USING COMPUTER LAB TRACE ANALYTICS TO SUPPORT LEARNERS' ENGAGEMENT IN LABORATORY ACTIVITIES
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Published
Apr 1, 2019
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Abstract
Laboratory based learning activities constitute an important part of students’ learning experiences today. As a consequence, it is interesting to investigate how to better support such activities using digital technologies. In this paper, the authors present a practical approach based on the use of Microsoft Families and artificial neural networks to analyse computer traces of students’ lab activities to identify students encountering difficulties and at risk of failure, and flag tutors for further corrective actions on demand. This work demonstrates how artificial neural networks allow the analysis of the traces of students’ work even outside of a Learning Management System and with no clue of possible algorithmic relationships between the traces and the students’ performance.
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Articles - Special Issue
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References
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Barile, S., Magna L., Marsella, M., & Miranda, S. (1999). A marketing decision problem solved by application of neural networks”, Proceedings of Third International Conference on Computational Intelligence and Multimedia Applications ICIIMA (September 1999).
Bishop, C. M., (2006). Pattern recognition and Machine Learning. New York, NY, USA: Springer-Verlag.
Brookhart, S. (2013). The use of teacher judgement for summative assessment in the USA. Assessment in Education: Principles, Policy & Practice, 20(1), 69-90. doi: 10.1080/0969594X.2012.703170
Brown, M. (2011). Learning Analytics: The coming third wave. Louisville, CO, USA: EDUCAUSE Learning Initiative.
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318-331. doi: 10.1504/IJTEL.2012.051815
Cooper, A. (2012). What is analytics? Definition and essential characteristics [White paper]. CETIS Analytics series 1(5). Manchester, UK: The University of Bolton.
De-la-Fuente-Valentìn, L., Corbi, A., Crespo, R. G., & Burgos, D. (2015). Learning Analytics. In M. Khosrow-Pour (Ed.), Encyclopedia of Information Science and Technology, Third Edition (pp. 2379-2387). Hershey, PA: Information Science Reference. doi: 10.4018/978-1-4666-5888-2.ch231
Gaeta, M., Marzano, A., Miranda, S., & Sandkuhl, K. (2017). The competence management to improve the learning engagement. Journal of Ambient Intelligence and Humanized Computing, 8(3), 405-417. doi: 10.1007/s12652-016-0399-7
Lal, P. (2016). Designing Online Learning Strategies through Analytics. In F. J. García-Peñalvo & A. M. Seoane Pardo (Eds.), Online Tutor 2.0: Methodologies and Case Studies for Successful Learning (pp. 1-15). Hershey, PA, USA: IGI Global. doi:10.4018/978-1-4666-5832-5.ch001
Marzano, A., & Vegliante, R. (2017). Training to teach: the laboratory experience of General Didactic and Educational Technology at the University of Salerno. Formazione & Insegnamento, 1, 283-303.
Miranda, S., & Ritrovato, P. (2015). Supporting Learning Object Repository by automatic extraction of metadata. Journal of e-Learning and Knowledge Society, 11(1), 43-54.
Pardo, A., & Kloos, C. D. (2011). Stepping out of the box: towards analytics outside the learning management system. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK '11). ACM, New York, NY, USA, 163-167. doi: 10.1145/2090116.2090142
Persico, D., & Pozzi, F. (2015). Informing learning design with learning analytics to improve teacher inquiry. British Journal of Educational Technology, 46(2), 230-248. doi: 10.1111/bjet.12207
Sampson, D. (2016). Learning Analytics: Analyze your lesson to discover more about your students, ELearning Industry. Retrieved from https://elearningindustry.com/learning-analytics-analyze-lesson
Scriven, M. (1967). The methodology of evaluation. In R. W. Tyler, R. M. Gagné, & M. Scriven (Eds.), Perspectives of curriculum evaluation (pp.39-83). Chicago, IL, USA: Rand-McNally.
Sah, M., & Hall, W. (2009). Building and managing personalized semantic portals. In Proceedings of the 16th International Conference on World Wide Web, 1227-1228.
Sergis, S., & Sampson, D. (2017). Teaching and Learning Analytics to support Teacher Inquiry: A Systematic Literature Review. In A. Peña-Ayala (Ed.), Learning Analytics: Fundaments, Applications, and Trends (pp. 25-63). New York, NY, USA: Springer.
Siemens, G., & Baker, R. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In S. Buckingham Shum, D. Gašević, & R. Ferguson (Eds.), LAK '12 Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 252-254). New York, NY, USA: ACM. doi: 10.1145/2330601.2330661
Trent, R. (June 29, 2015). Microsoft rolls out changes to family safety, renamed to Microsoft family. ITProToday. Retrieved from http://www.itprotoday.com/windows-server/microsoft-rolls-out-changes-family-safety-renamed-microsoft-family
Weurlander, M., Söderberg, M., Scheja, M., Hult, H., & Wernerson, A. (2012). Exploring formative assessment as a tool for learning: students’ experiences of different methods of formative assessment. Assessment & Evaluation in Higher Education, 37(6), 747-760. doi: 10.1080/02602938.2011.572153