Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018

Abstract

This systematic review of research on adaptive learning used a strategic search process to synthesize research on adaptive learning based on publication trends, instructional context, research methodology components, research focus, adaptive strategies, and technologies. A total of 61 articles on adaptive learning were analyzed to describe the current state of research and identify gaps in the literature. Descriptive characteristics were recorded, including publication patterns, instructional context, and research methodology components. The count of adaptive learning articles published fluctuated across the decade and peaked in 2015. During this time, the largest concentration of adaptive learning articles appeared in Computers and Education. The majority of the studies occurred in higher education in Taiwan and the United States, with the highest concentration in the computer science discipline. The research focus, adaptive strategies, and adaptive technologies used in these studies were also reviewed. The research was aligned with various instructional design phases, with more studies examining design and development, and implementation and evaluation. For examining adaptive strategies, the authors examined both adaptive sources based on learner model and adaptive targets based on content and instructional model. Learning style was the most observed learner characteristic, while adaptive feedback and adaptive navigation were the most investigated adaptive targets. This study has implications for adaptive learning designers and future researchers regarding the gaps in adaptive learning research. Future studies might focus on the increasing availability and capacities of adaptive learning as a learning technology to assist individual learning and personalized growth.

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Fig. 1

adapted from Shute and Towle (2003) and •Vandewaetere et al. (2011)

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References

Articles with bullet (•) are studies included in the systematic review

  1. Akbulut, Y., & Cardak, C. S. (2012). Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000–2011. Computers & Education,58(2), 835–842. https://doi.org/10.1016/j.compedu.2011.10.008.

    Google Scholar 

  2. •Bower, M. (2016). A framework for adaptive learning design in a web-conferencing environment. Journal of Interactive Media in Education,1(11), 1–21. https://doi.org/10.5334/jime.406.

    Google Scholar 

  3. Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education,13, 159–172.

    Google Scholar 

  4. •Cecilia, M. R., Vittorini, P., & di Orio, F. (2016). An adaptive learning system for developing and improving reading comprehension skills. Journal of Educational Research,10(4), 195–236.

    Google Scholar 

  5. •Chou, C.-Y., Lai, K. R., Chao, P.-Y., Lan, C.-H., & Chen, T.-H. (2015). Negotiation based adaptive learning sequences: Combining adaptivity and adaptability. Computers & Education,88, 215–226.

    Google Scholar 

  6. •Da-le-Fuente-ValentÃ-n, L., Pardo, A., & Kloos, C. D. (2011). Generic service integration in adaptive learning experiences using IMS learning design. Computers & Education,57(1), 1160–1170. https://doi.org/10.1016/j.compedu.2010.12.007.

    Google Scholar 

  7. •Dziuban, C. D., Moskal, P. D., Cassisi, J., & Fawcett, A. (2016). Adaptive learning in psychology: Wayfinding in the digital age. Online Learning,20(3), 74–96.

    Google Scholar 

  8. •Fasihuddin, H., Skinner, G., & Athauda, R. (2017). Towards adaptive open learning environments: Evaluating the precision of identifying learning styles by tracking learners’ behaviours. Education and Information Technologies,22(3), 807–825. https://doi.org/10.1007/s10639-015-9458-5.

    Google Scholar 

  9. •Griff, E. R., & Matter, S. F. (2013). Evaluation of online learning system. British Journal of Educational Technology,44(1), 170–176. https://doi.org/10.1111/j.1467-8535.2012.01300.x.

    Google Scholar 

  10. •Hammami, S., & Mathkour, H. (2013). Adaptive e-learning system based on agents and object petri nets (AELS-A/OPN). Computer Applications in Engineering Education,23(2), 170–190. https://doi.org/10.1002/cae.21587.

    Google Scholar 

  11. Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. London: Routledge.

    Google Scholar 

  12. •Hsu, P.-S. (2012). Learner characteristic based learning effort curve mode: The core mechanism on developing personalized adaptive elearning platform. Turkish Online Journal of Educational Technology,11(4), 210–220.

    Google Scholar 

  13. •Hsu, C.-K. (2015). Learning motivation and adaptive video caption filtering for EFL learners using handheld devices. ReCALL,27(1), 84–103. https://doi.org/10.1017/S0958344014000214.

