Causal Association Analysis Algorithm for MOOC Learning Behavior and Learning Effect

Wang, Hongyan; Hao, Xingwei; Jiao, Wenjiang; Jia, Xingwu

doi:10.1109/dasc-picom-datacom-cyberscitec.2016.53

Cited by 8 publications

(8 citation statements)

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“…Some other machine learning methods can be applied in causal analysis. The authors in [ 26 ] improved the Apriori algorithm to make causal analysis between learning behaviors and performance. This method is based on association analysis that can not express the connection between different rules.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the two traditional methods, a few studies utilized machine learning methods to perform causal analysis. Wang et al proposed a causal analysis algorithm by improving the Apriori algorithm to analyze the relationship between learning behaviors and performance, and provided an application direction for a daily inspection system based on the learning behaviors [ 26 ]. Ramirez-Arellano et al proposed a model that described the causal relationships concerning motivations, emotions, cognitive strategies, meta-cognitive strategies, learning strategies, and their impact on learning performance [ 27 ].…”

Section: Related Workmentioning

confidence: 99%

“…Causal analysis of learning performance is also an important direction in education [ 25 ]. A few studies conduct causal analysis between factors and learning performance [ 26 , 27 ]. That may support educators to make effective interventions, leading to successful applications such as intelligent tutoring, personalized recommendations, and learning evaluations.…”

Section: Introductionmentioning

confidence: 99%

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Causal Analysis of Learning Performance Based on Bayesian Network and Mutual Information

Chen

Feng

et al. 2019

Entropy

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Over the past few years, online learning has exploded in popularity due to the potentially unlimited enrollment, lack of geographical limitations, and free accessibility of many courses. However, learners are prone to have poor performance due to the unconstrained learning environment, lack of academic pressure, and low interactivity. Personalized intervention design with the learners’ background and learning behavior factors in mind may improve the learners’ performance. Causality strictly distinguishes cause from outcome factors and plays an irreplaceable role in designing guiding interventions. The goal of this paper is to construct a Bayesian network to make causal analysis and then provide personalized interventions for different learners to improve learning. This paper first constructs a Bayesian network based on background and learning behavior factors, combining expert knowledge and a structure learning algorithm. Then the important factors in the constructed network are selected using mutual information based on entropy. At last, we identify learners with poor performance using inference and propose personalized interventions, which may help with successful applications in education. Experimental results verify the effectiveness of the proposed method and demonstrate the impact of factors on learning performance.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Causal Analysis of Learning Performance Based on Bayesian Network and Mutual Information

Chen

Feng

et al. 2019

Entropy

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“…Um dos principais problemas identificados pelos professores é o engajamento dos alunos nos vídeos, pois geralmente eles não assistem uma videoaula inteira [8,9,12]. A retenção dos alunos nos vídeos é um dado importante a ser levado em consideração, pois o tempo que os alunos passam assistindo uma videoaula está correlacionado com o desempenho obtido por eles nos cursos [13,21].…”

Section: Análise De Engajamento Em Vídeosunclassified

VideoViz - Uma proposta de ferramenta de visualização de logs de interação em players de vídeo

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et al. 2018

Anais Estendidos Do XXIV Simpósio Brasileiro De Sistemas Multimídia E Web

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The usage of technologies for content authoring, storage, distribution and presentation supported the arise of Virtual Learning Environments (VLEs) such as Moodle and MOOCs (e.g., Udacity, Coursera, EdX). All students’ interaction in these environments can be stored in logs. For instance, video is one of the resources has a greater focus on VLEs. In this paper, we present a state-of-the-art overview of interaction analysis on video players. We noticed that teachers would like to know how and when students interact with videos. In order to fill this gap, we propose VideoViz, a tool to analyse logs from interaction on video players.

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“…I07 claims that motivation is one of the main supporters to student learning. In line with them, Wang et al (2016a) stated that online learning requires even more learner motivation and self-direction than traditional classroom-based instruction. According to I06, the instructor has to encourage students in order to keep them motivated, and Brinton et al (2014) showed that an active participation of the teaching staff in the forum is associated with a higher post volume.…”

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confidence: 89%

Supporting Instructors in Analyzing Student Logs From Virtual Learning Environments

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Causal Association Analysis Algorithm for MOOC Learning Behavior and Learning Effect

Cited by 8 publications

References 4 publications

Causal Analysis of Learning Performance Based on Bayesian Network and Mutual Information

Causal Analysis of Learning Performance Based on Bayesian Network and Mutual Information

VideoViz - Uma proposta de ferramenta de visualização de logs de interação em players de vídeo

Supporting Instructors in Analyzing Student Logs From Virtual Learning Environments

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