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[publication] Learning Analytics in Hochschulen und Künstliche Intelligenz. Eine Übersicht über Einsatzmöglichkeiten, erste Erfahrungen und Entwicklungen von KI-Anwendungen zur Unterstützung des Lernens und Lehrens. #KI #KünstlicheIntelligenz #Artificialintelligence #Learninganalytics #research #tugraz

Es freut uns sehr, dass unser Beitrag zu “Learning Analytics in Hochschulen und Künstliche Intelligenz. Eine Übersicht über Einsatzmöglichkeiten, erste Erfahrungen und Entwicklungen von KI-Anwendungen zur Unterstützung des Lernens und Lehrens.” nun erschienen ist. Das Ziel war den Unterschied herauszuarbeiten und einen ersten Einblick zu geben in das Themenfeld. Wir hatten seinerzeit auch ein Video, quasi als Teaser für das Kapitel erstellt:

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[full article @ ResearchGate]
[full article @ Verlag Homepage]

Zitation: Schön, Sandra; Leitner, Philipp; Lindner, Jakob & Ebner, Martin (2023). Learning Analytics in Hochschulen und Künstliche Intelligenz. Eine Übersicht über Einsatzmöglichkeiten, erste Erfahrungen und Entwicklungen von KI-Anwendungen zur Unterstützung des Lernens und Lehrens. In: Tobias Schmohl, Alice Watanabe, Kathrin Schelling (Hg.), Künstliche Intelligenz in der Hochschulbildung, Bielefeld: transkript, S. 27-49. Online zugänglich unter: https://www.transcript-verlag.de/media/pdf/c9/16/59/oa9783839457696.pdf

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[publication] Towards Trustworthy Learning Analytics Applications: An Interdisciplinary Approach Using the Example of Learning Diaries #LearningAnalytics #tugraz

Our poster/chapter about “Towards Trustworthy Learning Analytics Applications: An Interdisciplinary Approach Using the Example of Learning Diaries” at HCII 2022 conference is published and available.

Abstract:
Learning analytics (LA) is an emerging field of science due to its great potential to better understand, support and improve the learning and teaching process. Many higher education institutions (HEIs) have already included LA in their digitalisation strategies. This process has been additionally accelerated during the COVID-19 pandemic when HEIs transitioned from face-2-face learning environments to hybrid and e-learning environments and entirely relied on technology to continue operating. Undoubtedly, there was never a time when so much student data was collected, analysed, and reported, which brings numerous ethical and data protection concerns to the forefront. For example, a critical issue when implementing LA is to determine which data should be processed to fulfil pedagogical purposes while making sure that LA is in line with ethical principles and data protection law, such as the European General Data Protection Regulation (GDPR). This article contributes to the discussion on how to design LA applications that are not only useful and innovative but also trustworthy and enable higher education learners to make data-informed decisions about their learning process. For that purpose, we first present the idea and methodology behind the development of our interdisciplinary Criteria Catalogue for trustworthy LA applications intended for students. The Criteria Catalogue is a new normative framework that supports students to assess the trustworthiness of LA applications. It consists of seven defined Core Areas (i.e., autonomy, protection, respect, non-discrimination, responsibility and accountability, transparency, and privacy and good data governance) and corresponding criteria and indicators. Next, we apply this normative framework to learning diaries as a specific LA application. Our goal is to demonstrate how ethical and legal aspects could be translated into specific recommendations and design implications that should accompany the whole lifecycle of LA applications.

[article @ book’s homepage]
[draft @ ResearchGate]

Reference: Veljanova, H., Barreiros, C., Gosch, N., Staudegger, E., Ebner, M., Lindstaedt, S. (2022). Towards Trustworthy Learning Analytics Applications: An Interdisciplinary Approach Using the Example of Learning Diaries. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1582. Springer, Cham. https://doi.org/10.1007/978-3-031-06391-6_19

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[publication] Using learning analytics to improve the educational design of MOOCs #MOOC #imoox

Our article about “Using learning analytics to improve the educational design of MOOCs” got published now in the International Journal of Education and Learning.

Abstract:
In recent years, the interest in Massive Open Online Courses (MOOCs) and Learning Analytics research have highly increased in the areas of educational technologies. The emergence of new learning technologies requires new perspectives on Educational Design. When the areas of MOOCs, Learning Analytics and Instructional Design developed, the interest and connection between these three concepts became important for research. Learning Analytics provides progress information and other individualized support in MOOC settings where teachers are not able to provide learners with individual attention, which would be possible in a traditional face-to-face setting. Through collective views over the learning process, the overall progress and performance are indicated. Moreover, results can lead to Educational Design improvements. Every time a learner interacts with the system, data is created and collected. Many Educational Designers do not take advantage of this data and thereby, losing the possibility to impact the course design in a powerful way. This research work strongly focuses on the implication of Learning Analytics for Educational Design in MOOCs. Many methods and algorithms are used in the analytical learning process in MOOCs. Currently, a great variety of learning data exists. First, well-known Instructional Design patterns from different models were collected and listed. In a second step, through the collected data is used to point out which of these patterns can be answered by using Learning Analytics methods. The findings of the study show that it is possible to better understand which environments and experiences are best suited for learning by analyzing students’ behaviors online. These results have great potential for a rapidly and easier understanding and optimization of the learning process for educators.

