Evaluation of Student Performance with Predicted Learning Curve Based on Grey Models for Personalized Tutoring

Authors

  • Manqiang Liu Lanzhou University of Technology
  • Qingsheng Zhang Xi'an University of Posts and Telecommunications

DOI:

https://doi.org/10.3991/ijet.v14i13.9880

Keywords:

knowledge component, power law, predicted learning curve, learning factor analysis, grey models, personalized tutoring

Abstract


Learning time of student is precious, over-practice of target knowledge component wastes student’s time, however, under-practice may mean the student may not grasp target knowledge component properly. To any student, it is helpful if intelligent tutoring system can determine how many practice opportunities needed for mastery of knowledge component. In this paper, to improve student’s learning efficiency, a method of predicted learning curve based on grey models is proposed to determine the counts of practice op-portunity for mastery of knowledge component. The experimental results show that the predicted value on error rate of practice opportunity over knowledge component with the proposed method is much closer to the value of real learning curve than the predicted learning curve produced by learning factors analysis. It implies the proposed prediction method is potential to present reasonable practices for personalized tutoring.

Author Biographies

Manqiang Liu, Lanzhou University of Technology

Manqiang Liu is with Lanzhou University of Technology, Lanzhou, China. His research interests include industrial equipment diagnosis, data mining, and learning analytics. He has rich engineering experiences in the designing, development and onsite debugging and diagnosis of non-ferrous metal equipment control.

Qingsheng Zhang, Xi'an University of Posts and Telecommunications

Qingsheng Zhang is with Xi’an University of Posts and Telecommunications, Xi’an, China. His research interests include ubiquitous computing, context-aware computing applications, distributed computing and applications, data mining, and adaptive eLearning. He has published several papers in International conferences and journals.

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Published

2019-07-15

How to Cite

Liu, M., & Zhang, Q. (2019). Evaluation of Student Performance with Predicted Learning Curve Based on Grey Models for Personalized Tutoring. International Journal of Emerging Technologies in Learning (iJET), 14(13), pp. 157–171. https://doi.org/10.3991/ijet.v14i13.9880

Issue

Vol. 14 No. 13 (2019)

Section

Papers

License

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