quantitative research methods in mathematics education

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Key Challenges and Some Guidance on Using Strong Quantitative Methodology in Education Research

Genéa Stewart University of North Texas
Lee A. Bedford University of North Texas

The current article reviews several common areas of focus in quantitative methods with the hope of providing Journal of Urban Mathematics Education ( JUME ) readers and researchers with some guidance on conducting and reporting quantitative analyses. After providing some background for the discussion, the methodological nature of recent JUME articles is reviewed, followed by commentary on key challenges and recommendations for strong practice in quantitative methodology. The review addresses causal inferences, measurement issues, handling missing data, testing for assumptions, dealing with nested data, and providing evidence for outcomes. Enhanced quantitative training and resources for doctoral students, authors, reviewers, and editors is recommended.

Adler, J., Ball, D. Krainer, K., Lin, F., & Novotna, J. (2005). Reflections on an emerging field: Researching mathematics teacher education. Educational Studies in Mathematics, 60(3), 359–381. https://doi.org/10.1007/s10649-005-5072-6 DOI: https://doi.org/10.1007/s10649-005-5072-6

Aiken, L. S., West, S.g., & Millsap, R. E. (2008). Doctoral training in statistics, measurement, and methodology: Replication and extension of Aiken, West, Sechrest, and Reno's (1990) survey of PhD programs in North America. American Psychologist, 63(1), 32–50. https://doi.org/10.1037/0003-066X.63.1.32 DOI: https://doi.org/10.1037/0003-066X.63.1.32

Allen, M. J., & Yen, W. M. (1979). Introduction to measurement theory. Brooks/Cole Publishing Company.

American Psychological Association. (2010). Publication manual of the American Psychological Association (6th ed.).

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000 DOI: https://doi.org/10.1037/0000165-000

Austin, P. C. (2008). A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Statistics in Medicine, 27(12), 2037–2049. https://doi.org/10.1002/sim.3150 DOI: https://doi.org/10.1002/sim.3150

Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424. https://doi.org/10.1080/00273171.2011.568786 DOI: https://doi.org/10.1080/00273171.2011.568786

Beaujean, A. A., & Osterlind, S. J. (2008). Using item response theory to assess the Flynn effect in the National Longitudinal Study of Youth 79 Children and Young Adults data. Intelligence, 36(5), 455–463. https://doi.org/10.1016/j.intell.2007.10.004 DOI: https://doi.org/10.1016/j.intell.2007.10.004

Berliner, D. C. (2002). Comment: Educational research: The hardest science of all. Educational Researcher, 31(8), 18–20. https://doi.org/10.3102/0013189X031008018 DOI: https://doi.org/10.3102/0013189X031008018

Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2004). The concept of validity. Psychological Review, 111(4), 1061–1071. https://doi.org/10.1037/0033-295X.111.4.1061 DOI: https://doi.org/10.1037/0033-295X.111.4.1061

Cai, J., Morris, A., Hohensee, C., Hwang, S., Robison, V., Cirillo, M., Kramer, J., & Hiebert, J. (2019). Posing significant research questions. Journal for Research in Mathematics Education, 50(2), 114–120. https://doi.org/10.5951/jresematheduc.50.2.0114 DOI: https://doi.org/10.5951/jresematheduc.50.2.0114

Cai, J., Morris, A., Hohensee, C., Hwang, S., Robison, V., Cirillo, M., Kramer, S. L.., Hiebert, J., & Bakker, A. (2020). Addressing the problem of always starting over: Identifying, valuing, and sharing professional knowledge for teaching. Journal for Research in Mathematics Education, 51(2), 130–139. https://doi.org/10.5951/jresematheduc-2020-0015 DOI: https://doi.org/10.5951/jresematheduc-2020-0015

Casad, B. J., Hale, P., & Wachs, F. L. (2017). Stereotype threat among girls: Differences by gender identity and math education context. Psychology of Women Quarterly, 41(4), 513–529. https://doi.org/10.1177%2F0361684317711412 DOI: https://doi.org/10.1177/0361684317711412

Cochran-Smith, M., & Zeichner, K. M. (2005). Studying teacher educations, The report of the AERA Panel on Research and Teacher Education. Lawrence Erlbaum Associates.

Cohen, J. (1983). The cost of dichotomization. Applied Psychological Measurement, 7(3), 249–253. https://doi.org/10.1177/014662168300700301 DOI: https://doi.org/10.1177/014662168300700301

Connolly, P., Keenan, C., & Urbanska, K. (2018). The trials of evidence-based practice in education: A systematic review of randomised controlled trials in education research 1980–2016. Educational Research, 60(3), 276–291. https://doi.org/10.1080/00131881.2018.1493353 DOI: https://doi.org/10.1080/00131881.2018.1493353

Courville, T., & Thompson, B. (2001). Use of structure coefficients in published multiple regression articles: B is not enough. Educational and Psychological Measurement, 61(2), 229–248. https://doi.org/10.1177/0013164401612006 DOI: https://doi.org/10.1177/0013164401612006

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555 DOI: https://doi.org/10.1007/BF02310555

Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. http://doi.org/10.1037/h0040957 DOI: https://doi.org/10.1037/h0040957

Cumming, G., & Finch, S. (2005). Inference by eye: Confidence intervals and how to read pictures of data. American Psychologist, 60(2), 170–180. http://doi.org/10.1037/0003-066X.60.2.170 DOI: https://doi.org/10.1037/0003-066X.60.2.170

Demerath, P. (2006). The science of context: Modes of response for qualitative researchers in education. International Journal of Qualitative Studies in Education, 19(1), 97–113. https://doi.org/10.1080/09518390500450201 DOI: https://doi.org/10.1080/09518390500450201

Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Lawrence Erlbaum Associates. DOI: https://doi.org/10.1037/10519-153

Enders, C. K. (2010). Applied missing data analysis. Guilford Press.

Ferron, J. M., Hogarty, K. Y., Dedrick, R. F., Hess, M. R., Niles, J. D., Kromrey, J. D. (2008). Reporting results from multilevel analyses. In A. A. O'Connell & D. B. McCoach (Eds.), Multilevel modeling of educational data. Information Age Publishing.

Gutiérrez, R. (2002). Enabling the practice of mathematics teachers in context: Toward a new equity research agenda. Mathematical Thinking and Learning, 4(2–3), 145–187. https://doi.org/10.1207/S15327833MTL04023_4 DOI: https://doi.org/10.1207/S15327833MTL04023_4

Henson, R. K. (1999). Multivariate normality: What is it and how is it assessed? Advances in Social Science Methodology, 5, 193–211.

Henson, R. K. (2001). Understanding internal consistency reliability estimates: A conceptual primer on coefficient alpha. Measurement and Evaluation in Counseling and Development, 34(3), 177–189. https://doi.org/10.1080/07481756.2002.12069034 DOI: https://doi.org/10.1080/07481756.2002.12069034

Henson, R. K. (2002, April 1–5). The logic and interpretation of structure coefficients in multivariate general linear model analyses [Paper presentation]. Annual Meeting of the American Educational Research Association, New Orleans, LA, United States.

Henson, R. K. (2006). Effect-size measures and meta-analytic thinking in counseling psychology research. The Counseling Psychologist, 34(5), 601–629. https://doi.org/10.1177/0011000005283558 DOI: https://doi.org/10.1177/0011000005283558

Henson, R. K., Hull, D. M., & Williams, C. S. (2010). Methodology in our education research culture: Toward a stronger collective quantitative proficiency. Educational Researcher, 39(3), 229–240. https://doi.org/10.3102/0013189X10365102 DOI: https://doi.org/10.3102/0013189X10365102

Henson, R. K., Kogan, L. R., & Vacha-Haase, T. (2001). A reliability generalization study of the Teacher Efficacy Scale and related instruments. Educational and Psychological Measurement, 61(3), 404–420. https://doi.org/10.1177/00131640121971284 DOI: https://doi.org/10.1177/00131640121971284

Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393–416. https://doi.org/10.1177/0013164405282485 DOI: https://doi.org/10.1177/0013164405282485

Henson, R. K., & Williams, C. (2006, April 7–11). Doctoral training in research methodology: A national survey of education and related disciplines [Paper presentation]. Annual Meeting of the American Educational Research Association, San Francisco, CA, United States.

Hill, J. (2008). Discussion of research using propensity-score matching: Comments on ‘A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003’ by Peter Austin, Statistics in Medicine. Statistics in Medicine, 27(12), 2055–2061. https://doi.org/10.1002/sim.3245 DOI: https://doi.org/10.1002/sim.3245

Hogan, T. P., Benjamin, A., & Brezinski, K. L. (2000). Reliability methods: A note on the frequency of use of various types. Educational and Psychological Measurement, 60(4), 523–531. https://doi.org/10.1177/00131640021970691 DOI: https://doi.org/10.1177/00131640021970691

Howard, K. E., Romero, M., Scott, A., & Saddler, D. (2015). Success after failure: Academic effects and psychological implications of early universal algebra policies. Journal of Urban Mathematics Education, 8(1). https://doi.org/10.21423/jume-v8i1a248 DOI: https://doi.org/10.21423/jume-v8i1a248

Hughes, G. D., Onwuegbuzie, A. J., Daniel, L. G., & Slate, J. R. (2010). APA Publication Manual changes: Impacts on research reporting in the social sciences. Research in the Schools, 17(1), viii–xix.

Irvin, M., Byun, S. Y., Smiley, W. S., & Hutchins, B. C. (2017). Relation of opportunity to learn advanced math to the educational attainment of rural youth. American Journal of Education, 123(3), 475–510. https://doi.org/10.1086/691231 DOI: https://doi.org/10.1086/691231

Johnson, R. B., & Christensen, L. (2019). Educational research: Quantitative, qualitative, and mixed approaches. SAGE.

Journal of Urban Mathematics Education. (n.d.-a). Policies and procedures. Retrieved November 1, 2019, from https://jume-ojs-tamu.tdl.org/jume/index.php/jume/policiesandprocedures

Journal of Urban Mathematics Education. (n.d.-b). About the journal. Retrieved November 1, 2019, from https://journals.tdl.org/jume/index.php/jume/about

Kesselman, H. J., Huberty, C. J., Lix, L. M., Olejnik, S., Cribbie, R. A., Donahue, B., & Levin, J. R. (1998). Statistical practices of educational researchers: An analysis of their ANOVA, MANOVA, and ANCOVA analyses. Review of Educational Research, 68(3), 350–386. https://doi.org/10.3102/00346543068003350 DOI: https://doi.org/10.3102/00346543068003350

Kraha, A., Turner, H., Nimon, K., Zientek, L., & Henson, R. (2012). Tools to support interpreting multiple regression in the face of multicollinearity. Frontiers in Psychology, 3, 44. https://doi.org/10.3389/fpsyg.2012.00044 DOI: https://doi.org/10.3389/fpsyg.2012.00044

Kwok, O., Underhill, A., Berry, J. W., Luo, W., Elliott, T., & Yoon, M. (2008). Analyzing longitudinal data with multilevel models: An example with individuals living with lower extremity intra-articular fractures. Rehabilitation Psychology, 53(3), 370–386. https://doi.org/10.1037/a0012765 DOI: https://doi.org/10.1037/a0012765

Lee, L. S. (2018). Success of online mathematics courses at the community college level. Journal of Mathematics Education, 11(3), 69–89. https://doi.org/10.26711/007577152790033

Lekwa, A. J., Reddy, L. A., Dudek, C. M., & Hua, A. N. (2019). Assessment of teaching to predict gains in student achievement in urban schools. School Psychology, 34(3), 271–280. https://doi.org/10.1037/spq0000293 DOI: https://doi.org/10.1037/spq0000293

Lissitz, R. W., & Samuelson, K. (2007). A suggested change in terminology and emphasis regarding validity and education. Educational Researcher, 36(8), 437–448. https://doi.org/10.3102/0013189X07311286 DOI: https://doi.org/10.3102/0013189X07311286

Little, R. J. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83(404), 1198–1202. https://doi.org/10.1080/01621459.1988.10478722 DOI: https://doi.org/10.1080/01621459.1988.10478722

Matthews, J. S. (2018). When am I ever going to use this in the real world? Cognitive flexibility and urban adolescents’ negotiation of the value of mathematics. Journal of Educational Psychology, 110(5), 726–746. http://doi.org/10.1037/edu0000242 DOI: https://doi.org/10.1037/edu0000242

Maxwell, J. A. (2004). Causal explanation, qualitative research, and scientific inquiry in education. Educational Researcher, 33(2), 3–11. https://doi.org/10.3102%2F0013189X033002003 DOI: https://doi.org/10.3102/0013189X033002003

McCoach, D. B. (2010). Hierarchical linear modeling. In G. R. Hancock, R. O. Mueller, & L. M. Stapleton (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 123–140). Routledge.

Millsap, R. E. (2011). Statistical approaches to measurement invariance. Routledge. DOI: https://doi.org/10.4324/9780203821961

Morales-Chicas, J., & Agger, C. (2017). The effects of teacher collective responsibility on the mathematics achievement of students who repeat algebra. Journal of Urban Mathematics Education, 10(1), 52–73. https://doi.org/10.21423/jume-v10i1a287 DOI: https://doi.org/10.21423/jume-v10i1a287

Morgan, P. L., Frisco, M. L., Farkas, G., & Hibel, J. (2010). A propensity score matching analysis of the effects of special education services. Journal of Special Education, 43(4), 236–254. https://doi.org/10.1177/0022466908323007 DOI: https://doi.org/10.1177/0022466908323007

Onwuegbuzie, A. J., & Daniel, L. G. (2005). Evidence-based guidelines for publishing articles in Research in the Schools and beyond. Research in the Schools, 12(2), 1–11.

Osborne, J. W. (2013). Best practices in data cleaning: A complete guide to everything you need to do before and after collecting your data. SAGE. DOI: https://doi.org/10.4135/9781452269948

Peugh, J. L., & Enders, C. K. (2004). Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74(4), 525–556. https://doi.org/10.3102/00346543074004525 DOI: https://doi.org/10.3102/00346543074004525

Primi, C., Morsanyi, K., Donati, M. A., Galli, S., & Chiesi, F. (2017). Measuring probabilistic reasoning: The construction of a new scale applying item response theory. Journal of Behavioral Decision Making, 30(4), 933–950. https://doi.org/10.1002/bdm.2011 DOI: https://doi.org/10.1002/bdm.2011

Quintana, S. M., & Minami, T. (2006). Guidelines for meta-analyses of counseling psychology research. The Counseling Psychologist, 34(6), 839–877. https://doi.org/10.1177/0011000006286991 DOI: https://doi.org/10.1177/0011000006286991

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Sage.

