Influential factors on elementary students’ academic performance and identifying the appropriate performance group
Open Access
- 14 June 2024
- journal article
- research article
- Published by Springer Nature in Discover Education
- Vol. 3 (1) , 1-15
Abstract
Elementary education is critical as it lays the foundation for learning, critical thinking, social and emotional development, individual growth, and self-confidence. Hence, studying the elementary students’ educational progress is immensely important. This manuscript aims to investigate the factors that impact elementary students’ academic performance, predict their academic performance, and use the above factors to identify the student’s appropriate skilled level group based on their academic performance. In this study, the Dynamic Indicators of Basic Early Literacy Skills (DIBELS) score, a measurement to assess students’ early literacy skills for K-6 graders, is used to quantify students' academic performance. A sample of 185 first and second graders and their features, including student’s BMI (body mass index), amount of time the student spends watching television, student’s gender, number of hours that a student sleeps each night, amount of time that the student spends reading books, student’s race, and amount of time that the student spends on physical activity are used. Based on the regression analysis, second graders’ academic performances are significantly impacted by their BMI values (β = − 4.002, p < 0.05) and the amount of time students spend reading books (β = 29.14, p < 0.05). The first graders’ academic performances are significantly impacted by the amount of sleeping time (β = 41.89, p < 0.01) and their gender (β = − 37.129, p < 0.05). Furthermore, the experimental findings indicate that machine-learning techniques accurately predict the students' appropriate academic group. In the naive Byers classifier, students in the lowest academic performance group can be identified successfully with a sensitivity of 92%, and the students in the highest academic group can be identified with a specificity value of 100%.Keywords
Funding Information
- Internal Research Grant of Eastern New Mexico University, Portales, NM, USA
This publication has 31 references indexed in Scilit:
- Untangling the relationship between BMI and academic achievement in the elementary yearsBritish Journal of Nutrition, 2023
- Dropout early warning systems for high school students using machine learningChildren and Youth Services Review, 2019
- A feature dependent Naive Bayes approach and its application to the software defect prediction problemApplied Soft Computing, 2017
- Association of depression & health related quality of life with body composition in children and youth with obesityJournal of Affective Disorders, 2015
- The impact of physical education on obesity among elementary school childrenJournal of Health Economics, 2013
- Associations Between Obesity and Comorbid Mental Health, Developmental, and Physical Health Conditions in a Nationally Representative Sample of US Children Aged 10 to 17Academic Pediatrics, 2013
- A machine learning approach to reading level assessmentComputer Speech & Language, 2009
- Top 10 algorithms in data miningKnowledge and Information Systems, 2007
- Academic and Social Motivational Influences on Students' Academic PerformanceEducational Psychology Review, 1998
- Television Viewing as a Cause of Increasing Obesity Among Children in the United States, 1986-1990Archives of Pediatrics & Adolescent Medicine, 1996