Schlagwort: LAK

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[publication] Lessons Learned when transferring Learning Analytics Interventions across Institutions #lak18 #research

Our publication at this year LAK18-conference about “Lessons Learned when transferring Learning Analytics Interventions across Institutions” is now online available.

Abstract:

Learning Analytics is a promising research field, which is advancing quickly. Therefore, it finally impacts research, practice, policy, and decision making in the field of education. Nonetheless, there are still influencing obstacles when establishing Learning Analytics initiatives on higher education level. Besides the much discussed ethical and moral concerns, there is also the matter of data privacy. In 2015, the European collaboration project STELA started with the main goal to enhance the Successful Transition from secondary to higher Education by means of Learning Analytics. Together, the partner universities develop, test, and assess Learning Analytics approaches that focus on providing feedback to students. Some promising approaches are then shared between the partner universities. Therefore, the transferability of the Learning Analytics initiatives is of great significance. During the duration of our project, we found a variety of difficulties, we had to overcome to transfer one of those Learning Analytics initiatives, the Learning Tracker from one partner to the other. Despite, some of the difficulties can be categorized as small, all of them needed our attention and were time consuming. In this paper, we present the lessons learned while solving these obstacles.

[Full paper @ LAK 2018]

Reference: Leitner, P., Broos, T. & Ebner, M. (2018) Lessons Learned when transferring Learning Analytics Interventions across Institutions. In: Companion Proceedings 8th International Conference on Learning Analytcis & Knowledge. Sydney. pp. 621-629

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[publication] On Using Learning Analytics to Track the Activity of Interactive MOOC Videos #lak16

As part of the Workshop on Smart Environments and Analytics in Video-Based Learning at this year LAK`16 conference our contribution about “On Using Learning Analytics to Track the Activity of Interactive MOOC Videos” got published.
Abstract:

It is widely known that interaction, as well as communication, are very important parts of successful online courses. These features are
considered crucial because they help to improve students’ attention in a very significant way. In this publication, the authors present an innovative application, which adds different forms of interactivity to learning videos within MOOCs such as multiple-choice questions or the possibility to communicate with the teacher. Furthermore, Learning Analytics using exploratory examination and visualizations have been applied to unveil learners’ patterns and behaviors as well as investigate the effectiveness of the application. Based upon the quantitative and qualitative observations, our study determined common practices behind dropping out using videos indicator and suggested enhancements to increase the performance of the application as well as learners’ attention.

[Publication @ ResearchGate]

Reference: Wachtler, J., Khalil, M., Taraghi, B. & Ebner, M. (2016). On Using Learning Analytics to Track the Activity of Interactive MOOC Videos. Paper presented at LAK 2016 Workshop on Smart Environments and Analytics in Video-Based Learning, Edinburgh, United Kingdom, 26/04/16 – 26/04/16, pp. 8-17

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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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[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] De-Identification in Learning Analytics

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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[publication] On using markov chain to evidence the learning structures and difficulty levels of one digit multiplication

Our publication of this year Learning Analytics and Knowledge Conference (LAK 2014) about “On using markov chain to evidence the learning structures and difficulty levels of one digit multiplication” is now online available.

Abstract:

Understanding the behavior of learners within learning applications and analyzing the factors that may influence the learning process play a key role in designing and optimizing learning applications. In this work we focus on a specific application named “1×1 trainer” that has been designed for primary school children to learn one digit multiplications. We investigate the database of learners’ answers to the asked questions (N > 440000) by applying the Markov chains. We want to understand whether the learners’ answers to the already asked questions can affect the way they will answer the subsequent asked questions and if so, to what extent. Through our analysis we first identify the most difficult and easiest multiplications for the target learners by observing the probabilities of the different answer types. Next we try to identify influential structures in the history of learners’ answers considering the Markov chain of different orders. The results are used to identify pupils who have difficulties with multiplications very soon (after couple of steps) and to optimize the way questions are asked for each pupil individually.

Reference: Taraghi, B., Ebner, M., Saranti, A., Schön, M. (2014) On Using Markov Chain to Evidence the Learning Structures and Difficulty Levels of One Digit Multiplication, In: Proceedins of the Fourth International Conference on Learning Analytics And Knowledge, ACM, New York, p. 68-72

[Link Article]

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[publication] It’s Just About Learning the Multiplication Table

At this year LAK 2012 Conference on “Learning Analytics and Knowledge” we presented our first results of the netidee project “Multiplication Trainer“. Our publication is also online available:
Abstract:

One of the first and basic mathematical knowledge of schoolchildren is the multiplication table. At the age of 8 to 10 eachchild has to learn by training step by step, or more scientifically, by using a behavioristic learning concept. Due to this fact it can be mentioned that we know very well about the pedagogicalapproach, but on the other side there is rather less knowledgeabout the increase of step-by-step knowledge of the schoolchildren.In this publication we present some data documenting thefluctuation in the process of acquiring the multiplication tables.We report the development of an algorithm which is able to adaptthe given tasks out of a given pool to unknown pupils. For this purpose a web-based application for learning the multiplicationtable was developed and then tested by children. Afterwards so-called learning curves of each child were drawn and analyzed bythe research team as well as teachers carrying out interestingoutcomes. Learning itself is maybe not as predictable as we knowfrom pedagogical experiences, it is a very individualized processof the learners themselves.It can be summarized that the algorithm itself as well as thelearning curves are very useful for studying the learning success.Therefore it can be concluded that learning analytics will becomean important step for teachers and learners of tomorrow.

It’s Just About Learning the Multiplication Table

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Reference: Schön, M., Ebner, M., Kothmeier, G. (2012) It’s Just About Learning the Multiplication Table, Conferecnce Proceeding of LAK12: 2nd International Conference on Learning Analytics & Knowledge, 29 April – 2 May 2012, Vancouver, BC, Canada