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Measurement of students’ peer assessment motivation is critical to understand how they participate in such activities in higher education. The current study was conducted to develop and validate a brief scale that measures student peer assessment motivation in higher education using the Expectancy-Value Theory (EVT). Initial items were developed, revised, and administered to 369 students. Exploratory factor analyses suggested a three-factor model structure (ability belief, expectancy, and task value) aligning with EVT. Confirmatory factor analyses (n = 399) supported a higher-order factor structure with the three first-order factors (i.e. ability belief, expectancy, and task value) with a decent model fit. The 20-items Peer Assessment Motivation Scale (PAMS) had decent internal reliability, test-retest reliability, convergent validity, and discriminant validity, suggesting that it is a high-quality measure. This scale is beneficial for instructors and researchers who are interested in investigating peer assessment motivation in higher education.
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College student mental health has been a critical concern for professional counselors. Anxiety and depressive disorders have become increasingly prevalent over the past decade. Utilizing machine learning, a subset of artificial intelligence (AI), we developed predictive models (i.e., eXtreme Gradient Boosting [XGBoost], Random Forest, Decision Tree, and Logistic Regression) to identify US college students at heightened risk of diagnosable anxiety and depressive disorders. The dataset included 61,619 students from 133 US higher education institutions and was partitioned into a 90:10 ratio for training and testing the models. We employed hyperparameter tuning and cross-validation to optimize model performance and examined multiple measures of predictive performance (e.g., area under the receiver operating characteristic curve [AUC], accuracy, sensitivity). Results revealed strong discriminative power in our machine learning predictive models with AUC of 0.74 and 0.77, indicating current financial situation, sense of belonging on campus, disability status, and age as the top predictors of anxiety and depressive disorders. This study provides a practical tool for professional counselors to proactively identify students for anxiety and depressive disorders before these conditions escalate. Application of machine learning in counseling research provides data-driven insights that help enhance the understanding of mental health determinants, guide prevention and intervention strategies, and promote the well-being of diverse student populations through counseling.