The Framework Design of Intelligent Assessment Tasks Recommendation System for Personalized Learning

In teaching, assessment tasks are often used as an important way to evaluate students’ learning abilities. In traditional education, to design an assessment task, e.g., an assignment, teachers are often required to manually design by themselves. It is usually a challenging task to design high-quality assignments, especially for less-experienced teachers. In addition, students often have different learning abilities and it is often difficult and unreasonable to evaluate students’ learning abilities using only the same assignments. Moreover, to design an assignment with decent quality, teachers have to consider the knowledge items to be covered and the difficulty of the assignment. Therefore, it is worthwhile to do the research of automatically providing students with high-quality assessment tasks, taking into account the coverage of knowledge items and the appropriate difficulty of the designed assessment tasks, i.e., proposing an approach of personalized assessment task recommendation systems. The current literature on personalized assessment task recommendations shows that the recommendation process does not take into account the difficulty of the designed questions and students’ knowledge. To overcome the limitations of the current related work, this paper proposes a framework design of intelligent assessment tasks recommendation by considering several aspects, such as students’ learning ability, mastery of student knowledge, the difficulty of assessment tasks, and students’ forgetting characteristics. The proposed framework design consists of two components: data processing and personalized assessment task generation. The data processing component is designed to proceed with data, e.g., generating the initial question bank, analyses on the question bank generated, auto-generation of the assessment task, and the result collection of auto-correcting on the designed assessment task. Besides, the forgetting characteristics of students are also considered in this study for the intelligent assessment tasks recommendation framework design.