A cognition assessment authoring system for e-learning

Hung, Jonathan; Lin, Li-Chieh; Chang, Wen-Chih; Shih, Timothy K.; Hsu, Hui-Huang; Chang, Han-Bin; Chang, Hsuan-Pu; Huang, Kuan-Hao

doi:10.1109/icdcsw.2004.1284041

Cited by 5 publications

(3 citation statements)

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“…Hsieh et al (2003) introduced a model that presents test statistics and collects students' learning behaviours for generating analysis result and feedback to tutors. Hung et al (2004) proposed an analysis model based on Item Analysis (IA) that collects information such as item difficulty and discrimination indices, questionnaire and question style, etc. These data are combined with a set of rules in order to detect defective items, which are signalled using traffic lights.…”

Section: Related Workmentioning

confidence: 99%

LMS assessment: using IRT analysis to detect defective multiple-choice test items

Fotaris

Mastoras

2014

IJTEL

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Due to the computerization of assessment tests, the use of Item Response Theory (IRT) has become commonplace for educational assessment development, evaluation, and refinement. When used appropriately by a Learning Management System (LMS), IRT can improve the assessment quality, increase the efficiency of the testing process, and provide in-depth descriptions of item properties. This paper introduces a methodological and architectural framework which embeds an IRT analysis tool in an LMS so as to extend its functionality with assessment optimisation support. By applying a set of validity rules to the statistical indices produced by the IRT analysis, the enhanced LMS is able to detect several defective items from an item pool which are then reported for reviewing of their content. Assessment refinement is achieved by repeatedly employing this process until all flawed items are eliminated.

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Section: Related Workmentioning

confidence: 99%

LMS assessment: using IRT analysis to detect defective multiple-choice test items

Fotaris

Mastoras

2014

IJTEL

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“…A work closely related to ours is presented in (Hung et al, 2004). It proposes an e-testing system, where rules can detect defective items, which are signaled using traffic lights.…”

Section: Related Workmentioning

confidence: 99%

A Rule-Based System for Test Quality Improvement

Costagliola

Fuccella

2009

International Journal of Distance Education Technologies

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To correctly evaluate learners' knowledge, it is important to administer tests composed of good quality question items. By the term "quality" we intend the potential of an item in effectively discriminating between skilled and untrained students and in obtaining tutor's desired difficulty level. This paper presents a rulebased e-testing system which assists tutors in obtaining better question items through subsequent test sessions. After each test session, the system automatically detects items' quality and provides the tutors with advice about what to do with each of them: good items can be re-used for future tests; among items with lower performances, instead, some should be discarded, while some can be modified and then re-used. The proposed system has been experimented in a course at the University of Salerno.

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“…Hay otras soluciones más simples para tener en cuenta la dificultad del material de aprendizaje como las propuestas en (Hung et al 2004) (Jong et al 2006), que dan un peso a los materiales calculado como el cociente del número de veces que un ítem ha sido correcta o incorrectamente contestado entre el número total de veces que ha sido contestado.…”

Section: Búsqueda De La Soluciónunclassified

Contribución a la Aplicación de Técnicas de Inteligencia Artificial para el diseño efectivo de Sistemas Adaptativos de Aprendizaje Competitivo

Pérez¹

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Esta tesis doctoral se la dedico a mi familia, especialmente a mis padres porque sin su inestimable ayuda y su apoyo incondicional no hubiera podido realizar este trabajo, y a mis hijas Diana y Elena, que, sin saberlo, han sido mi fuente de energía y mi inyección de voluntad. Además, quería agradecer a mis compañeros y amigos María Jesús, Luisa y Juan Pablo, por su apoyo, consejos, y ayuda durante el desarrollo de esta tesis. Por último, quería también dar las gracias a todos los profesores y compañeros que de una manera u otra me han ayudado en el desarrollo

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A cognition assessment authoring system for e-learning

Cited by 5 publications

References 0 publications

LMS assessment: using IRT analysis to detect defective multiple-choice test items

LMS assessment: using IRT analysis to detect defective multiple-choice test items

A Rule-Based System for Test Quality Improvement

Contribución a la Aplicación de Técnicas de Inteligencia Artificial para el diseño efectivo de Sistemas Adaptativos de Aprendizaje Competitivo

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