Reise, S. P., Ainsworth, A. T., & Haviland, M. G. (2005). Item response theory: Fundamentals, applications, and promise in psychological research. Current Directions in Psychological Science, 14(2), 95–101. https://doi.org/10.1111/j.0963-7214.2005.00342.x DOI: https://doi.org/10.1111/j.0963-7214.2005.00342.x

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. https://doi.org/10.1093/biomet/70.1.41 DOI: https://doi.org/10.1093/biomet/70.1.41

Sadikovic, S., Milovanovic, I., & Oljaca, M. (2018). Another psychometric proof of the Abbreviated Math Anxiety Scale usefulness: IRT analysis. Primenjena Psihologija, 11(3), 301–323. https://doi.org/10.19090/pp.2018.3.301-323 DOI: https://doi.org/10.19090/pp.2018.3.301-323

Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147–177. https://doi.org/10.1037/1082-989X.7.2.147 DOI: https://doi.org/10.1037/1082-989X.7.2.147

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin Company.

Smith, P. A., & Hoy, W. K. (2007). Academic optimism and student achievement in urban elementary schools. Journal of Educational Administration, 45(5), 556–568. https://doi.org/10.1108/09578230710778196 DOI: https://doi.org/10.1108/09578230710778196

Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics (3rd ed.). Pearson.

Thompson, B. (1999). If statistical significance tests are broken/misused, what practices should supplement or replace them? Theory & Psychology, 9(2), 165–181. https://doi.org/10.1177/095935439992006 DOI: https://doi.org/10.1177/095935439992006

Thompson, B. (2002). What future quantitative social science research could look like: Confidence intervals for effect sizes. Educational Researcher, 31(3), 25–32. https://doi.org/10.3102/0013189X031003025 DOI: https://doi.org/10.3102/0013189X031003025

Vacha-Haase, T., Henson, R. K., & Caruso, J. C. (2002). Reliability generalization: Moving toward improved understanding and use of score reliability. Educational and Psychological Measurement, 62(4), 562–569. https://doi.org/10.1177/0013164402062004002 DOI: https://doi.org/10.1177/0013164402062004002

Vacha-Haase, T., Ness, C., Nilsson, J., & Reetz, D. (1999). Practices regarding reporting of reliability coefficients: A review of three journals. Journal of Experimental Education, 67(4), 335–341. https://doi.org/10.1080/00220979909598487 DOI: https://doi.org/10.1080/00220979909598487

Vogler, A. M., Prediger, S., Quasthoff, U., & Heller, V. (2018). Students’ and teachers’ focus of attention in classroom interaction — Subtle sources for the reproduction of social disparities. Mathematics Education Research Journal, 30(3), 299–323. https://doi.org/10.1007/s13394-017-0234-2 DOI: https://doi.org/10.1007/s13394-017-0234-2

Valero P. (2008). In between the global and the local: The politics of mathematics education reform in a globalized society. In B. Atweh, A. C. Barton, M. C. Borba, N. Gough, C. Keitel, C. Vistro-Yu, & R. Vithal (Eds.), Internationalisation and Globalisation in Mathematics and Science Education (pp. 421–439). Springer. https://doi.org/10.1007/978-1-4020-5908-7_23 DOI: https://doi.org/10.1007/978-1-4020-5908-7_23

Woltman, H., Feldstain, A., MacKay, J. C., & Rocchi, M. (2012). An introduction to hierarchical linear modeling. Tutorials in Quantitative Methods for Psychology, 8(1), 52–69. https://doi.org/10.20982/tqmp.08.1.p052 DOI: https://doi.org/10.20982/tqmp.08.1.p052

Young, D. J. (1997, March 24–28). A Multilevel Analysis of Science and Mathematics Achievement [Paper presentation]. Annual Meeting of the American Educational Research Association, Chicago, IL, United States.

Young, J. R., Young, J., Hamilton, C., & Pratt, S. (2019). Evaluating the effects of professional development on urban mathematics teachers TPACK using confidence intervals. REDIMAT – Journal of Research in Mathematics Education, 8(3), 312–338. http://doi.org/10.17583/redimat.2019.3065 DOI: https://doi.org/10.17583/redimat.2019.3065

Zientek, L. R., Capraro, M. M., & Capraro, R. M. (2008). Reporting practices in quantitative teacher education research: One look at the evidence cited in the AERA panel report. Educational Researcher, 37(4), 208–216. https://doi.org/10.3102/0013189X08319762 DOI: https://doi.org/10.3102/0013189X08319762

Zimney, G. H. (1961). Method in experimental psychology. Ronald Press. DOI: https://doi.org/10.1037/14006-000

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Examining purposeful researchable questions in mathematics education

Journal of Honai Math

Often general, and frequently involving scholarly concepts, research questions are the cornerstone of studies. Thus, from their precise wording to their context, research questions play a vital role in uncovering information, determining answers given by participants, and drawing conclusions. However, poorly structured research questions and misalignments to purpose within theoretical and empirical studies can lead to miscommunication and unanswered queries. To this point, this paper discusses the importance of asking purposeful researchable questions in mathematics education and examines what purposeful questioning in mathematics education using quantitative and qualitative research designs entails. An extensive review of literature, is presented with the purpose of identifying strategies for asking purposeful questions, exploring various criteria for judging researchable questions in mathematics education, and discussing the importance of aligning research questions to methodology...

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ICME-13 Monographs

Keith Jones

Jagdish M , Anna Sfard

Mathematics Teaching–Research Journal

Compendium for Research in Mathematics Education

IJEMST JOURNAL

Robin Dada , Sharon Bryant

Katie Makar

Athanassios Strantzalos

Mathematics Education Research Journal

Ana Lúcia Manrique

Igor' Kontorovich , Rina Zazkis

Russell Gersten

Psychology and Education: A Multidisciplinary Journal

Psychology and Education , Jayhad Michael B. Limana

CLASSPOINT APPLICATION AS A DIGITAL TRANSFORMATION IN MATHEMATICS EDUCATION

Abdelrady, A. H., & Akram, H. (2022). An Empirical Study of ClassPoint Tool Application in Enhancing EFL Students’ Online Learning Satisfaction. Systems, 10(5). https://doi.org/10.3390/systems10050154

Akram, H., & Abdelrady, A. H. (2023). Application of ClassPoint tool in reducing EFL learners’ test anxiety: an empirical evidence from Saudi Arabia. Journal of Computers in Education, 10(3), 529–547. https://doi.org/10.1007/s40692-023-00265-z

Anwar, Z., Kahar, M. S., Rawi, R. D. P., Nurjannah, N., Suaib, H., & Rosalina, F. (2020). Development of Interactive Video Based Powerpoint Media In Mathematics Learning. Journal of Educational Science and Technology (EST), 6(2), 167–177. https://doi.org/10.26858/est.v6i2.13179

Dubinina, G. A., Konnova, L. P., & Stepanyan, I. K. (2023). Compatibility of Edutainment and Traditional Methods in the University’s Educational Environment. Lecture Notes in Networks and Systems, 830 LNNS, 277–289. https://doi.org/10.1007/978-3-031-48020-1_22

Gabrielova, K., & Buchko, A. A. (2021). Here comes Generation Z: Millennials as managers. Business Horizons, 64(4), 489–499. https://doi.org/10.1016/j.bushor.2021.02.013

Hasan, B., Binti, R., Karim, A., Binti, Y., & Wahab, A. (2023). Implementing Book-end Division Approach using ClassPoint to Energize Electrical and Electronics Engineering Student Engagement. 7(November), 51–57.

Lefringhausen, K., Spencer-Oatey, H., & Debray, C. (2019). Culture, Norms, and the Assessment of Communication Contexts: Multidisciplinary Perspectives. Journal of Cross-Cultural Psychology, 50(10), 1098–1111. https://doi.org/10.1177/0022022119889162

Mazlan, N. A., Tan, K. H., Othman, Z., & Wahi, W. (2023). ClassPoint Application for Enhancing Motivation in Communication among ESL Young Learners. World Journal of English Language, 13(5), 520–526. https://doi.org/10.5430/wjel.v13n5p520

Nee, C. C., & Yunus, M. M. (2020). RollRoll Dice: An Effective Method to Improve Writing Skills among Year 3 Pupils in Constructing SVOA Sentences. Universal Journal of Educational Research, 8(6), 2368–2382. https://doi.org/10.13189/ujer.2020.080621

Querido, D. V. (2023). Effectiveness of Interactive Classroom Tool: A Quasi-Experiment in Assessing Students’ Engagement and Performance in Mathematics 10 using ClassPoint. Applied Quantitative Analysis, 3(1), 79–92. https://doi.org/10.31098/quant.1601

Quinn, D., & Aarão, J. (2020). Blended learning in first year engineering mathematics. ZDM - Mathematics Education, 52(5), 927–941. https://doi.org/10.1007/s11858-020-01160-y

Ramli, I. S. M., Maat, S. M., & Khalid, F. (2020). Game-Based Learning and Student Motivation in Mathematics. International Journal of Academic Research in Progressive Education and Development, 9(2), 449–455. https://doi.org/10.6007/ijarped/v9-i2/7487

Rondina, J. Q., & Roble, D. B. (2019). Game-Based Design Mathematics Activities and Students’ Learning Gains. The Turkish Online Journal of Design Art and Communication, 9(1), 1–7. https://doi.org/10.7456/10901100/001

Sanmugam, M., Abdullah, Z., Mohamed, H., Mohd Zaid, N., Aris, B., & Van Der Meijden, H. (2017). The impacts of infusing game elements and gamification in learning. 2016 IEEE 8th International Conference on Engineering Education: Enhancing Engineering Education Through Academia-Industry Collaboration, ICEED 2016, 131–136. https://doi.org/10.1109/ICEED.2016.7856058

Tossavainen, T., Rensaa, R. J., Haukkanen, P., Mattila, M., & Johansson, M. (2021). First-year engineering students’ mathematics task performance and its relation to their motivational values and views about mathematics. European Journal of Engineering Education, 46(4), 604–617. https://doi.org/10.1080/03043797.2020.1849032

van der Wal, N. J., Bakker, A., & Drijvers, P. (2019). Teaching strategies to foster techno-mathematical literacies in an innovative mathematics course for future engineers. ZDM - Mathematics Education, 51(6), 885–897. https://doi.org/10.1007/s11858-019-01095-z

Wanasek, S. (2024). How to Get Started with ClassPoint. https://www.classpoint.io/blog/classpoint-tutorial-getting-started

Wao, Y. P., Priska, M., & Peni, N. (2022). Persepsi Mahasiswa Terhadap Penggunaan Media Pembelajaran Interaktif Classpoint Pada Mata Kuliah Zoologi Invertebrata. Jurnal Inovasi Pembelajaran Biologi, 3(2), 76–87. https://doi.org/10.26740/jipb.v3n2.p76-87

Wijaya, H., Darmawan, I. P. A., Setiana, S. C., Helaluddin, H., & Weismann, I. Th. J. (2021). Active Reconnecting Learning Strategies to Increase Student Interest and Active Learning. Indonesian Journal of Instructional Media and Model, 3(1), 26. https://doi.org/10.32585/ijimm.v3i1.1290

Wilkins, J. L. M., Bowen, B. D., & Mullins, S. B. (2021). First mathematics course in college and graduating in engineering: Dispelling the myth that beginning in higher-level mathematics courses is always a good thing. Journal of Engineering Education, 110(3), 616–635. https://doi.org/10.1002/jee.20411

Zakariya, Y. F., Nilsen, H. K., Goodchild, S., & Bjørkestøl, K. (2022). Self-efficacy and approaches to learning mathematics among engineering students: empirical evidence for potential causal relations. International Journal of Mathematical Education in Science and Technology, 53(4), 827–841. https://doi.org/10.1080/0020739X.2020.1783006

The Effectiveness of AI on K-12 Students’ Mathematics Learning: A Systematic Review and Meta-Analysis

Published: 12 September 2024

Cite this article

quantitative research methods in mathematics education

Linxuan Yi 1 ,
Di Liu ORCID: orcid.org/0000-0003-0461-1012 1 ,
Tiancheng Jiang 1 &
Yucheng Xian 1

Artificial intelligence (AI) shows increasing potential to improve mathematics instruction, yet integrative quantitative evidence currently is lacking on its overall effectiveness and factors influencing success. This systematic review and meta-analysis investigate the effectiveness of AI on improving mathematics performance in K-12 classrooms compared to traditional classroom instruction. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched five databases from 2000 through December 2023, synthesizing findings from 21 relevant studies (40 samples) which met screening criteria. Results indicate a small overall effect size of 0.343 favoring AI under a random-effects model, showing a generally positive impact. Only one variable, AI type, was identified as having moderate effects, with AI demonstrating a greater impact when served as an intelligent tutoring system and adaptive learning system. Our findings establish an initial knowledge base for implementation and future research on the effective integration of AI into K–12 mathematics classrooms. This study also focuses on the appropriateness across age, mathematical contents, and AI design factors is aimed at further advancing the judicious adoption and success of classroom AI integration.

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Besimi, N., Besimi, A., & Cico, B. (2022). Artificial intelligence in education and learning (AI in research). In 2022 11th Mediterranean Conference on Embedded Computing (MECO) (pp. 1–6). IEEE. https://doi.org/10.1109/meco55406.2022.9797216

Chapter Google Scholar

Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2021). Introduction to meta-analysis (2nd ed.). John Wiley and Sons.

Book Google Scholar

Büchele, S., & Feudel, F. (2023). Changes in students’ mathematical competencies at the beginning of higher education within the last decade at a German University. International Journal of Science and Mathematics Education, 21 (8), 2325–2347. https://doi.org/10.1007/s10763-022-10350-x

Article Google Scholar

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access: Practical Innovations, Open Solutions , 8 , 75264–75278. https://doi.org/10.1109/access.2020.2988510

Chen, X., Xie, H., & Hwang, G. J. (2020). A multi-perspective study on Artificial Intelligence in Education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1 , 100005. https://doi.org/10.1016/j.caeai.2020.100005

de Morais, F., & Jaques, P. A. (2021). Does handwriting impact learning on math tutoring systems? Informatics in Education , 21 (1), 55–90. https://doi.org/10.15388/infedu.2022.03

Duval, S., & Tweedie, R. (2000). Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics , 56 (2), 455–463. https://doi.org/10.1111/j.0006-341x.2000.00455.x

Fang, Y., Ren, Z., Hu, X., & Graesser, A. C. (2018). A meta-analysis of the effectiveness of Aleks on learning. Educational Psychology , 39 (10), 1278–1292. https://doi.org/10.1080/01443410.2018.1495829

Hall, J. A., & Rosenthal, R. (1991). Testing for moderator variables in meta-analysis: Issues and methods. Communication Monographs , 58 (4), 437–448. https://doi.org/10.1080/03637759109376240

Hascoët, M., Giaconi, V., & Jamain, L. (2021). Family socioeconomic status and parental expectations affect mathematics achievement in a national sample of Chilean students. International Journal of Behavioral Development , 45 (2), 122–132. https://doi.org/10.1177/0165025420965731

Hillmayr, D., Ziernwald, L., Reinhold, F., Hofer, S. I., & Reiss, K. M. (2020). The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis. Computers & Education , 153 , 103897. https://doi.org/10.1016/j.compedu.2020.103897

Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of Artificial Intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics , 9 (6), 584. https://doi.org/10.3390/math9060584

Hwang, S. (2022). Examining the effects of artificial intelligence on Elementary Students’ mathematics achievement: A meta-analysis. Sustainability , 14 (20), 13185. https://doi.org/10.3390/su142013185

Li, Q., & Ma, X. (2010). A meta-analysis of the effects of computer technology on school students’ mathematics learning. Educational Psychology Review , 22 (3), 215–243. https://doi.org/10.1007/s10648-010-9125-8

Ma, W., Zhao, X., & Guo, Y. (2021). Improving the effectiveness of traditional education based on Computer Artificial Intelligence and neural network system. Journal of Intelligent & Fuzzy Systems , 40 (2), 2565–2575. https://doi.org/10.3233/jifs-189249

Mavrikis, M., Rummel, N., Wiedmann, M., Loibl, K., & Holmes, W. (2022). Combining exploratory learning with structured practice educational technologies to foster both conceptual and procedural fractions knowledge. Educational Technology Research and Development , 70 (3), 691–712. https://doi.org/10.1007/s11423-022-10104-0

Mohamed, M. Z., Hidayat, R., Suhaizi, N. N., Sabri, N., Mahmud, M. K., & Baharuddin, S. N. (2022). Artificial Intelligence in mathematics education: A systematic literature review. International Electronic Journal of Mathematics Education , 17 (3), em0694. https://doi.org/10.29333/iejme/12132

National Council of Teachers of Mathematics. (2022, October 24). 2022 NAEP math scores reinforce why systemic change is needed in mathematics education . NCTM Responds to 2022 Math NAEP Results . Retrieved from https://www.nctm.org/News-and-Calendar/News/NCTM-News-Releases/NCTM-Responds-to-2022-Math-NAEP-Results/

National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics . NCTM.

