Abstract
Adaptive learning systems have been on the rise ever since the beginnings of e-learning. Their ability to provide instructions, guidance and content tailored towards learners’ individual needs bears a lot of potential for optimising learning processes. Due to recent technological advances, adaptive systems have become more sophisticated and prevalent. This chapter serves the purpose of characterising research in the field of adaptive learning by referring to six questions central to the adaptive process. We hereby provide an overview over various options on how adaptive systems can be designed and implemented in terms of core models, reasons for and goals of adaptation, learner characteristics, contexts and instructional techniques and methods. This chapter also highlights the current state of the research in that area, focussing on the effectiveness and efficiency of adaptive learning systems, as well as learners’ satisfaction with them and their actual implementation in various contexts. We end the chapter by presenting practical implications and future potential adaptive learning holds.
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References
Adams Becker, S., Brown, M., Dahlstrom, E., Davis, A., DePaul, K., Diaz, V., & Pomerantz, J. (2018). NMC horizon report 2018: higher education edition (p. 60). Retrieved from https://library.educause.edu/{~}/media/files/library/2018/8/2018horizonreport.pdf
Afini Normadhi, N. B., Shuib, L., Md Nasir, H. N., Bimba, A., Idris, N., & Balakrishnan, V. (2019). Identification of personal traits in adaptive learning environment: Systematic literature review. Computers and Education, 130, 168–190. https://doi.org/10.1016/j.compedu.2018.11.005.
Albacete, P., & VanLehn, K. (2000). Evaluating the effectiveness of a cognitive tutor for fundamental physics concept. Proceedings of the 22nd Annual Meeting of the Cognitive Science Society (pp. 25–30). Mahwah, NJ: Erlbaum.
Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2017). Instruction based on adaptive learning technologies. In R. E. Mayer & P. Alexander (Eds.), Handbook of research on learning and instruction (pp. 522–560). New York: Routledge.
Alshammari, M., Anane, R., & Hendley, R. J. (2015). Design and usability evaluation of adaptive e-learning systems based on learner knowledge and learning style. In Human-computer interaction – Interact 2015 (Vol. 9297, pp. 584–591). https://doi.org/10.1007/978-3-319-22668-2.
Arroyo, I., Woolf, B.P., Burelson, W., Muldner, K., Rai, D., & Tai, M. (2014). A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. International Journal of Artificial Intelligence in Education,24(4), 387–426.
Bergamin, P. B., & Hirt, F. S. (2018). Who’s in charge? – Dealing with the self-regulation dilemma in digital learning environments. In K. North, R. Maier, & O. Haas (Eds.), Knowledge management in digital change – New findings and practical cases. Cham: Springer.
Bergamin, P. B., Werlen, E., Siegenthaler, E., & Ziska, S. (2012). The relationship between flexible and self-regulated learning in open and distance universities. International Review of Research in Open and Distance Learning, 13(2), 101–123. https://doi.org/10.19173/irrodl.v13i2.1124.
Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6(2–3), 87–129. https://doi.org/10.1007/BF00143964.
Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1/2), 87–110. https://doi.org/10.1023/A:1011143116306.
Cavanagh, T., Chen, B., Lahcen, R. A. M., & Paradiso, J. (2020). Constructing a design framework and pedagogical approach for adaptive learning in higher education: A practitioner’s perspective. The International Review of Research in Open and Distributed Learning, 21(1), 172–196. https://doi.org/10.19173/irrodl.v21i1.4557.
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. https://doi.org/10.1016/J.COMPEDU.2015.05.007.
Clarebout, G., & Elen, J. (2006). Tool use in computer-based learning environments: Towards a research framework. Computers in Human Behavior, 22(3), 389–411. https://doi.org/10.1016/J.CHB.2004.09.007.
Collins, A., Brown, J. S., & Newman, S. E. (1988). Cognitive apprenticeship. Thinking: The Journal of Philosophy for Children, 8(1), 2–10. https://doi.org/10.5840/thinking19888129.
