Digitale Lehre an und rund um der Technischen Universität Graz
Our contribution to this year LAK conference is about the “De-Identification”. Mohammad gave a presentation within the Workshop “Ethics and Privacy in Learning Analytics”. Here can your find his slides:
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Unsere Abteilungen beschäftigt sich nun schon seit einigen Jahren ganz stark mit Learning Analytics in der Primarstufe – angefangen hat es mit dem Einmaleinstrainer, heute haben wir auch einen mehrstelligen Multiplikationstrainer, Plus- und Minustrainer bzw. steht der Divisionstrainer kurz vor dem Start.
Alles findet man unter https://schule.learninglab.tugraz.at und kann frei von allen Schulen genutzt werden. Wir haben dies im Rahmen eines Workshops an der Fachdidaktik Informatik Tagung vorgestellt und dabei dieses Handout verteilt:
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Our publication about “Towards a Learning-Aware Application Guided by Hierarchical Classification of Learner Profiles” is published as part of the Special Issue on Learning Analytics.
Abstract:
Learner profiling is a methodology that draws a parallel from user profiling. Implicit feedback is often used in recommender systems to create and adapt user profiles. In this work the implicit feedback is based on the learner’s answering behaviour in the Android application UnlockYourBrain, which poses different basic mathematical questions to the learners. We introduce an analytical approach to model the learners’ profile according to the learner’s answering behaviour. Furthermore, similar learner’s profiles are grouped together to construct a learning behaviour cluster. The choice of hierarchical clustering as a means of classification of learners’ profiles derives from the observations of learners behaviour. This in turn reflects the similarities and subtle differences of learner behaviour, which are further analysed in more detail. Building awareness about the learner’s behaviour is the first and necessary step for future learning-aware applications.
Reference: Taraghi, B., Saranti, A., Ebner, M., Müller, V., Großmann, A. (2015) Towards a Learning-Aware Application Guided by Hierarchical Classification of Learner Profiles, Journal of Universal Computer Science, vol. 21, no. 1 (2015), 93-109
Our Special Issue about Learning Analytics is published within the Journal of Universal Computer Science. We like to thank all authors and reviewers for their valuable work. All readers we wish an enjoyable reading experience.
Already back in 2006 Retalis et al. proposed their first thoughts on Learning Analytics (LA) and considered interaction analysis as a promising way to better understand the learner’s behavior. A couple of years later, further activities were organized; especially Siemens and Long predicted that the most important factor shaping the future of higher education would be big data and analytics. Just few months later, the Horizon Report also described Learning Analytics as a big trend for the forthcoming years. Since then a number of conferences (for example LAK 11, LAK 12, …) have been organized and different projects have been started as well as the topic has been rising on Google trends. The number of research publications has also increased arbitrarily in different directions; for instance to define the upcoming research field, to gather practical experiences or simply to confine LA from other topics (especially from Educational Data Mining (EDM)) …
Table of Content:
Reference: Ebner, M., Kinshuk, Wohlhart, D., Taraghi, B., Kumar, V. (2015) Editorial: Learning Analytics J.UCS Special Issue, Journal of Universal Computer Science, vol. 21, no. 1 (2015), 1-6
Together with Behnam Taraghi, Anna Saranti and Sandra Schön we discussed and broad together what makes learning analytics smart – from our perspectives and experiences with some years of work (and several publications). Here your will find the whole publication or simply summarized as figure:
Abstract:
Learning Analytics (LA) is an emerging field; the analysis of a large amount of data helps us to gain deeper insights into the learning process. This contribution points out that pure analysis of data is not enough. Building on our own experiences from the field, seven features of smart learning analytics are described. From our point of view these features are aspects that should be considered while deploying LA.
Reference: Martin Ebner, Behnam Taraghi, Anna Saranti, Sandra Schön (2015). Seven features of smart learning analytics – lessons learned from four years of research with learning analytics. In: eLearning Papers, Issue 40, January 2015, pp. 51.55, URL: https://www.openeducationeuropa.eu/en/article/Assessment-certification-and-quality-assurance-in-open-learning_From-field_40_3?paper=164347
Christoph Prettenthaler hat im Rahmen seiner Masterarbeit ein webbasiertes Informationssystem entwickelt, welches es erlaubt interaktive Übungen in E-Books einzubauen bzw. online zu generieren. Wie es funktioniert zeigt er in seiner Präsentation:
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Our contribution about “Determining the Causing Factors of Errors for Multiplication Problems” at this year European Immersive Education Summit is now online available.
Abstract:
Literature in the area of psychology and education provides domain knowledge to learning applications. This work detects the difficulty levels within a set of multiplication problems and analyses the dataset on different error types as described and determined in several pedagogical surveys and investigations. Our research sheds light to the impact of each error type in simple multiplication problems and the course of error types in problem-size.
Reference: Taraghi, B., Frey, M., Saranti, A., Ebner, M., Müller, V. & Großmann, A. (2014) Determining the Causing Factors of Errors for Multiplication Problems, European Immersive Education Summit, 2014, Vienna, pp. 144 – 153 [Link to article]
Our contribution about “A Contribution to Collaborative Learning Using iPads for School Children” at this year European Immersive Education Summit is now online available.
Abstract:
Collaboration has a very positive effect on students’ learning experiences as well as their social interactions. Our research study aims towards enhancing the learning experience, stimulating communication and cooperative behavior to improve learning. Making use of recent technological advancements (tablets) and gaming as a motivational factor, a prototype application in form of a multiplayer learning game for iPads was designed and developed. In a face-to-face setting, connecting up to four devices, the players (learners) have to solve word puzzles in a collaborative way. Furthermore, a web-interface for teachers provides the possibility to create custom content as well as to receive feedback of the children’s performance. A first field study at two primary schools in Graz showed promising results for the learning behavior of school children.
Reference: Ebner, M., Kienleitner, B. (2014) A Contribution to Collaborative Learning Using iPads for School Children, European Immersive Education Summit, 2014, Vienna, pp. 87-97 [Link to article]
Im Rahmen der DINI-Zukunfstwerkstatt durfte ich zum Thema Learning Analytics reden – die Folien sind bereits hier zugänglich. Jetzt gibt es auch noch die Aufzeichnung 🙂
Our publication about “Adaptive Learner Profiling Provides the Optimal Sequence of Posed Basic Mathematical Problems” at this year EC-TEL conference is now available.
Abstract:
Applications that try to enhance learners’ knowledge can profit by the creation and analysis of learner profiles. This work deals with the derivation of an optimal sequence of questions by comparing similar learning behaviour of users of a mathematics training application. The adaptation of the learners’ clusters to the answers of the revised optimal question sequence improves learning
Reference: Taraghi, B., Saranti, A., Ebner, M., Großmann, A., Müller, V. (2014) Adaptive Learner Profiling Provides the Optimal Sequence of Posed Basic Mathematical Problems. In: Open Learning and Teaching in Educational Communities. Rensing, C., de Freitas, S., Ley, T., Muñoz-Merino, P. J. (Ed.). Lecture Notes in Computer Science Volume 8719, Springer 2014, pp. 592-59