Quantifying the Influence of Achievement Emotions for Student Learning in MOOCs

Liu, Bowen; Xing, Wanli; Zeng, Yifang; Wu, Yonghe

doi:10.1177/0735633120967318

Cited by 23 publications

(19 citation statements)

References 44 publications

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“…On one aspect, empirical evidence seemed to suggest the positive effects of negative emotions on online learning outcomes (e.g., Hilliard et al, 2020). On the other aspect, recent research found that positive achievement emotions had a negative influence on online learners' learning outcomes (Liu et al, 2021). Therefore, there has been little agreement on whether achievement emotions could be beneficial or detrimental to online learning outcomes.…”

Section: Roles Of Achievement Emotions In Online Learning Outcomesmentioning

confidence: 99%

“…Online learners could improve their performance because they have experienced positive achievement emotions (Parker et al, 2021). Nevertheless, some students feeling positive emotions have performed poorly in online learning (Liu et al, 2021). To date, the effects of achievement emotions on online learning performance have still not been systematically reviewed.…”

Section: Performancementioning

confidence: 99%

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Exploring the effects of achievement emotions on online learning outcomes: A systematic review

Yu²

2022

Front. Psychol.

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Recently, achievement emotions have attracted much scholarly attention since these emotions could play a pivotal role in online learning outcomes. Despite the importance of achievement emotions in online education, very few studies have been committed to a systematic review of their effects on online learning outcomes. This study aimed to systematically review studies examining the effects of achievement emotions on online learning outcomes in terms of motivation, performance, satisfaction, engagement, and achievement. According to the selection process of Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) principles, a total of 23 publications were included in this review. It was concluded that positive achievement emotions, such as enjoyment, pride, and relaxation, could generally exert a positive effect on online learning motivation, performance, engagement, satisfaction, and achievement. It should be noted that excessive positive emotions might be detrimental to online learning outcomes. On the other hand, it has been difficult to determine the effects of negative achievement emotions on online learning outcomes because of disagreement on the effects of negative achievement emotions. In order to improve online learners' learning outcomes, instructors should implement interventions that help online learners control and regulate their achievement emotions. Teaching interventions, technological interventions, and treatment interventions could benefit online learners emotionally and academically. Future studies could examine the moderating roles of contextual factors and individual variables in the effects of achievement emotions on online learning outcomes.

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Section: Roles Of Achievement Emotions In Online Learning Outcomesmentioning

confidence: 99%

Section: Performancementioning

confidence: 99%

Exploring the effects of achievement emotions on online learning outcomes: A systematic review

Yu²

2022

Front. Psychol.

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“…However, these existing studies simply explore the positive and negative emotions [32], while learners could have more complex achievement emotions, such as confusion, boredom, shame, which could affect the learning outcome [37]. A deeper understanding of the reasons of how students think and feel regarding the course could help to timely identify students at risk and help improve their engagement [21].…”

Section: Comment Analysismentioning

confidence: 99%

A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs

Sun

Harit

et al. 2021

Lecture Notes in Computer Science

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Massive Open Online Course (MOOC) systems have become prevalent in recent years and draw more attention, a.o., due to the coronavirus pandemic's impact. However, there is a well-known higher chance of dropout from MOOCs than from conventional off-line courses. Researchers have implemented extensive methods to explore the reasons behind learner attrition or lack of interest to apply timely interventions. The recent success of neural networks has revolutionised extensive Learning Analytics (LA) tasks. More recently, the associated deep learning techniques are increasingly deployed to address the dropout prediction problem. This survey gives a timely and succinct overview of deep learning techniques for MOOCs' learning analytics. We mainly analyse the trends of feature processing and the model design in dropout prediction, respectively. Moreover, the recent incremental improvements over existing deep learning techniques and the commonly used public data sets have been presented. Finally, the paper proposes three future research directions in the field: knowledge graphs with learning analytics, comprehensive social network analysis, composite behavioural analysis.

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“…10 Some methods for establishing MOOC prediction model are proposed in the literature, including k-nearest neighbor (K-NN), logistic regression, decision tree (DT), random forest (RF), and support vector machine (SVM). [11][12][13][14] This paper proposes an intelligent algorithm combining DT, SVM, and simulated annealing (SA) to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree MOOC platform. The SVM has good classification performance and the DT can generate decision rules.…”

Section: Introductionmentioning

confidence: 99%

Study on the university students' satisfaction of the wisdom tree massive open online course platform based on parameter optimization intelligent algorithm

et al. 2021

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Introduction: Curriculum learning through the wisdom tree massive open online course platform not only gets rid of the limitations of specialty, school and region, eliminates the limitations of time and space in traditional teaching, but also effectively solves the problem of educational equity. Objectives: This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. Methods: This study takes the university students in Fuzhou city information management department as the survey object, and adopts the electronic questionnaire survey method. A total of 1136 formal questionnaires were responded, and 1028 valid questionnaires were obtained after data cleaning and deleting invalid questionnaires (the effective rate was 90.49%). In this paper, the reliability and validity of the questionnaire were tested by IBM SPSS-20.0 software, and six explanatory variables including function, achievement, exercise, quality, richness, and interaction were obtained by principal component analysis. Then, the questionnaire data is converted to CSV (comma separated values) format for analysis. This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. In this paper, the proposed algorithm is compared with decision tree, random forest, k-nearest neighbor, and support vector machine to verify its performance. Results: The experimental results show that training set classification accuracy of decision tree, random forest, k-nearest neighbor, only support vector machine and the proposed algorithm (simulated annealing + support vector machine) are 92.21%, 96.10%, 95.67%, 97.29%, and 99.58%, respectively. Conclusion: The proposed algorithm simulated annealing + support vector machine does increase the classification accuracy. At the same time, the 11 decision rules generated by simulated annealing + decision tree can provide useful information for decision makers.

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Quantifying the Influence of Achievement Emotions for Student Learning in MOOCs

Cited by 23 publications

References 44 publications

Exploring the effects of achievement emotions on online learning outcomes: A systematic review

Exploring the effects of achievement emotions on online learning outcomes: A systematic review

A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs

Study on the university students' satisfaction of the wisdom tree massive open online course platform based on parameter optimization intelligent algorithm

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