Investigating the effect of an adaptive learning intervention on students’ learning

Educational Technology Research and Development

Volumn 65, Issue 6, 2017, Pages 1605-1625

  Liu, Min ^a   McKelroy, Emily ^a   Corliss, Stephanie B ^b   Carrigan, Jamison ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Adaptive learning; College teaching; Design; Math anxiety; Mixed methods; Personalized learning

Indexed keywords

EID: 85029739625     PISSN: 10421629     EISSN: 15566501     Source Type: Journal    
DOI: 10.1007/s11423-017-9542-1     Document Type: Article

References (49)

1. ACPE. (2015). Accreditation standards and key elements for the professional program in pharmacy leading to the doctor of pharmacy degree. Chicago, IL: Accreditation Council for Pharmacy Education.
2. Akin, A., & Kurbanoglu, I. N. (2011). The relationships between math anxiety, math attitudes, and self-efficacy: A structural equation model. Studia Psychologica, 53(3), 263.
3. Al-Dahir, S., Bryant, K., Kennedy, K. B., & Robinson, D. S. (2014). Online virtual-patient cases versus traditional problem-based learning in advanced pharmacy practice experiences. American Journal of Pharmaceutical Education. doi:10.5688/ajpe78476.
4. Allen, R. E., & Diaz, C., Jr. (2013). Use of preadmission criteria and performance in the Doctor of Pharmacy program to predict success on the North American pharmacists licensure examination. American Journal of Pharmaceutical Education, 77(9), 193.
5. Baghaei, N., Mitrovic, A., & Irwin, W. (2007). Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams. International Journal of Computer-Supported Collaborative Learning, 2(2), 159–190.
6. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. doi:10.1037/0033-295X.84.2.191.
7. Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147. doi:10.1037/0003-066X.37.2.122.
8. Benedict, N. (2010). Virtual patients and problem-based learning in advanced therapeutics. American Journal of Pharmaceutical Education, 74(8), 143.
9. Betz, N. E. (1978). Prevalence, distribution, and correlates of math anxiety in college students. Journal of Counseling Psychology, 25(5), 441–448. doi:10.1037/0022-0167.25.5.441.
10. Binet. A., & Simon, T. (1916). The development of intelligence in children. Baltimore, Williams & Wilkins. (Reprinted 1973, New York: Arno Press; 1983, Salem, NH: Ayer Company).
11. Blumberg, P. (2009). Maximizing learning through course alignment and experience with different types of knowledge. Innovative Higher Education, 34(2), 93–103.
12. Chamberlin, S. A. (2010). A review of instruments created to assess affect in mathematics. Journal of Mathematics Education, 3(1), 167–182.
13. Cooper, S. E., & Robinson, D. A. (1991). The relationship of mathematics self-efficacy beliefs to mathematics anxiety and performance. Measurement and Evaluation in Counseling and Development, 24(1), 4–11.
14. Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Thousand Oaks, CA: Sage Publications.
15. Dew, K. H., & Galassi, J. P. (1983). Mathematics anxiety: Some basic issues. Journal of Counseling Psychology, 30(3), 443. doi:10.1037/0022-0167.30.3.443.
16. Foshee, C. M., Elliott, S. N., & Atkinson, R. K. (2016). Technology-enhanced learning in college mathematics remediation. British Journal of Educational Technology, 47(5), 893–905. doi:10.1111/bjet.12285.
17. Hackett, G., & Betz, N. E. (1989). An exploration of the mathematics self-efficacy/mathematics performance correspondence. Journal for Research in Mathematics Education, 20(3), 261–273. doi:10.2307/749515
18. Ho, H.-Z., Senturk, D., Lam, A. G., Zimmer, J. M., Hong, S., Okamoto, Y.,… Wang, C.-P. (2000). The affective and cognitive dimensions of math anxiety: A cross-national study. Journal for Research in Mathematics Education, 31, 362–379.
19. Howlin, C. (2014). Realizeit at the University of Central Florida: Results from initial trials of Realizeit at the University of Central Florida: Dublin, Ireland
20. Hsieh, T.-C., Lee, M.-C. L., & Su, C.-Y. (2013). Designing and implementing a personalized remedial learning system for enhancing the programming learning. Journal of Educational Technology & Society, 16(4), 32–46.
21. Jameson, M. M., & Fusco, B. R. (2014). Math anxiety, math self-concept, and math self-efficacy in adult learners compared to traditional undergraduate students. Adult Education Quarterly, 64(4), 306–322. doi:10.1177/0741713614541461.
22. Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC Horizon Report: 2016 Higher (Education ed.). Austin, TX: The New Media Consortium.
23. Johnson, B. G., Phillips, F., & Chase, L. G. (2009). An intelligent tutoring system for the accounting cycle: Enhancing textbook homework with artificial intelligence. Journal of Accounting Education, 27(1), 30–39.
24. Johnson, D., & Samora, D. (2016). The potential transformation of higher education through computer-based adaptive learning systems. Global Education Journal, 2016(1), 1–17.
