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Related Concept Videos

Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus: Comparing...
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Biological approach
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Psychology, as a scientific discipline, aims to understand the mind and behavior through rigorous and systematic methods. The foundation of psychological research is evidence-based, relying heavily on the scientific method to derive and validate knowledge. This structured approach ensures that findings are reliable, valid, and applicable to broader contexts.
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The Scientific Method02:40

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Stress and Mental Health01:30

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Fundamental Attribution Error01:14

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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is called the fundamental attribution...

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Related Experiment Video

Updated: May 17, 2026

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
08:25

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

Published on: December 6, 2024

Understanding student mental health: An explainable pattern analysis approach.

Md Anisur Rahman1, Pubudu Sanjeewani2, Almond Pham1

  • 1La Trobe Business School, La Trobe University, Melbourne, VIC, 3086, Australia.

Acta Psychologica
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

Student mental health is impacted by coping difficulty, anger over poor performance, and difficulty relaxing. Explainable AI identified these key risk factors for stress and nervousness, enabling early intervention.

Keywords:
AnxietyDecision tree modelsDepressionExplainable artificial intelligenceFinancial stressInterpretable machine learningNervousnessPattern analysisStudents' mental healthSuicidal ideation

Related Experiment Videos

Last Updated: May 17, 2026

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
08:25

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

Published on: December 6, 2024

Area of Science:

  • Psychology
  • Artificial Intelligence
  • Education

Background:

  • Student mental health is a significant global issue impacting academic success.
  • Current research often uses opaque models, limiting actionable insights for intervention.
  • There's a need for interpretable methods to understand student psychological challenges.

Purpose of the Study:

  • To identify key psychosocial risk factors for student mental health using explainable pattern analysis.
  • To provide actionable insights for early intervention strategies.
  • To demonstrate the utility of interpretable AI in understanding student well-being.

Main Methods:

  • Analysis of three student mental health datasets using a decision tree algorithm.
  • Extraction of interpretable patterns linking psychosocial attributes to stress and nervousness.
  • Assessment of pattern influence using support and confidence metrics.

Main Results:

  • Coping difficulty was the most significant predictor of stress and nervousness (99.15% confidence).
  • Anger over poor performance and difficulty relaxing also elevated stress risk.
  • Stress was observed even in students without coping difficulties, indicating other contributing factors.

Conclusions:

  • Psychosocial factors collectively influence student mental health outcomes.
  • Explainable AI offers valuable, data-driven insights for proactive support systems in education.
  • Future research should integrate longitudinal and multimodal data for a comprehensive understanding.