Eliminating Anomalies in Learner Modeling Using Two-Partial Learner Model
Contributor(s)The Pennsylvania State University CiteSeerX Archives
Full recordShow full item record
AbstractAbstract—Sometimes, gathered information from tracking learner interactions is not precise. In fact, existing e-learning systems only know the number of previewed pages precisely. They can deduce that how much the learner is mastered using some learner&apos;s characteristics such as time of doing exercises and the number of mistakes. But these deductions may be not precise. Because the time of doing exercises depends on learner&apos;s emotional states and environmental conditions. Thus we have introduced a new concept as two-partial learner model. Our proposed model divides learner model into two parts: Permanent Learner Model (PLM) and Temporary Learner Model (TLM). In the two-partial learner model, system&apos;s deductions are placed in the TLM at first. Then, system should validate accuracy of these deductions. Valid deductions are used in the updating of PLM for making them usable in other sessions. Otherwise, they should be ignored. Two-partial learner model is suitable for example-based educational systems. Because these systems are making deductions about the learner based on his/her interactions with the system.
Copyright/LicenseMetadata may be used without restrictions as long as the oai identifier remains attached to it.
Showing items related by title, author, creator and subject.
Learner support: reflections on paradigms, models and pedagogical approaches for 21st Century learners in Higher EducationGatsha, Godson; Gruda, Michelle (Commonwealth of Learning (COL), 2016-06-06)Fifth lecture of the The Open University of Sri Lanka (OUSL) Distinguished Lecture Series, organized by the International Academic Relations Division (IRD) in collaboration with the Center for Educational Technology and Media (CETMe) on 27 March 2015 by Dr Godson Gatsha (co-written with Michelle Gruda), Commonwealth of Learning.
Simplifying is not always best : learners thrive when using multifaceted open social learner modelsShi, Lei (Researcher in Computer Science); Cristea, Alexandra I. (IEEE Computer Society, 2016)This paper explores open social learner modeling (OSLM) - a social extension of open learner modeling (OLM). A specific implementation of this approach is presented, by which learners' self-direction and self-determination in a social e- learning context could be potentially promoted. The proposed new approach, multifaceted-OSLM, allows, unlike in previous work, to seamlessly and adaptively embed visualization of both a learner's own model and other learning peers' models, into different parts of the learning content, for multiple axes of context, at any time during the learning process. It also demonstrates advantages of visualizing both learners' performance and their contribution to a learning community. An experimental study showed that, contradictory to previous research, the richness and complexity of this new approach impacted positively on the learning experience, in terms of effectiveness, efficiency and satisfaction perceived by the learners.
Görgün,I.,Türker,A.,Ozan,Y.,&amp;Heller,J.(2005).LearnerModelingtoFacilitatePersonalizedE- LearningExperience.InKinshuk,D.G.Sampson&amp;P.T.Isaías(Eds.),CELDA&apos;05:Cognitionand ExploratoryLearninginDigitalAge(pp.231-237).IADIS. LEARNER MODELING TO FACILITATE PERSONThe Pennsylvania State University CiteSeerX Archives; Lhami Görgün; Ali Türker; Assoc Prof; Yıldıray Ozan; Dr. Jürgen Heller (2013-07-25)This article describes a learner modeling strategy that is employed by an adaptive learning system in order to provide each learner with a personalized e-Learning experience. Parameters related to the learners ’ prior knowledge, goal and learning style constitute the basis of the personalization and the adaptivity of the mentioned learning system. The present paper focuses on the mechanisms concerning the learner’s prior knowledge and the goal parameters. The learner modeling that takes into account the prior knowledge of the learners is achieved by developing an ontological abstraction. Based on this ontological abstraction, a knowledge base is constructed in order to introduce the knowledge representations of the domain model and the curricular model, the knowledge and the learning structures. These knowledge representations specify how the prior knowledge of a learner will be represented, and also how it will be assessed and continuously updated.