656
Views
0
CrossRef citations to date
0
Altmetric
Research Paper

Case study of designing and evaluating an independent open learner model tool

ORCID Icon, ORCID Icon, , &
Article: 2237331 | Received 28 Nov 2022, Accepted 04 Jul 2023, Published online: 20 Jul 2023

References

  • Baber, H. (2020). Determinants of students’ perceived learning outcome and satisfaction in online learning during the pandemic of COVID19. Journal of Education and E-Learning Research, 7(3), 285–24. doi:10.20448/journal.509.2020.73.285.292
  • Biggs, J. (2014). Constructive alignment in university teaching. HERDSA Review of Higher Education, 1, 5–22.
  • Bodily, R., Kay, J., Aleven, V., Jivet, I., Davis, D., Xhakaj, F., & Verbert, K. (2018). Open learner models and learning analytics dashboards: A systematic review. In 8th International Conference on Learning Analytics and Knowledge (pp. 41–50). ACM. doi:10.1145/3170358.3170409
  • Bolliger, D., & Shepherd, C. (2010). Student perceptions of ePortfolio integration in online courses. Distance Education, 31(3), 295–314. doi:10.1080/01587919.2010.513955
  • Bull, S., Gakhal, I., Grundy, D., Johnson, M., Mabbott, A., & Xu, J. (2010). Preferences in multiple-view open learner models. In M. Wolpers, P. Kirschner, M. Scheffel, S. Lindstaedt, & V. Dimitrova Eds., (pp. 476–481). Springer Berlin Heidelberg. doi:10.1007/978-3-642-16020-2_40
  • Bull, S., & Gardner, P. (2009). Highlighting learning across a degree with an independent open learner model. Artificial Intelligence in Education: Building Learning Systems That Care: From Knowledge Representation to Affective Modelling (AIED), 200, 275–282.
  • Bull, S., Johnson, M., Alotaibi, M., Byrne, W., & Cierniak, G. (2013). Visualising multiple data sources in an independent open learner model. In H. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education (pp. 199–208). Memphis, United St: Springer Berlin Heidelberg. doi:10.1007/978-3-642-39112-5_21
  • Bull, S., & Kay, J. (2013). Open learner models as drivers for metacognitive processes. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 349–365). New York: Springer New York. doi:10.1007/978-1-4419-5546-3_23
  • 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. doi:10.1007/s40593-015-0090-8
  • Bull, S., Wasson, B., Kickmeier-Rust, M., Johnson, M., Moe, E., Hansen, C., & MeisslEgghart, G. (2012). Assessing English as a second language: From classroom data to a competence-based open learner model. In Proc. International Conference on Computers in Education, 26-30 November 2012, Singapore (pp. 618–622).
  • Cain, A. (2013). Constructive alignment for Introductory Programming [ Thesis]. Australia.
  • Chynał, P., Kozierkiewicz-Hetmańska, A., & Piet, M. (2017). Personalisation of learning process in intelligent tutoring systems using behavioural measures. In Multimedia and network information systems (pp. 407–417). Heidelberg: Springer International Publishing. doi:10.1007/978-3-319-43982-2_35
  • Conati, C., Porayska-Pomsta, K., & Mavrikis, M. (2018). AI in education needs interpretable machine learning: Lessons from open learner modelling. arXiv preprint arXiv:180700154.
  • Conejo, R., Trella, M., Cruces, I., & Garcia, R. (2011). INGRID: A web service tool for hierarchical open learner model visualization. Adjunct Proc (UMAP) Poster and Demo Track, Advances in User Modeling. 11-15 July 2011 Girona, Spain (pp. 406–409).
  • Evans, C. (2014). Exploring the use of a deep approach to learning with students in the process of learning to teach in D. In V.D. Gijbels, Richardson, J.T.E., and Vermunt, J. Eds., Learning patterns in higher education. Dimensions and research perspectives (pp. 187–213). London and New York: Routledge. EARLI Book Series
  • Evans, C., with Rutherford, S., Vieira, F., Erasmus+ team. (2021). A self-regulatory approach to assessment. Cardiff: Cardiff University.
  • Guerra, J., Hosseini, R., Somyurek, S., & Brusilovsky, P. (2016). An intelligent interface for learning content: Combining an open learner model and social comparison to support self-regulated learning and engagement. 21st International Conference on Intelligent User Interfaces, 7–10 March 2016, Sonoma, California (pp. 152–163).
  • Hofer, B., Yu, S., & Pintrich, P. (1998). Teaching college students to be self-regulated learners. In D. Schunk & B. Zimmerman (Eds.), Self-regulated learning: from teaching to self-reflective practice (pp. 57–85). New York: Guilford Press.
  • Hsiao, I., Bakalov, F., Brusilovsky, P., & König-Ries, B. (2011). Open social student modeling: Visualizing student models with parallel introspectiveviews. International Conference on User Modeling, Adaptation, and Personalization (pp. 171–182). doi:10.1007/978-3-642-22362-4_15
  • Kay, J., Kummerfeld, B., & Lauder, P. (2002). Personis: A server for user models. In P.D. Bra, P. Brusilovsky, & R. Conejo (Eds.), LNCS 2347 Adaptive Hypermedia and Adaptive Web Based Systems, Second Int. Conference (pp. 203–212). Springer.
  • Kelly, K., & Heffernan, N. (2015). Developing self-regulated learners through an intelligent tutoring system. International Conference on Artificial Intelligence in Education, 22-26 June 2015, Madrid, Spain (pp. 840–843).
