Abstract
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.
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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 Communication, 4(6), 389-394.
DOI: https://dx.doi.org/10.26855/acc.2023.12.008