ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC

Student score prediction: A knowledge-aware auto-encoder model

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2019.01.004
  • Received Date: 09 January 2018
  • Rev Recd Date: 30 June 2018
  • Publish Date: 31 January 2019
  • To reduce study burden and boost efficiency, online education systems offer personalized learning experience for students. In such systems, ability assessment is a fundamental task as reflected by a basic task, named score prediction. The main drawbacks of existing prediction methods are: ① Inability unable to fully exploit the potential of big data, ② cold start problem,③ lack of reasonable explanations. A novel knowledge-aware auto-encoder model (KAEM) is proposed to address these issues. Specifically, an exercise-knowledge-graph with education experts’ prior knowledge is introduced. Then students’ performance is modeled using auto-encoders with the combination of information in knowledge graph as regularization item. By encoding and integrating the experts’ prior knowledge, KAME can improve both prediction accuracy and model robustness and deal with the cold start problem well. Furthermore, reasonable explanations for recommendations can be generated using this model. KMAE has been applied to a famous online education system. Extensive experiments on large-scale real data clearly demonstrate its effectiveness.
    To reduce study burden and boost efficiency, online education systems offer personalized learning experience for students. In such systems, ability assessment is a fundamental task as reflected by a basic task, named score prediction. The main drawbacks of existing prediction methods are: ① Inability unable to fully exploit the potential of big data, ② cold start problem,③ lack of reasonable explanations. A novel knowledge-aware auto-encoder model (KAEM) is proposed to address these issues. Specifically, an exercise-knowledge-graph with education experts’ prior knowledge is introduced. Then students’ performance is modeled using auto-encoders with the combination of information in knowledge graph as regularization item. By encoding and integrating the experts’ prior knowledge, KAME can improve both prediction accuracy and model robustness and deal with the cold start problem well. Furthermore, reasonable explanations for recommendations can be generated using this model. KMAE has been applied to a famous online education system. Extensive experiments on large-scale real data clearly demonstrate its effectiveness.
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