Mining academic data to improve college student retention

Lauría, Eitel J. M.; Baron, Joshua D.; Devireddy, Mallika; Sundararaju, Venniraiselvi; Jayaprakash, Sandeep M.

doi:10.1145/2330601.2330637

Cited by 58 publications

(33 citation statements)

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“…In general, we found the same statistically significant elements as Purdue with similar correlation strengths. These initial findings on portability were included in a paper presented at the 2012 international Learning Analytics and Knowledge (LAK) conference (Lauría, Baron, Devireddy, Sundararaju, & Jayaprakash, 2012).…”

Section: Conducting Pilots Of the Academic Alert System At Partner Inmentioning

confidence: 99%

Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative

et al. 2014

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ABSTRACT:The Open Academic Analytics Initiative (OAAI) is a collaborative, multi-year grant program aimed at researching issues related to the scaling up of learning analytics technologies and solutions across all of higher education. The paper describes the goals and objectives of the OAAI, depicts the process and challenges of collecting, organizing and mining student data to predict academic risk, and report results on the predictive performance of those models, their portability across pilot programs at partner institutions, and the results of interventions on at-risk students.

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Section: Conducting Pilots Of the Academic Alert System At Partner Inmentioning

confidence: 99%

Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative

et al. 2014

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“…Conversely, students who infrequently participate in discussion forums and show patterns of disengagement are more likely to fail (Milne et al, 2012;Romero et al, 2013). The strength of the association ranges from weak (Lauría, Baron, Devireddy, Sundararaju, & Jayaprakash, 2012;Chanlin, 2012) to strong (Macfadyen & Dawson, 2010), and mere participation in discussion does not significantly distinguish students with the highest grades from average performers (Davies & Graff, 2005). Furthermore, discussion participation matters most for students in danger of failing (Davies & Graff, 2005), most likely because the feeling of being supported makes struggling students more likely to persist (Romero et al, 2013).…”

Section: Factors Related To Achievement In Online Coursesmentioning

confidence: 99%

Individual Differences Related to College Students’ Course Performance in Calculus II

Hart¹,

Daucourt²,

Ganley³

2017

Learning Analytics

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ABSTRACT. In this study, we explore student achievement in a semester-long flipped Calculus II course, combining various predictor measures related to student attitudes (math anxiety, math confidence, math interest, math importance) and cognitive skills (spatial skills, approximate number system), as well as student engagement with the online system (discussion forum interaction, time to submission of workshop assignments, quiz attempts), in predicting final grades. Data from 85 students enrolled in a flipped Calculus II course was used in dominance analysis to determine which predictors emerged as the most important for predicting final grades. Results indicated that feelings of math importance, approximate number system (ANS) ability, total amount of discussion forum posting, and time grading peer workshop submissions was the best combination of predictors of final grade, accounting for 17% of variance in a student's final grade. The point of this work was to determine which predictors are the most important in predicting student grade, with the end goal of building a recommendation system that could be implemented to help students in this traditionally difficult class. The methods used here could be used for any class.Keywords: Math performance, calculus, flipped classroom, math attitudes, cognitive performance, student engagement INTRODUCTIONOver the past few years, the attrition of STEM-focused undergraduates in the United States has become a critical national concern, with "a substantial number of undergraduate students initially enrolled in STEM degree programs [dropping] out in the first two years" (PCAST, 2012). In order to maintain highquality instructional practices in the face of STEM undergraduate attrition, growing demand for large (2017 130 numbers of graduates, and diminishing financial and human resources, many colleges and universities are turning to technologically aided teaching practices. This technological shift aims to integrate the traditional classroom environment with online course resources to enhance, replace, and supplement face-to-face instruction to reach more students in a cost-effective way (Garrison & Kanuka, 2004).To accommodate this shift toward technologically aided teaching methods, schools have implemented campus-wide Learning Management Systems (LMS), such as Moodle and Blackboard. These web-based management systems provide data related to the "user," with every action of the student tracked and recorded online. The underlying advantage of these LMS data is that they unobtrusively record individual student activity and interaction with course materials in real-time, providing a lens into traditionally unobservable learning-related behaviours and silently tracking individual students' learning progression (Macfadyen & Dawson, 2010; Gašević, Dawson, & Siemens, 2015). In traditional college courses, many performance measures, for example midterm or final exams (Lee, Speglia, Ha, Finch, & Nehm, 2015;Macfadyen & Dawson, 2010), are taken too late in the semester to identify st...

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“…Another very popular classification method, C4.5 decision trees (Quinlan, 1993) have been used to predict student retention multiple times in recent literature such as in Yadav, Bharadwaj, and Pal (2012), Nandeshwar, Menzies, and Nelson (2011), Lauría (2012), and Lin (2012. This method works by building a tree structure where split operations are performed on each node based on information gain values for each feature of the dataset and the respective class.…”

Section: C45 Decision Treesmentioning

confidence: 99%

“…Given this division, new instances can be easily verified to belong to one of the two classes. This approach has been used to predict student retention, and has often achieved highly accurate results (Luna, 2000;Fike & Fike, 2008;Lauría, 2012;Lin, Imbrie, & Reid, 2009;Zhang, Anderson, Ohland, & Thorndyke, 2004;Herzog, 2006;Veenstra, Dey, & Herrin, 2009). …”

Section: Logistic Regressionmentioning

confidence: 99%

Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Persistence

et al. 2014

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As providers of higher education begin to harness the power of big data analytics, one very fitting application for these new techniques is that of predicting student attrition. The ability to pinpoint students who might soon decide to drop out, or who may be following a suboptimal path to success, allows those in charge not only to understand the causes for this undesired outcome, but provides room for the development of early intervention systems. While making such inferences based on academic performance data alone is certainly possible, we claim that in many cases there is no substantial correlation between how well a student performs and his/her decision to withdraw. This is especially true when the overall set of students has a relatively similar academic performance. To address this issue, we derive measurements of engagement from students' electronic portfolios and show how these features can be used effectively to augment the quality of predictions.

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Mining academic data to improve college student retention

Cited by 58 publications

References 4 publications

Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative

Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative

Individual Differences Related to College Students’ Course Performance in Calculus II

Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Persistence

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