Teaching Tools to Enhance Student Engagement in Higher Education
Abstract
Objectives of the research: This project aims to identify the most effective tools for increasing student engagement.
Research methods: An ad hoc questionnaire to measure the engagement capacity of teaching tools, principal component analysis (PCA), and machine learning forward regression.
Structure of the article: Introduction, methodology and results (sampling, PCA, forward regression), discussion, and conclusions.
Research findings: Active interaction and modular organization promote student engagement. A student’s inability to respond to questions about improving a subject often indicates a lack of interest. Engagement increases when previous teaching experiences have not incorporated interactive tools. Pre-class homework assignments enhance interest and make courses more practical. Tools that facilitate teacher-student interaction improve engagement, regardless of whether the teaching style is based on the teacher’s practical experience or a student-centered approach.
Conclusions and recommendations: This research identifies several factors that significantly influence student engagement, including a modular structure, active classroom participation, pre- and post-class assignments, content quality, teaching style, and interaction through discussion platforms.
References
Alonso, F., López, G., Manrique, D., & Viñes, J. M. (2013). An instructional model for web-based e-learning education with a blended learning process approach. British Journal of Educational Technology, 34(4), Article 4. https://doi.org/10.1111/j.1467-8535.2003.00376.x
Anandarajan, M., & Simmers, C. A. (2017). Simulation game pedagogy: Bringing teamwork, problem-solving, and decision-making to the classroom. Journal of Education for Business, 92(2), Article 2.
Arabie, P. (1991). Multivariate Analysis of Data in Sensory Science. Elsevier Science Pub. Co.
Bandura, A. (1977). Social Learning Theory. Prentice Hall.
Barr, R. B., & Tagg, J. (1995). From teaching to learning: A new paradigm for undergraduate education. Change: The Magazine of Higher Learning, 27(6), Article 6.
Bartlett, M. S. (1950). The test of significance in factor analysis. Psychometrika, 15(3), Article 3.
Bollen, K. A., Glanville, J. L., & Stecklov, G. (2002). Economic status proxies in studies of fertility in developing countries: Does the measure matter? Population Studies, 56(1), 81–96. https://doi.org/10.1080/00324720213796
Bruner, J. (1960). The Process of Education. Harvard University Press.
Büchele, S. (2021). Evaluating the link between attendance and performance in higher education: The role of classroom engagement dimensions. Assessment & Evaluation in Higher Education, 46(1), 132–150. https://doi.org/10.1080/02602938.2020.1754330
Bušljeta Kardum, R., & Jurić Vukelić, D. (2021). The Challenges and Issues on the University of Zagreb during COVID-19 Crisis. Interdisciplinary Description of Complex Systems : INDECS, 19(3), 357–365. https://doi.org/10.7906/indecs.19.3.1
Caner, M. (2012). The Definition of Blended Learning in Higher Education. In Blended Learning Environments for Adults: Evaluations and Frameworks (pp. 19–34). IGI Global. https://doi.org/10.4018/978-1-4666-0939-6.ch002
Cattell, R. B. (1965). The Scientific Analysis of Personality (p. 399). Penguin Books.
Chiu, T. K. F. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(sup1), S14–S30. https://doi.org/10.1080/15391523.2021.1891998
Costello, A. B., & Osborne, J. W. (2005). Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most from your Analysis. Practical Assessment, Research & Evaluation.
Deng, R., Benckendorff, P., & Gannaway, D. (2020). Learner engagement in MOOCs: Scale development and validation. British Journal of Educational Technology, 51(1), 245–262. https://doi.org/10.1111/bjet.12810
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), Article 3.
Filmer, D., & Pritchett, L. H. (2001). Estimating wealth effects without expenditure data—or tears: An application to educational enrollments in states of India. Demography, 38(1), 115–132. https://doi.org/10.1353/dem.2001.0003
Freire, P. (1970). Pedagogy of the Oppressed. Bloomsbury.
Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
Heimbuch, B. K., & Lubbe, C. M. (2020). Incorporating Gamification into Higher Education: Motivation, Engagement, and Academic Performance. Journal of Education for Business, 95(1), Article 1.
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), Article 2.
Horta, H., Panova, A., Santos, J., & Yudkevich, M. (2022). The adaptation of academics to the Covid-19 crisis in terms of work time allocation. PLOS ONE, 17(8), e0273246. https://doi.org/10.1371/journal.pone.0273246
Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. John Wiley & Sons.
