Viewbrics Mental Model dataset

Learners can experience difficulties to imagine how to master a complex skill using solely a text-based analytic rubric (TR) within formative assessment (FA). TR’s deficiencies for skill-mastery might be remedied by adding video-modelling examples with embedded self-explanation prompts, turning TR into a new type of rubric format, so-called ‘video-enhanced analytic rubrics’ (VER). The current study contrasted two experimental conditions (TR, N = 54; VER, N = 49) for fostering growth of learners’ mental model of complex skills using the FA supporting Viewbrics-app and one control condition using the assessment of the school’s curriculum (N = 50). Learners’ mental models for three complex skills were measured through their constructed concept maps (before project one (T1), after project one (T2), and after project two (T3)). Mental model quality was measured by awarding point towards the number of concepts (width), hierarchical levels (depth) and relationships (strength) of a concept map. The total amount of points represents the quality of the mental model. The Viewbrics-app was expected to foster learners’ mental models’ growth in both experimental conditions, in particular for the VER condition because moving pictures are remembered better, contain more and different information, provide more cues to aid retrieval from long-term memory, attract more attention of learners and increase learner engagement. A multivariate multilevel regression analysis showed learners receiving VER developed richer mental models for information literacy (11.88 points above the control and 7.11 above TR), collaboration (10.42 points above the control and 3.68 above TR) and presentation (11.52 points above the control and 11.42 above TR). Finally, learners receiving TR developed richer mental models for information literacy (4.67 points above the control) and collaboration (6.74 points above the control) skills than the control group

Noise in classrooms data set

We present a data set comprising noise samples collected during 26 sessions of the subject “Technology” of Secondary Education studies with a mobile device. The data set includes rich metadata with the aim to facilitate the correlation with further studies, namely, type of session (i.e. traditional face-to-face lecture, collaborative workshop session, individual computer session), the number of students participating in the session, the percentage of male/female students, the mean age of the students, timestamp when the sample was collected, language of the session, country, city and location where it took place. The data is shared in different format to facilitate its management across platforms

Data for: Learning Analytics in European Higher Education-Trends and Barriers

This dataset contains responses to an institutional survey that investigated existing learning analytics initiatives, institutional infrastructures for learning analytics, adopted strategies and policies for learning analytics, considerations of legal and ethical issues, existing evaluation frameworks, and evaluations of the engagement of key stakeholders (i.e., teaching staff, students, and managers), success of learning analytics, institutional culture, data and research capabilities, legal and ethical awareness, and existing training