NotesFAQContact Us
Collection
Advanced
Search Tips
Back to results
Peer reviewed Peer reviewed
PDF on ERIC Download full text
ERIC Number: EJ1199900
Record Type: Journal
Publication Date: 2018
Pages: 15
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-1929-7750
EISSN: N/A
Student Ability Best Predicts Final Grade in a College Algebra Course
O'Connell, Kyle A.; Wostl, Elijah; Crosslin, Matt; Berry, T. Lisa; Grover, James P.
Journal of Learning Analytics, v5 n3 p167-181 2018
Historical student data can help elucidate the factors that promote student success in mathematics courses. Herein we use both multiple regression and principal component analyses to explore ten years of historical data from over 20,000 students in an introductory college-level Algebra course in an urban American research university with a diverse student population in order to understand the relationship between course success and student performance in previous courses, student demographic background, and time spent on coursework. We find that indicators of students' past performance and experience, including grade-point-average and the number of accumulated credit hours, best predict student success in this course. We also find that overall final grades are representative of the entire course and are not unduly weighted by any one topic. Furthermore, the amount of time spent working on assignments led to improved grade outcomes. With these baseline data, our team plans to design targeted interventions that can increase rates of student success in future courses.
Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Texas
Grant or Contract Numbers: N/A