Google Scholar

Parviainen, P., Eklund, K., Koivula, M., Liinamaa, T., & Rutanen, N. (2023). Teaching early mathematical skills to 3- to 7-year-old children — differences related to mathematical skill category, children’s age group and teachers’ characteristics. International Journal of Science and Mathematics Education, 21 (7), 1961–1983. https://doi.org/10.1007/s10763-022-10341-y

Peng, P., & Lin, X. (2019). The relation between mathematics vocabulary and mathematics performance among fourth graders. Learning and Individual Differences , 69 , 11–21. https://doi.org/10.1016/j.lindif.2018.11.006

Peng, P., Namkung, J., Barnes, M., & Sun, C. (2016). A meta-analysis of mathematics and working memory: Moderating effects of working memory domain, type of mathematics skill, and sample characteristics. Journal of Educational Psychology , 108 (4), 455–473. https://doi.org/10.1037/edu0000079

Qu, J., Zhao, Y., & Xie, Y. (2022). Artificial intelligence leads the reform of Education models. Systems Research and Behavioral Science , 39 (3), 581–588. https://doi.org/10.1002/sres.2864

Steenbergen-Hu, S., & Cooper, H. (2013). A meta-analysis of the effectiveness of intelligent tutoring systems on K–12 students’ mathematical learning. Journal of Educational Psychology , 105 (4), 970–987. https://doi.org/10.1037/a0032447

Steenbergen-Hu, S., & Cooper, H. (2014). A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. Journal of Educational Psychology , 106 (2), 331–347. https://doi.org/10.1037/a0034752

Tang, K. Y., Chang, C. Y., & Hwang, G. J. (2021). Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998–2019). Interactive Learning Environments , 31 (4), 2134–2152. https://doi.org/10.1080/10494820.2021.1875001

Thai, K. P., Bang, H. J., & Li, L. (2021). Accelerating early math learning with research-based personalized learning games: A cluster randomized controlled trial. Journal of Research on Educational Effectiveness , 15 (1), 28–51. https://doi.org/10.1080/19345747.2021.1969710

United Nations Educational, Scientific and Cultural Organization. (2019). Beijing consensus on AI and education . Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000368303

Xu, W., & Ouyang, F. (2022). The application of AI Technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education , 9 (59), 1–20. https://doi.org/10.1186/s40594-022-00377-5

Yang, S. J. H., Ogata, H., Matsui, T., & Chen, N. S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence , 2 , 100008. https://doi.org/10.1016/j.caeai.2021.100008

Yu, X., Xia, J., & Cheng, W. (2022). Prospects and challenges of equipping mathematics tutoring systems with personalized learning strategies . 2022 International Conference on Intelligent Education and Intelligent Research (IEIR), Wuhan, China. https://doi.org/10.1109/ieir56323.2022.10050082

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16 (1). https://doi.org/10.1186/s41239-019-0171-0

Zhang, S., & Chen, X. (2022). Applying artificial intelligence into early childhood math education: Lesson Design and course effect . 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), Hung Hom, Hong Kong. https://doi.org/10.1109/tale54877.2022.00109

Studies included in the meta-analysis

Arroyo, I., Royer, J., & Woolf, B. (2011). Using an intelligent tutor and math fluency training to improve math performance. International Journal of Artificial Intelligence in Education , 21 (1), 135–152. https://doi.org/10.3233/jai-2011-020

Bringula, R. P., Fosgate, I. C., Garcia, N. P., & Yorobe, J. L. (2017). Effects of pedagogical agents on students’ mathematics performance: A comparison between two versions. Journal of Educational Computing Research , 56 (5), 701–722. https://doi.org/10.1177/0735633117722494

del Olmo-Muñoz, J., González‐Calero, J. A., Diago, P. D., Arnau, D., & Arevalillo‐Herráez, M. (2022). Using intra‐task flexibility on an intelligent tutoring system to promote arithmetic problem‐solving proficiency. British Journal of Educational Technology , 53 (6), 1976–1992. https://doi.org/10.1111/bjet.13228

González-Calero, J. A., Arnau, D., Puig, L., & Arevalillo‐Herráez, M. (2014). Intensive scaffolding in an intelligent tutoring system for the learning of algebraic word problem solving. British Journal of Educational Technology , 46 (6), 1189–1200. https://doi.org/10.1111/bjet.12183

Harskamp, E. G., & Suhre, C. J. M. (2006). Improving mathematical problem solving: A computerized approach. Computers in Human Behavior , 22 (5), 801–815. https://doi.org/10.1016/j.chb.2004.03.023

Hou, X., Nguyen, H. A., Richey, J. E., Harpstead, E., Hammer, J., & McLaren, B. M. (2022). Assessing the effects of open models of learning and enjoyment in a digital learning game. International Journal of Artificial Intelligence in Education , 32 (1), 120–150. https://doi.org/10.1007/s40593-021-00250-6

Huang, X., Craig, S. D., Xie, J., Graesser, A., & Hu, X. (2016). Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program. Learning and Individual Differences , 47 , 258–265. https://doi.org/10.1016/j.lindif.2016.01.012

Hwang, G. J., Tseng, J. C. R., & Hwang, G. H. (2008). Diagnosing student learning problems based on Historical Assessment Records. Innovations in Education and Teaching International , 45 (1), 77–89. https://doi.org/10.1080/14703290701757476

Jaques, P. A., Seffrin, H., Rubi, G., de Morais, F., Ghilardi, C., Bittencourt, I. I., & Isotani, S. (2013). Rule-based expert systems to support step-by-step guidance in algebraic problem solving: The case of the tutor pat2math. Expert Systems with Applications , 40 (14), 5456–5465. https://doi.org/10.1016/j.eswa.2013.04.004

Jia, J., Li, S., Miao, Y., & Li, J. (2023). The effects of personalised mathematic instruction supported by an intelligent tutoring system during the COVID-19 epidemic and the post-epidemic era. International Journal of Innovation and Learning, 33 (3), 330–343. https://doi.org/10.1504/ijil.2023.130099

Khodeir, N., Wanas, N., & Elazhary, H. (2018). Constraint-based student modelling in probability story problems with scaffolding techniques. International Journal of Emerging Technologies in Learning (IJET) , 13 (01), 178–205. https://doi.org/10.3991/ijet.v13i01.7397

Kim, Y., Thayne, J., & Wei, Q. (2017). An embodied agent helps anxious students in mathematics learning. Educational Technology Research and Development, 65 (1), 219–235. https://doi.org/10.1007/s11423-016-9476-z

Kohn, J., Rauscher, L., Kucian, K., Käser, T., Wyschkon, A., Esser, G., & von Aster, M. (2020). Efficacy of a computer-based learning program in children with developmental dyscalculia. what influences individual responsiveness? Frontiers in Psychology , 11 , 1115. https://doi.org/10.3389/fpsyg.2020.01115

Li, Y., Zhao, K., & Xu, W. (2015). Developing an intelligent tutoring system that has automatically generated hints and summarization for algebra and geometry. International Journal of Information and Communication Technology Education , 11 (2), 14–31. https://doi.org/10.4018/ijicte.2015040102

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Cohen, W. W., Stylianides, G. J., & Koedinger, K. R. (2013). Cognitive anatomy of tutor learning: Lessons learned with Simstudent. Journal of Educational Psychology , 105 (4), 1152–1163. https://doi.org/10.1037/a0031955

Özyurt, Ö., Özyurt, H., Güven, B., & Baki, A. (2014). The effects of UZWEBMAT on the probability unit achievement of Turkish eleventh grade students and the reasons for such effects. Computers & Education , 75 , 1–18. https://doi.org/10.1016/j.compedu.2014.02.005

Pai, K. C., Kuo, B. C., Liao, C. H., & Liu, Y. M. (2020). An application of Chinese dialogue-based intelligent tutoring system in remedial instruction for Mathematics learning. Educational Psychology , 41 (2), 137–152. https://doi.org/10.1080/01443410.2020.1731427

Shih, S. C., Chang, C. C., Kuo, B. C., & Huang, Y. H. (2023). Mathematics intelligent tutoring system for learning multiplication and division of fractions based on diagnostic teaching. Education and Information Technologies , 28 (7), 9189–9210. https://doi.org/10.1007/s10639-022-11553-z

Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2023). When adaptive learning is effective learning: Comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 31 (2), 793–803. https://doi.org/10.1080/10494820.2020.1808794

Wu, H. M. (2018). Online individualised tutor for improving Mathematics learning: A cognitive diagnostic model approach. Educational Psychology , 39 (10), 1218–1232. https://doi.org/10.1080/01443410.2018.1494819

Xin, Y. P., Tzur, R., Hord, C., Liu, J., Park, J. Y., & Si, L. (2016). An intelligent tutor-assisted Mathematics Intervention Program for students with learning difficulties. Learning Disability Quarterly , 40 (1), 4–16. https://doi.org/10.1177/0731948716648740

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Di Liu had the idea for the article, and all authors contributed to the study design. Searching procedure, quality evaluation, data extraction and descriptive coding were conducted by Di Liu, Linxuan Yi, Tiancheng Jiang and Yucheng Xian. Data analysis was conducted by Linxuan Yi and Di Liu. The first draft of the manuscript was written by Linxuan Yi and it was critically revised by Di Liu. All authors commented on previous versions of manuscript. All authors read and approved the final manuscript.

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Yi, L., Liu, D., Jiang, T. et al. The Effectiveness of AI on K-12 Students’ Mathematics Learning: A Systematic Review and Meta-Analysis. Int J of Sci and Math Educ (2024). https://doi.org/10.1007/s10763-024-10499-7

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DOI : https://doi.org/10.1007/s10763-024-10499-7

