ERIC Number: EJ1432553
Record Type: Journal
Publication Date: 2024
Pages: 21
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: EISSN-2731-5525
Enhancing High-School Dropout Identification: A Collaborative Approach Integrating Human and Machine Insights
Discover Education, v3 Article 109 2024
Despite its proven success in various fields such as engineering, business, and healthcare, human-machine collaboration in education remains relatively unexplored. This study aims to highlight the advantages of human-machine collaboration for improving the efficiency and accuracy of decision-making processes in educational settings. High school dropout prediction serves as a case study for examining human-machine collaboration's efficacy. Unlike previous research prioritizing high accuracy with immutable predictors, this study seeks to bridge gaps by identifying actionable factors for dropout prediction through a framework of human--machine collaboration. Utilizing a large dataset from the High School Longitudinal Study of 2009 (HSLS:09), two machine learning models were developed to predict 9th-grade students' high school dropout history. Results indicated that the Random Forest algorithm outperformed the deep learning algorithm. Model explainability revealed the significance of actionable variables such as students' GPA in the 9th grade, sense of school belonging, self-efficacy in mathematics and science, and immutable variables like socioeconomic status in predicting high school dropout history. The study concludes with discussions on the practical implications of human-machine partnerships for enhancing student success.
Descriptors: High School Students, Dropouts, Identification, Man Machine Systems, Grade 9, Prediction, Artificial Intelligence, Group Membership, Predictor Variables
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://bibliotheek.ehb.be:2123/
Publication Type: Journal Articles; Reports - Research
Education Level: High Schools; Secondary Education; Grade 9; Junior High Schools; Middle Schools
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A