One of the essential problems, in educational data mining, is to predict students' performance on future learning materials, such as problems, assignments, and quizzes. Pioneer algorithms for predicting student performance mostly rely on two sources of information: students' past performance, and learning materials' domain knowledge model.
The domain knowledge model, traditionally curated by domain experts maps learning materials to concepts, topics, or knowledge components that are presented in them. However, creating a domain model by manually labeling the learning material can be a difficult and time-consuming task. In this paper, we propose a tensor factorization model for student performance prediction that does not rely on a predefined domain model. Our proposed algorithm models student knowledge as a soft membership of latent concepts. It also represents the knowledge acquisition process with an added rank-based constraint in the tensor factorization objective function. Our experiments show that the proposed model outperforms state-of-the-art algorithms in predicting student performance in two real-world datasets, and is robust to hyper-parameters.
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Proximity-based educational recommendations: A multi-objective framework
Personalized learning and educational recommender systems are integral parts of modern online education systems. In this context, the problem of recommending the best learning material to students is a perfect example of sequential multi-objective recommendation. Learning material recommenders need to optimize for and balance between multiple goals, such as adapting to student ability, adjusting the learning material difficulty, increasing student knowledge, and serving student interest, at every step of the student learning sequence. However, the obscurity and incompatibility of these objectives pose additional challenges for learning material recommenders. To address these challenges, we propose Proximity-based Educational Recommendation (PEAR), a recommendation framework that suggests a ranked list of problems by approximating and balancing between problem difficulty and student ability. To achieve an accurate approximation of these objectives, PEAR can integrate with any state-of-the-art student and domain knowledge model. As an example of such student and domain knowledge model, we introduce Deep Q-matrix based Knowledge Tracing model (DQKT), and integrate PEAR with it. Rather than static recommendations, this framework dynamically suggests new problems at each step by tracking student knowledge level over time. We use an offline evaluation framework, Robust Evaluation Matrix (REM), to compare PEAR with various baseline recommendation policies under three different student simulators and demonstrate the effectiveness of our proposed model. We experiment with different student trajectory lengths and show that while PEAR can perform better than the baseline policies with fewer data, it is also robust with longer sequence lengths.
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- Award ID(s):
- 2047500
- PAR ID:
- 10434440
- Date Published:
- Journal Name:
- The 2nd Workshop on Multi-Objective Recommender Systems (MORS’22)
- Page Range / eLocation ID:
- 1-17
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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