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Speaker 1: Welcome to this overview of quantitative research methods. This tutorial will give you the big picture of quantitative research and introduce key concepts that will help you determine if quantitative methods are appropriate for your project study. First, what is educational research? Educational research is a process of scholarly inquiry designed to investigate the process of instruction and learning, the behaviors, perceptions, and attributes of students and teachers, the impact of institutional processes and policies, and all other areas of the educational process. The research design may be quantitative, qualitative, or a mixed methods design. The focus of this overview is quantitative methods. The general purpose of quantitative research is to explain, predict, investigate relationships, describe current conditions, or to examine possible impacts or influences on designated outcomes. Quantitative research differs from qualitative research in several ways. It works to achieve different goals and uses different methods and design. This table illustrates some of the key differences. Qualitative research generally uses a small sample to explore and describe experiences through the use of thick, rich descriptions of detailed data in an attempt to understand and interpret human perspectives. It is less interested in generalizing to the population as a whole. For example, when studying bullying, a qualitative researcher might learn about the experience of the victims and the experience of the bully by interviewing both bullies and victims and observing them on the playground. Quantitative studies generally use large samples to test numerical data by comparing or finding correlations among sample attributes so that the findings can be generalized to the population. If quantitative researchers were studying bullying, they might measure the effects of a bully on the victim by comparing students who are victims and students who are not victims of bullying using an attitudinal survey. In conducting quantitative research, the researcher first identifies the problem. For Ed.D. research, this problem represents a gap in practice. For Ph.D. research, this problem represents a gap in the literature. In either case, the problem needs to be of importance in the professional field. Next, the researcher establishes the purpose of the study. Why do you want to do the study, and what do you intend to accomplish? This is followed by research questions which help to focus the study. Once the study is focused, the researcher needs to review both seminal works and current peer-reviewed primary sources. Based on the research question and on a review of prior research, a hypothesis is created that predicts the relationship between the study's variables. Next, the researcher chooses a study design and methods to test the hypothesis. These choices should be informed by a review of methodological approaches used to address similar questions in prior research. Finally, appropriate analytical methods are used to analyze the data, allowing the researcher to draw conclusions and inferences about the data, and answer the research question that was originally posed. In quantitative research, research questions are typically descriptive, relational, or causal. Descriptive questions constrain the researcher to describing what currently exists. With a descriptive research question, one can examine perceptions or attitudes as well as more concrete variables such as achievement. For example, one might describe a population of learners by gathering data on their age, gender, socioeconomic status, and attributes towards their learning experiences. Relational questions examine the relationship between two or more variables. The X variable has some linear relationship to the Y variable. Causal inferences cannot be made from this type of research. For example, one could study the relationship between students' study habits and achievements. One might find that students using certain kinds of study strategies demonstrate greater learning, but one could not state conclusively that using certain study strategies will lead to or cause higher achievement. Causal questions, on the other hand, are designed to allow the researcher to draw a causal inference. A causal question seeks to determine if a treatment variable in a program had an effect on one or more outcome variables. In other words, the X variable influences the Y variable. For example, one could design a study that answered the question of whether a particular instructional approach caused students to learn more. The research question serves as a basis for posing a hypothesis, a predicted answer to the research question that incorporates operational definitions of the study's variables and is rooted in the literature. An operational definition matches a concept with a method of measurement, identifying how the concept will be quantified. For example, in a study of instructional strategies, the hypothesis might be that students of teachers who use Strategy X will exhibit greater learning than students of teachers who do not. In this study, one would need to operationalize learning by identifying a test or instrument that would measure learning. This approach allows the researcher to create a testable hypothesis. Relational and causal research relies on the creation of a null hypothesis, a version of the research hypothesis that predicts no relationship between variables or no effect of one variable on another. When writing the hypothesis for a quantitative question, the null hypothesis and the research or alternative hypothesis use parallel sentence structure. In this example, the null hypothesis states that there will be no statistical difference between groups, while the research or alternative hypothesis states that there will be a statistical difference between groups. Note also that both hypothesis statements operationalize the critical thinking skills variable by identifying the measurement instrument to be used. Once the research questions and hypotheses are solidified, the researcher must select a design that will create a situation in which the hypotheses can be tested and the research questions answered. Ideally, the research design will isolate the study's variables and control for intervening variables so that one can be certain of the relationships being tested. In educational research, however, it is extremely difficult to establish sufficient controls in the complex social settings being studied. In our example of investigating the impact of a certain instructional strategy in the classroom on student achievement, each day the teacher uses a specific instructional strategy. After school, some of the students in her class receive tutoring. Other students have parents that are very involved in their child's academic progress and provide learning experiences in the home. These students may do better because they received extra help, not because the teacher's instructional strategy is more effective. Unless the researcher can control for the intervening variable of extra help, it will be impossible to effectively test the study's hypothesis. Quantitative research designs can fall into two broad categories, experimental and quasi-experimental. Classic experimental designs are those that randomly assign subjects to either a control or treatment comparison group. The researcher can then compare the treatment group to the control group to test for an intervention's effect, known as a between-subject design. It is important to note that the control group may receive a standard treatment or may receive a treatment of any kind. Quasi-experimental designs do not randomly assign subjects to groups, but rather take advantage of existing groups. A researcher can still have a control and comparison group, but assignment to the groups is not random. The use of a control group is not required. However, the researcher may choose a design in which a single group is pre- and post-tested, known as a within-subjects design. Or a single group may receive only a post-test. Since quasi-experimental designs lack random assignment, the researcher should be aware of the threats to validity. Educational research often attempts to measure abstract variables such as attitudes, beliefs, and feelings. Surveys can capture data about these hard-to-measure variables, as well as other self-reported information such as demographic factors. A survey is an instrument used to collect verifiable information from a sample population. In quantitative research, surveys typically include questions that ask respondents to choose a rating from a scale, select one or more items from a list, or other responses that result in numerical data. Studies that use surveys or tests need to include strategies that establish the validity of the instrument used. There are many types of validity that need to be addressed. Face validity. Does the test appear at face value to measure what it is supposed to measure? Content validity. Content validity includes both item validity and sampling validity. Item validity ensures that the individual test items deal only with the subject being addressed. Sampling validity ensures that the range of item topics is appropriate to the subject being studied. For example, item validity might be high, but if all the items only deal with one aspect of the subjects, then sampling validity is low. Content validity can be established by having experts in the field review the test. Concurrent validity. Does a new test correlate with an older, established test that measures the same thing? Predictive validity. Does the test correlate with another related measure? For example, GRE tests are used at many colleges because these schools believe that a good grade on this test increases the probability that the student will do well at the college. Linear regression can establish the predictive validity of a test. Construct validity. Does the test measure the construct it is intended to measure? Establishing construct validity can be a difficult task when the constructs being measured are abstract. But it can be established by conducting a number of studies in which you test hypotheses regarding the construct, or by completing a factor analysis to ensure that you have the number of constructs that you say you have. In addition to ensuring the validity of instruments, the quantitative researcher needs to establish their reliability as well. Strategies for establishing reliability include Test retest. Correlates scores from two different administrations of the same test. Alternate forms. Correlates scores from administrations of two different forms of the same test. Split half reliability. Treats each half of one test or survey as a separate administration and correlates the results from each. Internal consistency. Uses Cronbach's coefficient alpha to calculate the average of all possible split halves. Quantitative research almost always relies on a sample that is intended to be representative of a larger population. There are two basic sampling strategies, random and non-random, and a number of specific strategies within each of these approaches. This table provides examples of each of the major strategies. The next section of this tutorial provides an overview of the procedures in conducting quantitative data analysis. There are specific procedures for conducting the data collection, preparing for and analyzing data, presenting the findings, and connecting to the body of existing research. This process ensures that the research is conducted as a systematic investigation that leads to credible results. Data comes in various sizes and shapes, and it is important to know about these so that the proper analysis can be used on the data. In 1946, S.S. Stevens first described the properties of measurement systems that allowed decisions about the type of measurement and about the attributes of objects that are preserved in numbers. These four types of data are referred to as nominal, ordinal, interval, and ratio. First, let's examine nominal data. With nominal data, there is no number value that indicates quantity. Instead, a number has been assigned to represent a certain attribute, like the number 1 to represent male and the number 2 to represent female. In other words, the number is just a label. You could also assign numbers to represent race, religion, or any other categorical information. Nominal data only denotes group membership. With ordinal data, there is again no indication of quantity. Rather, a number is assigned for ranking order. For example, satisfaction surveys often ask respondents to rank order their level of satisfaction with services or programs. The next level of measurement is interval data. With interval data, there are equal distances between two values, but there is no natural zero. A common example is the Fahrenheit temperature scale. Differences between the temperature measurements make sense, but ratios do not. For instance, 20 degrees Fahrenheit is not twice as hot as 10 degrees Fahrenheit. You can add and subtract interval level data, but they cannot be divided or multiplied. Finally, we have ratio data. Ratio is the same as interval, however ratios, means, averages, and other numerical formulas are all possible and make sense. Zero has a logical meaning, which shows the absence of, or having none of. Examples of ratio data are height, weight, speed, or any quantities based on a scale with a natural zero. In summary, nominal data can only be counted. Ordinal data can be counted and ranked. Interval data can also be added and subtracted, and ratio data can also be used in ratios and other calculations. Determining what type of data you have is one of the most important aspects of quantitative analysis. Depending on the research question, hypotheses, and research design, the researcher may choose to use descriptive and or inferential statistics to begin to analyze the data. Descriptive statistics are best illustrated when viewed through the lens of America's pastimes. Sports, weather, economy, stock market, and even our retirement portfolio are presented in a descriptive analysis. Basic terminology for descriptive statistics are terms that we are most familiar in this discipline. Frequency, mean, median, mode, range, variance, and standard deviation. Simply put, you are describing the data. Some of the most common graphic representations of data are bar graphs, pie graphs, histograms, and box and whisker graphs. Attempting to reach conclusions and make causal inferences beyond graphic representations or descriptive analyses is referred to as inferential statistics. In other words, examining the college enrollment of the past decade in a certain geographical region would assist in estimating what the enrollment for the next year might be. Frequently in education, the means of two or more groups are compared. When comparing means to assist in answering a research question, one can use a within-group, between-groups, or mixed-subject design. In a within-group design, the researcher compares measures of the same subjects across time, therefore within-group, or under different treatment conditions. This can also be referred to as a dependent-group design. The most basic example of this type of quasi-experimental design would be if a researcher conducted a pretest of a group of students, subjected them to a treatment, and then conducted a post-test. The group has been measured at different points in time. In a between-group design, subjects are assigned to one of the two or more groups. For example, Control, Treatment 1, Treatment 2. Ideally, the sampling and assignment to groups would be random, which would make this an experimental design. The researcher can then compare the means of the treatment group to the control group. When comparing two groups, the researcher can gain insight into the effects of the treatment. In a mixed-subjects design, the researcher is testing for significant differences between two or more independent groups while subjecting them to repeated measures. Choosing a statistical test to compare groups depends on the number of groups, whether the data are nominal, ordinal, or interval, and whether the data meet the assumptions for parametric tests. Nonparametric tests are typically used with nominal and ordinal data, while parametric tests use interval and ratio-level data. In addition to this, some further assumptions are made for parametric tests that the data are normally distributed in the population, that participant selection is independent, and the selection of one person does not determine the selection of another, and that the variances of the groups being compared are equal. The assumption of independent participant selection cannot be violated, but the others are more flexible. The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups, and especially appropriate as the method of analysis for a quasi-experimental design. When choosing a t-test, the assumptions are that the data are parametric. The analysis of variance, or ANOVA, assesses whether the means of more than two groups are statistically different from each other. When choosing an ANOVA, the assumptions are that the data are parametric. The chi-square test can be used when you have non-parametric data and want to compare differences between groups. The Kruskal-Wallis test can be used when there are more than two groups and the data are non-parametric. Correlation analysis is a set of statistical tests to determine whether there are linear relationships between two or more sets of variables from the same list of items or individuals, for example, achievement and performance of students. The tests provide a statistical yes or no as to whether a significant relationship or correlation exists between the variables. A correlation test consists of calculating a correlation coefficient between two variables. Again, there are parametric and non-parametric choices based on the assumptions of the data. Pearson R correlation is widely used in statistics to measure the strength of the relationship between linearly related variables. Spearman-Rank correlation is a non-parametric test that is used to measure the degree of association between two variables. Spearman-Rank correlation test does not assume any assumptions about the distribution. Spearman-Rank correlation test is used when the Pearson test gives misleading results. Often a Kendall-Taw is also included in this list of non-parametric correlation tests to examine the strength of the relationship if there are less than 20 rankings. Linear regression and correlation are similar and often confused. Sometimes your methodologist will encourage you to examine both the calculations. Calculate linear correlation if you measured both variables, x and y. Make sure to use the Pearson parametric correlation coefficient if you are certain you are not violating the test assumptions. Otherwise, choose the Spearman non-parametric correlation coefficient. If either variable has been manipulated using an intervention, do not calculate a correlation. While linear regression does indicate the nature of the relationship between two variables, like correlation, it can also be used to make predictions because one variable is considered explanatory while the other is considered a dependent variable. Establishing validity is a critical part of quantitative research. As with the nature of quantitative research, there is a defined approach or process for establishing validity. This also allows for the findings transferability. For a study to be valid, the evidence must support the interpretations of the data, the data must be accurate, and their use in drawing conclusions must be logical and appropriate. Construct validity concerns whether what you did for the program was what you wanted to do, or whether what you observed was what you wanted to observe. Construct validity concerns whether the operationalization of your variables are related to the theoretical concepts you are trying to measure. Are you actually measuring what you want to measure? Internal validity means that you have evidence that what you did in the study, i.e., the program, caused what you observed, i.e., the outcome, to happen. Conclusion validity is the degree to which conclusions drawn about relationships in the data are reasonable. External validity concerns the process of generalizing, or the degree to which the conclusions in your study would hold for other persons in other places and at other times. Establishing reliability and validity to your study is one of the most critical elements of the research process. Once you have decided to embark upon the process of conducting a quantitative study, use the following steps to get started. First, review research studies that have been conducted on your topic to determine what methods were used. Consider the strengths and weaknesses of the various data collection and analysis methods. Next, review the literature on quantitative research methods. Every aspect of your research has a body of literature associated with it. Just as you would not confine yourself to your course textbooks for your review of research on your topic, you should not limit yourself to your course texts for your review of methodological literature. Read broadly and deeply from the scholarly literature to gain expertise in quantitative research. Additional self-paced tutorials have been developed on different methodologies and techniques associated with quantitative research. Make sure that you complete all of the self-paced tutorials and review them as often as needed. You will then be prepared to complete a literature review of the specific methodologies and techniques that you will use in your study. Thank you for watching.

Open access
Published: 10 September 2024

Being old is like…: perceptions of aging among healthcare profession students

Mustafa Aktekin 1 ,
Nafiye Cigdem Aktekin 2 ,
Hatice Celebi 3 ,
Cihan Kocabas 4 &
Cevahir Kakalicoglu 5

BMC Medical Education volume 24 , Article number: 985 ( 2024 ) Cite this article

Metrics details

This research explores the perspectives and attitudes of university students in health sciences towards aging and older adults. Given the intricate interplay of factors influencing attitudes toward aging, coupled with the demographic shift in Turkey from a youthful to an aging population, the study aims to discern how a cohort of university students perceives the aging process.

Employing a mixed-methods research strategy, which enhances the depth of data interpretation, the study utilized a questionnaire for quantitative data collection. Additionally, qualitative insights were gathered through a metaphor stem-completion item appended to the questionnaire and semi-structured interviews with students. The participants were selected from the Health Sciences Faculty and School of Medicine at a Turkish university.

The study revealed that participating students generally hold positive attitudes and demonstrate respect towards older adults. However, they also associate old age with negative aspects such as loss of autonomy and a constant need for assistance. Furthermore, older individuals are perceived as emotionally challenging and challenging to work with, irrespective of the nature and duration of interactions during their academic programs. These findings suggest a potential pathologizing perspective towards aging adults among health science students, who are prospective health professionals.

Conclusions

This paper discusses the implications of the study and offers insights for program coordinators, curriculum designers, and faculty members in health sciences. The results underscore the necessity for a heightened emphasis on gerontology-related subjects within health science curricula. This emphasis is crucial for cultivating a comprehensive understanding among students of the social, psychological, cognitive, and biological changes associated with aging.

Peer Review reports

Introduction

In Turkey, individuals aged 65 and above are classified as “senior/elder/older adults” [ 1 ]. However, differing perspectives on aging exist across communities, and historically, the term “older people” was applied to much younger adults before advancements in health sciences. Presently, Turkey is undergoing demographic shifts, marked by a decline in fertility rates and an increase in life expectancy. Projections from 2016 to 2018 indicate a continued trend, with average life expectancy expected to reach 75.6 for men and 81 for women [ 1 , 2 , 3 ]. The percentage of the population aged 65 and above is projected to rise from 8.2% in 2015 to 10.2% by 2023 [ 2 ]. These demographic shifts align with a global pattern, positioning the 21st century in Turkey as “the century of the older generation” [ 4 ].

Given the socio-structural transformations resulting from recent demographic and economic changes in Turkey [ 5 ], it becomes crucial to examine intergenerational relationships. Understanding the dynamics of knowledge transfer between generations and raising awareness of tensions arising from demographic and economic changes are essential. Education, in its broad sense, is intertwined with societal changes, as societal shifts significantly influence education, and education, in turn, can shape anticipated impacts of societal changes. The project “The Interplay between Intergenerational Solidarity, Family, and School” (TUBITAK-Scientific and Technological Research Council of Turkey, Project no: 116K245), from which this paper emerges, was designed to explore these relationships in light of recent demographic changes in Turkey.

While the project aimed to investigate intergenerational solidarity, family, and education, a specific focus was directed towards observing stereotypes and beliefs about aging among students. Perceptions of old age not only influence societal behavior towards older adults but also impact the relationships of younger professionals interacting with older generations in their workplaces. This paper specifically delves into the perceptions and attitudes of students in healthcare-related education, as they are the future providers of services and support to older adults. Given Turkey’s rapid demographic transition, the country is anticipated to experience accelerated population aging and an increase in care-related needs [ 6 ]. The study’s findings provide valuable insights for revising healthcare service education policies, facilitating collaboration between healthcare professionals, scholars, and students to deliver more compassionate and high-quality care-related services in the face of the impending challenges posed by increased demand for social and healthcare services.

Design, setting and participants

In this study, we employed a mixed-methods research approach, chosen for its capacity to yield a more nuanced interpretation of both the data and the phenomenon under scrutiny [ 7 ]. Specifically, we adhered to a convergent parallel mixed-methods design, wherein the researcher concurrently conducted the quantitative and qualitative components, treating them with equal importance and independently analyzing each before integrating the results [ 8 ]. The overarching objective was to leverage the advantages offered by both quantitative and qualitative methodologies [ 9 ], recognizing that each approach contributes distinct techniques and procedures aligned with their respective strengths and logical frameworks [ 10 ].

