Parallel Prediction Method of Knowledge Proficiency Based on Bloom’s Cognitive Theory

Tiancheng Zhang (), Hanyu Mao, Hengyu Liu, Yingjie Liu, Minghe Yu, Wenhui Wu, Ge Yu, Baoze Wei and Yajuan Guan
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Tiancheng Zhang: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Hanyu Mao: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Hengyu Liu: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Yingjie Liu: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Minghe Yu: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Wenhui Wu: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Ge Yu: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Baoze Wei: Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
Yajuan Guan: Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark

Mathematics, 2023, vol. 11, issue 24, 1-23

Abstract: Knowledge proficiency refers to the extent to which students master knowledge and reflects their cognitive status. To accurately assess knowledge proficiency, various pedagogical theories have emerged. Bloom’s cognitive theory, proposed in 1956 as one of the classic theories, follows the cognitive progression from foundational to advanced levels, categorizing cognition into multiple tiers including “knowing”, “understanding”, and “application”, thereby constructing a hierarchical cognitive structure. This theory is predominantly employed to frame the design of teaching objectives and guide the implementation of teaching activities. Additionally, due to the large number of students in real-world online education systems, the time required to calculate knowledge proficiency is significantly high and unacceptable. To ensure the applicability of this method in large-scale systems, there is a substantial demand for the design of a parallel prediction model to assess knowledge proficiency. The research in this paper is grounded in Bloom’s Cognitive theory, and a Bloom Cognitive Diagnosis Parallel Model (BloomCDM) for calculating knowledge proficiency is designed based on this theory. The model is founded on the concept of matrix decomposition. In the theoretical modeling phase, hierarchical and inter-hierarchical assumptions are initially established, leading to the abstraction of the mathematical model. Subsequently, subject features are mapped onto the three-tier cognitive space of “knowing”, “understanding”, and “applying” to derive the posterior distribution of the target parameters. Upon determining the objective function of the model, both student and topic characteristic parameters are computed to ascertain students’ knowledge proficiency. During the modeling process, in order to formalize the mathematical expressions of “understanding” and “application”, the notions of “knowledge group” and “higher-order knowledge group” are introduced, along with a parallel method for identifying the structure of higher-order knowledge groups. Finally, the experiments in this paper validate that the model can accurately diagnose students’ knowledge proficiency, affirming the scientific and meaningful integration of Bloom’s cognitive hierarchy in knowledge proficiency assessment.

Keywords: knowledge proficiency; cognitive diagnosis; Bloom’s cognitive taxonomy; matrix factorization; parallel and distributed processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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