Developing Student Model Using Kohonen Network in Adaptive Hypermedia Learning System

Yusob, Bariah; Shamsuddin, Siti Mariyam; Ahmad, Nor Bahiah

doi:10.1109/isda.2009.103

Cited by 1 publication

(1 citation statement)

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“…Machine learning techniques have been applied in several research studies to construct student models in the context of intelligent tutoring systems. [4][5][6][7] The use of intelligent tutoring systems has been very limited in higher educations as most educational institutions are in favour of using virtual learning environments (VLEs), which provide a set of software tools to support learning and teaching. Examples of VLEs are Moodle, WebCT and Blackboard.…”

Section: Introductionmentioning

confidence: 99%

Identifying student group profiles for diagnostic feedback using snap-drift modal learning neural network

Habte

Palmer-Brown

Kang

et al. 2015

AIR

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The aim of this paper is to propose a novel method for identifying student group profiles based on student responses to a set of multiple choice questions for the purpose of constructing diagnostic feedback using snap-drift modal learning neural network. The proposed method is capable of supporting tutors without the knowledge of machine learning in identifying useful student groups and constructing diagnostic feedback. Trials were conducted and analysis of the result showed that the snap-drift modal learning neural network was able to identify distinct student groups and represented student group profiles were helpful in revealing gaps of understanding and misconceptions that facilitate construction of diagnostic feedback. Moreover, the result showed that all student responses gathered were assigned to their appropriate student group profiles and the diagnostic feedback constructed based on the identified student group profiles had a positive impact on improving the learning performance of the students.

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Section: Introductionmentioning

confidence: 99%