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• With WiDeST, you can score the quality of paper-based concept maps and, thus, the richness of a mental model. • You can also track mental model development over time without computer-based applications. • Based on two studies, WiDeST is reliable across time, skills, and educational levels. Drawing a paper-based concept map gives students more freedom to express their mental model than digital concept mapping tools. However, this freedom can hinder a uniform structure and make determining the structural quality of students' mental models more difficult. In this dual-study paper, we develop and determine the reliability of our Width, Depth and Strength Tool (WiDeST) for assessing paper-based concept maps in secondary and tertiary education. In the first study, 157 secondary education students created 1377 concept maps in a longitudinal design over 24 weeks. The first study's results indicate that WiDeST is reliable, with an Omega Total of 0.81. Test-retest stability (ICCk2) ranges between 0.72 and 0.84. To test whether WiDeST remained reliable in tertiary education, we undertook a second study in which 80 students created 80 concept maps. The second study's results show that WiDeST is reliable with an Omega total of 0.70. WiDeST remained reliable while the structural complexity of the mental models increased from secondary education to tertiary education. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a Framework for Improving Learning Through Webcams And Microphones (FILTWAM). It proposes an overarching framework comprising conceptual and technical frameworks for enhancing the online communication skills of lifelong learners. Our approach interprets the emotional state of people using webcams and microphones and combines relevant and timely feedback based upon learner''s facial expressions and verbalizations (like sadness, anger, disgust, fear, happiness, surprise, and neutral). The feedback generated from the webcams is expected to enhance learner''s awareness of their own behavior. Our research enhances flexibility and scalability in contrast with face- to-face trainings and better helps the interests of lifelong learners who prefer to study at their own pace, place and time. Our small-scale proof of concept study exemplifies the practical application of FILTWAM and provides first evaluation results on that. This study will guide future development of software, training materials, and research. It will validate the use of webcam data for a real-time and adequate interpretation of facial expressions into emotional states. Participants’ behaviour is recorded on videos so that videos will be replayed, rated, annotated and evaluated by expert observers and contrasted with participants’ own opinions in future research. [Copyright &y& Elsevier]

Recommender systems for e-learning demand specific pedagogy-oriented and hybrid recommendation strategies. Current systems are often based on time-consuming, top down information provisioning combined with intensive data-mining collaborative filtering approaches. However, such systems do not seem appropriate for Learning Networks where distributed information can often not be identified beforehand. Providing sound way-finding support for lifelong learners in Learning Networks requires dedicated personalised recommender systems (PRS), that offer the learners customised advise on which learning actions or programs to study next. Such systems should also be practically feasible and be developed with minimized effort. Currently, such so called light-weight PRS systems are scarcely available. This study shows that simulation studies can support the analysis and optimisation of PRS requirements prior to starting the costly process of their development, and practical implementation (including testing and revision) during field experiments in real-life learning situations. This simulation study confirms that providing recommendations leads towards more effective, more satisfied, and faster goal achievement. Furthermore, this study reveals that a light-weight hybrid PRS-system based on ratings is a good alternative for an ontology-based system, in particular for low-level goal achievement. Finally, it is found that rating-based light-weight hybrid PRS-systems enable more effective, more satisfied, and faster goal attainment than peer-based light-weight hybrid PRS-systems (incorporating collaborative techniques without rating). [ABSTRACT FROM AUTHOR]

Lifelong learners who select learning activities to attain certain learning goals need to know which are suitable and in which sequence they should be performed. Learners need support in this way-finding process, and we argue that this could be provided by using Personalised Recommender Systems (PRSs). To enable personalisation, collaborative filtering could use information about learners and learning activities, since their alignment contributes to learning efficiency. A model for way-finding presents personalised recommendations in relation to information about learning goals, learning activities and learners. A PRS has been developed according to this model, and recommends to learners the best next learning activities. Both model and system combine social-based (i.e., completion data from other learners) and information-based (i.e., metadata from learner profiles and learning activities) approaches to recommend the best next learning activity to be completed. [ABSTRACT FROM AUTHOR]