Search

This paper explores machine learning algorithms that can be used to predict student results in an assignment of a Software Engineering course, based on weekly cumulative average source code submissions to GitLab. GitLab is a source code version control system, commonly used in Software Engineering courses in Higher Education. The aim of this work is to create models that can be used to predict if a group of students in a team will pass or fail an assignment. In this paper, we present results from Decision Tree, Random Forest, Extra Trees, Ada Boost and Gradient Boosting machine learning models. These models were evaluated using cross-validation, with Ada Boost achieving the highest average score.

Learning Analytics Dashboards (LAD) provide a means to leverage data to support learners, teachers, and counselors. This paper reports on an in-depth analysis of how learners interact with a LAD. N=1,406 first-year students in 12 different study programs were invited to use a LAD to support them in their transition from secondary to higher education. The LAD provides actionable feedback about five of the learning skills assessed by the Learning and Study Strategies Inventory (LASSI): concentration, anxiety, motivation, test strategies, and time management. We logged access to and behavior within the LAD and analyzed their relationship with these learning skills. While eight out of ten students accessed the LAD, students with lower time management scores tend to have a lower click-trough rate. Once within the LAD, students with lower scores for specific learning skills are accessing the corresponding information and remediation possibilities more often. Regardless of their scores for any of the other learning skills, learners with higher motivation scores are reading the remediation possibilities for the other four learning skills more often. Gender and study program have an influence on how learners use the LAD. Our findings may help both researchers and practitioners by creating awareness about how LAD use in itself may depend on the context and profile of the learner. ispartof: CEUR Workshop Proceedings ispartof: Proceedings of the 7th Workshop on Awareness and Reflection in Technology Enhanced Learning co-located with the 12th European Conference on Technology Enhanced Learning (EC-TEL 2017) vol:1997 ispartof: ARTEL Workshop @ EC-TEL 2017 location:Tallin, Estonia status: published

In this chapter, the concept of Personal Learning Environment is first refined taken into account recent advances and the experience gathered in a European research project dedicated to responsive open learning environments. A prototypal implementation of a Web 2.0 platform enabling the construction, the sharing, and the repurposing of personal learning environments is then introduced. Participatory design and validation activities carried out in the framework of higher education test beds aiming at understanding the benefits of personal learning environments in academic institutions are presented. Finally, the broaden applica- tion framework for the deployment and the adoption of open user-centric environ- ments for learning and knowledge management is tackled with perspectives in terms of supporting inquiry learning at school with online laboratories, implementing connectivist massive open online courses or enabling agile informa- tion systems for nongovernmental organizations