Personalized e-learning system using Item Response Theory

Chen, Chih‐Ming; Lee, Hahn-Ming; Chen, Yahui

doi:10.1016/j.compedu.2004.01.006

Cited by 401 publications

(185 citation statements)

References 15 publications

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“…The results of these works are promising but their application to standard LMSs can be difficult. From a perspective based on the design, analysis and scoring of tests, the personalization of e-learning systems has been approached by using the Item Response Theory (PEL-IRT) which, by considering the difficulty of the learning materials to be provided and the ability of the students, finds personalized learning paths (Chen, Lee, & Chen, 2005). Another work based on the students' results of pre-tests, has led to a genetic-based customized e-learning system which conducts to a personalized curriculum sequencing (Chen, 2008).…”

Section: Learning Management Systemsmentioning

confidence: 99%

Student-oriented planning of e-learning contents for Moodle

Caputi

Garrido

2015

Journal of Network and Computer Applications

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We present a way to automatically plan student-oriented learning contents in Moodle. Rather than offering the same contents for all students, we provide personalized contents according to the students' background and learning objectives. Although curriculum personalization can be faced in several ways, we focus on Artificial Intelligence (AI) planning as a very useful formalism for mapping actions, i.e. learning contents, in terms of preconditions (precedence relationships) and causal effects to find plans, i.e. learning paths that best fit the needs of each student. A key feature is that the learning path is generated and shown in Moodle in a seamless way for both the teacher and student, respectively. We also include some experimental results to demonstrate the scalability and viability of our approach.

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Section: Learning Management Systemsmentioning

confidence: 99%

Student-oriented planning of e-learning contents for Moodle

Caputi

Garrido

2015

Journal of Network and Computer Applications

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“…Other researchers found a positive effect of IRT-based adaptive item sequencing on learning. More precisely, adaptive environments in which items were selected because the learner had a 50% probability of answering them correctly yielded faster learning than a non-adaptive learning environment [44,45]. Furthermore, research on CAT suggests that administering easier items would foster motivation and lead to a higher performance score, especially for persons with a low proficiency level [46].…”

Section: Adaptive Item Sequencing In Item-based Learning Environmentsmentioning

confidence: 99%

When Easy Becomes Boring and Difficult Becomes Frustrating: Disentangling the Effects of Item Difficulty Level and Person Proficiency on Learning and Motivation

Moeyaert

Wauters²,

Desmet

et al. 2016

Systems

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Abstract:The research on electronic learning environments has evolved towards creating adaptive learning environments. In this study, the focus is on adaptive curriculum sequencing, in particular, the efficacy of an adaptive curriculum sequencing algorithm based on matching the item difficulty level to the learner's proficiency level. We therefore explored the effect of the relative difficulty level on learning outcome and motivation. Results indicate that, for learning environments consisting of questions focusing on just one dimension and with knowledge of correct response, it does not matter whether we present easy, moderate or difficult items or whether we present the items with a random mix of difficulty levels, regarding both learning and motivation.

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“…Chen et al [9] developed a personalised adaptive elearning system using item response theory to which they enabled personalized learning according to difficulty parameters of course materials and learners' responses. Chen et al [10] also proposed a personalized mobile English vocabulary learning system based on Item Response Theory and learning memory cycles, which recommends appropriate English vocabulary for learning according to individual learner's vocabulary ability and memory cycle.…”

Section: Special Focus Paper Towards Adaptive E-learning Using Decisimentioning

confidence: 99%

Towards Adaptive E-Learning using Decision Support Systems

Yarandi

Jahankhani

Tawil

2013

Int. J. Emerg. Technol. Learn.

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Abstract-The significance of personalization towards learners' needs has recently been agreed by all web-based instructional researchers. This study presents a novel ontology semantic-based approach to design an e-learning Decision Support System (DSS) which includes major adaptive features. The ontologically modelled learner, learning domain and content are separately designed to support personalized adaptive learning. The proposed system utilise captured learners' models during the registration phase to determine learners' characteristics. The system also tracks learner's activities and tests during the learning process. Test results are analysed according to the Item Response Theory in order to calculate learner's abilities. The learner model is updated based on the results of test and learner's abilities for use in the adaptation process. Updated learner models are used to generate different learning paths for individual learners. In this study, the proposed system is implemented on the "Fraction topic" of the mathematics domain. Experimental test results indicated that the proposed system improved learning effectiveness and learner's satisfaction, particularly in its adaptive capabilities.

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Personalized e-learning system using Item Response Theory

Cited by 401 publications

References 15 publications

Student-oriented planning of e-learning contents for Moodle

Student-oriented planning of e-learning contents for Moodle

When Easy Becomes Boring and Difficult Becomes Frustrating: Disentangling the Effects of Item Difficulty Level and Person Proficiency on Learning and Motivation

Towards Adaptive E-Learning using Decision Support Systems

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