Dataset Information

An Online Experiment During COVID-19: Testing the Influences of Autonomy Support Toward Emotions and Academic Persistence.

ABSTRACT: Students' academic persistence is a critical component of effective online learning. Promoting students' academic persistence could potentially alleviate learning loss or drop-out, especially during challenging time like the COVID-19 pandemic. Previous research indicated that different emotions and autonomy support could all influence students' academic persistence. However, few studies examined the multidimensionality of persistence using an experimental design with students' real-time emotions. Using an experimental design and the Contain Intelligent Facial Expression Recognition System (CIFERS), this research explored the dynamic associations among real-time emotions (joy and anxiety), autonomy support (having choice and no choice), self-perceived persistence, self-reliance persistence, and help-seeking persistence. 177 college students participated in this study online via Zoom during COVID-19 university closure. The results revealed that having choice and high intensity of joy could promote students' self-reliance persistence, but not help-seeking persistence. Interestingly, students who perceived themselves as more persistent experienced more joy during experiment. The theoretical and practical implications on facilitating students' academic persistence were discussed.

SUBMITTER: Wang Y

PROVIDER: S-EPMC8542910 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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An Online Experiment During COVID-19: Testing the Influences of Autonomy Support Toward Emotions and Academic Persistence.

Wang Yurou Y Zhang Jihong J Lee Halim H

Frontiers in psychology 20211011

Students' academic persistence is a critical component of effective online learning. Promoting students' academic persistence could potentially alleviate learning loss or drop-out, especially during challenging time like the COVID-19 pandemic. Previous research indicated that different emotions and autonomy support could all influence students' academic persistence. However, few studies examined the multidimensionality of persistence using an experimental design with students' real-time emotions ...[more]

PMID: 34707547

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Project description:BackgroundWhile online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge.ObjectiveThe objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs.MethodsWe initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features.ResultsThe best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others.ConclusionsWe developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs.

Dataset Information

An Online Experiment During COVID-19: Testing the Influences of Autonomy Support Toward Emotions and Academic Persistence.

Publications

An Online Experiment During COVID-19: Testing the Influences of Autonomy Support Toward Emotions and Academic Persistence.

Similar Datasets

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