Kategorie: Learning Analytics

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[publication] De-Identification in Learning Analytics #LA #research

Our publication about „De-Identification in Learning Analytics“ got published in the Journal of Learning Analytics.
Abstract:

Learning Analytics has reserved its position as an important field in the educational sector. However, the large-scale collection, processing and analyzing of data have steered the wheel beyond the border lines and faced an abundance of ethical breaches and constraints. Revealing learners’ personal information and attitudes, as well as their activities, are major aspects that lead to personally identify individuals. Yet, de-identification can keep the process of Learning Analytics in progress while reducing the risk of inadvertent disclosure of learners’ identities. In this paper, the authors talk about de-identification methods in the context of learning environment and propose a first prototype conceptual approach that describes the combination of anonymization strategies and Learning Analytics techniques.

[Full Paper @ ResearchGate]

[Full Paper @ Journal’s Homepage]

Reference: Khalil, M. & Ebner, M. (2016) De-Identification in Learning Analytics. Journal of Learning Analytics. 3(1). pp. 129 – 138

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[publication] What is Learning Analytics about? A Survey of Different Methods Used in 2013-2015 #LearningAnalytics #tugraz

Our publication about „What is Learning Analytics about? A Survey of Different Methods Used in 2013-2015“ for this year Smart Learning Excellence Conference in Dubai is now online available. The slides have been already published here.
Abstract:

The area of Learning Analytics has developed enormously since the first International Conference on Learning Analytics and Knowledge (LAK) in 2011. It is a field that combines different disciplines such as computer science, statistics, psychology and pedagogy to achieve its intended objectives. The main goals illustrate in creating convenient interventions on learning as well as its environment and the final optimization about learning domain’s stakeholders (Khalil & Ebner, 2015b). Because the field matures and is now adapted in diverse educational settings, we believe there is a pressing need to list its own research methods and specify its objectives and dilemmas. This paper surveys publications from Learning Analytics and Knowledge conference from 2013 to 2015 and lists the significant research areas in this sphere. We consider the method profile and classify them into seven different categories with a brief description on each. Furthermore, we show the most cited method categories using Google scholar. Finally, the authors raise the challenges and constraints that affect its ethical approach through the meta-analysis study. It is believed that this paper will help researchers to identify the common methods used in Learning Analytics, and it will assist by establishing a future forecast towards new research work taking into account the privacy and ethical issues of this strongly emerged field.

[Full text @ ResearchGate]

Reference: Khalil, M., Ebner, M. (2016). What is Learning Analytics about? A Survey of Different Methods Used in 2013-2015. Conference proceeding of the 8th e-Learning Excellence Conference, 2016. Dubai, UAE. pp. 1-12

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[presentation] Learning Analytics in MOOCs: Can Data Improve Students Retention and Learning? #edmediaconf #tugraz

Our presentation at this year ED-Media Conference in Vancouver about „Learning Analytics in MOOCs: Can Data Improve Students Retention and Learning? “ is now online available. Here are the slides:

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[presentation] Bayesian Modelling of Student Misconceptions in the one-digit Multiplication with Probabilistic Programming #lak16 #research

Our presentation at this year conference on Learning Analytics (LAK 16) was about „Bayesian Modelling of Student Misconceptions in the one-digit Multiplication with Probabilistic Programming„. Here you can find the slides:

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[presentation] Engaging Learning Analytics in MOOCs: the good, the bad, and the ugly #tugraz #research

Mohammad is presenting our research work about „Engaging Learning Analytics in MOOCs: the good, the bad, and the ugly“ at this year END conference in Lubijana. Here are his presentation:

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[publication] Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming #lak16

Our contribution to this year Learning Analytics Conference was about „Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming„.
Abstract:

One-digit multiplication errors are one of the most extensively analysed mathematical problems. Research work primarily emphasises the use of statistics whereas learning analytics can go one step further and use machine learning techniques to model simple learning misconceptions. Probabilistic programming techniques ease the development of probabilistic graphical models (bayesian networks) and their use for prediction of student behaviour that can ultimately influence learning decision processes.