[full article @ journal’s homepage]
[full article @ ResearchGate]

Reference: Khalil, H., Ebner, M., & Leitner, P. (2022). Using learning analytics to improve the educational design of MOOCs. International Journal of Education and Learning, 4(2), 100-108. doi:https://doi.org/10.31763/ijele.v4i2.641

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[keynote] 9th International Conference on Computer Technology Applications (ICCTA 2023) #conference #research

Martin is invited to give a keynote talk at 9th International Conference on Computer Technology Applications (ICCTA 2023) in May 2023. He will give an overview and insights about our running projects on MOOCs, Learning Analytics and Open Educational Resources.

Abstract of the talk:
In this talk the different research fields of MOOCs, Learning Analytics and OER are addressed and brought together. Over a couple of years now, different proponents announcing MOOS or OER or Learning Analytics. Nevertheless, the idea is to put all the research results together and provide it as a service for education, especially in Higher Education.
Using the example from Graz University of Technology the talk will provide insights how we can deal with it in the future based on research findings and hands-on experiences with real student data.

We would be happy if you join us in Vienna next year: [conference website]

With this announcement I would like to wish you merry christmas and a happy, healthy year 2023 – I will be back after my holidays in January 2023. Hopefully we got a little bit of snow in our mountains, because I would love to make some tours with my skis 🙂
Feel free to take a look to my tour descriptions to get an idea what I am taking about: [Link to profile @ Alpenverein]

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[publication] Patterns of quiz attempts in a MOOC. The full-points-pattern and other patterns on the way to a successful MOOC in a lecture setting #tugraz #edil22 #MOOC #imoox #LearningAnalytics

Our research about “Patterns of quiz attempts in a MOOC. The full-points-pattern and other patterns on the way to a successful MOOC in a lecture setting” was presented at EdMedia + Innovate Learning 2022:

Abstract:
The analysis of learner data in MOOCs provides numerous opportunities to look for patterns that may indicate participants’ learning strategies. In this article, we investigated how participants in a MOOC (N=1,200), in which they must successfully complete a quiz in each unit, deal with the fact that they can repeat this quiz up to five times. On the one hand, patterns can be identified regarding the success of the quiz attempts: For example, 32.7% of the course participants always repeat the quizzes up to a full score, while about 16.0% of the participants repeat, but only until they pass all quizzes. Regarding the number of attempts, independent of the success, there is only a uniformity in “single attempt”; 12.6% of the participants only take exactly one attempt at each of the quizzes in the MOOC. An analysis of a subgroup of 80 learners which were students of a course where the MOOC was obligatory, shows that the proportion of learners attributed to patterns making more attempts is generally bigger. It can be shown as well that learners who uses several attempts, even after a full score results, tend to get better exam. The article concludes by discussing how these patterns can be interpreted and how they might influence future MOOC developments.

[full article @ conference homepage]
[draft @ ResearchGate]

Reference: Mair, B., Schön, S., Ebner, M., Edelsbrunner, S., Leitner, P., Schlager, A., Teufel, M. & Thurner, S. (2022). Patterns of quiz attempts in a MOOC. The full-points-pattern and other patterns on the way to a successful MOOC in a lecture setting. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 1169-1179). New York City, NY, United States: Association for the Advancement of Computing in Education (AACE). Retrieved July 13, 2022 from https://www.learntechlib.org/primary/p/221430/

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[publication] MOOCs, Learning Analytics and OER: An Impactful Trio for the Future of Education! #imoox #OER #tugraz #LearningAnalytics

Our contribution to the CSEDU 2020 conference about “MOOCs, Learning Analytics and OER: An Impactful Trio for the Future of Education!” got finally published. I did the presentation in May 2020 in the very beginning of the COVID19-pandemic and it was my first complete online keynote 🙂

Abstract:
This paper discusses the general thesis that massive open online courses (in short MOOC), open educational resources (in short OER) and learning analytics are an impactful trio for future education, especially if combined. The contribution bases upon our practical experience as service providers and researchers in the department “Educational Technology” at Graz University of Technology (TU Graz) in Austria. The team members provide support to lecturers, teachers and researchers in these addressed fields for several years now, for example as host of the MOOC platform iMooX.at, providing only OER since 2015. Within this contribution, we will show, against some doubtful or conflicting opinions and positions, that (a) MOOCs are opening-up education; (b) learning analytics give insights and support learning, not only online learning, if implemented in MOOCs; and (c) that OER has the potential for sustainable resources, innovations and even more impact, especially if implemented in MOOCs.

[full article @ book’s homepage]
[draft @ ResearchGate]

Reference: Ebner M., Schön S. (2021) MOOCs, Learning Analytics and OER: An Impactful Trio for the Future of Education!. In: Lane H.C., Zvacek S., Uhomoibhi J. (eds) Computer Supported Education. CSEDU 2020. Communications in Computer and Information Science, vol 1473. Springer, Cham. https://doi.org/10.1007/978-3-030-86439-2_2