Fuzzy Cognitive Diagnosis for Modelling Examinee Performance

Abstract

Recent decades have witnessed the rapid growth of educational data mining (EDM), which aims at automatically extracting valuable information from large repositories of data generated by or related to people's learning activities in educational settings. One of the key EDM tasks is cognitive modelling with examination data, and cognitive modelling tries to profile examinees by discovering their latent knowledge state and cognitive level (e.g. the proficiency of specific skills). However, to the best of our knowledge, the problem of extracting information from both objective and subjective examination problems to achieve more precise and interpretable cognitive analysis remains underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees' cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then, we combine fuzzy set theory and educational hypotheses to model the examinees' mastery on the problems based on their skill proficiency. Finally, we simulate the generation of examination score on each problem by considering slip & guess factors. In this way, the whole diagnosis framework is built. For further comprehensive verification, we apply our FuzzyCDF to three classical cognitive assessment tasks, i.e., predicting examinee performance, slip & guess detection and cognitive diagnosis visualization. Extensive experiments on three real-world datasets for these assessment tasks prove that FuzzyCDF can reveal the knowledge states and cognitive level of the examinees effectively and interpretatively.

Preference Cognitive Diagnosis for Student Performance Prediction

“…Although the prediction results of traditional CDMs are more interpretable, they are usually not accurate enough. To improve the effectiveness of prediction, many researchers have developed CDMs [3], [6], [30], [31]. For example, the authors in [3] proposed NeuralCD, which applied a neural network to learn the complex interactions between students and exercises.…”

“…The results tested by the two datasets were approximately 71.9% and 80.4% in accuracy. The authors in [6] designed FuzzyCDF to predict students' scores on subjective and objective types of exercises. The fuzzy set and educational hypotheses were considered to be an effective means for measuring students' performance.…”

“…on specific knowledge concepts [6]. Generally, the effectiveness of knowledge states modeling can be verified by predicting students' possible scores on a series of exercises, i.e., predicting student performance (PSP).…”

“…Generally, the effectiveness of knowledge states modeling can be verified by predicting students' possible scores on a series of exercises, i.e., predicting student performance (PSP). PSP aims to evaluate whether students can respond to the corresponding exercises correctly (correct is "1" and incorrect is "0") [6], [7], which can be further extended to some educational applications, such as exercise recommendation [8], [9] and teaching plan improvement [10].…”

Exploiting Cognitive Structure for Adaptive Learning

“…Usually, examinations, e.g., two tests in Figure 1 on D, are used to retrieve the learning effects. The personalized learning path helps the learner understand the new learning items efficiently [19,33]. Recently, adaptive learning has become a crucial component for many applications such as on-line education systems (e.g., KhanAcademy.org, junyiacademy.org).…”