IRT model fit from different perspectives

Khalid, Muhammad Naveed

doi:10.3990/1.9789036529006

Cited by 2 publications

(2 citation statements)

References 132 publications

(202 reference statements)

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“…The example pertains to the scale for 'Attitude towards English Reading' which consisted of 50 items with five response categories for each. The data is based on the instrument reported by Khalid [32], who has evaluated the psychometric properties of the scale and found it to be appropriate for similar studies. The scale was administered to 8th grade students in a number of elementary schools in Pakistan.…”

Section: An Empirical Examplementioning

confidence: 99%

A scale purification procedure for evaluation of differential item functioning

Khalid

Glas

2014

Measurement

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Section: An Empirical Examplementioning

confidence: 99%

A scale purification procedure for evaluation of differential item functioning

Khalid

Glas

2014

Measurement

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“…For static student models, this process can be as straightforward as assessing the power of a model to predict successes and failures from a subset of student response outcomes. Validation has also been extensively studied for static learner models such as IRT, for which statistics such as person-fit and item-fit are a standard part of most software packages (see Khalid 2009 for a recent review). However, validation is more complex for dynamically changing learner models such as CT and CBM tutors.…”

Section: Assessing Learner Modelsmentioning

confidence: 99%

A review of recent advances in learner and skill modeling in intelligent learning environments

Desmarais

Baker

2011

User Model User-Adap Inter

332

137

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In recent years, learner models have emerged from the research laboratory and research classrooms into the wider world. Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users. Probabilistic models for skill assessment are playing a key role in these advanced learning environments. In this paper, we review the learner models that have played the largest roles in the success of these learning environments, and also the latest advances in the modeling and assessment of learner skills. We conclude by discussing related advancements in modeling other key constructs such as learner motivation, emotional and attentional state, meta-cognition and self-regulated learning, group learning, and the recent movement towards open and shared learner models.

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IRT model fit from different perspectives

Cited by 2 publications

References 132 publications

A scale purification procedure for evaluation of differential item functioning

A scale purification procedure for evaluation of differential item functioning

A review of recent advances in learner and skill modeling in intelligent learning environments

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