Schlagwort: learning anayltics

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[publication] Diagnose leicht gemacht (Learning Analytics in der Schule) #LearningAnalytics #tugraz

Wir freuen uns, dass unser Beitrag zu Learning Analytics in der Schule nun veröffentlicht wurde:

Zusammenfassung:
Wenn digitale Informationssysteme im schulischen Kontext genutzt werden, entstehen auch Sammlungen von Daten zum Lernverhalten. Einige Anwendungen sammeln und analysieren diese Daten gezielt, um damit Lernende zu unterstützen. Das Forschungsfeld dazu heißt “Learning Analytics” und ist erst rund 10 Jahre alt (Leitner et al., 2019). In diesem Beitrag möchten wir zwei Anwendungen aus dem Schulkontext vorstellen, die Datenanalysen einsetzen um Lernfortschritte von Schüler/innen zu beobachten und Rückmeldung in Echtzeit zu geben (s. Ebner, Leitner, Ebner 2020). Wir nennen zudem Chancen und Herausforderungen von Learning Analytics im Schulkontext.

[Preprint @ ResearchGate]

Referenz: Schön, S., Ebner, M. (2021) Diagnose leicht gemacht. In: on. Lernen in der digitalen Welt. Schiefner-Rohs, M. & Aufenanger, S. (Betr.). Heft 5/2021 (Jg. 2). S. 10-11. ISSN: 2700-1091

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[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]

[Draft @ ResearchGate]

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

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[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]

[article @ ResearchGate]

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

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[app] 1×1 Trainer #tugraz #math

Wir haben den 1×1 Trainer grundlegend überarbeitet und mit Handschrifterkennung ausgestattet. Ab sofort können die Kinder also die Zahlen per Finger eingeben und wir hoffen so, dass es noch einfacher wird mit der App umzugehen. Die Registierung kann direkt in der App oder auch über unsere LearningLab gemacht werden.

The 1×1 Trainer App with handwriting recognition is supposed to help children with learning multiplication tables. The numbers can be written with the finger.

[Link zur App]


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[presentation] On Using Learning Analytics to Track the Activity of Interactive MOOC Videos #LAK16 #research

Today Behnam Taraghi is presenting our activities using learning vidoes in MOOCs to enhance the interactivity: “On Using Learning Analytics to Track the Activity of Interactive MOOC Videos“. The presentation is part of the “Workshop on Smart Environments and Analytics in Video-Based Learning (SE@VBL)” of 2016 Learning Analytics Conference in Edinburgh.
The slides are already available here:

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