    Google Scholar 

  14. •Huang, S.-L., & Shiu, J.-H. (2012). A user-centric adaptive learning system for e-Learning 2.0. Educational Technology & Society,15(3), 214–225.

    Google Scholar 

  15. •Huang, S.-L., & Yang, C.-W. (2009). Designing a semantic bliki system to support different types of knowledge and adaptive learning. Computers & Education,53(3), 701–712. https://doi.org/10.1016/j.compedu.2009.04.011.

    Google Scholar 

  16. Jhangiani, R., Tarry, H., & Stangor, C. (2014). Principles of social psychology-1st international edition. BC Campus Open Education. Retrieved June 15, 2019, from https://opentextbc.ca/socialpsychology/.

  17. •Jong, B. S., Chen, C. M., Chan, T. Y., Hsia, Y. T., & Lin, T. W. (2012). Applying learning portfolios and thinking styles to adaptive remedial learning. Computer Applications in Engineering Education,20, 45–61. https://doi.org/10.1002/cae.20372.

    Google Scholar 

  18. •Jonsdottir, A. H., Jakobsdottir, A., & Stefansson, G. (2015). Development and use of an adaptive learning environment to research online study behavior. Educational Technology & Society,18(1), 132–144.

    Google Scholar 

  19. Kerr, P. (2016). Adaptive learning. ETL Journal,70(1), 88–93. https://doi.org/10.1093/elt/ccv055.

    Google Scholar 

  20. •Kolekar, S. V., Pai, R. M., & Manohara Pai, M. M. (2017). Prediction of learner’s profile based on learning styles in adaptive e-learning system. International Journal of Emerging Technologies in Learning,12(6), 31–51. https://doi.org/10.3991/ijet.v12i06.6579.

    Google Scholar 

  21. Kumar, A., Singh, N., & Ahuja, N. J. (2017). Learning styles based adaptive intelligent tutoring systems: Document analysis of articles published between 2001 and 2016. International Journal of Cognitive Research in Science, Engineering and Education,5(2), 83–97. https://doi.org/10.5937/ijcrsee1702083k.

    Google Scholar 

  22. •Liu, M., Kang, J., Zou, W. T., Lee, H., Pan, Z. L., & Corliss, S. (2017a). Using data to understand how to better design adaptive learning. Technology, Knowledge and Learning,22(3), 271–298. https://doi.org/10.1007/s10758-017-9326-z.

    Google Scholar 

  23. •Liu, M., McKelroy, E., Corliss, S. B., & Carrigan, J. (2017b). 43-Investigating the effect of an adaptive learning intervention on students’ learning. Educational Technology Research and Development,65(6), 1605–1625. https://doi.org/10.1007/s11423-017-9542-1.

    Google Scholar 

  24. •Louhab, F. E., Bahnasse, A., & Talea, M. (2018). Considering mobile device constraints and context-awareness in adaptive mobile learning for flipped classroom. Education and Information Technologies,23(6), 2607–2632. https://doi.org/10.1007/s10639-018-9733-3.

    Google Scholar 

  25. Lowendahl, J. M., Thayer, T. L. B., & Morgan, G. (2016). Top 10 strategic technologies impacting higher education in 2016. Research Note G00294732, 15.

  26. Lynch, D. J., & Howlin, C. P. (2014). Uncovering Latent Knowledge: A Comparison of Two Algorithms. UMAP 2014, LNCS 8538 (pp. 363–368). Cham: Springer International Publishing.

  27. •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. https://doi.org/10.1016/j.compedu.2010.11.018.

    Google Scholar 

  28. •Marković, S., Jovanović, Z., Jovanović, N., Jevremović, A., & Popović, R. (2013). Adaptive distance learning and testing system. Computer Applications in Engineering Education,21(S1), E2–E13. https://doi.org/10.1002/cae.20510.

    Google Scholar 

  29. Martin, F., & Markant, D. (2019). Adaptive learning modules. In M. E. David & M. J. Amey (Eds.), The SAGE encyclopedia of higher education. London: Sage.

