## 2 Related Works
### Knowledge Tracing  
Knowledge tracing [1] is a method used in educational technology to model and predict student knowledge and learning progress over time. It involves the analysis of student data to infer their mastery of specific concepts or skills. The goal of knowledge tracing is to provide personalized feedback and support to students, thereby helping them to improve their learning outcomes. Traditionally, knowledge tracing has been based on statistical models that use observed student responses to questions or tasks to estimate their current knowledge level. Bayesian knowledge tracing (BKT) [1; 19] is a probabilistic modeling technique that uses Bayesian inference to estimate students' knowledge states. BKT assumes that students have a latent knowledge state that evolves over time and can be influenced by observables, such as their responses to learning tasks or assessment items. By capturing the probability of students mastering specific concepts, BKT helps in identifying their knowledge gaps and predicting their performance on future tasks. The Bayesian framework allows for updating the knowledge estimates as new information becomes available, thereby resulting in increasingly accurate predictions. The factor analysis model (FAM) [20; 21] is a statistical technique that aims to identify the underlying latent factors responsible for student performance on learning tasks. It assumes that learners' knowledge and skills can be represented by a set of unobservable factors. FAM uses factor analysis to estimate the relationships between these factors and observable variables, such as student responses to assessment items or tasks. By capturing these latent factors, FAM helps in understanding the structure of students' knowledge and predicting their performance on future tasks. Despite being a nascent field of research, knowledge tracing has already found diverse applications across various scenarios, such as learning resource recommendations and adaptive learning. [PERSON] et al. [22] propose two extensions to the knowledge tracing model. The first extension takes into account the difficulty levels of exercises, while the second extension considers students' multiple attempts at solving exercises. These extensions are integrated into the knowledge tracing algorithm, which is used to recommend exercises based on students' knowledge states. Concretely, the algorithm begins by determining the expected range of scores for each exercise. It then calculates an expected score that the student should achieve in order to attain mastery, taking into account their current knowledge state. Then, the algorithm suggests the exercise with a predicted score closest to the expected score. As the knowledge state for a specific concept improves, the algorithm recommends more challenging exercises. [PERSON] et al. [23] propose three additional objectives that are beneficial and specific for education. The first objective, \"review and explore\", aims to enhance students' understanding of nonmastered concepts through timely reviews and provide opportunities for exploring new knowledge. The second objective, \"smoothness of difficulty level\", suggests that the difficulty levels of consecutive exercises should be within a narrow range as students gradually learn new material. The third objective, \"student engagement\", focuses on recommending exercises that align with students' preferences, thereby promoting their enthusiasm during the learning process. To support online intelligent education with these specific objectives, the researchers developed a more advanced framework called multiobjective deep reinforcement learning. This framework incorporates three novel reward functions that capture and quantify the effects of the above objectives via knowledge tracing technology. [PERSON] et al. [19] implement knowledge tracing on the edX MOOC platform, specifically focusing on a 14-week online course that consists of weekly video lectures and accompanying lecture problems.