Our publication about “Towards a Learning-Aware Application Guided by Hierarchical Classification of Learner Profiles” is published as part of the Special Issue on Learning Analytics.
Learner profiling is a methodology that draws a parallel from user profiling. Implicit feedback is often used in recommender systems to create and adapt user profiles. In this work the implicit feedback is based on the learner’s answering behaviour in the Android application UnlockYourBrain, which poses different basic mathematical questions to the learners. We introduce an analytical approach to model the learners’ profile according to the learner’s answering behaviour. Furthermore, similar learner’s profiles are grouped together to construct a learning behaviour cluster. The choice of hierarchical clustering as a means of classification of learners’ profiles derives from the observations of learners behaviour. This in turn reflects the similarities and subtle differences of learner behaviour, which are further analysed in more detail. Building awareness about the learner’s behaviour is the first and necessary step for future learning-aware applications.
[Link to full article]
Reference: Taraghi, B., Saranti, A., Ebner, M., Müller, V., Großmann, A. (2015) Towards a Learning-Aware Application Guided by Hierarchical Classification of Learner Profiles, Journal of Universal Computer Science, vol. 21, no. 1 (2015), 93-109
Our second publication at this year HCII conference in Crete, Greece is about “Markov Chain and Classification of Difficulty Levels Enhances the Learning Path in One Digit Multiplication“.
In this work we focus on a specific application named “1×1 trainer” that has been designed to assist children in primary school to learn one digit multiplications. We investigate the database of learners’ answers to the asked questions by applying Markov chain and classification algorithms. The analysis identifies different clusters of one digit multiplication problems in respect to their difficulty for the learners. Next we present and discuss the outcomes of our analysis considering Markov chain of different orders for each question. The results of the analysis influence the learning path for every pupil and offer a personalized recommendation proposal that optimizes the way questions are asked to each pupil individually.
Reference: Taraghi, B., Saranti, A., Ebner, M., Schön, M. (2014) Markov Chain and Classification of Difficulty Levels Enhances the Learning Path in One Digit Multiplication. Learning and Collaboration Technologies. Designing and Developing Novel Learning Experiences. Panayiotis, Z., Ioannou, A. (Ed.), Springer Lecture Notes, pp. 322-322
Beni did the presentation about “Markov Chain and Classification of Difficulty Levels Enhances the Learning Path in One Digit Multiplication” at this HCII conference in Creete, Greece. Here are his slides:
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