Decoding Minds: Estimation of Stress Level in Students using Machine Learning DOI Open Access
Salma S. Shahapur,

Praveen Chitti,

Shahak Patil

et al.

Indian Journal of Science and Technology, Journal Year: 2024, Volume and Issue: 17(19), P. 2002 - 2012

Published: May 14, 2024

Objectives: Develop a predictive model to categorize student’s stress levels and support early interventions based on self-reported data, academic performance, study load. This will help receive diagnosis treatment. Methods: In this work the data set used was downloaded from website called KAGGLE. The dataset has more than 6000 samples, parameters considered in are Anxiety level, self-esteem, mental_health_history, depression, headache, blood pressure, sleep_quality, breathing_problem, noise_level, living conditions, Safety, basic needs, study_load, teacher_student_relationship, future_career_concerns, social support, peer_pressure, extracurricular_activities bullying which directly or indirectly an effect mental health of students, so basically here 20 different types factors taken into consideration. specific Research Work employs Machine Learning (ML) approaches analyze students stress-level text data. Logistic Regression (LR) with 89.46%, KNeighbors 92.8%, Decision Tree 94.5%, Random Forest 95%, Gradient Boosting 90.15%, algorithms determine levels. Findings: Several significant findings have emerged research predicting using machine learning. Studies feature importance emphasize sleep quality, participation extracurricular activities several other as critical criteria for accurate prediction. Multimodal techniques that integrate history, family records provide complete picture life. Temporal dynamics important, fluctuate throughout time result personal events. Some goes beyond prediction, investigating intervention options tailored management suggestions. Novelty: order anticipate stress, presents novel machine-learning architecture. methodology attempts give identification students’ at risk by leveraging diverse sources learning very high accuracy level. Keywords: Stress Level, Students, Learning, Tree, Physio Bank

Language: Английский

Challenges and opportunities in using interpretable AI to develop relationship interventions DOI
Daniel J. Puhlman, Chaofan Chen

Family Relations, Journal Year: 2025, Volume and Issue: unknown

Published: April 3, 2025

Abstract Objective Although still in its infancy, research shows promise that artificial intelligence (AI) models can be integrated into relationship interventions, and the potential benefits are substantial. This article articulates challenges opportunities for developing interventions integrate AI. Background After defining AI differentiating machine learning from deep learning, we review key concepts strategies related to AI, specifically natural language processing, interpretability, human‐in‐the‐loop strategies, as approaches needed develop interventions. Method We explore how is currently family life literature has served foundation further integrating The use of therapy contexts examined, identify ethical need addressed this technology develops. Results examine using focusing on four areas: diagnosis problems, providing autonomous treatment, predicting successful treatment outcomes (prognosis), biomarkers monitor client reactions. Opportunities explored include development data‐efficient training methods, creating interpretable focused relationships, integration clinical expertise during model development, combining biomarker data with other modalities. Conclusion Despite obstacles, provide families personalized support strengthen bonds overcome relational challenges. Implications emerging intersection science pioneer innovative solutions diverse needs.

Language: Английский

Citations

1
Effectiveness of XR‐Based Exposure Therapy for Phobic Disorders DOI Creative Commons
Richard Lamb,

Jason Perry,

Elizabeth Sutherland

et al.

Journal of Counseling & Development, Journal Year: 2025, Volume and Issue: unknown

Published: March 19, 2025

ABSTRACT Research on anxiety and posttraumatic stress disorder (PTSD) indicates that virtual reality related technologies are effective tools for therapy. Given the similar underlying mechanism of these disorders to phobias, it is thought by researchers in mental health care VR‐based exposure therapies would have treatment outcomes. The purpose this research examine effectiveness XR‐based therapy using physiological markers combination with patient perceptions phobic response. primary question study as follows: what an disorder? Forty‐five participants (22 males 23 females) took part study. Results from repeated measures analysis variance illustrate statistically significant differences over time main effect group. three groups (1) XR exposure, (2) traditional (3) time‐delay comparison. offers multiple advantages vivo imaginative exposure.

