Driscoll (Eds.), Handbook of research for educational communications and technology (3rd ed., pp. Extend your portfolio of analytics capabilities. Practical automated process and product metric collection and analysis in a classroom setting: Lessons learned from Hackystat-UH. M., Zhang, Q., Kagawa, A., & Yamashita, T. Proceedings of the 2014 SIGED: IAIM Conference, Paper 2. Learning Analytics applied to curriculum analysis. Eye tracking: A comprehensive guide to methods and measures. Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. Semantic visual analytics for today’s programming courses. Introduction to smart learning analytics: Foundations and developments in video-based learning. The use of triangulation for completeness purposes. International Journal of Technology Enhanced Learning, 4(5–6), 304–317.įenech Adami, M., & Kiger, A. Learning analytics: Drivers, developments and challenges. Web-CAT: Automatically grading programming assignments. Using software testing to move students from trial-and-error to reflection-in-action. Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. VISLA: Visual aspects of learning analytics. ACM.ĭuval, E., Verbert, K., Klerkx, J., Wolpers, M., Pardo, A., Govaerts, S., …, & Parra, D. Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. B., Quinto, I., Bachler, M., & Cannavacciuolo, L. Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education (pp. Exploring influences on student adherence to test-driven development. Proceedings of the First International Workshop on Visual Aspects of Learning Analytics (VISLA‘ 15) in Xonjunction with the 5th Learning Analytics & Knowledge Conference (pp. INSIGHT: A semantic visual analytics for programming discussion forums. When adaptive and visual learning analytics capabilities are incorporated, it is vital to follow minimal and well-integrated design, focus on meaningful representations for the learners and the tutors, include descriptive visual analytics, and consider incorporating adaptability characteristics. It was found that visual learning analytics coupled with some adaptability foster students’ learning awareness, support learning by reflection, and increase students’ self-confidence. This chapter presents an exploratory study, which provides valuable insights through the lens of the collected evidence from students’ use employing semistructured interviews and eye-tracking techniques. The environment monitored students’ programming progression in order to track their behavior and visualize metrics associated with it while the students developed programs in Java. To this end, this chapter discusses mechanisms of capturing and analyzing the debugging habits and the quality of the design solutions provided by the students in the context of an object-oriented programming course. The focus of this case study is the usage of visualized learning analytics coupled with the provision of feedback and support provided to the students and their impact in provoking change at student programming habits.
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