The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Utilizing early engagement and machine learning to predict student outcomes
Open accessAbstract
Abstract Finding a solution to the problem of student retention is an often-required task across Higher Education. Most often managers and academics alike rely on intuition and experience to identify the potential risk students and factors. This paper examines the literature surrounding current methods and measures in use in Learning Analytics. We find that while tools are available, they do not focus on earliest possible identification of struggling students. Our work defines a new descriptive statistic for student attendance and applies modern machine learning tools and techniques to create a predictive model. We demonstrate how students can be identified as early as week 3 (of the Fall semester) with approximately 97% accuracy. We, furthermore, situate this result within an appropriate pedagogical context to support its use as part of a more comprehensive student support mechanism.
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Cites
Relationships matter: linking teacher support to student engagement and achievement.
Klem, A. M., & Connell, J. P. (2004) · Journal of School Health · doi:10.1111/j.1746-1561.2004.tb08283.x · Cited by 2,082 · cited 1×
“mechanics of the intervention are important, the relationship between student and their support is critically important [55].”
BackgroundSocial-Psychological Interventions in Education: They’re Not Magic
Yeager, D. S., & Walton, G. M. (2011) · Review of Educational Research · doi:10.3102/0034654311405999 · Cited by 1,345
Ethical and privacy principles for learning analytics
Pardo, A., & Siemens, G. (2014) · British Journal of Educational Technology · doi:10.1111/bjet.12152 · Cited by 710 · cited 1×
“This position is also advocated by several ethical investigations [48, 49].”
BackgroundBig Data and analytics in higher education: Opportunities and challenges
Daniel, B. (2015) · British Journal of Educational Technology · doi:10.1111/bjet.12230 · Cited by 610
Predictors of First-Year Student Retention in the Community College
Fike, D. S., & Fike, R. (2008) · Community College Review · doi:10.1177/0091552108320222 · Cited by 416
Affect-aware tutors: recognising and responding to student affect
Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009) · International Journal of Learning Technology · doi:10.1504/ijlt.2009.028804 · Cited by 371
Lonn, S., Aguilar, S. J., & Teasley, S. D. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.07.013 · Cited by 152
Cited by
Application and theory gaps during the rise of artificial intelligence in education
Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100002 · Cited by 1,003
Namoun, A., & Alshanqiti, A. (2020) · Applied Sciences · doi:10.3390/app11010237 · Cited by 473
Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review
Rastrollo-Guerrero, J. L., Gómez-Pulido, J. A., & Durán-Domínguez, A. (2020) · Applied Sciences · doi:10.3390/app10031042 · Cited by 367
Li, J., & Xue, E. (2023) · Behavioral Sciences · doi:10.3390/bs13010059 · Cited by 280
Adnan, M., Habib, A., Ashraf, J., Mussadiq, S., Raza, A. A., Abid, M., Bashir, M., & Khan, S. U. (2021) · IEEE Access · doi:10.1109/access.2021.3049446 · Cited by 250
Metadata
- Authors
- Gray, C. C., & Perkins, D.
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 131
- Pages
- 22-32
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2905931716
- Cited by
- 199
- References
- 7
- FWCI
- 19.36
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
