Assessing learning engagement based on facial expression recognition in MOOC’s scenario

Shen, Junge; Yang, Haopeng; Li, Jiawei; Cheng, Zhiyong

doi:10.1007/s00530-021-00854-x

Cited by 39 publications

(18 citation statements)

References 26 publications

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“…According to the survey, MOOCs are very beneficial to learners who have completed the courses [4]. Previous surveys also suggested that 61% of the respondents said that MOOCs have educational benefits and 72% believed that MOOCs have professional benefits, which confirms the significance of online learning, especially during the epidemic of COVID-19 [5].…”

Section: Introductionmentioning

confidence: 94%

Online Course Recommendation Using Deep Convolutional Neural Network with Negative Sequence Mining

Gao

Luo

2022

Wireless Communications and Mobile Computing

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Massive Open Online Course (MOOC) has been criticized for low completion rates, and one of the major reasons is that it fails to offer personalized course recommendations for different users with different demands. To solve this problem, this paper proposes a personalized course recommendation model based on convolutional neural network combined with negative sequence pattern mining. The model first models the course-learning sequence as a negative sequence pattern according to the user’s course registration, degree of completion, and final grades, in which, the negative term means that students should not choose and misoperate the principle of courses. Then, it employs a convolutional neural network structure to extract the internal features of negative sequence patterns for representation learning. Finally, through the convolutional sequence-embedding model, each user is recommended with a course list that includes the user’s maximized needs in recent temporal terms and the courses that are easy to be misselected. Experiment results show that the recommended model achieves higher recommendation performance with lower course dropout rate compared to baselines, which provides a new insight for both online and offline course recommendation.

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Section: Introductionmentioning

confidence: 94%

Online Course Recommendation Using Deep Convolutional Neural Network with Negative Sequence Mining

Gao

Luo

2022

Wireless Communications and Mobile Computing

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“…This method usually divides learning engagement into several categories. For example, Shen et al (2021) proposed a new facial expression recognition method based on a CNN using domain adaptation; their network can recognize the four most common facial expressions (understanding, neutral, disgust, and doubt). Then, they applied a formula to classify students according to learning engagement (high, moderate, or low).…”

Section: Classification Of Learning Engagementmentioning

confidence: 99%

“…Massive open online courses (MOOCs) provide online learning opportunities for learners worldwide (Gallego-Romero et al, 2020), allowing them to learn anytime and anywhere (Shen et al, 2021). In addition to the high flexibility of learning whenever and wherever, MOOCs also enable the sharing and open access of high-quality, top university course resources (Atiaja and Proenza, 2016), which promotes educational equity.…”

Section: Introductionmentioning

confidence: 99%

Learning engagement in massive open online courses: A systematic review

Wang

Cao

et al. 2022

Front. Educ.

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Although massive open online courses (MOOCs) increase the number of choices in higher education and enhance learning, their low completion rate remains problematic. Previous studies have shown that learning engagement is a crucial factor influencing learning success and learner retention. However, few literature reviews on learning engagement in MOOCs have been conducted, and specific data analysis methods are lacking. Moreover, the internal and external factors that affect learning engagement have not been fully elucidated. Therefore, this systematic literature review summarized articles pertaining to learning engagement in MOOCs published from 2015 to 2022. Thirty articles met the inclusion and quality assurance criteria. We found that (1) learning engagement can be measured through analysis of log, text, image, interview, and survey data; (2) measures that have been used to analyze learning engagement include self-report (e.g., the Online Learning Engagement Scale, Online Student Engagement Questionnaire, and MOOC Engagement Scale) and automatic analysis methods [e.g., convolutional neural network (CNN), bidirectional encoder representations from transformers-CNN, K-means clustering, and semantic network analysis]; and (3) factors affecting learning engagement can be classified as internal (learning satisfaction, etc.) or external (curriculum design, etc.). Future research should obtain more diverse, multimodal data pertaining to social engagement. Second, researchers should employ automatic analysis methods to improve measurement accuracy. Finally, course instructors should provide technical support (“scaffolding”) for self-regulated learning to enhance student engagement with MOOCs.

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“…Because scientific scenarios include controversial situations that will enable argumentation in the solution process of problems based on daily life examples in the scenario (Rebello et al, 2013;Suephatthima & Faikhamta, 2018). Many researchers (Garrecht et al, 2020;Günter, 2020;Mills et al, 2021;Mio et al, 2019;Sailer et al, 2021;Shen et al, 2021;Zeidler et al, 2019) mention the benefits and effectiveness of using scenarios with different strategies, methods and techniques in education. In this context, scientific scenarios are used in studies such as problem-based teaching (Günter, 2020), online learning (Mio et al, 2019;Sailer et al, 2021;Shen et al, 2021) and socioscientific issues (Garrecht et al, 2020;Zeidler et al al., 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers (Garrecht et al, 2020;Günter, 2020;Mills et al, 2021;Mio et al, 2019;Sailer et al, 2021;Shen et al, 2021;Zeidler et al, 2019) mention the benefits and effectiveness of using scenarios with different strategies, methods and techniques in education. In this context, scientific scenarios are used in studies such as problem-based teaching (Günter, 2020), online learning (Mio et al, 2019;Sailer et al, 2021;Shen et al, 2021) and socioscientific issues (Garrecht et al, 2020;Zeidler et al al., 2019). Genetic issues, which we face frequently in daily life, are also discussed within the scope of SSIs.…”

Section: Introductionmentioning

confidence: 99%

Teaching Socioscientific Issues through Scientific Scenarios: A Case Evaluation Based on Secondary School Students’ Views

ŞAŞMAZÖREN

DEMİRER

SARI

et al. 2023

Bartın Üniversitesi Eğitim Fakültesi Dergisi

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The aim of this study is to make a situation assessment of student views on the processing of socioscientific issues through scientific scenarios.As a sample of socioscientific issues, the 'DNA and Genetic Code' unit, which is included in the secondary school science curriculum, was taken as the basis. The study was conducted with 8th grade students in a public school. As a method, a case study was used, which allowed the students' views to be examined in depth. The data were collected with a opinion form and semi-structured interviews. The opinion form data were analysed by content analysis, and semi-structured interviews were analysed by descriptive analysis. At the end of the study about the teaching of the socioscientific issues of ‘DNA and Genetic Code’ unit through scientific scenarios, it was revealed that the students thought that their academic achievement increased, they provided permanent learning, scientific scenarios were effective in associating lessons with daily life, and their awareness of social issues increased. It was also understood that they discovered skills such as critical thinking, decision-making and empathy in themselves at the end of the applications. According to the findings obtained from the students, the teaching of socioscientific issues through scientific scenarios increases the interest and motivation towards the lesson, makes the lesson enjoyable and fun, and facilitates understanding. In line with the experiences and findings obtained from the applications, suggestions were made for the use of scientific scenarios in the teaching of socioscientific issues.

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Assessing learning engagement based on facial expression recognition in MOOC’s scenario

Cited by 39 publications

References 26 publications

Online Course Recommendation Using Deep Convolutional Neural Network with Negative Sequence Mining

Online Course Recommendation Using Deep Convolutional Neural Network with Negative Sequence Mining

Learning engagement in massive open online courses: A systematic review

Teaching Socioscientific Issues through Scientific Scenarios: A Case Evaluation Based on Secondary School Students’ Views

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