To determine students’ attitudes towards older adults, we employed a combination of quantitative and qualitative data collection methods within the university setting of Acıbadem University in Istanbul, specializing in health sciences. The quantitative data were gathered from 242 students using the questionnaire devised by Bousfield and Hutchison [ 11 ]. Additionally, qualitative insights were obtained through semi-structured interviews conducted with 20 students and an extra metaphor stem-completion item incorporated into the questionnaire [ 12 ].

The quantitative data collected from the questionnaire adapted and translated from Bousfield and Hutchison [ 11 ] were analyzed using SPSS 17.0 (SPSS Inc.,Chicago), and descriptive statistics were obtained. The qualitative data collected through the semi-structured interviews were coded in order to pull out any themes across the data, which were then thematically grouped and analyzed using NVivo 11 (NVivo qualitative data analysis software; QSR International Pty Ltd. Version 11, 2014). Lastly, the additional metaphor stem-completion item was dissected from the questionnaire, and the semantically related metaphors were grouped manually and then analyzed. Figure 1 represents data collection instruments and subscales.

Data collection instruments and subscales

The participating students were selected from the departments of nursing (NRS), physiotherapy and rehabilitation (P&R), nutrition and dietetics (N&D), healthcare management (HM), and the School of Medicine (SoM). A convenience sampling method was employed in this study. When using convenience sampling, the researcher looks for participants who are most easily selected and available to participate in the research study [ 13 ]. Since the university specializes in the health sciences, the researchers had access to several different departments and populations of students. Hence, when this research study was announced, several students offered to participate.

Data collection instruments

The questionnaire we used on attitudes towards older adults (Bousfield & Hutchison, 2010) originally contained 19 5-band Likert-type statements and 10 ranking statements but, to aid our qualitative data collection, we added a metaphor stem-completion item to it. Before it was given to the participants, the questionnaire was translated into Turkish and reverse translated. The Brislin [ 14 ] back-translation method was used to achieve equivalence between two languages. We worked with an expert to translate the instrument from its source language (English) into the target language (Turkish). To confirm clarity and detect linguistic mistakes, we consulted secondary expert opinions from the health sciences departments. Then another English language expert was consulted to translate the survey from Turkish back to English (backward translation). After that, the backward translation was compared with the original version for accuracy. To ensure cross-cultural equivalence, this process was repeated twice until the translated survey was jointly agreed to be equivalent and clear. To obtain the cross-cultural adaptation, 2 professors from the nursing and psychology departments were asked to review and approve its adaptation, and necessary modifications were made accordingly. To ensure the validation of the survey in Turkish, the questionnaire was tested by 41 students; the same students answered the survey twice within a two-week period. Table 1 demonstrates the correlation values of the items.

Reliability was assessed using Cronbach’s alpha internal consistency correlation measure with the following value of 0.71. In terms of subdimensions, the adapted version has a better internal consistency with a total Cronbach’s alpha of 0.71 compared to the original measurement, as shown in Table 2 .

The survey instrument employed in this study has been referenced in numerous publications closely related to the subject matter [ 15 , 16 , 17 ], suggesting that its content and face validity have been well-established. This is further supported by the fact that the survey underwent a thorough review by subject matter experts, particularly during the language equivalence stage. Moreover, to evaluate the construct validity of the translated survey, the results of a confirmatory factor analysis were analysed. As demonstrated in Table 3 , the translated version is consistent with the original measurement tool, thereby confirming the achievement of construct validity.

In the first part of the questionnaire, we aimed to collect demographic information. Participants were also asked to define the age which they considered to be old. In the following parts, the questionnaire aimed to measure five constructs, namely intergroup contact , consisting of two dimensions: contact frequency and contact quality, intergroup anxiety , aging anxiety , attitudes , and behavioral intentions . As for the intergroup contact, contact frequency and contact quality were measured by asking participants to specify how often they had contact with older adults, on a scale ranging from 1 (almost never) to 5 (every day), and how they would rate the quality of that contact, from 1 (very bad) to 5 (very good). Responses to four items (“I feel awkward/restricted/happy/self-conscious/relaxed around older adults”) measured participants’ intergroup anxiety concerning interactions with older adults. Anxiety about personal aging was measured based on responses to five items: “I am anxious about getting old”; “I am worried that I will lose my independence when I am old”; “I am concerned that my mental abilities will suffer when I am old”; “I am concerned that my physical abilities will suffer when I am old”; and “I do not want to get old because it means I am closer to dying”. Ten adjective pairs were used to measure attitudes towards older adults, and a 5-point scale separated each adjective pair. The first six pairs asked participants to indicate how they evaluated older adults in general on scales with endpoints labeled as follows: independent/dependent, grumpy/pleasant, knowledgeable/ignorant, narrow-minded/open to new ideas or values, healthy/unwell, and withdrawn/sociable. The last four items required participants to rate their feelings when they thought of older adults on scales with endpoints labeled as follows: negative/positive, friendly/hostile, contempt/respect, and admiration/disgust. Intentions to engage in positive behaviors towards older adults were assessed using five items under the behavioral intentions part: “I would not give money to someone collecting for an organization that helps older adults”; “I would support a small increase in taxes if the money went towards supporting older adults”; “I would offer help to an older adult if they were clearly in need of it (for example, crossing the road or carrying shopping)”; “I would be happy to take a job that involved regular contact with older adults”; and “I would want to spend some of my free time on an activity supporting older adults”.

The semi-structured interviews were held with 20 students, selected based on their availability, who were asked the following six questions: (1) Can you describe your level of experience working with geriatric populations? (2) Geriatric patients can have multiple complex health conditions; how do you best work with patients with these complex conditions? (3) Older adults can sometimes be impatient and frustrated with medical providers; how do you deal with angry patients? (4) Do you think your current education will provide you with sufficient information about how to care for older adults? If not, what would you like to learn about caring for older adults? (5) In your future career, would you like to work intensively with older adults? and (6) In your view, what do the older adults think about the young, and what do the young think about the older adults? The questions for the semi-structured interviews were created considering the purpose and the scope of the study and after a rigorous reading of the literature. An external audit was also done by a colleague from the nursing department. The data were processed using NVivo 11.

The additional section in the questionnaire asked the respondents to complete the stem “[B]eing old is like … because.” to elicit the metaphors. A metaphor is a device for illuminating the lesser-known through the better-known, with the “source domain” yielding insights into the “target domain” [ 23 ]. The researchers added the metaphor stem-completion item to complement the quantitative data to gain insights into and descriptions of how the subjects perceive old age, as well as to reach a deeper understanding of the qualitative themes emerging from the semi-structured interviews [ 12 ].

In their study, Forero et al. [ 24 ] adapted the criteria of Lincoln and Guba [ 25 ] in qualitative research, known as credibility, dependability, confirmability and transferability. To be clearer about the procedures of our study, we adapted Forero’s ‘The Four-Dimensions Criteria’. Table 4 illustrates which strategies were used in our study.

Quantitative phase

The questionnaire.

According to Bousfield and Hutchison [ 11 ], the 16 to 25 age group is commonly used in research focused on young people; similarly, the average age of the participants in this study was approximately 21 years old. The majority of the participants were women, due to the high female populations in these departments. The average age of the participants’ mothers was 46 years old, while their fathers’ average age was 51. Table 5 shows the demographic characteristics of the participants.

The average age that the respondents selected to describe a person as “old” was 64. Only 23 students (10%) did not have any grandparents, while 86 (36%) had three or more living grandparents. Table 6 shows the subscale statistics, that is, the means and standard deviations of the variables collected from the questionnaire.

According to the results of the Independent Sample T-Test, which was conducted to identify any differences in the responses between male and female participants, there were no significant differences between the genders in terms of the subscales of contact frequency (t=,02; p> ,05), contact quality (t=-,73; p> ,05), and attitude (t=,96; p> ,05). However, in other subscales, significant differences were identified between male and female respondents. In terms of the subscales of intergenerational anxiety (t = 3,26; p< ,05), aging anxiety (t = 3,55; p< ,05), and behavioral intentions, the male participants were found to score more highly than the females. male These results indicate that students in this study reported greater levels of intergenerational and aging anxiety, as well as stronger anxiety-related intentions, compared to their female counterparts. Table 7 represents gender differences considering the subscales.

Of all those who filled out the questionnaire, 106 (44%) were “often” or in contact with older adults, i.e. every two days or once a week, while the remaining 134 (55%) had a low frequency of contact, i.e. once a month or once every two months. A total of 157 (65%) of the students considered the quality of their contact as good, while 72 (30%) had “normal” interactions with older adults. Only 65 (27%) of the participants felt awkward or restricted around older adults, whereas 153 (63%) felt happy and relaxed.

Of all those who filled out the questionnaire in a valid manner, 129 (53%) had anxiety about aging, with 119 (49%) feeling they will lose their independence, 147 (65%) believing their mental abilities will suffer, and 181 (75%) fearing their physical abilities will suffer. A total of 132 (55%) participants interpreted “getting old” as “getting closer” to death. Additionally, 147 (61%) students indicated that they would gladly accept a job in which they have contact with older adults and 138 (57%) of them would like to spend some of their spare time doing activities planned for older adults. Table 8 demonstrates these findings.

Based on the ten adjective pairs used to measure attitudes towards older adults, the majority of the respondents indicated that they view older adults as dependent, unhealthy, and grumpy. Most of them believed that older adults are knowledgeable but not open to new ideas. When they thought of old people, the participants reported feeling positive towards them and holding respect and admiration for them.

Qualitative phase

Semi-structured interviews.

Students participating in the semi-structured interviews were drawn from six distinct university departments, each of which includes at least one course focused on geriatric studies. Following the analysis of qualitative data, the students’ responses were categorized into three primary themes for subsequent examination: the frequency and quality of contact, mutual perceptions, and the sufficiency of current educational offerings and future training.

Contact frequency and quality

The students’ perceptions of older adults are influenced by the frequency and quality of their contact. Even when students reported having at least two older adults in their family or among relatives and neighbors, with a perceived good quality of contact, it was notable that their actual contact with the older population at home or during their education was limited. Students highlighted the prevalent role of the second generation (their parents) in caring for older adults in the family or the presence of hired caregivers for basic daily needs. Consequently, the students’ viewpoints were primarily shaped by their experiences during hospital internships. Some students expressed the belief that theoretical courses in gerontology alone did not equip them with effective communication skills for older patients, emphasizing that caring for the older adults required hands-on experience, patience, and a genuine willingness to engage. One of the nursing students stated that, based on her observations, the nurses in the hospitals in which she had completed her internship paid scant attention to communicating with older patients, and, instead, mostly interacted with the relatives accompanying them.

Upon their experience with their families, relatives, and field visits, some participants stated that old people are to be treated differently because: they are like children; they are dependent and in need of attention and help; they are sensitive, resentful, and fragile; and they do not get enough nutrition. The majority of the students expressed that they felt unprepared to deal with the older population. They believed that older adults had complex health problems and were not easy to communicate with. One of the interviewees indicated this view as follows:

The old are like children , but when you treat them like children , they get angry and edgy .

The overall opinion of the students was that greater and better-quality exposure to elder populations would be linked to more favorable attitudes and maintaining such favorable attitudes could then be possible during their education through professional encounters. However, they still expressed that the communication when they are dealing with older patients is unique and could not be compared to the communication they had with their older household members as the following quote illustrates:

Communicating with grandmas and grandpas is not like dealing with older patients.

Mutual perceptions

Responses to the question “What do older adults think about the young?” indicated that participants believed that the negative attitudes they held towards older adults were reciprocal in that the older adults perceived the young also negatively. The majority of the participants expressed the belief about the reciprocity of negative attitudes offering numerous reasons. Their reasons included the generation gap and differences in upbringing; the difficulty of adjusting to one another; the place of technology in young people’s lives and older adults’ lack of sympathy for the technological reality; and the ideas older people had about the changes in young people’s behaviors towards them. However, the participants did not convey these feelings and thoughts in a complaining or reproachful tone; they simply stated that this despondency was a phenomenon that should be accepted with maturity.

A few of the participants indicated that they appreciated that older adults did not exert authority to prevent the young from making different choices than the ones they made in the past in their lives. On the contrary, the participants expressed that there were cases where the older encouraged the young intangibly and helped them financially to realize their aims. The following quote illustrates the above interpretation:

To me , they think of us as lucky , as a generation. Yes , there are slight complaints , but compared to their generation , they let us be freer and don’t meddle. They help us to study in other cities; for example , they give us support , both morally and financially .

Some participants commented that the young find the old to be resentful, sensitive, childlike, and highly expectant of being respected and admired. However, some participants criticized the young, blaming them for not being sufficiently empathetic and for being impatient.

The adequacy of their current education and future training

Several participants indicated that they did not feel well-equipped or ready to interact with the geriatric population. They were aware of the need to receive specific education to work with older adults. They believed that such education should consider both health and psychological dimensions. Accordingly, some students suggested that more courses should be given in geriatric psychology, especially in the early years of their education, rather than only in their final year. 18 interviewees stated that they did not intend to work solely with older adults in their future careers, while three of them said they had changed their minds and would now prefer to work with older adults after having taken care of their grandparents during their summer holidays. One of these three students expressed the need for more psychology courses as part of their current education:

…and the psychological aspect of this issue is very important. I want to give an example from my grandfather. This year , he has had three operations; it is not only my grandfather but also the caregivers around him who have had difficult times; they have become mentally depressed. And the fear of death is so intense in older adults that everyone around has been affected psychologically .

One of the senior-year nursing students underlined the advantages of having a geriatrics course in the final year, saying:

… having the course in our final year is kind of advantageous. We are in the clinics four days a week; we have had the chance to practice what we have studied. If we were given the course in the early years of our education , we couldn’t have put theory into practice.

To summarize, despite the great variety in the study participants’ interactions with older adults, all of the students considered the experience they had to be valuable. Moreover, although geriatrics courses are usually offered in the final year of the university’s curriculum, some interviewees believed that earlier input about the geriatric population would have been beneficial for them. In addition, they expressed appreciation for having theoretical courses followed by the opportunity to put the ideas into practice during their internship programs. While they were aware that they would have the chance to specialize after their first study cycle and choose geriatrics as a postgraduate degree program, only a few participants seemed eager to take this path. Overall, the young participants’ perceptions of the older adults appeared to be positive, and marked by a sense of respect; despite this, however, a sole focus on geriatric care was not part of most of their future career plans.

Metaphor stem-completion

The additional metaphor stem-completion item on the questionnaire was analyzed separately and qualitatively. For classification purposes, after all of the metaphors were collected through stem-completion, 87 in total were selected; metaphors that appeared to be semantically related were grouped, and seven categories emerged. 87 metaphors were selected for classification because not all participants provided valid metaphors; some students either left this section incomplete, misunderstood the task, or offered responses that were not suitable for metaphorical analysis. The remaining 87 metaphors were those that clearly expressed relevant ideas and could be meaningfully grouped into distinct categories based on their semantic similarities. A minimum of five metaphors was considered necessary to count as one category. Categorization was conducted separately by three researchers. The formulation of the categories was based on finding the same keywords in different metaphors, as in the study of Aktekin [ 26 ]; for example, “oak tree” and “plane tree” led to the formulation of the category “Trees”. The conceptual aspects of the metaphors were also considered, and categorization was made accordingly; for example, the two metaphors used to portray older adults: “aging like wine” and “becoming vinegary” were categorized differently based on their positive or negative connotations, with the former categorized as “becoming precious”, and the latter as “losing flavor”. Table 9 summarizes the seven categories identified from the students’ metaphors, with three examples given to illustrate each category. The first approach to analyzing the metaphors is social psychology in nature, and can show ‘how these metaphors reveal the underlying conceptualization of the worlds [these students] inhabit’ [Ellis, 1998, p. 37, cited in 26 ].