    Google Scholar 

  30. •Matthews, K., Janicki, T., He, L., & Patterson, L. (2012). Implementation of an automated grading system with an adaptive learning component to affect student feedback and response time. Journal of Information Systems Education,23(1), 71–83.

    Google Scholar 

  31. •Mei, J., Guo, Y. H., & Li, X. K. (2017). Adaptive learning mode of a multimedia-based “English literature” learning system. International Journal of Emerging Technologies in Learning,12(1), 71–83. https://doi.org/10.3991/ijet.v12i01.6483.

    Google Scholar 

  32. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine,151(4), 264–269.

    Google Scholar 

  33. Nakic, J., Granic, A., & Glavinic, V. (2001to). Anatomy of student models in adaptive learning systems: A systematic literature review of individual differences from 2001to 2013. Journal of Educational Computing Research,51(4), 459–489. https://doi.org/10.2190/EC.51.4.e.

    Google Scholar 

  34. •Neubrand, C., & Harms, U. (2017). Tackling the difficulties in learning evolution: Effects of adaptive self-explanation prompts. Journal of Biological Education,51(4), 336–348. https://doi.org/10.1080/00219266.2016.1233129.

    Google Scholar 

  35. New Media Consortium. (2018). NMC Horizon Report: 2018 Education Edition. Retrieved June 15, 2019, from https://library.educause.edu/~/media/files/library/2018/8/2018horizonreport.pdf.

  36. Normadhi, N. B. A., Shuib, L., Nasir, H. N. M., Bimba, A., Idris, N., & Balakrishnan, V. (2019). Identification of personal traits in adaptive learning environment: Systematic literature review. Computers & Education,130, 168–190. https://doi.org/10.1016/j.compedu.2018.11.005.

    Google Scholar 

  37. •Ortigosa, A., Paredes, P., & Rodriguez, P. (2010). AH-questionnaire: An adaptive hierarchical questionnaire for learning styles. Computers & Education,54(4), 999–1005. https://doi.org/10.1016/j.compedu.2009.10.003.

    Google Scholar 

  38. Paramythis, A., & Loidl-Reisinger, S. (2004). Adaptive leanring environments and e-Learning standards. Electronic Journal on e-Learning,2(1), 181–194.

    Google Scholar 

  39. •Polat, E., Adiguzel, T., & Akgun, O. E. (2012). Adaptive web-assisted learning system for students with specific learning disabilities: A needs analysis study. Educational Sciences: Theory and Practice,12, 3243–3258.

    Google Scholar 

  40. •Premlatha, K. R., Dharani, B., & Geetha, T. V. (2016). Dynamic learner profiling and automatic learner classification for adaptive e-learning environment. Interactive Learning Environments,24(6), 1054–1075. https://doi.org/10.1080/10494820.2014.948459.

    Google Scholar 

  41. Rosita, C. M., Vittorini, P., & di Orio, F. (2016). An adaptive learning system for developing and improving reading comprehension skills. Journal of Education Research,10(4), 195–236.

    Google Scholar 

  42. •Salahli, M. A., Özdemir, M., & Yaşar, C. (2013). Concept based approach for adaptive personalized course learning system. International Education Studies,6(5), 92–103. https://doi.org/10.5539/ies.v6n5p92.

    Google Scholar 

  43. •Sfenrianto, S., Hartarto, Y. B., Akbar, H., Mukhtar, M., Efriadi, E., & Wahyudi, M. (2018). An adaptive learning system based on knowledge level for English learning. International Journal of Emerging Technologies in Learning,13(2), 191–200.

    Google Scholar 

  44. •She, H. C., & Liao, Y. W. (2010). Bridging scientific reasoning and conceptual change through adaptive web-based learning. Journal of Research in Science Teaching,47(1), 91–119. https://doi.org/10.1002/tea.20309.

    Google Scholar 

  45. Shute, V., & Towle, B. (2003). Adaptive e-learning. Educational Psychologist,38(2), 105–114. https://doi.org/10.1207/S15326985EP3802_5.