Conati, C. (2013). Modeling and scaffolding self-explanation across domains and activities. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 367–383). New York: Springer. https://doi.org/10.1007/978-1-4419-5546-3_24.
Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing. User Modeling and User-Adapted Interaction, 4(4), 253–278.
Corbett, A. T., McLaughlin, M., & Scarpinatto, K. C. (2000). Modeling student knowledge: Cognitive tutors in high school and college. User Modeling and User-Adapted Interaction, 10(2/3), 81–108. https://doi.org/10.1023/A:1026505626690.
Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods: A handbook for research on interactions. New York: Irvington.
D’Mello, S. K., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., et al. (2010). A time for emoting: When affect-sensitivity is and isn’t effective at promoting deep learning. https://doi.org/10.1007/978-3-540-69132-7.
D’Mello, S. K., Olney, A., Williams, C., & Hays, P. (2012). Gaze tutor: A gaze-reactive intelligent tutoring system. International Journal of Human Computer Studies, 70(5), 377–398. https://doi.org/10.1016/j.ijhcs.2012.01.004.
Dollinger, M., & Lodge, J. M. (2018). Co-creation strategies for learning analytics. In Proceedings of the 8th international conference on learning analytics and knowledge – LAK’18 (pp. 97–101). New York: ACM Press. https://doi.org/10.1145/3170358.3170372.
Dounas, L., Salinesi, C., & El Beqqali, O. (2019). Requirements monitoring and diagnosis for improving adaptive e-learning systems design. Journal of Information Technology Education: Research, 18, 161–184. https://doi.org/10.28945/4270.
Dziuban, C., Moskal, P., Johnson, C., & Evans, D. (2017). Adaptive learning: A tale of two contexts. Current Issues in Emerging eLearning, 4(1). Retrieved from https://scholarworks.umb.edu/ciee/vol4/iss1/3/
Eau, G., Judah, K., & Shahid, H. (2019). How can adaptive platforms improve student learning outcomes? A case study of open educational resources and adaptive learning platforms. http://dx.doi.org/10.2139/ssrn.3478134
Eldenfria, A., & Al-Samarraie, H. (2019). The effectiveness of an online learning system based on aptitude scores: An effort to improve students’ brain activation. Education and Information Technologies, 24(5), 2763–2777. https://doi.org/10.1007/s10639-019-09895-2.
Ennouamani, S., & Mahani, Z. (2019). Towards adaptive learning systems based on fuzzy-logic. In Advances in intelligent systems and computing (Vol. 997, pp. 625–640). Cham: Springer. https://doi.org/10.1007/978-3-030-22871-2_42.
Essa, A. (2016). A possible future for next generation adaptive learning systems. Smart Learning Environments, 3. https://doi.org/10.1186/s40561-016-0038-y.
Freda, B. (2016). Clearing the hurdles to adaptive learning. Retrieved from https://universitybusiness.com/clearing-the-hurdles-to-adaptive-learning/
Ge, Z., Xi, M., & Li, Y. (2019). A literature review of the adaptive algorithms adopted in adaptive learning systems. In 2019 IEEE 4th international conference on signal and image processing, ICSIP 2019 (pp. 254–258). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SIPROCESS.2019.8868893.
Ghergulescu, I., Flynn, C., & O’Sullivan, C. (2016). Learning effectiveness of adaptive learning in real world context. In EdMedia+ innovate learning (pp. 1391–1396). Waynesville: Association for the Advancement of Computing in Education (AACE).
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT Press.
Granić, A., & Nakić, J. (2010). Enhancing the learning experience: Preliminary framework for user individual differences. In (pp. 384–399). Berlin/Heidelberg: Springer. https://doi.org/10.1007/978-3-642-16607-5_26.
Griff, E. R., & Matter, S. F. (2013). Evaluation of an adaptive online learning system. British Journal of Educational Technology, 44(1), 170–176. https://doi.org/10.1111/j.1467-8535.2012.01300.x.
