Automatic Discovery of Complementary Learning Resources

Abstract

Abstract. Students in a learning experience can be seen as a commu-nity working simultaneously (and in some cases collaboratively) in a set of activities. During these working sessions, students carry out nu-merous actions that affect their learning. But those actions happening outside a class or the Learning Management System cannot be easily observed. This paper presents a technique to widen the observability of these actions. The set of documents browsed by the students in a course was recorded during a period of eight weeks. These documents are then processed and the set with highest similarity with the course notes are selected and recommended back to all the students. The main problem is that this user community visits thousands of documents and only a small percent of them are suitable for recommendation. Using a combination of lexican analysis and information retrieval techniques, a fully automatic procedure to analyze these documents, classify them and select the most relevant ones is presented. The approach has been validated with an em-pirical study in an undergraduate engineering course with more than one hundred students. The recommended resources were rated as "relevant to the course" by the seven instructors with teaching duties in the course.

The relations between Czech undergraduates’ motivation and emotion in self-regulated learning, learning engagement, and academic success in blended course designs: Consistency between theory-driven and data-driven approaches

“…Our findings that students' reporting about their self-efficacy and anxiety significantly contributed to their final exam results corroborated previous findings (Romero-Zaldivar et al, 2012;Joksimović et al, 2015). Similar to previous research (Pardo et al, 2016a,b), we also found that self-efficacy positively affected students' learning achievement.…”

Monitoring student progress using virtual appliances: A case study

“…The events recorded with the browser offer a glimpse of the type of documents that students browse while working in the activities. A significant proportion of these documents will be part of the official course material, but there will be additional documents not part of the course but related to the topic (Romero-Zaldivar, Crespo García, Burgos, Delgado Kloos, Pardo, 2011). The collected events could be used in a crowd-sourcing approach to detect resources that are not included in the course material but are relevant to the topic.…”

Peeking into the black box: visualising learning activities

“…Furthermore, they can be used as basis of recommender systems and for evaluating recommendation algorithms, as proposed by Verbert et al (2011). Such an application is reported by Romero Zaldívar et al (2011).…”