Howe, C., Hennessy, S., Mercer, N., Vrikki, M., & Wheatley, L. (2019). Teacher–Student Dialogue During Classroom Teaching: Does It Really Impact on Student Outcomes? Journal of the Learning Sciences, 28(4–5), 462–512. https://doi.org/10.1080/10508406.2019.1573730
Jolliffe, I. T. (1986). Principal component analysis. Springer-Verlag New York.
Jones, L. (2007). The student-centered classroom. Cambridge University Press.
Kaiser, H. F. (1970). A second generation little jiffy. Psychometrika, 35(4), 401–415. https://doi.org/10.1007/BF02291817
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), Article 1.
Khalil, M., & Ebner, M. (2013). Learning analytics: Principles and constraints. EDM, 1–8. https://doi.org/10.1145/2491504.2491529
Kolenikov, S. & Angeles, G. (2009). Socioeconomic Status Measurement With Discrete Proxy Variables: Is Principal Component Analysis A Reliable Answer? Review of Income and Wealth, 55(1), 128–165. https://doi.org/10.1111/j.1475-4991.2008.00309.x
Lightner, C. A., & Lightner-Laws, C. A. (2016). A blended model: Simultaneously teaching a quantitative course traditionally, online, and remotely. Interactive Learning Environments, 24(1), 224–238. https://doi.org/10.1080/10494820.2013.841262
McKenzie, D. (2005). Measuring inequality with asset indicators. Journal of Population Economics, 18(2), 229–260.
Mishra, J., & Attri, V. (2020). Governance, Public Service Delivery and Trust in Government. Studies in Indian Politics, 8(2), 186–202. https://doi.org/10.1177/2321023020963518
Mortimer, E. F., & Scott, P. H. (2003). Meaning making in secondary science classrooms. McGraw-Hill Education.
Mustafa, S., Qiao, Y., Yan, X., Anwar, A., Hao, T., & Rana, S. (2022). Digital Students’ Satisfaction With and Intention to Use Online Teaching Modes, Role of Big Five Personality Traits. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.956281
Piaget, J. (1970). Science of Education and the Psychology of the Child. Viking Press.
Ramoshaba, N. D. J., & Kgarose, M. F. (2022). Analysing coping strategies of students for online teaching and learning during the COVID-19 pandemic. International Journal of Research in Business and Social Science (2147–4478), 11(9). https://doi.org/10.20525/ijrbs.v11i9.2192
Rijst, R. V. der, Guo, P., & Admiraal, W. (2023). Student engagement in hybrid approaches to teaching in higher education. Revista de Investigación Educativa, 41(2), Article 2. https://doi.org/10.6018/rie.562521
Skinner, E. A. (2023). Four Guideposts toward an Integrated Model of Academic Motivation: Motivational Resilience, Academic Identity, Complex Social Ecologies, and Development. Educational Psychology Review, 35(3), 80. https://doi.org/10.1007/s10648-023-09790-w
Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of Educational Psychology, 85(4), 571–581. https://doi.org/10.1037/0022-0663.85.4.571
Song, Y., Westerhuis, J. A., Aben, N., Michaut, M., Wessels, L. F. A., & Smilde, A. K. (2019). Principal component analysis of binary genomics data. Briefings in Bioinformatics, 20(1), 317–329. https://doi.org/10.1093/bib/bbx119
Stevens, J. (1996). Applied multivariate statistics for the social sciences. Lawrence Erlbaum Associates.
Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
Vyas, S., & Kumaranayake, L. (2006). Constructing socio-economic status indices: How to use principal components analysis. Health Policy and Planning, 21(6), 459–468. https://doi.org/10.1093/heapol/czl029
Vygotsky, L. (1930). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press.
Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219–246. https://doi.org/10.1177/0095798418771807
Yang, J., Huang, H., & Ren, L. (2020). The Influence of Gamification on Students’ Learning Engagement and Performance in Higher Education. International Journal of Information and Education Technology, 10(3), Article 3.
Yeomans, K. A., & Golder, P. A. (1982). The Guttman-Kaiser criterion as a predictor of the number of common factors. Journal of the Royal Statistical Society. Series D (The Statistician), 31(3), 221–229.
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