The most frequently used metaphors referred to the respectability and dignity of older people, which were associated with mighty, old trees. However, the participants also indicated that with age, people lose their strength, and physical decline sets in. Therefore, metaphors about “falling leaves” and “autumn” were also high in number. The metaphors of the third category indicate that the young view being old as being childlike. In conceptual terms, these metaphors highlight the fact that older adults need care and help because they become like children. The richness of older people’s lives, their experience, wisdom, and their value were categorized into three semantically related metaphor groups; all of these groups show the students’ appreciation of the fact of aging. The last category relates to negative perceptions of getting old, and may indicate aging anxiety.

The second approach employed to analyze the metaphors, a social discursive approach, focuses on the metaphoric construction of a belief space, which is, in part, shared and shaped by others and in which various possible scenarios are acted out [ 26 ]. This approach reveals students’ conflicting feelings about aging. Table 10 presents the metaphors as constructions of paradoxes. Concepts relating to ending, perishing, fading away, and struggling to survive can all be deduced from these metaphors.

This study employed a mixed-methods research approach to determine whether the quantitative data—specifically participants’ responses to a questionnaire—aligned with the qualitative data gathered from semi-structured interviews and metaphor-completion task comments. The quantitative approach allows for generalizations by using measurable methods for data collection and analysis, while the qualitative approach seeks to understand and appreciate human thought and behavior within a social context, covering a broad range of phenomena. We found that the mixed-methods approach used in this study provided us with some variability in the findings in terms of both negative and positive attitudes and perceptions towards the older, which could be further explored by future studies.

Attitudes play a key role in people’s behavior and are shaped by values and beliefs. Therefore, shedding light on these attitudes can support efforts aimed at raising social awareness and reformulating education policies. Attitudes towards older people and older patients among healthcare professionals have been an increasing concern and as such more recently systematic reviews examined and compared attitudes across the various professions that provide healthcare to older people [ 27 ]. On a general level, in our study, it was found that the students studying at Acıbadem University continue to hold positive attitudes, and find older adults wise and experienced but at the same time, they are mostly reluctant to work with the older in their profession. In the findings, it appears that the young have limited interaction with the older despite the presence of older adults in their communities. Although they would like to spend their time doing activities planned for the older adults, the participants express that they would not readily accept a job concerning older adults as they are concerned with the issues that may arise due to the generational gap, differences in upbringing and communication problems. Additionally, participants believe the generation gap and differences in upbringing are the two main reasons why there are miscommunications between the young and the older adults. Other studies in the literature report about participants’ positive intentions towards older adults [ 28 , 29 , 30 , 31 , 32 ] and similarly in our study, the majority of the participants express respect and admiration towards older adults; however, they also express that older adults’ expectations of the young are high. According to our results, students also find older adults in need of help, sometimes bad-tempered, and difficult to work with. Indeed, many studies [ 34 , 35 , 36 , 37 , 38 ] have identified nursing students’ declining interest in working with older adults during their nursing degree programs. In the studies of Adıbelli et al. [ 39 ], and Celik et al. [ 40 ]. the majority of participants were found to hold negative views about aging; nevertheless, most participants reported that they behaved positively towards older adults and showed sensitivity and respect while caring for them. Comparing the findings of a recent study by Murakami et al. [ 41 ] with our findings, we observe notable differences between Japanese students’ perspectives of older adults and old age and our participants in terms of articulation of respect and the degree the articulation is transferred to caring in practice. Our findings suggest that although our participants respect older adults, this respect is sometimes overshadowed by the challenges they face in communication and caregiving, as reflected in their metaphorical associations with aging. For instance, while they view older adults as wise and dignified (comparable to ‘mighty old trees’), they also perceive them as frail and in decline (akin to ‘falling leaves’). This dual perception indicates that, unlike the Japanese students, who maintain more consistent and perhaps idealized respect, our participants’ respect is tempered by their first-hand experiences with the complexities of aging, such as physical and cognitive decline. Thus, our findings support Yıldırım’s [ 5 ] argument that demographic and economic changes and socio-structural developments have started to change the relationships between the generations in Turkey. It can therefore be suggested that in Turkey’s case, it cannot be taken for granted that students in the health profession will unquestionably carry positive attitudes they hold toward the older adults in their profession and would willingly and effectively work with the older towards maximizing their well-being. Based on our findings we propose that understanding the attitudes of student healthcare professionals and the associated factors can provide an evidential basis upon which to develop initial education curricula that can train new enrollees to meet the needs of future healthcare systems. Seeking out students who hold negative views of older adults may be worthwhile as it can provide insights into what can be done to improve attitudes and increase sensitivity.

In terms of gender differences, our findings indicate that the male students’ levels of intergenerational anxiety and aging anxiety are lower than those of the female students, while the males’ behavioral intention levels are higher than those of the females. The results of our study also revealed that the female students on average had more positive attitudes towards older people than the males did. According to Barrett and Von Rohr [ 42 ], gender and aging are inextricably linked, which adds another layer to the complexity of how aging is perceived by different communities and individuals. Women are twice as likely as men to celebrate their 85th birthday [ 43 ] and the research reveals that women tend to have more positive attitudes towards the older adults than men do [ 44 , 45 ]. In light of the findings of earlier studies and our study, we maintain that a curricular approach in health sciences that addresses the complexities genders may bring to caring for older adults could help promote more positive perceptions of the profession, regardless of the gender of the students.

In our study, participants noted that their interaction frequency with older adult patients during their education was not adequate. They believed they would be more comfortable toward working with older adults if they could have more previous contact. Although some of the students may never primarily work with older adults, increasing sensitivity to this population would still benefit the profession [ 46 ]. They also highlighted the positive effect of putting their theoretical knowledge into practice during their clinical training period. These results are in line with the studies of Dussen & Weaver, and Oh & Bong [ 47 ]. These findings raise intriguing questions regarding the nature and extent of intergenerational solidarity and its implications for healthcare science departments and wider societal dynamics.

Earlier studies investigating attitudes among physical therapy undergraduates found that the students hold both negative and positive biases about aging. Beling’s [ 48 ] study showed that an increase in knowledge about older adults did not necessarily lead to changes in attitudes or imply negativity in the students’ behavior. According to Beling [ 48 ], these findings among physical therapy students are similar to those reported in some other studies with nursing and medical students. In another study Hobbs et al. [ 49 ]. found that the physiotherapy students began their degrees with somewhat positive attitudes towards older people and that their knowledge of older people improved throughout the program. Nevertheless, the low levels of knowledge and relatively negative attitudes found in some of these studies suggested that the educational sector and workplaces both need to address some real issues in preparing a workforce to care for older people. According to the study of Gonçalves et al. [ 50 ], interest in working with older adults was significantly related to positive attitudes, more knowledge and formal previous contact. Positive attitudes towards older adults can be promoted through interaction with faculty members, experts, healthy and impaired older adults [ 50 ]. Academics could consistently demonstrate respect and empathy toward older adults, both in their teaching materials and through any direct interactions that students might observe. For instance, when discussing case studies, professors could highlight the dignity and value of older adults, ensuring that the language used is respectful and acknowledges their contributions and experiences. Given that students in our study reported challenges in communicating with older adults, academics can model effective communication strategies in their teaching. For example, they could demonstrate how to adapt language, tone, and body language when interacting with older adults, emphasizing the importance of patience, active listening, and empathy. Lastly, academics could encourage students to reflect on their experiences with older adults, whether through internships, family interactions, or classroom activities. Reflective journals, discussion groups, or assignments that prompt students to consider their attitudes and behaviors can help them internalize the lessons learned and develop more favorable attitudes.

Meanwhile, studies investigating dietetics students’ knowledge and attitudes in relation to aging and their interest in working with the older adult population found that the students possessed low levels of knowledge about aging and neutral attitudes towards older adults [ 51 ]. However, it was also found that the students who preferred working with older adults had more positive attitudes towards working with older adults compared to those who did not share this preference. Previous experience with older adults was found to be strongly associated with higher comfort levels and self-efficacy in working with the older adults, improved attitudes towards this age group, and a preference for working with them [ 52 ]. The findings reported in this study appear to reflect complexities and inconsistencies referred to in the mentioned studies and as such provide a basis as to why healthcare curriculum should be planned in consonance with social dynamics and attitudes and perceptions of both students and the populations the groups will work with.

Academics who teach undergraduate courses could foster more favorable attitudes among students by acting as role models [ 49 ]. There is an urgent need to recognize the increasing demand for healthcare for older people and, hence, to positively promote care for the aged as an attractive and valuable career path. Promoting healthcare students’ attitudes toward the older adults is significant in providing high-quality care. The practical inference of these findings is that academic programs should focus on accomplishing specific goals, such as increasing student knowledge about older adults. Furthermore, extensive education programs must be provided on aging and gerontology. In rapidly aging, modern societies, healthcare curricula should include topics related to healthy aging and avoid focusing only on the pathologies and illnesses associated with old age. Involving older people in the design, development, and delivery of a module on aging in the curriculum would be innovative. Healthcare curricula, therefore, incorporate content and learning outcomes relevant to aging, and they may encourage contact between healthcare students and older patients [ 53 ].

This study explored the views on and attitudes to aging among healthcare students at a Turkish university that specializes in the health sciences. The insights gathered through the study can support future studies and educational programs about aging. The findings reveal that there is a need for a greater emphasis on gerontology-related subjects in health science curricula for the students to have a deeper understanding of the social and psychological, as well as the cognitive and biological, changes associated with aging. Health science programs could provide enhanced training to maximize the well-being of older people despite the physical decline. Understanding healthcare students’ perceptions of aging is of great importance, especially, given that these students may have preferences for working or not working with particular groups of people in the future; if they have concerns about working with older adults, the Turkish healthcare system may face challenges in providing adequate care to its older population. As Turkey’s population ages and its functional dependency increases, this will have a direct effect on the nation’s public health and social care systems. Educational initiatives designed to alter medical students’ perceptions of older adults have been found to result in enhanced positive attitudes and diminished negative age stereotypes [ 54 , 55 , 56 ]. Short-term training programs can be integrated in the curriculum of the departments.

In light of the rapid demographic changes taking place in Turkey and the population’s increased longevity, it is essential that universities and healthcare programs undertake two key tasks: (1) understand students’ attitudes towards aging, while also providing them with opportunities to reflect on their expectations regarding their profession; and (2) promote the idea that the healthcare sciences mirror demographic transformations and that students are and will be participants in the mid-and long-term social, health, and economic policy-making concerning gerontology. An evaluation of demographic changes in a country is necessary as the changes could be reflected in university curricula, which then will open up new possibilities for developing aging-related policies together with healthcare professionals with expertise in gerontology.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.

TUIK. İstatistiklerle yaşlılar, [Statistics on the older adults]. Retrieved from http://www.tuik.gov.tr/PreHaberBultenleri.do?id=21520 . Published 2018. Accessed April 26, 2020.

TUIK. (2018). Turkish Statistical Institute statistics on family 2016. Retrieved from http://www.turkstat.gov.tr/PreHaberBultenleri.do?id=24646 . Published 2018. Accessed April 26, 2020.

Eurostat, Key Figures on Europe. : 2017 Edition. Retrieved from https://ec.europa.eu/eurostat/documents/3217494/8309812/KS-EI-17-001-EN-N.pdf/b7df53f5-4faf-48a6-aca1-c650d40c9239 Published 2017. Accessed April 26, 2020.

Tamer MG. Kuşaklararası dayanışma ve işbirliği çerçevesinde gençlerin yaşlı ve yaşlılık algısının değerlendirilmesi [An evaluation of young people’s perceptions of older adults and old age within the framework of intergenerational solidarity and collaboration]. Toplum Bilimleri Dergisi. 2014;8(15):7–28.

Google Scholar

Yıldırım F. Çocukların dünyasına yaşlıları dahil etmek: Okul temelli kuşaklararası dayanışma modelleri [Including seniors in children’s worlds: School-based intergenerational solidarity models]. Türkiye Sosyal Araştırmalar Dergisi. 2015;19(1):275–96.

Arun Ö, Holdsworth JK. Integrated social and health care services among societies in transition: insights from Turkey. J Aging Stud. 2020;53:100850. https://doi.org/10.1016/j.jaging.2020.100850 .

Article Google Scholar

Creswell JW, Plano Clark VL. Designing and conducting mixed methods research. Thousand Oaks, CA: Sage; 2011.

Johnson RB, Onwuegbuzie AJ, Turner LA. Toward a definition of mixed methods research. J Mixed Methods Res. 2007;1(2):112–33. https://doi.org/10.1177/1558689806298224 .

Eyisi D. The usefulness of qualitative and quantitative approaches and methods in researching problem-solving ability in Science Education Curriculum. J Educ Pract. 2016;7(15):91–100.

Creswell J. Qualitative inquiry & research design: choosing among five approaches. 3rd ed. Thousand Oaks, CA: Sage; 2013.

Bousfield C, Hutchison P. Contact, anxiety, and Young people’s attitudes and behavioral intentions towards the Elderly. Educ Gerontol. 2010;36(6):451–66. https://doi.org/10.1080/03601270903324362 .

Hoyle E, Wallace M. Beyond metaphors of management: the case for metaphoric re-description in education. Br J Educational Stud. 2007;55(4):426–42.

Lambrinou E, Sourtzi P, Kalokerinou A, Lemonidou C. Attitudes and knowledge of the Greek nursing students towards older people. Nurse Educ Today. 2009;29(6):617–22. https://doi.org/10.1016/j.nedt.2009.01.011 .

Brislin RW. Back-translation for cross-cultural research. J Cross-Cultural Psychol. 1970;1(3):185–216.

Drury L, Hutchison P, Abrams D. Direct and extended intergenerational contact and young people’s attitudes towards older adults. Br J Soc Psychol. 2016;55(3):522–43.

Paleari FG, Brambilla M, Fincham FD. When prejudice against you hurts others and me: the case of ageism at work. J Appl Soc Psychol. 2019;49(11):704–20.

Visintin EP. Contact with older people, ageism, and containment behaviours during the COVID-19 pandemic. J Community Appl Social Psychol. 2021;31(3):314–25.

Hooper D, Coughlan J, Mullen M. Structural equation modelling: guidelines for determining Model Fit. Electron J Bus Res Methods. 2008;6(1):53–60.

Anderson JC, Gerbing DW. The effect of sampling error on convergece, improper solutions and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika. 1984;49:155–73.

