| dc.description.abstract | In past research, it has been found that machine learning can better predict students′ learning outcomes, and it can provide explanations for the model′s predictions through explainable artificial intelligence methods. However, different explanation methods may yield different interpretations for the same data, leaving teaching teams uncertain.
This study is based on the LBLS-467 dataset, which records behavioral logs and questionnaire data of university students in an online learning environment. Using machine learning techniques, high-risk and low-risk students are identified.
Additionally, we further utilize explainable artificial intelligence methods (LIME, SHAP) to extract explanations for the model′s predictions. We evaluate the stability and
faithfulness of the explanations using six evaluation criteria, helping teachers select better explanations and gain a deeper understanding of students′ current learning status.
The research results show that the classifiers identified the majority of at-risk students, meaning that with the LBLS-467 dataset, it is possible to identify students at
risk. Through the use of explainable artificial intelligence methods, explanations for the model′s predictions can be obtained, further assisting teachers in understanding
students′ learning situations. By utilizing the six evaluation criteria for explaining, high quality explanations can be identified, enabling teaching teams to select better explanations. | en_US |