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A new e-learning achievement evaluation model based on RBF-NN and similarity filter

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Abstract

As Internet rises fast in recent decades, teaching and learning tools based on Internet technology are rapidly applied in education. Learning through Internet can make learners absorb knowledge without the limitations on learning time and distance. Therefore, in academy, e-learning is one of the popular learning assistant instruments. Recently, “student-centered” instruction has become one of the primary approaches in education, and the e-learning system, which can provide the learning environment of personalization and adaptability, is more and more popular. By using e-learning system, teachers can adjust the learning schedule instantly for learners according to their learning achievements, and build more adaptive learning environments. However, in some cases, bias assessments are given for student achievements under specific uncontrollable conditions (i.e. tiredness, preference). In dire need of overcoming this predicament, a new model based on radial basis function neural networks (RBF-NN) and similarity filter to evaluate learning achievements is proposed. The proposed model includes three phases to reduce bias assessments: (1) preprocess: select important features (attributes) to enhance classification performance by feature selection methods and utilize minimal entropy principle approach (MEPA) to fuzzify the quantitative data, (2) similarity filter: select linguistic values for each feature and delete inconsistent data by the similarity threshold (similarity filter) and (3) construct classification model and accuracy evaluation: build the proposed model based on RBF-NN and evaluate model performance. To verify the proposed model, a practical achievement dataset, collected from e-learning online examination system in a university of Taiwan, is used as experiment dataset, and the performance of the proposed model is compared with the listing models in this paper. From the empirical study, it is shown that the proposed model provided more proper achievement evaluations than the listing models.

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Authors and Affiliations

  1. Department of Information Management, National Yunlin University of Science and Technology, 123, Sect. 3, University Road, Touliu, Yunlin, 640, Taiwan

    Ching-Hsue Cheng & Liang-Ying Wei

  2. Department of Information Management and Communication, Wenzao Ursuline College of Languages, 900 Mintsu 1st Road, Kaohsiung, 807, Taiwan

    Tai-Liang Chen

  3. Institute of Biomedical Sciences, Academia Sinica, 128, Sect. 2, Academia Road, Nankang, Taipei, 115, Taiwan

    Liang-Ying Wei

  4. Department of Computer Science and Information Management, Hungkuang University, No. 34, Chung-Chie Road, Shalu, Taichung, 433, Taiwan

    Jr-Shian Chen

Authors
  1. Ching-Hsue Cheng
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  2. Tai-Liang Chen
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  4. Jr-Shian Chen
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Correspondence to Ching-Hsue Cheng.

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Cheng, CH., Chen, TL., Wei, LY. et al. A new e-learning achievement evaluation model based on RBF-NN and similarity filter. Neural Comput & Applic 20, 659–669 (2011). https://doi.org/10.1007/s00521-009-0280-0

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  • DOI: https://doi.org/10.1007/s00521-009-0280-0

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