Sümer N. Yapısal eşitlik modelleri: Temel kavramlar ve örnek uygulamalar. Türk Psikoloji Yazıları. 2000;3(6):74–9.

Portela DMP. Contributo das técnicas de análise fatorial para o estudo do programa ocupação científica de jovens nas férias. 2012. (Doctoral dissertation).

Hu LT, Bentler PM. Cutoff criteria for fit indexes in Covariance structure analysis: conventional criteria Versus New Alternatives. Struct Equ Model. 1999;6(1):1–55.

Christensen L, Johnson R, Turner L. Research methods, design, and analysis. 12th ed. Upper Saddle River, NJ: Pearson Education; 2014.

Forero R, Nahidi S, De Costa J, et al. Application of four-dimension criteria to assess rigour of qualitative research in emergency medicine. BMC Health Serv Res. 2018;18. https://doi.org/10.1186/s12913-018-2915-2 .

Lincoln YS, Guba EG. But is it rigorous? Trustworthiness and authenticity in naturalistic evaluation. New Dir Program Evaluation. 1986;1986(30):73–84. https://doi.org/10.1002/ev.1427 .

Kramsch C. Metaphor and the subjective construction of beliefs. In: Kalaja P, Barcelos AF, editors. Beliefs about SLA: new research approaches. Dordrecht: Kluwer Academic; 2003. pp. 109–27.

Chapter Google Scholar

Liu Y-E, Norman IJ, While AE. Nurses’ attitudes towards older people: a systematic review. Int J Nurs Stud. 2013;50(9):1271–82. https://doi.org/10.1016/j.ijnurstu.2012.11.021 .

Wang C, Liao W, Kao M, Chen YJ, Lee MC, Lee MF, Yen CH. Taiwanese medical and nursing student interest levels in and attitudes towards geriatrics. Annals Acad Med Singap. 2009;38(3):230–6.

Henderson J, Xiao L, Siegloff L, Kelton M, Paterson J. Older people have lived their lives’: first year nursing students’ attitudes to older people. Contemp Nurse. 2008;30(1):32–45.

Aktekin NC. İngilizce öğretmenlerinin ve yabancı dil öğrenen öğrencilerin tutum ve inançlarının metafor yoluyla ortaya çıkarılması [Revealing ESL teachers’ and students’ attitudes and beliefs through metaphors]. Uludağ Üniversitesi Eğitim Fakültesi Dergisi. 2013;26(2):405–22.

Runkawatt V, Gustafsson C, Engström G. Different cultures but similar positive attitudes: a comparison between Thai and Swedish nursing students’ attitudes toward older people. Educ Gerontol. 2013;39(2):92–102. https://doi.org/10.1080/03601277.2012.689934 .

Carlson E, Idvall E. Who wants to work with older people? Swedish student nurses’ willingness to work in elderly care-a questionnaire study. Nurse Educ Today. 2015;35(7):849–53. Epub 2015 Mar 24. PMID.

McCarthy F, Winter R, Levett T. An exploration of medical student attitudes towards older persons and frailty during undergraduate training. Eur Geriatr Med. 2021;12:347–53.

Courtney M, Tong S, Walsh A. Acute-care nurses’ attitudes towards older patients: a literature review. Int J Nurs Pract. 2002;6(2):62–9.

Abbey J, Abbey B, Bridges P, Elder R, Lemke P, Liddle J, Thornton R. Clinical placements in residential aged care facilities: the impact on nursing student’s perception of aged care and the effect on career plans. Australian J Adv Nurs. 2006;23(4):14–9.

Mansouri Arani M, Aazami S, Azami M, Borji M. Assessing attitudes toward elderly among nurses working in the city of Ilam. Int J Nurs Sci. 2017;4(3):311–3. https://doi.org/10.1016/j.ijnss.2017.06.009 .

Aud MA, Bostick JE, Marek KD, McDaniel RW. Introducing baccalaureate student nurses to gerontological nursing. J Prof Nurs. 2006;22(2):73–8. https://doi.org/10.1016/j.profnurs.2006.01.005 .

Happell B. Nursing home employment for nursing students: valuable experience or a harsh deterrent? J Adv Nurs. 2002;39(6):529–36. https://doi.org/10.1046/j.1365-2648.2002.02321.x .

Adıbelli D, Türkoğlu N, Kılıç D. Views of nursing students about ageing and their attitudes toward older people. Dokuz Eylül Üniversitesi Hemşirelik Fakültesi Elektronik Dergisi. 2013;6(1):2–8.

Celik SS, Kapucu S, Tuna Z, Akkus Y. Views and attitudes of nursing students towards aging and older patients. Australian J Adv Nurs. 2010;2(4):24–30.

Murakami I, Çoban M, Baş AM, Yücel G, Gündem EK. Eğitim Fakültesi öğrencilerinin yaşliya ve yaşliliğa bakiş açisi: türkiye ve japonya karşilaştirmasi [Perspectives on aging and older people among the students in the faculty of education: a comparison of Japan and Turkey]. Yaşlı Sorunları Araştırma Dergisi. 2018;(2):16–23.

Barrett AE, von Rohr C. Gendered perceptions of aging: an examination of college students. Int J Aging Hum Dev. 2008;67(4):359–86. https://doi.org/10.2190/AG.67.4.d .

Gist YJ, Hetzel LI. (2004). We the people: Aging in the United States (Census 2000 special reports). U.S. Census Bureau. Retrieved from http://www.census.gov/prod/2004pubs/censr-19.pdf (Accessed April 26, 2020).

Laditka SB, Fischer M, Laditka JN, Segal DR. Attitudes about aging and gender among young, middle age, and older college-based students. Educ Gerontol. 2004;30(5):403–21. https://doi.org/10.1080/03601270490433602 .

Rupp DE, Vodanovich SJ, Credé M. The multidimensional nature of ageism: construct validity and group differences. J Soc Psychol. 2005;145(3):335–62. https://doi.org/10.3200/SOCP.145.3.335-362 .

Wang D, Chonody J. Social workers’ attitudes toward older adults: a review of the literature. J Social Work Educ. 2013;49(1):150–72. https://doi.org/10.1080/10437797.2013.755104 .

Oh C, Bong J. Aging through their eyes: college students’ attitudes toward older adults before and after nursing home observations. Educational Gerontol 2021 Sep 2:47(9):393–405. https://doi.org/10.1080/03601277.2021.1989219

Beling J. Impact of service learning on physical therapist students’ knowledge of and attitudes toward older adults and on their critical thinking ability. J Phys Therapy Educ. 2004;18(1):13–21.

Hobbs C, Dean CM, Higgs J, Adamson B. Physiotherapy students’ attitudes towards and knowledge of older people. Australian J Physiotherapy. 2006;52(2):115–9. https://doi.org/10.1016/S0004-9514(06)70046-0 .

Gonçalves, Daniela C, et al. Attitudes, knowledge, and interest: preparing university students to work in an aging world. Int Psychogeriatr. 2011;23(2):315–21.

Kaempfer D, Wellman N, Himburg S. Dietetics students’ low knowledge, attitudes, and work preferences toward older adults indicate need for improved education about aging. J Am Diet Assoc. 2002;102(2):197–202. https://doi.org/10.1016/S0002-8223(02)90048-9 .

Lee SY, Hoerr SL, Weatherspoon L, Schiffman RF. Previous experience with older adults positively affects nutrition students’ attitudes toward this age group. J Nutr Educ Behav. 2007;39(3):150–6. https://doi.org/10.1016/j.jneb.2006.08.029 .

Tullo E, Greaves L, Wakeling L. Involving older people in the design, development, and delivery of an innovative module on aging for undergraduate students. Educ Gerontol. 2016;42(10):698–705. https://doi.org/10.1080/03601277.2016.1218705 .

Jeste DV, Avanzino J, Depp CA, Gawronska M, Tu X, Sewell DD, Huege SF. Effect of short-term research training programs on medical students’ attitudes toward aging. Gerontol Geriatr Educ. 2018;39(2):214–22. https://doi.org/10.1080/02701960.2017.1340884 .

Atkinson HH, Lambros A, Davis BR, Lawlor JS, Lovato J, Sink KM, Demons JL, Lyles MF, Watkins FS, Callahan KE, Williamson JD. Teaching medical student geriatrics competencies in 1 week: an efficient model to teach and document selected competencies using clinical and community resources. J Am Geriatr Soc. 2013;61(7):1182–7. https://doi.org/10.1111/jgs.12314 .

Laks J, Wilson LA, Khandelwal C, Footman E, Jamison M, Roberts E. Service-learning in communities of elders (SLICE): development and evaluation of an introductory geriatrics course for medical students. Teach Learn Med. 2016;28(2):210–8. https://doi.org/10.1080/10401334.2016.1146602 .

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NCA, MA and HC initiated and designed the study. CK and CeK collected the data. NCA, HC and CK analyzed and interpreted the results. CK designed the tables and figures. NCA wrote the original draft of the manuscript. NCA, MA, HC and CK contributed to the writing and editing of the manuscript. All authors read and approved the final manuscript.

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Aktekin, M., Aktekin, N.C., Celebi, H. et al. Being old is like…: perceptions of aging among healthcare profession students. BMC Med Educ 24 , 985 (2024). https://doi.org/10.1186/s12909-024-05959-1

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STEM experts from across the world join the University of Minnesota

The University of Minnesota College of Science and Engineering (CSE) welcomes 26 faculty members this 2023-24 academic year—on its way to achieving its goal to hire 60 faculty in three years.

The expertise of this new group of CSE researchers and educators is broad. They range in areas such as hybrid intelligence systems, the reconstruction of past environments and climates, electric machines and magnetic levitation, reinforced concrete structures, and mathematical models to predict the electronic properties of novel materials.

Meet our new science and engineering faculty:

Rene Boiteau is an assistant professor of chemistry. He joins Minnesota from Oregon State University, where he held a joint faculty appointment in the Pacific Northwest National Laboratory. Boiteau earned a bachelor’s in chemistry at Northwestern University, a master’s in earth sciences at University of Cambridge, and a Ph.D. in chemical oceanography at Massachusetts Institute of Technology and Woods Hole Oceanographic Institution. Much of his work is focused on developing analytical chemical approaches, especially mass spectrometry.

Zhu-Tian Chen is an assistant professor of computer science and engineering. He received his bachelor’s in software engineering from South China University of Technology and Ph.D. in computer science from Hong Kong University of Science and Technology. Prior to Minnesota, Chen served as a postdoctoral fellow at Harvard University and postdoctoral researcher at the University of California San Diego. His recent work focuses on enhancing human-data and human-AI interactions in both AR/VR environments—with applications in sports, data journalism, education, biomedical, and architecture.

Gregory “Greg” Handy is an assistant professor of mathematics . He comes to Minnesota from the University of Chicago, where he was a postdoctoral scholar in the Departments of Neurobiology and Statistics. As an applied mathematician and theoretical biologist, Handy’s research strives to use biological applications as inspiration to create new mathematical techniques, and to combine these techniques with classical approaches to examine the mechanisms driving biological processes. This fall, he is teaching Math 2142: Elementary Linear Algebra.

Jessica Hoover is a professor of chemistry. She joins the University of Minnesota from West Virginia University, where she has been a faculty member since 2012. Hoover’s interest in catalysis has been the focus of her work since her undergraduate studies. She graduated with a bachelor’s from Harvey Mudd College before arriving at the University of Washington to pursue her Ph.D. She was a postdoctoral researcher at the University of Wisconsin, Madison.

Harman Kaur is an assistant professor of computer science and engineering—and a University of Minnesota alumna (2016 bachelor’s in computer science). Her research areas are human-centered artificial intelligence, explainability and interpretability, and hybrid intelligence systems. She is affiliated with the GroupLens Research Lab, a group of faculty and students in her department that’s focused on human computing interaction. Prior to Minnesota, Kaur served as a graduate researcher in the interactive Systems Lab and comp.social Lab at the University of Michigan, where she received both her master’s and Ph.D.

Yulong Lu is an assistant professor of mathematics. He joins the faculty from University of Massachusetts, Amherst. Lu received his Ph.D. in mathematics and statistics at the University of Warwick. His research lies at the intersection of applied and computational mathematics, statistics, and data sciences. His recent work is focused on the mathematical aspects of deep learning. This fall, Lu is teaching Math 2573H: Honors Calculus III to undergraduates and Math 8600: Topics in Applied Mathematics, Theory of Deep Learning to graduate students.

Ben Margalit is an assistant professor of physics and astronomy. As a theoretical astrophysicist, he studies the fundamental physics of star explosions, collisions and other examples of intergalactic violence such as a black hole passing near a galaxy and “shredding it to spaghetti.” As part of his job, Margalit works closely with observational astronomers in selecting the kinds of places to look for transient events. He holds bachelor’s and master’s degrees from the Hebrew University of Jerusalem, and a Ph.D. from Columbia University.

Maru Sarazola is an assistant professor of mathematics. She joins Minnesota from Johns Hopkins University, where she was a J.J. Sylvester Assistant Professor. Sarazola received her Ph.D. from Cornell University. Her research is focused on algebraic topology—specifically, her interest lies in homotopy theory (a field that studies and classifies objects up to different notions of "sameness") and category theory (“the math of math,” which looks to abstract all structures to study their behavior). This fall, she is teaching Math 5285H: Honors Algebra I.

Eric Severson is an associate professor of mechanical engineering—and University of Minnesota alumnus (2008 bachelor’s and 2015 Ph.D. in electrical engineering). He returns to his alma mater after being on the University of Wisconsin-Madison faculty for six years. Severson leads research in electric machines and magnetic levitation, with a renewed focus in addressing grand challenges in energy and sustainability through multidisciplinary collaborations. His interests include extreme efficiency, bearingless machines, flywheel energy storage, and electric power grid technology.

Kelsey Stoerzinger is an associate professor of chemical engineering and materials science. She was on the faculty at Oregon State University, with a joint appointment in the Pacific Northwest National Laboratory. She studies the electrochemical transformation of molecules into fuels, chemical feedstocks, and recovered resources. Her research lab designs materials and processes for the storage of renewable electricity. Stoerzinger holds a bachelor’s from Northwestern University, master’s from University of Cambridge, and Ph.D. from MIT.

Lynn Walker is a professor—and the L.E. Scriven Chair in the Department of Chemical Engineering and Materials Science. Previously, she was on the faculty at Carnegie Mellon University. Her research focuses on developing the tools and fundamental understanding necessary to efficiently process soft materials and complex fluids. This expertise is being used to develop systematic approaches to incorporate sustainable feedstocks in consumer products. Walker holds a bachelor’s from the University of New Hampshire and Ph.D. from the University of Delaware. She was a postdoctoral researcher at Katholieke Universiteit Leuven in Belgium.

Alexander “Alex” Watson is an assistant professor of mathematics—and former University of Minnesota postdoctoral researcher in the School of Mathematics. Watson earned his Ph.D. at Columbia University. He works on mathematical models used to predict the electronic properties of materials, especially novel 2D materials such as graphene and twisted multilayer “moiré materials.” In summer 2022 and 2023, he presented at the U’s MathCEP Talented Youth Mathematics Program on topics related to materials research at the University of Minnesota.

