Publikationen

Learning dashboards (LD) are commonly applied for monitoring and visual analysis of learning activities. The main purpose of LDs is to increase awareness, to support self assessment and reflection and, when used in collaborative learning platforms (CLP), to improve the collaboration among learners. Collaborative learning platforms serve astools to bring learners together, who share the same interests and ideas and are willing to work and learn together – a process which, ideally, leads to effective knowledge building. However, there are collaborationand communications factors which affect the effectiveness of knowledge creation – human, social and motivational factors, design issues, technical conditions, and others. In this paper we introduce a learning dashboard – the Visualizer – that serves the purpose of (statistically) analyzing andexploring the behaviour of communities and users. Visualizer allows a learner to become aware of other learners with similar characteristics and also to draw comparisons with individuals having similar learninggoals. It also helps a teacher become aware of how individuals working in the groups (learning communities) interact with one another and across groups.

This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.

Purpose – The goal of this study is to understand what drives people (i.e., their motivations, autonomous learning attitudes and learning interests) to volunteer as mentors for a program that helps families to ideate technological solutions to community problems.Design/methodology/approach – A three-phase method was used to i) create volunteer mentor profiles; ii) elicit topics of interest; and iii) establish relationships between those. The mentor profiles were based on self-assessments of motivation, attitudes towards lifelong learning and self-regulated learning strategies. The topics of interests were defined by analyzing answers to reflection questions. Statistical methods were applied to analyze the relationships between the interests and the mentor profiles.Findings –Three mentor groups (G1 “low,” G2 “high” and G3 “medium”) were identified based on pre-survey data via bottom-up clustering. Content analysis was used to define the topics of interest: communication skills; learning AI; mentoring; prototype development; problem solving skills; and working with families. Examining relationships between the mentor profile and the topics of interest showed that group G3 “medium” with strong intrinsic motivation had significantly more interest in working with families. The group with overall highest scores (G2 “high”) expressed substantial interest in learning about AI. However, there was a high variability between members of this group. Originality/value –The study established different types of learning interests of volunteer mentors and related them to the mentor profiles based on motivation, self-regulated learning strategies and attitudes towards lifelong learning. Such knowledge can help organizations shape volunteering experience, offering more value to volunteers. Furthermore, the reflection questions can be used by: i) volunteers as an instrument of reflection; and ii) organizations for eliciting learning interests of volunteers.