scholarly journals Supporting knowledge monitoring ability: open learner modeling vs. open social learner modeling

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Sibel Somyürek ◽  
Peter Brusilovsky ◽  
Julio Guerra
Keyword(s):  
Learner Modeling ◽  
Knowledge Monitoring

scholarly journals Using AI Techniques in a Serious Game for Socio-Moral Reasoning Development

2020 ◽  
Vol 34 (09) ◽  
pp. 13420-13427
Author(s):  
Ange Tato ◽  
Roger Nkambou ◽  
Aude Dufresne
Keyword(s):  
Machine Learning ◽  
Moral Reasoning ◽  
Serious Game ◽  
Machine Learning Techniques ◽  
Learning Gain ◽  
Learner Modeling ◽  
Learner Model ◽  
Learning Techniques ◽  
Adaptation Rules ◽  
Computerized Task

We present a serious game designed to help players/learners develop socio-moral reasoning (SMR) maturity. It is based on an existing computerized task that was converted into a game to improve the motivation of learners. The learner model is computed using a hybrid deep learning architecture, and adaptation rules are provided by both human experts and machine learning techniques. We conducted some experiments with two versions of the game (the initial version and the adaptive version with AI-Based learner modeling). The results show that the adaptive version provides significant better results in terms of learning gain.

scholarly journals Computerized testing as efficient form of objective knowledge monitoring in studying of technical subjects

2019 ◽  
pp. 11-11
Author(s):  
Vitaly Kuznetsov ◽  
Galina Polekhina ◽  
Yulia Shaposhnikova
Keyword(s):  
Computerized Testing ◽  
Efficient System ◽  
Academic Activity ◽  
Computer Testing ◽  
Level Ii ◽  
New Information ◽  
The Subject ◽  
Knowledge Monitoring ◽  
Level I ◽  
Problematic Situations

Introduction. Objective and regular students’ knowledge monitoring in technical subjects can be implemented by means of special tests allowing for the required mastering level of the matter and the reliable consolidation of the acquired knowledge. Various aspects of the application of tests in the academic activity were considered. Materials and methods. Tests used in practical studies should meet specific requirements, such as: validity, definiteness, simplicity, unambiguity, reliability. The identification of mastering levels makes it possible to “troubleshoot” and to improve the academic activity and the mastering degree of the competences by the students. Based on the assessment of the studying pattern of the forthcoming activity, one could point out four mastering levels of the subject matter. Level I tests include recognition, discrimination and classification. Level II tests monitor the mastering of the subject in the level of “reproduction” allowing for retrieval of information from the memory and its analysis, for routine assignment solutions. Level III tests impose special assignments challenging a student with quests for which no ready algorithms are catered, whereas the solutions found lead to obtaining of subjectively new information. Level IV tests reveal students’ capability to take decisions in new problematic situations, the solutions found, being a result of creative activity, are followed by obtaining of objectively new information. Results. To establish an efficient system of monitoring tests in a certain subject, a number of basic prerequisites is required, such as a data base, a sample group of with the required number of assignments, at least 30 and maximum 70, a time limit in accordance with the required labor intensity, assessment of the assignments and its criteria, the output of the results. Conclusions. If there is a required number of computers of at least one PC per two students, correctly arranged computer testing considerably reduces time demand of a monitoring event, increases the responsibility and the progress of the students, guarantees the objectiveness of the knowledge monitoring and helps to avoid conflicts.

Intelligent Market Based Learner Modeling

2006 ◽  
pp. 101-111 ◽  
Author(s):  
Maryam Ashoori ◽  
Chun Yan Miao ◽  
Angela Eck Soong Goh ◽  
Wang Qiong
Keyword(s):  
Learner Modeling

A Learner Model Based on Bayesian Networks in Adaptive Educational Hypermedia Systems

2018 ◽  
pp. 65-94
Keyword(s):  
Computational Modeling ◽  
Bayesian Networks ◽  
Educational System ◽  
Probabilistic Framework ◽  
Adaptive Hypermedia ◽  
Learner Modeling ◽  
Learner Model ◽  
Learning Path ◽  
Hypermedia System ◽  
Probabilistic Context

The work presented in this chapter lies within learner modeling in an adaptive educational system construed as a computational modeling of the learner. All actions of the learner in a learning situation on an adaptive hypermedia system are not limited to valid or invalid actions (true and false), but they are a set of actions that characterize the learning path of formation. Thus, one cannot represent the information from the system of each learner using relative data. It requires putting the work in a probabilistic context due to the changes in the learner model information during formation. In this chapter, the authors propose to use Bayesian networks as a probabilistic framework to resolve the issue of dynamic management and update of the learner model. The experiments and results presented in this work are arguments in favor of the hypothesis and can also promote reusing the modeling obtained through different systems and similar modeling situations.

Intelligent Market Based Learner Modeling

2006 ◽  
pp. 101-111
Author(s):  
Maryam Ashoori ◽  
Chun Yan Miao ◽  
Angela Eck Soong Goh ◽  
Wang Qiong
Keyword(s):  
Learner Modeling

From Non-Adaptive to Adaptive Educational Hypermedia

2006 ◽  
pp. 302-330 ◽  
Author(s):  
Michael J. Jacobson
Keyword(s):  
Learner Modeling ◽  
Design Elements ◽  
Learning Agent ◽  
Rigorous Research ◽  
Adaptive Hypermedia Systems ◽  
Hypermedia Design ◽  
Learning Efficacy ◽  
Research Findings ◽  
Theory Research ◽  
Educational Hypermedia

In this chapter, it is argued that research involving adaptive educational hypermedia will be advanced by attention to two main areas: (a) the articulation of principled design features for adaptive hypermedia systems and (b) rigorous research documenting the learning efficacy of particular design approaches for different domains and learner groups. As an example of design and research in these two areas, a case study of a program of hypermedia research related to the knowledge mediator framework (KMF) is provided. First, a discussion of non-adaptive KMF hypermedia design elements and learning tasks is provided, followed by a short overview of the research findings from studies involving the use of different KMF systems. Next, current efforts are discussed to create adaptive KMF hypermedia using a learning agent module that employs semantic assessment and learner modeling in order to provide adaptive content and adaptive learner scaffolding. A general consideration of theory, research, and methodological issues related to current work in the field of adaptive educational hypermedia is also provided.

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