Anna Weigandt is an assistant professor of mathematics. She comes to Minnesota from the Massachusetts Institute of Technology, where she was an instructor. Weigandt completed her Ph.D. at the University of Illinois, and she was a postdoctoral assistant professor in the Center for Inquiry Based Learning at University of Michigan. She works in algebraic combinatorics, specifically Schubert calculus. This fall 2023, she is teaching Math 5705: Enumerative Combinatorics.

Michael Wilking is a professor of physics—and University of Minnesota alumnus (2001 bachelor’s in chemical engineering). He holds a master’s and Ph.D. from the University of Colorado. Prior to his return to the Twin Cities campus, Wilking served on the faculty at Stony Brook University. He completed his post-doc at TRIUMF, Canada's national particle accelerator center. Wilking was part of the Stony Brook research team honored with the 2016 Breakthrough Prize in Fundamental Physics.

Benjamin "Ben" Worsfold is an assistant professor of civil engineering —and a licensed professional engineer in both California and Costa Rica. His research interest lies in large-scale structural testing, finite element analysis of reinforced concrete structures, and anchoring to concrete. Worsfold earned his master’s and Ph.D. from the University of California, Berkeley, and bachelor’s from the University of Costa Rica.

Yogatheesan Varatharajah is an assistant professor of computer science and engineering —and a visiting scientist in neurology at the Mayo Clinic. His research lies broadly in machine learning for health. Varatharajah earned his master’s and Ph.D. from the University of Illinois Urbana-Champaign. Prior to Minnesota, he was a research assistant professor of bioengineering at the University of Illinois and faculty affiliate for the Center for Artificial Intelligence Innovation with the National Center for Supercomputing Applications.

Starting in January 2024:

Emily Beverly is an incoming assistant professor of earth sciences. Prior to joining the University of Minnesota, she was on the faculty at University of Houston. She earned a bachelor’s from Trinity University, a master’s from Rutgers University, and a Ph.D. from Baylor University. Beverly was a postdoctoral researcher at Georgia State University and University of Michigan. Her research focuses on understanding environmental drivers of human and hominin evolution. Beverly uses stable isotopes and geochemistry to answer questions about past and future climates with a firm foundation in sedimentary geology and earth surface processes.

Alexander “Alex” Grenning is an assistant professor of chemistry. He comes to Minnesota from the University of Florida, where he was a tenured faculty. Grenning earned a bachelor’s in chemistry and music from Lake Forest College, and a Ph.D. in organic chemistry from the University of Kansas. He was a postdoctoral researcher at Boston University. His work is focused on chemical synthesis and drug discovery.

Rachel Gelhar is an incoming assistant professor of mechanical engineering. Her research focuses on developing and implementing nonlinear model-based control strategies for powered prosthetic legs, to improve generalizability of control methods across prosthesis users. She earned a B.S. 2016, Mechanical Engineering, University of St. Thomas., and both a master’s and Ph.D. in mechanical engineering from California Institute of Technology.

Yu Cao is an incoming professor of electrical and computer engineering. Prior to Minnesota, Cao was a professor at Arizona State University. He holds a bachelor’s in physics from Peking University and a master’s in biophysics plus a Ph.D. in electrical engineering and computer sciences from the University of California-Berkeley. His research includes neural-inspired computing, hardware design for on-chip learning, and reliable integration of nanoelectronics. Cao served as associate editor of the Institute of Electrical and Electronics Engineers’s monthly Transactions on CAD .

Edgar Peña is an incoming assistant professor of biomedical engineering—and a University of Minnesota alumnus (2017 Ph.D. in biomedical engineering). He is a neuromodulation scholar who is interested in vagus nerve stimulation. Peña earned his bachelor’s degrees in electrical engineering and biomedical engineering from the University of California, Irvine. During his doctoral studies at the University of Minnesota Twin Cities, he used computational models to optimize deep brain stimulation.

Seongjin Choi is an incoming assistant professor of civil engineering. He received his bachelor’s, master’s, and Ph.D. from the Korea Advanced Institute of Science and Technology. He was a postdoctoral researcher at McGill University. His work involves using data analytics to draw valuable insights from urban mobility data and applying cutting-edge AI technologies in the field of transportation.

Pedram Mortazavi is an incoming assistant professor of civil engineering— and a licensed structural engineer in Canada . His interests lie in structural resilience, steel structures, large-scale testing, development of damping and isolation systems, advanced simulation methods, and hybrid simulation. Mortazavi holds a bachelor’s from the University of Science and Culture in Iran, a master’s from Carleton University in Ottawa, and Ph.D. from the University of Toronto.

Gang Qiu is an incoming assistant professor of electrical and computer engineering. He received his bachelor’s degree from Peking University in microelectronics and his Ph.D. in electrical and computer engineering from Purdue University. (He is currently a postdoctoral researcher at the University of California, Los Angeles.) Qiu’s research focuses on novel low-dimensional materials for advanced electronics and quantum applications. His current interest includes employing topological materials for topological quantum computing.

Qianwen Wang is an incoming assistant professor of computer science and engineering. She received her bachelor’s from Xi’an Jiao Tong University and her Ph.D. from Hong Kong University of Science and Technology. Prior to Minnesota, Wang served as a post-doctoral researcher at Harvard University in the Department of Biomedical Informatics. As a visualization researcher, she created interactive visualization tools that enable humans to better interpret AI and generate insights from their data.

Katie (Yang) Zhao is an incoming assistant professor of electrical and computer engineering. Her research interest resides in the intersection between Domain-Specific Acceleration Chip and Computer Architecture. In particular, her work centers around enabling AI-powered intelligent functionalities on resource-constrained edge devices. Zhao received her bachelor’s and master’s from Fudan University, China, and Ph.D. from Rice University. (She is currently a postdoctoral researcher at Georgia Institute of Technology.)

If you’d like to support faculty research in the University of Minnesota College of Science and Engineering, visit our CSE Giving website .

Join our winning team

Our unique combination of science and engineering within one college in a vibrant, metropolitan area means more opportunities for you. Learn about faculty openings.

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(PDF) Robustness of Quantitative Research Methods in Mathematics Education
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Quantitative Research, Qualitative Research
Quantitative and Qualitative Research Methods
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PDF Research trends in mathematics education: A quantitative content
Research trends in mathematics education: A quantitative content analysis of major journals 2017-2021 . Katibe Gizem Yığ. 1. Burdur Mehmet Akif Ersoy University, Turkey (ORCID: 0000-0001-5783-3861) This research aims to uncover current trends and key issues by examining the research in mathematics education during the period 2017-2021.
Robustness of Quantitative Research Methods in Mathematics Education
Quantitative research methods are essential in mathematics education for standardized testing, data analysis, objectivity, and trend analysis, supporting student learning assessment and policy-making.
A quantitative methodology for analyzing the impact of the formulation
After a review of the related literature in Mathematics education and a review of the methodologies used until now to investigate this research issue, we describe in depth our quantitative approach, providing motivations and examples of its statistical relevance and its potentiality in making interesting phenomena emerge, to be interpreted with ...
The Combination of Qualitative and Quantitative Research Methods in
Research about education in mathematics is influenced by the ongoing dispute about qualitative and quantitative research methods. Especially in the domain of professional knowledge of teachers one can find a clear distinction between qualitative, interpretive studies on the one hand and large-scale quantitative assessment studies on the other hand.
(PDF) Quantitative Research in Education
The. quantitative research methods in education emphasise basic group designs. for research and evaluation, analytic metho ds for exploring re lationships. between categorical and continuous ...
PDF Prevalence of Mixed Methods Research in Mathematics Education
published research in mathematics education journals includes what is commonly known as mixed methods (or mixed) research (Johnson & Onwuegbuzie, 2004). For the purposes of this paper, we view qualitative research, quantitative research, and mixed methods research as representing the three major research or methodological paradigms.
Quantitative Reasoning in Mathematics Education: Directions in Research
Literacy (QL) - also known as Numeracy or Quantitative Reasoning (QR) - is a "habit of mind," competency, and comfort in working with numerical data.". Notably, working with numbers. plays a ...
Experimental methods in mathematics education research
1. We surveyed the 263 articles published in 2012 by eight leading mathematics education journals (Educational Studies in Mathematics, For the Learning of Mathematics, Journal of Mathematical Behavior, Journal of Mathematics Teacher Education, Journal for Research in Mathematics Education, Mathematical Thinking and Learning, Research in Mathematics Education and ZDM).
PDF Introduction to quantitative research
Mixed-methods research is a flexible approach, where the research design is determined by what we want to find out rather than by any predetermined epistemological position. In mixed-methods research, qualitative or quantitative components can predominate, or both can have equal status. 1.4. Units and variables.
PDF MATHEMATICS EDUCATION RESEARCH: A Guide for the Research Mathematician
Mathematics education research is research using the paradigms and methods of educational research and, more specifically, its applications to mathematics teach-ing and learning. Even applied to undergraduate mathematics education, this re-search uses methods and models not typically familiar to research mathematicians.
Handbook of Quantitative Methods for Educational Research
This handbook serves to act as a reference for educational researchers and practitioners who desire to acquire knowledge and skills in quantitative methods for data analysis or to obtain deeper insights from published works. Written by experienced researchers and educators, each chapter in this handbook covers a methodological topic with ...
Robustness of Quantitative Research Methods in Mathematics Education
This study explored the robustness of quantitative research methods in mathematics education citing the challenges and importance of developing theoretical, conceptual, and analytical models to enhance teaching and learning in mathematics. This review systematically examines existing literature to investigate the strengths and limitations of quantitative methods in evaluating educational ...
Mixed methods integration strategies used in education: A systematic
Mixed methods research (MMR), which has been defined as collecting both qualitative and quantitative data sets then integrating both components to answer the research question in a single study (Creswell, 2015; Creswell and Plano Clark, 2018), has been self-evident by its burgeoning body of cross-discipline literature, including education, psychology, and health sciences, etc.
Quantitative research in education : Background information
Educational research has a strong tradition of employing state-of-the-art statistical and psychometric (psychological measurement) techniques. Commonly referred to as quantitative methods, these techniques cover a range of statistical tests and tools. The Sage encyclopedia of educational research, measurement, and evaluation by Bruce B. Frey (Ed.)
Research Methods in Mathematics Teacher Education
Hart, Smith, Swars and Smith surveyed the research methods used in mathematics education by examining the use of qualitative, quantitative and mixed methods in articles published between 1995 and 2005 in major research journals in mathematics education. A subset of their data corpus consisted of all the articles published in JMTE, ESM and JRME ...
PDF Artificial intelligence in mathematics education: A systematic
reviewed studies used quantitative research methods. The types of themes for AI in mathematics education were categorized into advantages and disadvantages, conceptual understanding, factors, role, idea suggestion, ... under the existing education conditions (Wu, 2021). In mathematics education in particular, the animation of figure and of ...
Key Challenges and Some Guidance on Using Strong Quantitative ...
The current article reviews several common areas of focus in quantitative methods with the hope of providing Journal of Urban Mathematics Education (JUME) readers and researchers with some guidance on conducting and reporting quantitative analyses. After providing some background for the discussion, the methodological nature of recent JUME articles is reviewed, followed by commentary on key ...
Research trends in mathematics education: A quantitative content
According to the common findings from both analysis approaches, in the 2017-2021 period, the most focused and prominent research issues in the field of mathematics education have revealed ...
Critical Quantitative Literacy: An Educational Foundation for Critical
Quantitative research in the social sciences is undergoing a change. After years of scholarship on the oppressive history of quantitative methods, quantitative scholars are grappling with the ways that our preferred methodology reinforces social injustices (Zuberi, 2001).Among others, the emerging fields of CritQuant (critical quantitative studies) and QuantCrit (quantitative critical race ...
Doing Quantitative Research in Education with SPSS
Doing Quantitative Research in Education with SPSS. achievement, attitudes, grade point average, population, pupils, self-concept, …. This book provides an introduction to using quantitative methods in educational research. The author writes for non-mathematical students, avoiding the use of mathematical formulae wherever possible.
Examining purposeful researchable questions in mathematics education
When asking purposeful researchable questions in mathematics education it is generally best practice to employ the following strategies: (a) use a coherent theoretical framework, (b) clearly state the research questions and hypotheses, (c) employ a sound research design and methods, (d) support the results and implications of the study with ...
Factors Affecting Attitude Toward Learning Mathematics: A Case of
This quantitative research study explores the factors that impact the attitude toward math in higher education institutions in the UAE. Higher education students in the UAE were identified with a positive attitude toward math with no gender-based differences. ... Mathematics Education Research Group of Australasia, Paper presented at the Annual ...
Classpoint Application As a Digital Transformation in Mathematics Education
This research is quantitative descriptive research with the research subjects of 31 first-semester students of the Informatics Engineering study program in one of the universities in East Java. The instruments used in this research are activeness set in ClassPoint, assessment results from student assignments, and response questionnaires.
The Effectiveness of AI on K-12 Students' Mathematics ...
Artificial intelligence (AI) shows increasing potential to improve mathematics instruction, yet integrative quantitative evidence currently is lacking on its overall effectiveness and factors influencing success. This systematic review and meta-analysis investigate the effectiveness of AI on improving mathematics performance in K-12 classrooms compared to traditional classroom instruction ...
Comprehensive Guide to Quantitative Research Methods in Education
The research design may be quantitative, qualitative, or a mixed methods design. The focus of this overview is quantitative methods. The general purpose of quantitative research is to explain, predict, investigate relationships, describe current conditions, or to examine possible impacts or influences on designated outcomes.
The Combination of Qualitative and Quantitative Research Methods in
Research about education in mathematics is influenced by the ongoing dispute about qualitative and quantitative research methods. Especially in the domain of professional knowledge of teachers one ...
Review of mathematics education in the age of artificial intelligence
Manolis Mavrikis is a Professor at the UCL Knowledge Lab, an interdisciplinary research centre at the UCL Faculty of Education and Society. With a research agenda spanning over 20 years, Manolis has contributed to the field through various international projects and partnerships with schools and third-sector organisations.
Being old is like…: perceptions of aging among healthcare profession
Design, setting and participants. In this study, we employed a mixed-methods research approach, chosen for its capacity to yield a more nuanced interpretation of both the data and the phenomenon under scrutiny [].Specifically, we adhered to a convergent parallel mixed-methods design, wherein the researcher concurrently conducted the quantitative and qualitative components, treating them with ...
CSE welcomes 26 new faculty in 2023-24
His research lies at the intersection of applied and computational mathematics, statistics, and data sciences. His recent work is focused on the mathematical aspects of deep learning. This fall, Lu is teaching Math 2573H: Honors Calculus III to undergraduates and Math 8600: Topics in Applied Mathematics, Theory of Deep Learning to graduate ...

Quantitative research in education : Background information

Key Challenges and Some Guidance on Using Strong Quantitative Methodology in Education Research

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Being old is like…: perceptions of aging among healthcare profession students

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CSE welcomes 26 new faculty in 2023-24

STEM experts from across the world join the University of Minnesota

Meet our new science and engineering faculty:

Starting in January 2024:

Join our winning team

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