Résumés
Abstract
Personalization is crucial for achieving smart learning environments in different lifelong learning contexts. There is a need to shift from one-size-fits-all systems to personalized learning environments that give control to the learners. Recently, learning analytics (LA) is opening up new opportunities for promoting personalization by providing insights and understanding into how learners learn and supporting customized learning experiences that meet their goals and needs. This paper discusses the Personalization and Learning Analytics (PERLA) framework which represents the convergence of personalization and learning analytics and provides a theoretical foundation for effective analytics-enhanced personalized learning. The main aim of the PERLA framework is to guide the systematic design and development of effective indicators for personalized learning.
Keywords:
- personalization,
- self-regulated learning,
- user-centered learning analytics,
- learning analytics reference model,
- goal-oriented learning analytics
Parties annexes
Bibliography
- Blikstein, P. (2013). Multimodal learning analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK 13), 102-106, New York, NY, USA. ACM.
- Boekaerts, M. (1992). The adaptable learning process: Initiating and maintaining behavioural change. Applied Psychology, 41(4), 377-397.
- Boekaerts, M. (1996). Coping with stress in childhood and adolescence. In M. Zeidner & N. S. Endler (Eds.), Handbook of coping: Theories, research, application (pp. 452-484). New York, NY: Wiley.
- Boekaerts, M. (2011). Emotions, emotion regulation, and self-regulation of learning. In B. J. Zimmerman and D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 408-425). New York, NY: Routledge.
- Boekaerts, M., & Cascallar, E. (2006). How far have we moved toward the integration of theory and practice in self-regulation? Educational Psychology Review, 18(3), 199-210. doi: 10.1007/s10648-006-9013-4
- Boekaerts, M., & Niemivirta, M. (2000). Self-regulated learning: Finding a balance between learning goals and ego-protective goals. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 417-451). San Diego, CA: Academic Press. doi: 10.1016/b978-012109890-2/50042-1
- Brusilovsky, P., & Millan, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web (pp. 3-53). Berlin Heidelberg: Springer.
- Bull, S., & Kay, J. (2016). SMILI☺: A framework for interfaces to learning data in open learner models, learning analytics and related fields. International Journal of Artificial Intelligence in Education, 26(1), 293-331.
- Chatti, M. A. (2010). Personalization in technology enhanced learning: A social software perspective. Aachen: Shaker Verlag.
- Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning (IJTEL), 4(5-6), 318-331.
- Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U. ... Schroeder, U. (2014). Learning analytics: Challenges and future research directions. E-learning and Education, 10(1).
- Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an open learning analytics ecosystem. In Kei Daniel B. (Eds.), Big data and learning analytics in higher education (pp. 195-219). Springer.
- Daniel, B., & Butson, R. (2014). Foundations of big data and analytics in higher education. In Proceedings of the 9th International Conference on Analytics Driven Solutions (ICAS 2014 ). Retrieved from http://archive.engineering.nyu.edu/files/ICAS2014-Proceedings-Publication.pdf#page=51
- Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist, 46(1), 6-25.
- Ferguson, R., & Clow, D. (2017). Where is the evidence? A call to action for learning analytics. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 56-65. ACM, New York, USA.
- Gašević, D., Dawson, S., & Siemens, G. (2015). Let's not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.
- Griffiths, D., Hoel, T., & Cooper, A. (2016). Learning analytics interoperability: Requirements, specifications, and adoption. Learning Analytics Community Exchange (LACE) Deliverable D7.4. Retrieved from http://www.laceproject.eu/wp-content/uploads/2016/01/LACE_D7-4.pdf
- Lombardo, M. M., & Eichinger, R. W. (1996). The career architect development planner (1st ed.). Minneapolis: Lominger.
- Kay, J., & Kummerfeld, B. (2011). Lifelong learner modeling. In P. J. Durlach, & A.M. Lesgold (Eds.), Adaptive technologies for training and education (pp. 140-164). Cambridge: Cambridge University Press.
- Marzouk, Z., Rakovic, M., Liaqat, A., Vytasek, J., Samadi, D., Stewart-Alonso, J. ... Nesbit, J. C. (2016). What if learning analytics were based on learning science? Australasian Journal of Educational Technology, 32(6), 1-18.
- Muslim, A., Chatti, M., Mughal, M., & Schroeder, U. (2017). The Goal - Question - Indicator approach for personalized learning analytics. In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017). doi: 10.5220/0006319803710378
- Norman, D. (2013). The design of everyday things. New York, NY: Basic Books.
- Nussbaumer, A., Hillemann, E. C., Gütl, C., & Albert, D. (2015). A competence‐based service for supporting self‐regulated learning in virtual environments. Journal of Learning Analytics, 2(1), 101-133.
- Ochoa, X. (2017). Multimodal learning analytics. In C. Lang, G. Siemens, A. Wise, & D. Gaševic (Eds.), Handbook of learning analytics (pp. 129-141). Alberta, Canada: Society for Learning Analytics Research (SoLAR). doi: 10.18608/hla17
- Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8 ( 422). doi: 10.3389/fpsyg.2017.00422
- Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438-450.
- Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 452-502). San Diego, CA: Academic Press.
- Roll, I., & Winne, P. H. (2015). Understanding, evaluating, and supporting self‐regulated learning using learning analytics. Journal of Learning Analytics, 2(1), 7-12.
- Siemens, G., & Gašević, D. (2012). Special issue on learning and knowledge analytics. Educational Technology & Society, 15(3), 1-163.
- Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529.
- Thüs, H., Chatti, M. A., Yalcin, E., Pallasch, C. Kyryliuk, B., Mageramov, T., & Schroeder, U. (2012). Mobile learning in context. International Journal of Technology Enhanced Learning, 4(5-6), 332-344.
- Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2014). Learning dashboards: An overview and future research opportunities. Personal and Ubiquitous Computing, 18(6), 1499-1514.
- Winne, P. H. (2011). A cognitive and metacognitive analysis of self-regulated learning. In B. J. Zimmerman, & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 15-32). New York, NY: Routledge.
- Winne, P. H., & Baker, R. S. J. D. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. Journal of Educational Data Mining, 5(1), 1-8.
- Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated engagement in learning. In D. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277-304). Hillsdale, NJ: Erlbaum.
- Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-40). San Diego, CA: Academic Press. doi: 10.1016/b978-012109890-2/50031-7
- Zimmerman, B. J. (2013). From cognitive modeling to self-regulation: A social cognitive career path. Educational Psychologist, 48(3), 135-147. doi: 10.1080/00461520.2013.794676
- Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 299-315). New York, NY: Routledge.