Preference Cognitive Diagnosis for Student Performance Prediction

Knowledge states modeling is a fundamental issue in online education. One of its tasks is to discover the potential knowledge capacity of students for predicting their performance (i.e., scores on exercises). Current studies either depend on cognitive diagnosis approaches or apply collaborative filtering. However, the prediction accuracy of traditional cognitive diagnosis is insufficient, and collaborative filtering has difficulty ensuring the interpretability of prediction. Actually, students usually read some auxiliary text learning materials that they are interested in, namely, preferred learning material, to consolidate what they have learned. Preference cognitive diagnosis means that the preferred learning materials can reflect the students’ knowledge states (i.e., proficiency for knowledge concepts) to some extent, which is beneficial for predicting students’ performance. Therefore, we propose a preference cognitive diagnosis method (
PreferenceCD
) to model students’ knowledge states. Specifically, we first design the
Direct-Indirect
method to acquire students’ preferred learning materials. This method mines important information from students’ reading content that can reflect their preference for learning materials to acquire those preferred learning materials directly. Moreover, it discovers preferred learning materials indirectly by analyzing the similarity of students’ learning behaviors during the reading process. Subsequently, we calculate students’ preference degree for knowledge concepts based on the acquired preferred learning materials and diagnose their proficiency for knowledge concepts by applying a cognitive diagnosis model. After that, we combine the above two aspects to model students’ knowledge states and further predict their scores on exercises. The experimental results on a real-world dataset demonstrate the effectiveness of
PreferenceCD
with both accuracy and interpretability. The accuracy, root mean square error (RMSE), and mean absolute error (MAE) of
PreferenceCD
are 0.7614, 0.4805, and 0.2386, respectively, which outperforms related works by about 2-12% in terms of these evaluation metrics.