Hou, M., & Fidopiastis, C. (2017). A generic framework of intelligent adaptive learning systems: From learning effectiveness to training transfer. Theoretical Issues in Ergonomics Science, 18(2), 1–30.
Imhof, C., Bergamin, P. B., Moser, I., & Holthaus, M. (2018). Implementation of an adaptive instructional design for a physics module in a learning management system. In Proceedings of the 15th international conference on cognition and exploratory learning in the digital age, CELDA 2018.
Jameson, A. (2003). Adaptive interfaces and agents. In J. A. Jacko & A. Sears (Eds.), The human-computer interaction handbook (pp. 305–330). Hillsdale: Lawrence Erlbaum. Retrieved from https://chusable.com/archives/homepage-2017/pdf/hci-handbook.jameson.pdf
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19(4), 509–539. https://doi.org/10.1007/s10648-007-9054-3.
Kalyuga, S. (2009). Instructional designs for the development of transferable knowledge and skills: A cognitive load perspective. Computers in Human Behavior, 25(2), 332–338. https://doi.org/10.1016/j.chb.2008.12.019.
Kalyuga, S., & Singh, A.-M. (2016). Rethinking the boundaries of cognitive load theory in complex learning. Educational Psychology Review, 28(4), 831–852. https://doi.org/10.1007/s10648-015-9352-0.
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31. https://doi.org/10.1207/S15326985EP3801_4.
Khosravi, H., Gasevic, D., & Sadiq, S. (2020). Development and adoption of an adaptive learning system. https://doi.org/10.1145/3328778.3366900.
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106, 166–171. https://doi.org/10.1016/J.COMPEDU.2016.12.006.
Kirschner, P. A., & van Merriënboer, J. J. G. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. https://doi.org/10.1080/00461520.2013.804395.
Knutov, E. (2012). Generic adaptation framework for unifying adaptive web-based systems. Eindhoven: Technische Universiteit Eindhoven. https://doi.org/10.6100/IR732111
Kobsa, A., Koenemann, J., & Pohl, W. (2001). Personalised hypermedia presentation techniques for improving online customer relationships. The Knowledge Engineering Review, 16(2), 111–155. https://doi.org/10.1017/s0269888901000108.
Koedinger, K. R., & Aleven, V. (2007). Exploring the assistance dilemma in experiments with cognitive tutors. Educational Psychology Review, 19(3), 239–264. https://doi.org/10.1007/s10648-007-9049-0.
Koedinger, K. R., Brunskill, E., Baker, R. S. J., McLaughlin, E. A., & Stamper, J. C. (2013). New potentials for data-driven intelligent tutoring system development and optimization. AI Magazine, 34(3), 27. https://doi.org/10.1609/aimag.v34i3.2484.
Kolekar, S. V., Pai, R. M., & Manohara Pai, M. M. (2019). Rule based adaptive user interface for adaptive e-learning system. Education and Information Technologies, 24(1), 613–641. https://doi.org/10.1007/s10639-018-9788-1.
Lee, C. H., & Kalyuga, S. (2014). Expertise reversal effect and its instructional implications. In Applying science of learning in education: Infusing psychological science into the curriculum (pp. 32–44). Washington, DC: Society for the Teaching of Psychology. Retrieved from http://psycnet.apa.org/record/2013-44868-003
Lerís, D., Sein-Echaluce, M. L., Hernández, M., & Bueno, C. (2017). Validation of indicators for implementing an adaptive platform for MOOCs. Computers in Human Behavior, 72, 783–795. https://doi.org/10.1016/j.chb.2016.07.054.
Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers & Education, 48(2), 185–204. https://doi.org/10.1016/J.COMPEDU.2004.12.004.
Long, Y., & Aleven, V. (2013). Skill diaries: Improve student learning in an intelligent tutoring system with periodic self-assessment. In International Conference on Artificial Intelligence in Education (pp. 249–258). Springer, Berlin, Heidelberg.
Lu, J., Yu, C.-S., & Liu, C. (2003). Learning style, learning patterns, and learning performance in a WebCT-based MIS course. Information & Management, 40(6), 497–507. https://doi.org/10.1016/S0378-7206(02)00064-2.
