Favouring reflexivity in technology-enhanced learning systems: towards smart uses of traces

Abstract

During learning activities, reflexive processes allow learners to realise what they have done, understand why, decide on new actions and gain motivation. They help learners to regulate their actions by themselves, that is, to develop metacognitive regulation skills. Computer environments can support reflexive processes to support human learning, for example by analysing traces of learner activity and providing synthetic views by the way of indicators. Nevertheless, traces are underused in technologyenhanced learning (TEL) systems. In this article, we draw up the potential and limits of TEL systems based on traces for reflexive purposes. We then highlight the characteristics which TEL systems should have to use traces to foster reflexivity. We finally discuss how systems could enrich the self-confrontation process with traces.

Audience participation digital drama‐based learning activities for situational learning in the classroom

“…An environment that is designed for authentic learning tasks reflects how knowledge and skills are used in real-life situations (Bosman & Fernhaber, 2019). Therefore, it allows learners to invest themselves in educational activities through co-construction methods to understand that learning happens as a result of sensemaking through interpretation or reinterpretation of real-life situations (George et al, 2016). In this study, the students can interpret, analyse, enquire about ideas, and solve problems through case-based learning in an authentic manner, as well as transfer knowledge in realworld situations (Kopp et al, 2014;Lee et al, 2009).…”

Structuring International University Students’ Reflection and Meta-reflection Experiences Online

“…Notwithstanding the increasing focus on the use of web-based learning environments that foster students' reflexivity [27,28], the question of how critical reflection can/should be worked out within higher education hybrid courses is still an open issue [29]. In this paper we address this gap by making explicit the design rationale of an inquiry-based, hybrid learning course on Social Science Methods within the particular study context presented in Sect.…”

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

“…With the development of artificial intelligence, the intelligent recommendation system has become a necessary tool for learners (Shao et al, 2021; Wu et al, 2022). It can help the learners to obtain appropriate learning resources based on their preferences and learning goals (George et al, 2016; Joy & Pillai, 2022). It is very suitable for self-directed learning in the aspects of personal information collection, system guidance and personalized control (Buder & Schwind, 2012).…”