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Improvement of Self-Assessment Effectiveness by Activity Monitoring and Analysis

Improvement of Self-Assessment Effectiveness by Activity Monitoring and Analysis

Dumitru Dan Burdescu, Marian Cristian Mihaescu
Copyright: © 2012 |Pages: 20
ISBN13: 9781613504567|ISBN10: 161350456X|EISBN13: 9781613504574
DOI: 10.4018/978-1-61350-456-7.ch711
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MLA

Burdescu, Dumitru Dan, and Marian Cristian Mihaescu. "Improvement of Self-Assessment Effectiveness by Activity Monitoring and Analysis." Computer Engineering: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, IGI Global, 2012, pp. 1779-1798. https://doi.org/10.4018/978-1-61350-456-7.ch711

APA

Burdescu, D. D. & Mihaescu, M. C. (2012). Improvement of Self-Assessment Effectiveness by Activity Monitoring and Analysis. In I. Management Association (Ed.), Computer Engineering: Concepts, Methodologies, Tools and Applications (pp. 1779-1798). IGI Global. https://doi.org/10.4018/978-1-61350-456-7.ch711

Chicago

Burdescu, Dumitru Dan, and Marian Cristian Mihaescu. "Improvement of Self-Assessment Effectiveness by Activity Monitoring and Analysis." In Computer Engineering: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, 1779-1798. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-456-7.ch711

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Abstract

Self-assessment is one of the crucial activities within e-learning environments that provide learners with feedback regarding their level of accumulated knowledge. From this point of view, the authors think that guidance of learners in self-assessment activity must be an important goal of e-learning environment developers. The scope of the chapter is to present a recommender software system that runs along the e-learning platform. The recommender software system improves the effectiveness of self-assessment activities. The activities performed by learners represent the input data and the machine learning algorithms are used within the business logic of the recommender software system that runs along the e-learning platform. The output of the recommender software system is represented by advice given to learners in order to improve the effectiveness of self-assessment process. The methodology for obtaining improvement of self-assessment is based on embedding knowledge management into the business logic of the e-learning platform. Naive Bayes Classifier is used as machine learning algorithm for obtaining the resources (e.g., questions, chapters, and concepts) that need to be further accessed by learners. The analysis is accomplished for disciplines that are well structured according to a concept map. The input data set for the recommender software system is represented by student activities that are monitored within Tesys e-learning platform. This platform has been designed and implemented within Multimedia Applications Development Research Center at Software Engineering Department, University of Craiova. Monitoring student activities is accomplished through various techniques like creating log files or adding records into a table from a database. The logging facilities are embedded in the business logic of the e-learning platform. The e-learning platform is based on a software development framework that uses only open source software. The software architecture of the e-learning platform is based on MVC (model-view-controller) model that ensures the independence between the model (represented by MySQL database), the controller (represented by the business logic of the platform implemented in Java) and the view (represented by WebMacro which is a 100% Java open-source template language).

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