Predicting the Performance Fluctuation of Students Based on the Long-Term and Short-Term Data

Zhang, Tao; Xu, Yangsheng; Pan, Qi; Li, Xin; Hu, Guoping

doi:10.1109/eitt.2017.38

Cited by 2 publications

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“…In this study, we are also interested in predicting users' academic performance and computing their psychological attributes and mental health from different social media such as Twitter, Reddit, etc. Students' academic performances may fluctuate [84,85] for different reasons, and we would like to investigate whether users' psychological and mental health attributes are the vital factors.…”

Section: Discussionmentioning

confidence: 99%

Predicting Academic Performance: Analysis of Students’ Mental Health Condition from Social Media Interactions

Mukta

Islam

Shatabda

et al. 2022

Behavioral Sciences

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Social media have become an indispensable part of peoples’ daily lives. Research suggests that interactions on social media partly exhibit individuals’ personality, sentiment, and behavior. In this study, we examine the association between students’ mental health and psychological attributes derived from social media interactions and academic performance. We build a classification model where students’ psychological attributes and mental health issues will be predicted from their social media interactions. Then, students’ academic performance will be identified from their predicted psychological attributes and mental health issues in the previous level. Firstly, we select samples by using judgmental sampling technique and collect the textual content from students’ Facebook news feeds. Then, we derive feature vectors using MPNet (Masked and Permuted Pre-training for Language Understanding), which is one of the latest pre-trained sentence transformer models. Secondly, we find two different levels of correlations: (i) users’ social media usage and their psychological attributes and mental health status and (ii) users’ psychological attributes and mental health status and their academic performance. Thirdly, we build a two-level hybrid model to predict academic performance (i.e., Grade Point Average (GPA)) from students’ Facebook posts: (1) from Facebook posts to mental health and psychological attributes using a regression model (SM-MP model) and (2) from psychological and mental attributes to the academic performance using a classifier model (MP-AP model). Later, we conduct an evaluation study by using real-life samples to validate the performance of the model and compare the performance with Baseline Models (i.e., Linguistic Inquiry and Word Count (LIWC) and Empath). Our model shows a strong performance with a microaverage f-score of 0.94 and an AUC-ROC score of 0.95. Finally, we build an ensemble model by combining both the psychological attributes and the mental health models and find that our combined model outperforms the independent models.

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Section: Discussionmentioning

confidence: 99%