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Gonzalez, Julie M. Byers; DesJardins, Stephen L.

doi:10.1023/a:1014423925000

Cited by 37 publications

(2 citation statements)

References 29 publications

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“…Given the utility of gradient boosting in this setting, especially in providing better predictions for under-served populations, this paper recommends that this method be more prevalent in the education research community. Additionally, other methods should be examined such as artificial neural networks [67].…”

Section: Resultsmentioning

confidence: 99%

Predicting time to graduation at a large enrollment American university

et al. 2020

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The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto’s Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).

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Section: Resultsmentioning

confidence: 99%

Predicting time to graduation at a large enrollment American university

et al. 2020

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“…Higher education researchers began using supervised ML techniques to predict student outcomes in the early 2000s (e.g., Aulck et al, 2016;Delen, 2010Delen, , 2011González & DesJardins, 2002;Herzog, 2005;Nandeshwar et al, 2011;Raju & Schumacker, 2015). Despite the time invested in studying ML techniques, it is difficult to find consistent evidence regarding which algorithms yield better results.…”

Section: Algorithmsmentioning

confidence: 99%

How Accurately Can Short-Term Outcomes Approximate Long-Term Outcomes? Examining the Predictive Power of Early Momentum Metrics for Community College Credential Completion Using Machine Learning

Yanagiura

2023

Community College Review

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Objective: This study examines how accurately a small set of short-term academic indicators can approximate long-term outcomes of community college students so that decision-makers can take informed actions based on those indicators to evaluate the current progress of large-scale reform efforts on long-term outcomes, which in practice will not be observed until several years later. Method: Using transcript-level data of approximately 50,000 students at over 30 institutions in two states, I compare the out-of-sample predictive power of the early momentum metrics (EMMs), 13 short-term academic indicators suggested in the literature, to that of more complex, Machine Learning (ML)-based models that employ 497 predictors. Results: This study found that EMMs accurately predict credential completion for 75% to 77% of students in an out-of-sample dataset, with a predictive power largely comparable to that of ML-based models. This study also found similar results among the gender and race/ethnicity groups. However, the predictive power for certificate completion is lower than that for associate and bachelor’s degrees by 5 percentage points, implying that this set of EMMs are likely to be less relevant to certificate completion. Contribution: This study validates EMMs as informative predictors of credential completion, confirming that decision makers can use them to understand the probable long-term impact of current reforms on credential outcomes. However, room for continued research and refinement of EMMs remains, especially for certificate.

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Luan

Zhao

2006

New Drctns for Instit Rsrch

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Untitled

Cited by 37 publications

References 29 publications

Predicting time to graduation at a large enrollment American university

Predicting time to graduation at a large enrollment American university

How Accurately Can Short-Term Outcomes Approximate Long-Term Outcomes? Examining the Predictive Power of Early Momentum Metrics for Community College Credential Completion Using Machine Learning

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