Towards a Competence Based System for Recommending Study Materials (CBSR)

Nitchot, Athitaya; Gilbert, Lester; Wills, Gary

doi:10.1109/icalt.2010.179

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…In addition, the authoring process for a user model is difficult since this is a complex task, there is a lack of good user models, and there are no standardized approaches to using a user model in supporting the adaptivity of AH or ITS systems. None of the adaptive systems support a learner seeking to achieve an intended learning outcome (Nitchot, Gilbert, & Wills, 2010). Finally, Sitthisak, Gilbert, Davis, and Gobbi (2007) highlighted the inconsistency of adaptive assessment systems in estimating a learner's knowledge level.…”

Section: User Modelingmentioning

confidence: 99%

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Competence-Based System Self-Study System for Suggesting Study Materials Links

Nitchot

Gilbert

Wills

2014

Journal of Educational Technology Systems

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The article proposes a self-study system which suggests web links to learners. The suggestions depend upon the learner's chosen competences selected from a competence structure for a particular knowledge domain. Three experiments were conducted, where the first compared the perceived usefulness and value of the links generated by different learning paths selected to navigate the competence structure. The second experiment evaluated the acceptability of the self-study system, and the third experiment compared ratings of the links generated by two different search engines (Google, iSEEK). The results showed that the competence-based self-study system was accepted by learners, that learning paths which included more competence nodes were more useful than paths with fewer nodes, and that there was no significant difference between the ratings of the competence-based links generated by the two types of search engine.

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Section: User Modelingmentioning

confidence: 99%

“…The COMBA model supports consistency in recording the learner's levels of performance, as these levels are specified in terms of the learner's capability and the context in which the performance is demonstrated (Nitchot et al, 2010).…”

Section: Comba Competency Modelmentioning

confidence: 99%

Competence-Based System Self-Study System for Suggesting Study Materials Links

Nitchot

Gilbert

Wills

2014

Journal of Educational Technology Systems

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“…How to effectively represent, learn or adapt user models [4], [5] and how to model competency for learning systems, e.g. for teaching models and student models [6], is a topic of great interest in the scientific learning community ( [7], [8], [9]) and won't be further detailed in this paper.…”

Section: A Conceptmentioning

confidence: 99%

Enhanced test evaluation for web based adaptive learning paths

Szentes

Bargel

Streicher

et al. 2011

2011 7th International Conference on Next Generation Web Services Practices

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In our work we describe an approach for the automatic evaluation of student test results by a web based e-learning application, which considers more criteria than the obvious correctness. This evaluation is focused on the use in adaptive learning paths, meaning that the student's learning path is adapted based on the test results. We describe a concept which integrates the evaluation of more criteria than correctness and how an implementation of such adaptive learning paths could look like in a web based e-learning environment. Our work shows a working prototype which uses the enhanced criteria speed and certainty based upon an evaluation with fuzzy logic algorithms. Finally we present some first results about the acceptance of web based adaptive learning paths

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“…In criteria, we basically set competencies [2] that can be reached by every learner. These competency criteria then drive to expect learning outcome.…”

Section: Introductionmentioning

confidence: 99%

Personalized Virtual School Environment Using Classifier Algorithm and Semantic Advisor-Assisting Framework

Maniyar

L.M.

2017

Asian J Pharm Clin Res

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As we knew that every individual is different than each other and their brain levels are also different, some students are very bright and some are not, so there is a need to develop such system which will teach them as per their thought process and learning habits. This research aims to develop a personalized virtual school environment for students which help them to learn the things same as they learn in school physically, but the thing here is each student will be treated differently as per his/her ability. Hence, this system would be a virtual school for any student, which resembles the CBSE school system in India. In this virtual school, all things are present such as teachers, homework, games, and exams which present in actual school except one which is the physical classroom because in virtual environment the course and things are personalized for each student as per his/her thoughts and brain level, so here the student can seat in his/her home and can learn at any time with help of computer. The system is a webbased environment and machine learning would be used for doing personalization on family and knowledge context of student. This web system contains four components: (1) Classification based on family context, (2) classification based on knowledge context, (3) learning material selection algorithm, (4) web-based learning system on top of above three, are discussed in this paper.

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Towards a Competence Based System for Recommending Study Materials (CBSR)

Cited by 10 publications

References 6 publications

Competence-Based System Self-Study System for Suggesting Study Materials Links

Competence-Based System Self-Study System for Suggesting Study Materials Links

Enhanced test evaluation for web based adaptive learning paths

Personalized Virtual School Environment Using Classifier Algorithm and Semantic Advisor-Assisting Framework

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