Mobile formative assessment tool based on data mining techniques for supporting web-based learning

  • Authors:

  • Chih-Ming Chen;Ming-Chuan Chen

  • Affiliations:

  • Graduate Institute of Library, Information and Archival Studies, National Chengchi University, No. 64, Section 2, ZhiNan Road, Wenshan District, Taipei City 116, Taiwan, ROC;Graduate Institute of Learning Technology, National Dong Hwa University (Meilun Campus), No. 123, Hua-Hsi Road, Hualien 970, Taiwan, ROC

  • Venue:
  • Computers & Education
  • Year:
  • 2009

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Abstract

Current trends clearly indicate that online learning has become an important learning mode. However, no effective assessment mechanism for learning performance yet exists for e-learning systems. Learning performance assessment aims to evaluate what learners learned during the learning process. Traditional summative evaluation only considers final learning outcomes, without concerning the learning processes of learners. With the evolution of learning technology, the use of learning portfolios in a web-based learning environment can be beneficially adopted to record the procedure of the learning, which evaluates the learning performances of learners and produces feedback information to learners in ways that enhance their learning. Accordingly, this study presents a mobile formative assessment tool using data mining, which involves six computational intelligence theories, i.e. statistic correlation analysis, fuzzy clustering analysis, grey relational analysis, K-means clustering, fuzzy association rule mining and fuzzy inference, in order to identify the key formative assessment rules according to the web-based learning portfolios of an individual learner for the performance promotion of web-based learning. Restated, the proposed method can help teachers to precisely assess the learning performance of individual learner utilizing only the learning portfolios in a web-based learning environment. Hence, teachers can devote themselves to teaching and designing courseware, since they save a lot of time in measuring learning performance. More importantly, teachers can understand the main factors influencing learning performance in a web-based learning environment based on the interpretable learning performance assessment rules obtained. Experimental results indicate that the evaluation results of the proposed scheme are very close to those of summative assessment results and the factor analysis provides simple and clear learning performance assessment rules. Furthermore, the proposed learning feedback with formative assessment can clearly promote the learning performances and interests of learners.