A Qualitative Comparison of Techniques for Student Modeling in Intelligent Tutoring Systems

González, Carolina; Burguillo, Juan C.; Llamas, Martin

doi:10.1109/fie.2006.322537

Cited by 33 publications

(19 citation statements)

References 11 publications

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“…Uyarlanabilir bir sistem tasarlamak için öncelikle öğrencinin mevcut özellikleri nedir? sorusuna yanıt aranması gerekir (Gonzalez, Burguillo ve Llamas, 2006). Öğrenci özelliklerinin bir kısmı statik, diğer bir kısmı ise dinamiktir.…”

Section: Introductionunclassified

Content Analysis of Studies Conducted on Adaptive Educational Hypermedia Environments in Turkey

Güyer

Çebi

2015

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Section: Introductionunclassified

Content Analysis of Studies Conducted on Adaptive Educational Hypermedia Environments in Turkey

Güyer

Çebi

2015

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“…• "Domain Independent Information": It is information about the student that "may include learning goals, cognitive aptitudes, measures for motivation state, preferences about the presentation method, factual and historic data, etc." With this information the system "adapts the learning sequence accordingly to help him/her in navigating through the course" [18].…”

Section: Recommendationsmentioning

confidence: 99%

“…They guide the user basing their decisions on user actions and knowledge (data) about the learning project, which is known and controlled by the system. In order to adapt the actions to each user, ITSs employ a user model that consists of two parts [18]:…”

Section: Recommendationsmentioning

confidence: 99%

Proposal of a Learning Organization Tool With Support for Metacognition

Manso-Vázquez

Nistal

2015

IEEE R. Iberoamericana Tecnologias Aprendizaje

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Building and using a personal learning environment is challenging and requires the students to have their metacognitive skills developed. Many students need help to develop these skills, to learn how to manage and evaluate their learning process in order to improve it. We designed an organizer with metacognitive support to help users to meet their learning goals. Here, we propose: 1) embedding learning strategies to support metacognitive learning; 2) a learning strategies recommender; 3) our vision of a learning analytics module for learners to support learning awareness and the reflection process; and 4) our solution for recommendations based on tag relationships, tag enrichment, and folksonomies.

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“…Bayesian networks are also being used to address user modeling problems in different types of systems (Heckerman, Horvitz, & Rathwani, 1989;Horvitz, Breese, Heckerman, Hovel, & Rommelse, 1998;Jameson, 1996;Vegas, 1995). Several studies conducted on the comparison of machine learning techniques used in student modeling systems show that Bayesian networks are effective for meeting the student modeling challenge (Gonzalez, Burguillo, & Llamas, 2006;Hamalainen & Vinni, 2006;Kotsiantis, Pierrakeas, & Pintelas, 2003;MinaeliBigdoli, Kashy, Kortemeyer, & Punch, 2003). Hence, a dynamic Bayesian inference mechanism for students modeling has been proposed in this study.…”

Section: Methodsmentioning

confidence: 99%

Effect of Bayesian Student Modeling on Academic Achievement in Foreign Language Teaching (University Level English Preparatory School Example)

Aslan¹,

Öztürk²,

İnceoğlu³

2014

ESTP

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Considering the increasing importance of adaptive approaches in CALL systems, this study implemented a machine learning based student modeling middleware with Bayesian networks. The profiling approach of the student modeling system is based on Felder and Silverman's Learning Styles Model and Felder and Soloman's Index of Learning Styles Questionnaire. The questionnaire was adapted to Turkish for this experimental study conducted with respect to the visual/verbal and active/reflective dimensions of the model. A topic in EFL was chosen for the learning content design, which was also carried into the digital domain and remastered as separate learning scenes for different learning styles. Computer software was also implemented to carry out the experimental learning processes. A quasi-experimental pre-test, post-test design was conducted with 46 volunteers, with 23 students assigned each to a control and an experimental group to compare academic achievement between student-based learning and conventional computer-based learning. No significant difference was found in academic achievement between the control and experimental groups after the experimental treatment. The diagnostic performance of the proposed student modeling system was also compared with performances from similar studies. This student modeling system had a successful prediction rate of 41% on the visual/verbal dimension and 54% on the active/reflective dimension, respectively.

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A Qualitative Comparison of Techniques for Student Modeling in Intelligent Tutoring Systems

Cited by 33 publications

References 11 publications

Content Analysis of Studies Conducted on Adaptive Educational Hypermedia Environments in Turkey

Content Analysis of Studies Conducted on Adaptive Educational Hypermedia Environments in Turkey

Proposal of a Learning Organization Tool With Support for Metacognition

Effect of Bayesian Student Modeling on Academic Achievement in Foreign Language Teaching (University Level English Preparatory School Example)

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