    Google Scholar 

  46. •Soflano, M., Connolly, T. M., & Hainey, T. (2015). Learning style analysis in adaptive GBL application to teach SQL. Computers & Education,86, 105–119. https://doi.org/10.1016/j.compedu.2015.02.009.

    Google Scholar 

  47. Tortorella, R. A. W., & Graf, S. (2017). Considering learning styles and contexts-awareness for mobile adaptive leanring. Education and Information Technologies,22(1), 297–315. https://doi.org/10.1007/s10639-015-9445-x.

    Google Scholar 

  48. •Tosheva, S., & Martinovska, C. (2012). Adaptive e-learning system in secondary education. International Journal of Emerging Technologies in Learning. https://doi.org/10.3991/ijet.v7iS1.1913.

    Google Scholar 

  49. Truong, H. M. (2016). Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior,55, 1185–1193. https://doi.org/10.1016/j.chb.2015.02.014.

    Google Scholar 

  50. •Tseng, J. C. R., Chu, H.-C., Hwang, G.-J., & Tsai, C.-C. (2008). Development of an adaptive learning system with two sources of personalization information. Computers & Education,51(2), 776–786. https://doi.org/10.1016/j.compedu.2007.08.002.

    Google Scholar 

  51. U.S. Department of Education, Institute of Education Sciences. (2017). What Works Clearinghouse procedures and standards handbook, version 3.0. Washington, DC: Institute of Education Sciences. Retrieved June 15, 2009, from https://ies.ed.gov/ncee/wwc/Docs/referenceresources/wwc_procedures_v3_0_standards_handbook.pdf.

  52. •van Seters, J. R., Ossevoort, M. A., Tramper, J., & Goedhart, M. J. (2012). The influence of student characteristics on the use of adaptive e-learning material. Computers & Education,58, 942–952. https://doi.org/10.1016/j.compedu.2011.11.002.

    Google Scholar 

  53. •Vandewaetere, M., Desmet, P., & Clarebout, G. (2011). The contribution of learner characteristics in the development of computer-based adaptive learning environments. Computers in Human Behavior,27(1), 118–130. https://doi.org/10.1016/j.chb.2010.07.038.

    Google Scholar 

  54. Verdú, E., Regueras, L. M., Verdú, M. J., De Castro, J. P., & Perez, M. Á. (2008). Is adaptive learning effective? A review of the research. The 7th WSEAS International Conference On Applied Computer & Applied Computational Science, Hangzhou, China, April 6–8.

  55. •Walkington, C. (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,105(4), 932–945. https://doi.org/10.1037/a0031882.

    Google Scholar 

  56. •Wang, C. Y. (2016). Comparisons of adult learners’ self-regulated learning literacy, learning preferences, and adaptive teaching in formal, non-formal, and informal education institutions. International Journal of Continuing Education and Lifelong Learning,8(2), 47–66.

    Google Scholar 

  57. •Wang, Y. H., & Liao, H. C. (2011). Adaptive learning for ESL based on computation. British Journal of Educational Technology,42(1), 66–87. https://doi.org/10.1111/j.1467-8535.2009.00981.x.

    Google Scholar 

  58. •Yang, T.-C., Hwang, G.-J., & Yang, S. J.-H. (2013). Development of an adaptive learning system with multiple perspectives based on students' learning styles and cognitive styles. Educational Technology & Society,16(4), 185–200.

  59. •Yang, Y. T. C., Gamble, J., Hung, Y.-W., & Lin, T. Y. (2014). An online adaptive learning environment for criticial-thinking-infused English literacy instruction. British Journal of Educational Technology,45(4), 723–747. https://doi.org/10.1111/bjet.12080.

  60. •Zafar, A., & Albidewi, I. (2015). Evaluation study of eLGuide: A framework for adaptive e-Learning. Computer Applications in Engineering Education,23, 542–555. https://doi.org/10.1002/cae.21625.

    Google Scholar 

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Martin, F., Chen, Y., Moore, R.L. et al. Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Education Tech Research Dev (2020). https://doi.org/10.1007/s11423-020-09793-2

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Keywords

  • Adaptive learning
  • Adaptive strategy
  • Adaptive technology
  • Adaptive target
  • Adaptive source