An Investigation of an Early College Entrance Program’s Ability to Impact Intellectual and Social Development

Wu, Jiaju; Assouline, Susan G.; McClurg, Virginia M.; McCallum, R. Steve

doi:10.1080/02783193.2022.2043503

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Integrated Sampling Neural Network (ES-ANN) models [7][8]: after obtaining all the data features needed for model training, it is the specific data that is substituted into the model for computation, for supervised neural network models, called EnsemblesamplingANN, which can make good use of the entire data set [9][10]. d denotes all datasets, D training denotes the training set, D test denotes the test set, DT denotes the set of positive samples and DN denotes the set of negative samples as in Table 1.…”

Section: Student Psychological Stress Assessment Modelmentioning

confidence: 99%

A Machine Learning-supported Analysis of University Students' Mental Health

2021

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Psychological stress is a psychological stress response that occurs when a person is faced with an overwhelming situation. Research has shown that the right level of stress can lead to progress and the realisation of one's potential. For students, appropriate levels of stress can enhance their learning and contribute to their growth and development. However, excessive psychological stress can be physically and psychologically distressing, and can even lead to suicidal behaviour. If students with abnormal psychological stress can be identified in time, schools can provide timely help and intervention to alleviate the psychological stress. The main objective of this paper is to develop a study on the analysis of university students' mental health based on machine learning. To investigate the difference between supervised and unsupervised learning, the LOF algorithm was also introduced for comparison. Experiments show that the G-mean value of ES-ANN improves by approximately 8 percentage points and the F1 value by approximately 4 percentage points over the best benchmark algorithm. Compared to traditional questionnaire-based methods, the daily campus data has a higher degree of authenticity and real time, which helps schools to identify students with high psychological stress in a timely manner. The research in this paper suggests that this idea is potentially feasible and deserves further validation and improvement in practice.

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Section: Student Psychological Stress Assessment Modelmentioning

confidence: 99%

A Machine Learning-supported Analysis of University Students' Mental Health

2021

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Gifted, high achieving, and accelerated Learners' mental health through the lens of positive psychology

Shaunessy-Dedrick,

Suldo

2024

Gifted Education International

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The study of mental health among gifted and accelerated learners has continued to evolve since the publication of a chapter focused on their subjective well-being. This article provides an updated review of mental health research focusing on gifted learners, specifically through understanding flourishing is routed in their experiences of positive emotions, engagement, relationships, meaning, and accomplishment (PERMA theory). We explore literature published between 2017 and 2024 addressing PERMA as it relates to gifted, high-achieving, and accelerated youth. Recommendations for future research and practice are provided.

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An Investigation of an Early College Entrance Program’s Ability to Impact Intellectual and Social Development

Cited by 2 publications

References 27 publications

A Machine Learning-supported Analysis of University Students' Mental Health

A Machine Learning-supported Analysis of University Students' Mental Health

Gifted, high achieving, and accelerated Learners' mental health through the lens of positive psychology

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