Language: Английский

Citations

0
Adaptive Artificial Intelligence for Students with Specific Learning Disabilities in Reading Science Content DOI
Richard Lamb, Danielle Malone, Tosha L. Owens

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

Abstract The growing integration of generative artificial intelligence (AI) technologies, including systems such as ChatGPT, into educational environments in science presents new opportunities to support learning. However, mainstream AI tools often fail adequately assist students with specific learning disabilities reading, dyslexia. Students reading require specialized instruction tailored the unique challenges posed by difficulties comprehension, decoding, and retaining multi-step directions present complex texts. While current technologies can provide basic explanations, they lack real-time, adaptive guidance step-by-step feedback personalized individual learners. Additionally, predominantly text-based does not suit needs who benefit from interactive, multimodal strategies visual aids. To better serve neurodiverse learners classrooms, must evolve a focus on inclusivity. Potential improvements include algorithms based upon use neurological data, enhanced formative assessment techniques, incorporation graphics other multisensory features. With innovative designs that align principles universal learning, AI-based could individualized skill development for all students. This will sustained efforts develop is responsive diverse needs.

Language: Английский

Citations

0
Exploring Predictors of Bullying Perpetration Among Adolescents Using Machine Learning Approach DOI
Huiling Zhou,

Qingying Zheng,

Huaibin Jiang

et al.

Journal of Interpersonal Violence, Journal Year: 2025, Volume and Issue: unknown

Published: May 19, 2025

This study used machine learning methods to detect risk and protective factors for bullying perpetration in adolescents. The sample consisted of 777 students with an age range 11 16 years old. Multidimensional data covering both individual environmental levels were collected. Individual included moral disengagement, normative beliefs about aggression, neuroticism, self-control; parent–child relationships, deviant peer affiliation, school connection, violent media exposure. current tested compared six algorithms: Logistic Regression, Random Forest, Gradient Boosting Decision Tree, XGBoost, LightGBM, Stacking, behavior. results demonstrated that: (a) the Forest algorithm performed optimally, recall, F1 score, area under curve values 0.9394, 0.8516, 0.8043, respectively; (b) Gini importance SHapley Additive exPlanations (SHAP) identified self-control as most significant factor, while disengagement was influential factor. recommended model not only provides application value preventing but also a scientific basis developing targeted interventions.

Language: Английский

Citations

0
Decoding Minds: Estimation of Stress Level in Students using Machine Learning DOI Open Access
Salma S. Shahapur,

Praveen Chitti,

Shahak Patil

et al.

Indian Journal of Science and Technology, Journal Year: 2024, Volume and Issue: 17(19), P. 2002 - 2012

Published: May 14, 2024

Objectives: Develop a predictive model to categorize student’s stress levels and support early interventions based on self-reported data, academic performance, study load. This will help receive diagnosis treatment. Methods: In this work the data set used was downloaded from website called KAGGLE. The dataset has more than 6000 samples, parameters considered in are Anxiety level, self-esteem, mental_health_history, depression, headache, blood pressure, sleep_quality, breathing_problem, noise_level, living conditions, Safety, basic needs, study_load, teacher_student_relationship, future_career_concerns, social support, peer_pressure, extracurricular_activities bullying which directly or indirectly an effect mental health of students, so basically here 20 different types factors taken into consideration. specific Research Work employs Machine Learning (ML) approaches analyze students stress-level text data. Logistic Regression (LR) with 89.46%, KNeighbors 92.8%, Decision Tree 94.5%, Random Forest 95%, Gradient Boosting 90.15%, algorithms determine levels. Findings: Several significant findings have emerged research predicting using machine learning. Studies feature importance emphasize sleep quality, participation extracurricular activities several other as critical criteria for accurate prediction. Multimodal techniques that integrate history, family records provide complete picture life. Temporal dynamics important, fluctuate throughout time result personal events. Some goes beyond prediction, investigating intervention options tailored management suggestions. Novelty: order anticipate stress, presents novel machine-learning architecture. methodology attempts give identification students’ at risk by leveraging diverse sources learning very high accuracy level. Keywords: Stress Level, Students, Learning, Tree, Physio Bank

Language: Английский

Citations

0