Mavroudi, A., Giannakos, M., & Krogstie, J. (2018). Supporting adaptive learning pathways through the use of learning analytics: Developments, challenges and future opportunities. Interactive Learning Environments, 26(2), 206–220. https://doi.org/10.1080/10494820.2017.1292531.
Mazziotti, C., Holmes, W., Wiedmann, M., Loibp, K., Rummel, N., Mavrikis, M., et al. (2015). Robust student knowledge: Adapting to individual student needs as they explore the concepts and practice the procedures of fractions. CEUR Workshop Proceedings, 1432, 32–40.
McQuiggan, S. W., Mott, B. W., & Lester, J. C. (2008). Modeling self-efficacy in intelligent tutoring systems: An inductive approach. User Modeling and User-Adapted Interaction, 18(1–2), 81–123. https://doi.org/10.1007/s11257-007-9040-y.
Mirata, V., & Bergamin, P. B. (2019). Developing an implementation framework for adaptive learning: A case study approach. In Proceedings of the 18th European conference on e-learning. Copenhagen, Denmark, 7–8 November 2019.
Muldner, K., & Conati, C. (2007). Evaluating a decision-theoretic approach to tailored example selection. In IJCAI 2007, proceedings of the 20th international joint conference on artificial intelligence. Hyderabad, India. Retrieved from https://www.ijcai.org/Proceedings/07/Papers/076.pdf
Murray, M. C., & Pérez, J. (2015). Informing and performing: A study comparing adaptive learning to traditional learning. Informing Science, 18(1), 111–125. https://doi.org/10.28945/2165.
Nakić, J., Granić, A., & Glavinić, 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. https://doi.org/10.2190/EC.51.4.e.
Own, Z. (2006). The application of an adaptive web-based learning environment on oxidation–reduction reactions. International Journal of Science and Mathematics Education, 4(1), 73–96. https://doi.org/10.1007/s10763-004-8129-6.
Oxman, S., & Wong, W. (2014). White Paper: Adaptive learning systems. Retrieved from http://kenanaonline.com/files/0100/100321/DVx{\_}Adaptive{\_}Learning{\_}White{\_}Paper.pdf
Pancar, T., Holthaus, M., & Bergamin, P. B. (2019). Enhanced system usability scale for adaptive courses. In The twelfth international conference on advances in human-oriented and personalized mechanisms, technologies, and services, centric 2019, Valencia, Spain, 24–28 November 2019.
Pliakos, K., Joo, S. H., Park, J. Y., Cornillie, F., Vens, C., & Van den Noortgate, W. (2019). Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems. Computers and Education, 137, 91–103. https://doi.org/10.1016/j.compedu.2019.04.009.
Rey, G. D., & Buchwald, F. (2011). The expertise reversal effect: Cognitive load and motivational explanations. Journal of Experimental Psychology: Applied, 17(1), 33–48. https://doi.org/10.1037/a0022243.
Rus, V., Baggett, W., Gire, E., Franceschetti, D., Conley, M., & Graesser, A. (2013). Towards learner models based on learning progressions in DeepTutor. Retrieved from www.semanticsimilarity.org
Scanlon, E., Sharples, M., Fenton-O’Creevy, M., Fleck, J., Cooban, C., Ferguson, R., et al. (2013). Beyond prototypes: Enabling innovation in technology-enhanced learning. Milton Keynes: Open University. Retrieved from http://beyondprototypes.com/
Scheiter, K., Fillisch, B., Krebs, M.-C., Leber, J., Ploetzner, R., Renkl, A., et al. (2017). How to design adaptive information environments to support self-regulated learning with multimedia. In Informational environments (pp. 203–223). Cham: Springer. https://doi.org/10.1007/978-3-319-64274-1_9.
Schertzer, C. B., & Schertzer, S. M. B. (2004). Student satisfaction and retention: A conceptual model. Journal of Marketing for Higher Education, 14(1), 79–91. https://doi.org/10.1300/J050v14n01.
