Research on Causal Reasoning Method for MOOC Course Effectiveness Based on Multi-path Network Search


Min Guo

Taiyuan University of Technology, Taiyuan, Shanxi, China.

*Corresponding author: Min Guo

Published: January 18,2024


Massive open online learning platforms, through the teaching process to use the Internet, means to put the platform users, for many learners to provide a new way of learning. The current research focuses on the correlation between learners' learning effect and learning behavior in MOOC platforms. In contrast to causality, correlation analysis often leads to biased conclusions. In this paper, we implement the selection of independent variables for causal networks in MOOC data based on counterfactual reasoning and propose a heuristic network search method based on multiple paths. It is fully demonstrated that the algorithm model proposed in this study can effectively improve the inefficient problem in the generation of multi-node causal networks and effectively generate causal network groups in the process of causal network generation without reducing the accuracy of the result. The aim is to explore the causal relationship construction method between user behavior and learning effect in MOOC data, so as to improve the teaching completion degree of platform courses and obtain better learning effect.


[1] Di Pietro L, Mugion R G, Musella F, et al. Reconciling Internal and External Performance in A Holistic Approach: A Bayesian Network Model in Higher Education [J]. Expert Systems with Application, 2019, 42(5): 2691-2702.

[2] Kui Xiang Gou, Gong Xiu Jun, & Zheng Zhao. Learning Bayesian Network Structure from Distributed Homogeneous Data [C]//Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on. IEEE, 2021.

[3] Lim S L & Goh O S. Intelligent Conversational Bot for Massive Online Open Courses (MOOCs) [J]. 2016.

[4] Roni Stern, Scott Kiesel, Rami Puzis, Ariel Felner, & Wheeler Ruml. Max Is More than Min: Solving Maximization Problems with Heuristic Search [C]// International Conference on Artificial Intelligence. AAAI Press, 20158.

[5] Seaton D T, Bergner Y, Chuang I, et al. Who Does What in A Massive Open Online Course? [J]. Communications of the ACM, 2019, 57(4):58-65.

[6] Thadhani R, Appelbaum E, Pritchett Y, et al. Vitamin D Therapy and Cardiac Structure and Function in Patients with Chronic Kidney Disease: The PRIMO Randomized Controlled Trial [J]. Jama, 2012, 307(7): 674-684.

[7] Trabelsi G, Leray P, Ayed M B, et al. Dynamic MMHC: A Local Search Algorithm for Dynamic Bayesian Network Structure Learning [C]// Twelfth International Symposium on Intelligent Data Analysis. 2019.

[8] Wang H, Hao X, Jiao W, et al. Causal Association Analysis Algorithm for MOOC Learning Behavior and Learning Effect [C]//2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). IEEE, 2018.

How to cite this paper

Research on Causal Reasoning Method for MOOC Course Effectiveness Based on Multi-path Network Search

How to cite this paper: Min Guo. (2023) Research on Causal Reasoning Method for MOOC Course Effectiveness Based on Multi-path Network Search. Advances in Computer and Communication4(6), 389-394.