I feel honored to give the keynote presentation for this year CSEDU 2020 conference. Due to the CoVid-crisis it was done completely online. Here you can find my slides:
Learning Analytics
[publication] Mobile Learning Applications for Android und iOS for German Language Acquisition based on Learning Analytics Measurements #LearningAnlytics #IdERblog
We did a contribution to the International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI) titled „Mobile Learning Applications for Android und iOS for German Language Acquisition based on Learning Analytics„
Abstract:
Journal’s homepage
The use of digital media is increasingly being promoted in school teaching. Since this aspect changes the interaction between teachers and pupils, this research is concerned with the development of a prototype of a mobile application for Android and iOS, in which different learning applications for language acquisition are offered on the basis of learning analytical measurements provided by experts in the field. By logging and collecting interactions of the user, it is possible to create a variety of statistical evaluations and thus respond to the needs and weaknesses of students. For the evaluation of the application, a user experience test was carried out, whereby the child-friendly operation of the application was tested. Due to the very positive feedback, the design was found to be good and can therefore be further developed.
[Link to full article @ ResearchGate]
[Link to full article @ journal’s homepage]
Reference: Friedl, Markus, Ebner, Markus, Ebner, Martin (2020) Mobile Learning Applications for Android und iOS for German Language Acquisition based on Learning Analytics Measurements. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI). 2(1). pp. 4-13
[presentation] Implementation of Interactive Learning Objects for German Language Acquisition #master #tugraz
[publication] Learning Analytics: Einsatz an österreichischen Hochschulen #fnma #LearningAnalytics
Unsere gemeinsaem Publikation zu „Learning Analytics: Einsatz an österreichischen Hochschulen“ versucht ein gemeinsames Verständnis für das Themenfeld zu schaffen und ist nun auch online zugänglich:
Zusammenfassung:
Learning Analytics ist sowohl international als auch national ein immer mehr an Bedeutung gewinnendes Themenfeld, welches dabei helfen kann, Lehr- und Lernprozesse besser zu verstehen und gezielt zu optimieren. Dieses Whitepaper soll eine erste Orientierung zu diesem Thema geben und dabei speziell die österreichische Hochschullandschaft adressieren.
Ausgehend von der Definition: “Learning Analytics umfasst die Analyse, Darstellung und Interpretation von Daten aus Lehr- und Lernsettings mit dem Zweck, dass Lernende ihr Lernen unmittelbar verändern können” werden Herausforderungen benannt und der Status Quo in Österreich präsentiert. Daraus werden sechs Argumente für Learning Analytics abgeleitet und vier konkrete Handlungsempfehlungen ausgesprochen.
[Link zum Whitepaper auf Researchgate]
Zitation: Leitner, P., Ebner, M., Ammenwerth, E., Andergassen, M., Csanyi, G., Gröblinger, O., Kopp, M., Reichl, F., Schmid, M., Steinbacher, H.-P., Handle-Pfeiffer, D., Zitek, A., Zöserl, E., Zwiauer, C. (2019) Learning Analytics: Einsatz an österreichischen Hochschulen. Whitepaper, Forum Neue Medien in der Lehre (FNMA). 24s.
[presentation] Mobile Learning Applications for German Language Acquisition #master #iderblog
[presentation] Learning Anaytics: Einsatz an österreichischen Hochschulen #fnma #LearningAnalytics
Ich habe zusammen mit vielen Kolleginnen und Kollegen im Rahmen einer Arbeitsgruppe des Forums Neue Medien in der Lehre Austria (FNMA) ein Whitepaper zu „Learning Analaytics: Einsatz an österreichischen Hochschulen“ erstellt und darf diese heute im Rahmen einer Veranstaltung präsentieren. Hier die Folien dazu:
[workshop] Emerging technologies and emerging trends in education #tugraz #reserach
Im Rahmen des CAS eLearning Zertifikatkurses darf ich heuer eine neues Modul rund um „Emerging Technologies and Trends“ abhalten. Die dafür notwendigen Unterlagen sind hier nochmals übersichtlich dargestellt.
VORMITTAG: EMERGING TECHNOLOGIES – from social to mobile and seamless learning
NACHMITTAG: EMERGING TRENDS – Learning Analytics, Maker Education & Lernen mit Videos
Als weiterführende Literatur wird das Lehrbuch für Lernen und Lehren mit Technologien empfohlen mit den entsprechenden Kapiteln, sowie das Kapitel „Mobile Seamless Learning – Die nahtlose Integration mobiler Geräte beim Lernen und im Unterricht„.
Zur Info für Nicht-Teilnehmer/innen: Es handelt sich um einen ganztägigen Workshop.
[publication] Insights into Learning Competence Through Probabilistic Graphical Models #tugraz #research
We contributed to this year „Third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference (CD-MAKE 2019)“ with a publication titled „Insights into Learning Competence Through Probabilistic Graphical Models„.
Abstract:
One-digit multiplication problems is one of the major fields in learning mathematics at the level of primary school that has been studied over and over. However, the majority of related work is focusing on descriptive statistics on data from multiple surveys. The goal of our research is to gain insights into multiplication misconceptions by applying machine learning techniques. To reach this goal, we trained a probabilistic graphical model of the students’ misconceptions from data of an application for learning multiplication. The use of this model facilitates the exploration of insights into human learning competence and the personalization of tutoring according to individual learner’s knowledge states. The detection of all relevant causal factors of the erroneous students answers as well as their corresponding relative weight is a valuable insight for teachers. Furthermore, the similarity between different multiplication problems – according to the students behavior – is quantified and used for their grouping into clusters. Overall, the proposed model facilitates real-time learning insights that lead to more informed decisions.
[Proceedings online @ Springer]
Reference: Saranti, A., Taraghi, B., Ebner, M., Holzinger, A. (2019) Insights into Learning Competence Through Probabilistic Graphical Models. In: Machine Learning and Knowledge Extraction. Third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2019, Canterbury, UK, August 26–29, 2019, Proceedings. pp. 250-271
[presentation] Was lernen wir von Learning Analytics #tugraz #research #LearningAnalytics
Im Rahmen des ÖFEB-Kongress darf ich heute über Learning Analytics reden – die Folien findet man hier:
[publication] Scheduling Interactions in Learning Videos: A State Machine Based Algorithm #tugraz #Interactive
We did an article about „Scheduling Interactions in Learning Videos: A State Machine Based Algorithm“ for the first issue of the „International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI)„.
Abstract:
Based on the currently developing trend of so called Massive Open Online Courses it is obvious that learning videos are more in use nowadays. This is some kind of comeback because due to the maxim “TV is easy, book is hard” [1][2] videos were used rarely for teaching. A further reason for this rare usage is that it is widely known that a key factor for human learning is a mechanism called selective attention [3][4]. This suggests that managing this attention is from high importance. Such a management could be achieved by providing different forms of interaction and communication in all directions. It has been shown that interaction and communication is crucial for the learning process [6]. Because of these remarks this research study introduces an algorithm which schedules interactions in learning videos and live broadcastings. The algorithm is implemented by a web application and it is based on the concept of a state machine. Finally, the evaluation of the algorithm points out that it is generally working after the improvement of some drawbacks regarding the distribution of interactions in the video.
[article @ journal’s homepage]
Reference: Wachtler, J., Ebner, M. (2019) Scheduling Interactions in Learning Videos: A State Machine Based Algorithm. IN: International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI). 2019(1). pp. 58-76