  • Khosravi, H., Demartini, G., Sadiq, S., & Gasevic, D. (2021). Charting the design and analytics agenda of learnersourcing systems. LAK21: 11th International Learning Analytics and Knowledge Conference, 12-16 April 2021, Irvine, CA, USA (pp. 32–42).
  • Kizilcec, R., Pérez-Sanagustín, M., & Mal, J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Computers & Education, 104, 18–33. doi:10.1016/j.compedu.2016.10.001
  • Lee, D., Watson, S., & Watson, W. (2019). Systematic literature review on self-regulated learning in massive open online courses. Australasian Journal of Educational Technology, 35(1), 28–41. doi:10.14742/ajet.3749
  • Liu, S., Cui, W., Wu, Y., & Liu, M. (2014). A survey on information visualization: Recent advances and challenges. The Visual Computer, 30(12), 1373–1393. doi:10.1007/s00371-013-0892-3
  • Long, Y., & Aleven, V. (2017). Enhancing learning outcomes through self-regulated learning support with an open learner model. User Modeling and User-Adapted Interaction, 27(1), 55–88. doi:10.1007/s11257-016-9186-6
  • López-Crespo, G., Blanco-Gandía, M., Valdivia-Salas, S., Fidalgo, C., & Sánchez-Pérez, N. (2022). The educational e-portfolio: Preliminary evidence of its relationship with student’s self-efficacy and engagement. Education and Information Technologies, 27(4), 5233–5248. doi:10.1007/s10639-021-10827-2
  • Mabbott, A., & Bull, S. (2004). Alternative views on knowledge: Presentation of open learner models. In J. Lester, R. Vicari, & F. Paraguacu (Eds.), Intelligent Tutoring Systems: 7th International Conference ITS (pp. 689–698). Springer.
  • Mahasneh, O. (2020). A proposed model for the university students’ e-portfolio. Journal of Education and E-Learning Research, 7, 28–33. doi:10.20448/journal.509.2020.71.28.33
  • Mahmood, S. (2021). Instructional strategies for online teaching in COVID-19 pandemic. Human Behavior and Emerging Technologies, 3(1), 199–203. doi:10.1002/hbe2.218
  • 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
  • Pérez-Álvarez, R., Maldonado-Mahauad, J., & Pérez-Sanagustín, M. (2018). Tools to support self-regulated learning in online environments: Literature review. In V. Pammer-Schindler, M. Pérez-Sanagustín, H. Drachsler, R. Elferink, & M. Scheffel (Eds.), Lifelong technology-enhanced learning (pp. 16–30). Switzerland: Springer. doi:10.1007/978-3-319-98572-5_2
  • Schunk, D. (1983). Progress self-monitoring: Effects on children’s self-efficacy and achievement. The Journal of Experimental Education, 51(2), 89–93. doi:10.1080/00220973.1982.11011845
  • Schunk, D. (1989). Social cognitive theory and self-regulated learning. In B. Zimmerman & D. Schunk (Eds.), Self-regulated learning and academic achievement: Theory, research, and practice (pp. 83–110). New York: Springer Verlag. doi:10.1007/978-1-4612-3618-4_4
  • Self, J. (1990). Bypassing the intractable problem of student modelling. In C. Frasson & G. Gauthier (Eds.), IntelligenT tutoring systems: At the crossroads of artificial intelligence and education (pp. 107–123). Norwood, N.J: Ablex.
  • Shroff, R., Trent, J., & Ng, E. (2013). Using e-portfolios in a field experience placement: Examining student-teachers’ attitudes towards learning in relationship to personal value, control and responsibility. Australasian Journal of Educational Technology, 29. doi:10.14742/ajet.51
  • Siadaty, M., Gašević, D., Jovanović, J., Pata, K., Milikić, N., Holocher-Ertl, T., Jeremić, Z., Ali, L., Giljanović, A., & Hatala, M. (2012). Self-regulated workplace learning: A pedagogical framework and semantic web-based environment. Journal of Educational Technology & Society, 15, 75–88.
  • Slepcevic-Zach, P., & Stock, M. (2018). ePortfolio as a tool for reflection and self-reflection. Reflective Practice, 19(3), 291–307. doi:10.1080/14623943.2018.1437399
  • Thirouard, M., Bernaert, O., Dhorne, L., Bianchi, S., Pidol, L., & Petit, Y. (2015). Learning by doing: Integrating a serious game in a MOOC to promote new skills. European MOOCs Stakeholders Summit 2015, 18-20 May 2015, Belgium (pp. 92–96).
  • Vermunt, J.D., & Donche, V. (2017). A learning patterns perspective on student learning in higher education: State of the art and moving forward. Educational Psychology Review, 29(2), 269–299. doi:10.1007/s10648-017-9414-6
  • Vermunt, J.D., & Verloop, N. (1999). Congruence and friction between learning and teaching. Learning and Instruction, 9(3), 257–280. doi:10.1016/S0959-4752(98)00028-0
  • Zapata-Rivera, J.D. (2021). Open student modeling research and its connections to educational assessment. International Journal of Artificial Intelligence in Education, 31(3), 380–396. doi:10.1007/s40593-020-00206-2
  • Zapata-Rivera, J.D., & Greer, J. (2004). Inspectable Bayesian student modelling servers in multi-agent tutoring systems. International Journal of Human-Computer Studies, 61(4), 535–563. doi:10.1016/j.ijhcs.2003.12.017
  • Zimmerman, B. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. doi:10.1207/s15430421tip4102_2
  • Zimmerman, B., & Campillo, M. (2003). Motivating self-regulated problem solvers. In J. Davidson & R. Sternberg (Eds.), The psychology of problem solving (pp. 233–262). UK: Cabidge niversity Press. doi:10.1017/CBO9780511615771.009