Open Access
Extending Moodle Functionalities to Adaptive Testing Framework
Komi Sodoké,Martin Riopel,Gilles Raîche,Roger Nkambou,Martin Lesage +4 more
- 15 Oct 2007
- Vol. 2007, Iss: 1, pp 476-482
TL;DR: This paper will present some of the principles, the architectural elements and the algorithms used in an exploratory integration of adaptive testing functionalities within the Moodle platform.
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Abstract: E-learning has advanced considerably in the last decades allowing the interoperability of different systems and different kinds of adaptation to the student profile or the learning objectives. But, some of its aspects, such as E-testing are still in their early age. As a consequence, most of the actual E-learning platforms offer only basic E-testing functionalities. In addition, in most those platforms, the tests are in the traditional format despite their known limitations and precision problems. However, by making efficient use of well known techniques in artificial intelligence, existing psychometric theories and standards in E-learning, it could be possible to integrate adaptive and more informative E-testing functionalities in the actual E-learning platforms. In this paper, we will present some of the principles, the architectural elements and the algorithms used in an exploratory integration of adaptive testing functionalities within the Moodle platform.
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Citations
Design based learning by knowledge reuse: Towards its application to e-learning
Noriyuki Iwane,Hiroaki Ueda,Makoto Yoshida +2 more
- 06 Sep 2011
TL;DR: A framework of design based learning by knowledge reuse and a method for implementing with an e-learning system are proposed, explained from the feasibility and the reusability.
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Adaptive Virtual Learning System Using Raspberry-Pi
Busisiwe N. Ncube,Pius A. Owolawi,Temitope Mapayi +2 more
- 01 Aug 2020
TL;DR: A virtual learning environments integrated with adaptive testing functionalities using Raspberry pi is presented and it is suggested that personalized learning enhances learning effectiveness in terms of self-efficacy.
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IMS-QTI sub-standards in computerised adaptive testing and interfacing
TL;DR: The authors are developing a Macromedia Flash E-Learning Web application able to fully include item data input and adaptive testing capabilities using Item Response Theory (IRT).
6
•Dissertation
A service-orientated architecture for adaptive and collaborative e-learning systems
Maram Meccawy
- 23 Jul 2009
TL;DR: This research proposes a new architecture for Adaptive Educational Hypermedia Systems (AEHS), which addresses the limitations of AEHS regarding interoperability, reusability, openness, flexibility, and limited tools for collaborative and social learning.
•Journal Article
Una Revisión de Herramientas Asistidas por Ordenador para la Evaluación del Conocimiento
TL;DR: This paper presents a review of e-assessment tools bearing in mind the use of standards and pedagogical practices, and shows the necessity of covering some empty areas.
1
References
•Book
Computerized Adaptive Testing: A Primer
Howard Wainer,Neil J. Dorans,Ronald Flaugher,Bert F. Green,Robert J. Mislevy +4 more
- 01 Feb 1990
TL;DR: The author discusses the challenges faced in implementing large-scale computerized testing in the rapidly changing environment and some of the strategies used to deal with these challenges.
1.4K
•Book
Item Response Theory: Parameter Estimation Techniques
Frank B. Baker,Seock-Ho Kim +1 more
- 01 Jul 2004
TL;DR: The Item Characteristic Curve: Dichotomous Response Estimating the Parameters of an item characteristic curve Maximum Likelihood Estimation of Examinee Ability Maximum Like likelihood Procedures for Estimating Both Ability and Item Parameters as discussed by the authors.
999
Evaluating the Feasibility of Learning Student Models from Data
Anders Jonsson,Jeff Johns,Hasmik Mehranian,Ivon Arroyo,Beverly Park Woolf,Andrew G. Barto,Donald L. Fisher,Sridhar Mahadevan +7 more
- 01 Jan 2005
TL;DR: A machine learning approach that uses Expectation-Maximization to learn the parameters of a dynamic Bayesian network with hidden variables indicates that it is possible to learnThe parameters of hidden variables given enough sequential data of training sessions on similar problems.
•Proceedings Article
Exam Question Recommender System
Hicham Hage,Esma Aïmeur +1 more
- 06 May 2005
TL;DR: This paper proposes using recommendation techniques to help a teacher search for and select questions from a shared and centralized IMS QTI-compliant question bank, using a hybrid, feature-augmentation, recommendation approach.
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