[Full paper @ ResearchGate]

[Full paper @ ACM Library]

Reference: Taraghi, B., Saranti, A., Legenstein, R. & Ebner, M. (2016) Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edingburg, United Kingdom, 25/04/16 – 29/04/16, pp. 449-453., 10.1145/2883851.2883895

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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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[publication] Portraying MOOCs Learners: a Clustering Experience Using Learning Analytics #emoocs2016 #mooc

Our paper on „Portraying MOOCs Learners: a Clustering Experience Using Learning Analytics“ at this year EMOOCs conference is now online available.
Abstract:

Massive Open Online Courses are remote courses that excel in their students‘ heterogeneity and quantity. Due to the peculiarity of being massiveness, the large datasets generated by MOOCs platforms require advance tools to reveal hidden patterns for enhancing learning and educational environments. This paper offers an interesting study on using one of these tools, clustering, to portray learners‘ engagement in MOOCs. The research study analyse a university mandatory MOOC, and also opened to the public, in order to classify students into appropriate profiles based on their engagement. We compared the clustering results across MOOC variables and finally, we evaluated our results with an eighties students‘ motivation scheme to examine the contrast between classical classes and MOOCs classes. Our research pointed out that MOOC participants are strongly following the Cryer’s scheme of Elton (1996).

[Full paper @ ResearchGate]

[Conference Proceeding @ ResearchGate]

Reference: Khalil, M., Kastl, C., Ebner, M. (2016) Portraying MOOCs Learners: a Clustering Experience Using Learning Analytics. In: Proceedings of the European Stakeholder Summit on experiences and best practices in and around MOOCs. Khalil, M., Ebner, M., Kopp, M., Lorenz, A., Kalz. M. (Eds.). BookOnDemand, Norderstedt. pp. 265 – 278

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[presentation] What is Learning Analytics about? #LearningAnalytics #IA9 #research

Today, Mohammad Khalil is presenting our publication about „What is Learning Analytics about?“ at Smart Learning Excellence Conference in Dubai. Here are his presentation slides:

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[publication] Design für eine Plattform zum Schreibenlernen im Grundschulalter #LearningAnalytics

IDeR BlogIm Rahmen der heurigen DeLFI-Tagung hab ich kurz unser kürzliches begonnenes EU-Projekt, den IDeR-Blog, vorstellen können. Hier gibt es nun noch auch die zugehörige Publikation.
Zusammenfassung:

Viele Jugendliche haben mit Lese- und Schreib- oder Rechtschreibproblemen zu kämpfen. Werden diese nicht erkannt und gefördert, wirkt sich das im Erwachsenenalter negativ aus [SMH08]. In diesem Beitrag beschreiben wir ein Informationssystem für den deutschsprachigen Raum, das sich derzeit im Aufbau befindet und mit Hilfe von Learning Analytics versucht personalisiertes Lernen zu fördern. Die Zielgruppe des Forschungsprojekts sind Kinder im Alter zwischen 8 und 12 Jahren. Schülerinnen und Schüler können Texte in Form von Blogeinträgen verfassen, welche automatisiert auf orthografische Fehler ausgewertet werden. Die qualitative Analyse wird mit Hilfe eines eigens dafür entwickelten Wörterbuches umgesetzt und den Lehrkräften zur Verfügung gestellt.

[Link zum Beitrag]

Zitation: Ebner, M., Taraghi, B., Ebner, M., Aspalter, C., Biermeier, S., Edtstadler, K., Gabriel, S., Goor, G., Gros, M., Huppertz, A., Martich, S., Steinhauer, N., Ullmann, M., Ziegler, K. (2015) Design für eine Plattform zum Schreibenlernen im Grundschulalter. In: Proceedings of DeLFI Workshops 2015 co-located with 13th e-Learning Conference of the German Computer Society (DeLFI 2015)München, Germany, September 1, 2015, S. 118-122.