KNOWLEDGECHECKR: Intelligent Techniques for Counteracting Forgetting

Martin Stettinger, Trang Tran, Ingo Pribik, Gerhard Leitner, Alexander Felfernig, Ralph Samer, Müslüm Atas, Manfred Wundara

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Existing e-learning environments primarily focus on theaspect of providing intuitive learning contents and to recommendlearning units in a personalized fashion. The major focus of theKNOWLEDGECHECKR environment is to take into account forget-ting processes which immediately start after a learning unit has beencompleted. In this context, techniques are needed that are able to pre-dict which learning units are the most relevant ones to be repeated infuture learning sessions. In this paper, we provide an overview of therecommendation approaches integrated in KNOWLEDGECHECKR.Examples thereof areutility-based recommendationthat helps toidentify learning contents to be repeated in the future,collaborativefilteringapproaches that help to implement session-based recommen-dation, andcontent-based recommendationthat supports intelligentquestion answering. In order to show the applicability of the pre-sented techniques, we provide an overview of the results of empiricalstudies that have been conducted in real-world scenarios
Original languageEnglish
Title of host publication24th European Conference on Artificial Intelligence (ECAI '20)
Number of pages6
Publication statusAccepted/In press - 2020
EventECAI 2020 - 24th European Conference on Artificial Intelligence - Virtuell
Duration: 8 Jun 202012 Jun 2020
Conference number: 24


ConferenceECAI 2020 - 24th European Conference on Artificial Intelligence
Abbreviated titleECAI 2020
Internet address

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