Shelle, G., Earnesty, D., Pilkenton, A., & Powell, E. (2018). Adaptive learning: An innovative method for online teaching and learning. Journal of Extension, 56(5). Retrieved from https://joe.org/joe/2018september/pdf/JOE{\_}v56{\_}5a5.pdf
Shute, V., & Towle, B. (2003). Adaptive e-learning. Educational Psychologist, 38(2), 105–114. https://doi.org/10.1207/S15326985EP3802_5.
Somyürek, S. (2015). The new trends in adaptive educational hypermedia systems. The International Review of Research in Open and Distance Learning, 16(1), 221–241. https://doi.org/10.19173/irrodl.v16i1.1946.
Sottilare, R. A., & Goodwin, G. A. (2017). Adaptive instructional methods to accelerate learning and enhance learning capacity. Retrieved from https://gifttutoring.org/attachments/download/2427/Sottilare and Goodwin DHSS2017.pdf
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.
Tai, M., Arroyo, I., & Woolf, B. P. (2013). Teammate relationships improve help-seeking behavior in an intelligent tutoring system. International Conference on Artificial Intelligence in Education (pp. 239–248). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_25.
van Merriënboer, J. J. G., & Sluijsmans, D. M. A. (2009). Toward a synthesis of cognitive load theory, four-component instructional design, and self-directed learning. Educational Psychology Review, 21(1), 55–66. https://doi.org/10.1007/s10648-008-9092-5.
van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147–177. https://doi.org/10.1007/s10648-005-3951-0.
Vagale, V., & Niedrite, L. (2012). Learner model’ s utilization in the e-learning environments. In Tenth international Baltic conference on databases and information systems. Vilnius, Lithuania, pp. 162–174.
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, 118–130. https://doi.org/10.1016/j.chb.2010.07.038.
Verdú, E., Regueras, L. M., Verdú, M. J., De Castro, J. P., & Pérez, M. Á. (2008). An analysis of the research on adaptive learning: The next generation of e-learning. WSEAS Transactions on Information Science and Applications, 5(6), 859–868.
Vesin, B., Mangaroska, K., & Giannakos, M. (2018). Learning in smart environments: User-centered design and analytics of an adaptive learning system. Smart Learning Environments, 5(1). https://doi.org/10.1186/s40561-018-0071-0.
Vygotsky, L. S. (1978). Mind in society: the development of higher psychological processes (p. 159). Cambridge, MA: Harvard University Press.
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, 105(4), 932–945. https://doi.org/10.1037/a0031882.
Wauters, K., Desmet, P., & Van Den Noortgate, W. (2010). Adaptive item-based learning environments based on the item response theory: Possibilities and challenges. Journal of Computer Assisted Learning, 26(6), 549–562. https://doi.org/10.1111/j.1365-2729.2010.00368.x.
Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599. https://doi.org/10.1016/J.COMPEDU.2019.103599.
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 and Society, 16(4), 185–200.
Yuan, X. (2019). Model and implementation of personalized adaptive learning and analysis technology based on large data. International Conference on Artificial Intelligence and Advanced Manufacturing (pp. 202–205), Dublin, Ireland. https://doi.org/10.1109/AIAM48774.2019.00048.
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166–183. https://doi.org/10.3102/0002831207312909.
Zliobaite, I., Bifet, A., Gaber, M., Gabrys, B., Gama, J., Minku, L., & Musial, K. (2012). Next challenges for adaptive learning systems. ACM SIGKDD Explorations Newsletter, 14(1), 48. https://doi.org/10.1145/2408736.2408746.
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Imhof, C., Bergamin, P., McGarrity, S. (2020). Implementation of Adaptive Learning Systems: Current State and Potential. In: Isaias, P., Sampson, D.G., Ifenthaler, D. (eds) Online Teaching and Learning in Higher Education. Cognition and Exploratory Learning in the Digital Age. Springer, Cham. https://doi.org/10.1007/978-3-030-48190-2_6
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