25. Kauffman, H. (2015). A review of predictive factors of student success in and satisfaction with online learning. Research in Learning Technology, 23(1), 26507.
26. Kelly, D. (2008). Adaptive versus learner control in a multiple intelligence learning environment. Journal of Educational Multimedia & Hypermedia, 17(3), 307–336.
27. Kennedy, D., Hyland, A., & Ryan, N. (2007). Writing and using learning outcomes: A practical guide. Retrieved from https://www.researchgate.net/publication/238495834_Writing_and_Using_Learning_Outcomes_A_Practical_Guide.
28. Kerr, P. (2016). Adaptive learning. ELT Journal, 70(1), 88–93. doi:10.1093/elt/ccv055.
29. Koedinger, K. R., & Aleven, V. (2007). Exploring the assistance dilemma in experiments with cognitive tutors. Educational Psychology Review, 19(3), 239–264. doi:10.1007/s10648-007-9049-0.
30. Kong, S. C., Chan, T.-W., Griffin, P., Ulrich, H., Huang, R., Kinshuk,… Yu, S. (2014). E-learning in school education in the coming 10 years for developing 21st century skills: Critical research issues and policy implications. Journal of Educational Technology & Society, 17(1), 70–78
31. Lin, C. C., Guot, K. H., & Lin, Y. C. (2016). A simple and effective remedial learning system with a fuzzy expert system. Journal of Computer Assisted Learning, 32(6), 647–662. doi:10.1111/jcal.12160.
32. Mampadi, F., Chen, S. Y., Ghinea, G., & Chen, M. P. (2011). Design of adaptive hypermedia learning systems: A cognitive style approach. Computers & Education, 56(4), 1003–1011. doi:10.1016/j.compedu.2010.11.018.
33. Messick, S. (1979). Potential uses of non-cognitive measurement in education. Journal of Educational Psychology, 71, 281–292.
34. Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative data analysis: A methods sourcebook (3rd ed.). Thousand Oaks, CA: Sage Publications.
35. Mullis, I. V. S., Martin, M. O., Gonzalez, E. J., & Chrostowski, S. J. (2004). TIMSS 2003 International Mathematics: Report findings from IEA’s Trends in International Mathematics and Science Study at the Fourth and Eighth Grades. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.
36. Nakic, J., Granic, A., & Glavinic, V. (2015). Anatomy of student models in adaptive learning systems: A systematic literature review of individual differences from 2001 to 2013. Journal of Educational Computing Research, 51(4), 459–489. doi:10.2190/EC.51.4.e.
37. Pajares, F., & Kranzler, J. (1995). Self-efficacy beliefs and general mental ability in mathematical problem-solving. Contemporary Educational Psychology, 20(4), 426–443. doi:10.1006/ceps.1995.1029.
38. Salvatori, P. (2001). Reliability and validity of admissions tools used to select students for the health professions. Advances in Health Sciences Education, 6(2), 159–175.
39. Sancho, P., Moreno-Ger, P., Fuentes-Fernández, R., & Fernández-Manjón, B. (2009). Adaptive role playing games: An immersive approach for problem based learning. Part of A Special Issue: New Directions in Advanced Learning Technologies, 12(4), 110–124.
40. Seifert, T. A., Pascarella, E. T., Wolniak, G. C., & Cruce, T. M. (2006). Impacts of good practices on cognitive development, learning orientations, and graduate degree plans during the first year of college. Journal of College Student Development, 47(4), 365–383. doi:10.1353/csd.2006.0042.
41. Simon-Campbell, E. L., & Phelan, J. (2016). Effectiveness of an adaptive quizzing system as an institutional-wide strategy to improve student learning and retention. Nurse Educator, 41(5), 246–251. doi:10.1097/NNE.0000000000000258.
42. Smith, M. A., Mohammad, R. A., & Benedict, N. (2014). Use of virtual patients in an advanced therapeutics pharmacy course to promote active, patient-centered learning. American Journal of Pharmaceutical Education, 78(6), 125. doi:10.5688/ajpe786125.
43. Somyürek, S. (2015). The new trends in adaptive educational hypermedia systems. International Review of Research in Open and Distributed Learning, 16(1), 221–241.
44. U.S. Department of Education. (2013). Expanding evidence approaches for learning in a digital world. Washington, D.C: U.S. Department of Education.
45. van Seters, J. R., Wellink, J., Tramper, J., Goedhart, M. J., & Ossevoort, M. A. (2012). A web-based adaptive tutor to teach PCR primer design. Biochemistry and Molecular Biology Education, 40(1), 8–13. doi:10.1002/bmb.20563.
46. Walkington, C. A. (2013). Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology. doi:10.1037/a0031882.
47. Wiggins, G. P., & McTighe, J. (2011). The understanding by design guide to creating high-quality units. Alexandria: ASCD.
48. Yang, Y.-T. C., Gamble, J. H., Hung, Y.-W., & Lin, T.-Y. (2014). An online adaptive learning environment for critical-thinking-infused English literacy instruction. British Journal of Educational Technology, 45(4), 723–747. doi:10.1111/bjet.12080.
49. Yarnall, L., Means, B., & Wetzel, T. (2016). Lessons learned from early implementations of adaptive courseware. Retrieved from SRI International website: https://www.sri.com/sites/default/files/brochures/almap_final_report.pdf