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Utilizing Early Engagement and Machine Learning to Predict Student Outcomes

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

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Utilizing Early Engagement and Machine Learning to Predict Student Outcomes. / Gray, Cameron C.; Perkins, Dave.
Yn: Computers and Education, Cyfrol 131, 01.04.2019, t. 22-32.

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

HarvardHarvard

Gray, CC & Perkins, D 2019, 'Utilizing Early Engagement and Machine Learning to Predict Student Outcomes', Computers and Education, cyfrol. 131, tt. 22-32. https://doi.org/10.1016/j.compedu.2018.12.006

APA

Gray, C. C., & Perkins, D. (2019). Utilizing Early Engagement and Machine Learning to Predict Student Outcomes. Computers and Education, 131, 22-32. https://doi.org/10.1016/j.compedu.2018.12.006

CBE

Gray CC, Perkins D. 2019. Utilizing Early Engagement and Machine Learning to Predict Student Outcomes. Computers and Education. 131:22-32. https://doi.org/10.1016/j.compedu.2018.12.006

MLA

Gray, Cameron C. a Dave Perkins. "Utilizing Early Engagement and Machine Learning to Predict Student Outcomes". Computers and Education. 2019, 131. 22-32. https://doi.org/10.1016/j.compedu.2018.12.006

VancouverVancouver

Gray CC, Perkins D. Utilizing Early Engagement and Machine Learning to Predict Student Outcomes. Computers and Education. 2019 Ebr 1;131:22-32. Epub 2018 Rhag 21. doi: 10.1016/j.compedu.2018.12.006

Author

Gray, Cameron C. ; Perkins, Dave. / Utilizing Early Engagement and Machine Learning to Predict Student Outcomes. Yn: Computers and Education. 2019 ; Cyfrol 131. tt. 22-32.

RIS

TY - JOUR

T1 - Utilizing Early Engagement and Machine Learning to Predict Student Outcomes

AU - Gray, Cameron C.

AU - Perkins, Dave

PY - 2019/4/1

Y1 - 2019/4/1

N2 - 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 amore comprehensive student support mechanism.

AB - 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 amore comprehensive student support mechanism.

KW - Machine Learning

KW - Student Retention

KW - Learning Analytics

U2 - 10.1016/j.compedu.2018.12.006

DO - 10.1016/j.compedu.2018.12.006

M3 - Article

VL - 131

SP - 22

EP - 32

JO - Computers and Education

JF - Computers and Education

SN - 0360-1315

ER -