Machine Learning based model for predicting Stress Level in Online Education Due to Coronavirus Pandemic: A Case Study of Bangladeshi Students

Siddique, Sunzida; Baidya, Sajal; Rahman, Md. Sadekur

doi:10.1109/eict54103.2021.9733612

Cited by 6 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results suggest that the ML-based prediction model developed in this study has the potential to accurately anticipate human stress levels in PSS during a pandemic situation. A similar study found that the Random Forest algorithm was found to play the most significant role in predicting the stress level detection in online classes, and the accuracy was 73.91% [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach

Ratul

Nishat

Faisal

et al. 2023

Heliyon

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach

Ratul

Nishat

Faisal

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…From the publications (n = 35), the best-performing machine learning algorithms are DT [20], [25], [41], [42], [50], SVM [19], [24], [33], [39], [41], [43] and LR [19], [30] [32], [35] stood exceptional performances. According to [50], when comparing the performance of machine learning and deep learning models, as well as comparing machine learning and deep learning models to each other, the K-Nearest Neighbor (KNN) algorithm was found to have the highest accuracy.…”

Section: Rq2: Which Machine Learning or Deep Learning Classifier Is F...mentioning

confidence: 99%

Role of Artificial Intelligence in Online Education: A Systematic Mapping Study

et al. 2023

View full text Add to dashboard Cite

Artificial intelligence (AI) comprises various sub-fields, including machine learning (ML) and deep learning (DL) perform a key role in the transformation of many industries, including education. It changes traditional learning methods by using its Innovative techniques and applications. Using its applications, the teachers may keep track of each student's development, paying close attention to the areas in which they struggle. Many researchers are working with ML and DL to exploit its discoveries and insights. In education, traditional education methods (TEM) are the same for each student, which means each student is taught in the same way as ML and DL, making this process flexible and creative for solving complex problems and enhancing productivity. Nowadays, each institution adopts E-learning methods as the primary way of learning, especially during the pandemic. Despite this evolution of creativity, delivering quality education, making strategies for analyzing performance and future goals, and career counseling for students still pose challenges. The current study aims to offer a complete overview of the significance of ML approaches in online education. To accomplish this purpose, the study synthesizes information from multiple scientific papers that investigate (a) the methodology used to construct learning analysis tools, (b) the key data resources used, and (c) the scopeÂ of data sources now available. This systematic literature review (SLR) examines the research conducted between 1961 and 2022, focusing on various machine learning (ML) and deep learning (DL) techniques. Its aim is to provide insights into the applications of these techniques and offer optimal solutions to the research questions at hand. We are convinced that our complete assessment will be a dependable resource for the research group in ascertainment the best approach and information source for their unique needs. Moreover, our findings provide valuable insights on the subject matter that could aid the research community in their future endeavors in the related field.

show abstract