Programming Learning Supported by the Open Social Model of the Student
DOI:
https://doi.org/10.17398/1695-288X.19.2.83Keywords:
Scaffolding, Progress Monitoring, Metacognition, Programming, Educational TechnologyAbstract
This article presents the implementation of the Open Social Student Model in the virtual learning environment of an introductory computer programming course in higher education. The Open Social Student Model presents, in a visual and interactive way, a representation of the students' performance and that of their colleagues, thus allowing them to compare, evaluate and reflect about their own performance. As introductory programming courses have a high failure and dropout rate, the aim of this study has been to investigate how the metacognitive scaffolding provided by the interaction with the Open Social Student Model could contribute to the learning processes. To achieve this goal, a quasi-experiment was carried out involving 40 students during one semester, following a quali-quantitative approach. Results showed that the students used the Open Social Student Model to evaluate and monitor their own performance, to organize their studies and identify colleagues who could provide them support or who could be helped by them,. However, changes in the students' meta-cognitive awareness were not observed when comparing pre-test and post-test results. Thus, the study led to the conclusion that students who already master their metacognitive processes and know how to apply them in their learning process are the ones who can benefit the most with the use of the Open Social Student Model.
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Cham. https://doi.org/10.1007/978-3-319-61425-0_2
Bergin, S., Reilly, R., & Traynor, D. (2005). Examining the role of self-regulated learning on introductory programming performance. Proceedings of First International Workshop on Computing Education Research, 81-86. https://doi.org/10.1145/1089786.1089794
Brusilovsky P., Somyürek S., Guerra J., Hosseini R., & Zadorozhny V. (2015). The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling. In: Ricci F., Bontcheva K., Conlan O., Lawless S. (Eds), User Modeling, Adaptation and Personalization. UMAP 2015 (pp. 44-55). Lecture Notes in Computer Science, 9146. Springer, Cham. https://doi.org/10.1007/978-3-319-20267-9_4
Brusilovsky, P., Somyürek, S., Guerra, J., Hosseini, R., Zadorozhny, V., & Durlach, P. J. (2016). Open Social Student Modeling for Personalized Learning. IEEE Transactions on Emerging Topics in Computing, 4 (3), 450-461. https://doi.org/10.1109/TETC.2015.2501243
Bull, S., & Kay, J. (2007). Student Models that Invite the Learner. In The SMILI: Open Learner Modelling Framework. I. J. Artificial Intelligence in Education, 17, 89-120.
Bull S., & Kay J. (2013). Open Learner Models as Drivers for Metacognitive Processes. In: Azevedo R., Aleven V. (Eds), International Handbook of Metacognition and Learning Technologies (pp. 349-365). Springer International Handbooks of Education, 28. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5546-3_23
Festinger, L. (1954). A Theory of Social Comparison Processes. Human Relations, 7(2), 117–140. https://doi.org/10.1177/001872675400700202
Figueiredo, J., & García-Peñalvo, J. F. (2018). Building Skills in Introductory Programming. Proceedings of the 6th International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM 2018) (5 pages). Ed. ACM, New York, NY, USA. https://doi.org/10.1145/3284179.3284190
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive developmental inquiry. American Psychologist, 34 (10), 906-911. https://doi.org/10.1037/0003-066X.34.10.906
Gama, C. A. (2004). Integrating Metacognition Instruction in Interactive Learning Environments. PhD Thesis, University of Sussex. Disponível em https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413355
Gomes, A., & Mendes, A. (2014). A teacher's view about introductory programming teaching and learning: Difficulties, strategies and motivations. Proceedings of IEEE Frontiers in Education Conference (FIE), Madrid, 1-8. https://doi.org/10.1109/FIE.2014.7044086
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. Proceedings of the 21st International Conference on Intelligent User Interfaces (IUI ’16). Association for Computing Machinery, New York, NY, USA, 152-163. https://doi.org/10.1145/2856767.285678
Hooshyar D., Pedaste M., Saks K., Leijen Ä., Bardone E. & Wang M. (2020). Open learner models in supporting self-regulated learning in higher education: A systematic literature review, Computers & Education, In-Press. https://doi.org/10.1016/j.compedu.2020.103878.
Hsiao, IH., Bakalov, F., Brusilovsky, P., & König-Ries, B. (2011) Open Social Student Modeling: Visualizing Student Models with Parallel IntrospectiveViews. In: Konstan J.A., Conejo R., Marzo J.L., Oliver N. (Eds) User Modeling, Adaption and Personalization. UMAP 2011(pp. 171-182). Lecture Notes in Computer Science, 6787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22362-4_15
Luckin, R., & Hammerton, L. Getting to Know Me: Helping Learners Understand Their Own Learning Needs through Metacognitive Scaffolding. In: Cerri, S.A., Gouardères, G., & Paraguaçu, F. (Eds) (2002). Intelligent Tutoring Systems. ITS (pp.759-771) . Lecture Notes in Computer Science, 2363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47987-2_76
Medeiros, R. P., Ramalho, G. L., & Falcão, T. P. (2019). A Systematic Literature Review on Teaching and Learning Introductory Programming in Higher Education. IEEE Transactions on Education, 62 (2), 77-90. https://doi.org/10.1109/TE.2018.2864133
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In: Boekaerts, M., Pintrich, P. R., & Zeidner, M. (Eds.). Handbook of self-regulation (pp. 451–502). San Diego, CA: Academic. https://doi.org/10.1016/B978-012109890-2/50043-3
Robins, A., Rountree, J., & Rountree, N. (2003) Learning and Teaching Programming: A Review and Discussion. Computer Science Education, 13 (2), 137-172. https://doi.org/10.1076/csed.13.2.137.14200
Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19 (4), 460-75. https://doi.org/10.1006/ceps.1994.1033
Tobias, S., & Everson, H. T. (2009). The importance of knowing what you know: A knowledge monitoring framework for studying metacognition in education. In: Hacker, D. J., Dunlosky, J. & Graesser, A. C. (Eds.). The educational psychology series. Handbook of metacognition in education (pp. 107–127). Routledge/Taylor & Francis Group. Disponível em https://psycnet.apa.org/record/2010-06038-007
Watson, C., & Li, F. W. B. (2014) Failure rates in introductory programming revisited. In: Proceedings of Innovation And Technology In Computer Science Education (ITiCSE) (pp. 39-44). New York: ACM. https://doi.org/10.1145/2591708.2591749
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