Doing it twice, getting it right? The effects of grade retention and course repetition in higher education

Tafreschi, Darjusch; Thiemann, Petra

doi:10.1016/j.econedurev.2016.10.003

Cited by 28 publications

(19 citation statements)

References 25 publications

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“…Even the group with the weakest 'gain' in performance (Y4 resitters into Y5), still do actually improve in their Year 5 performance on average compared to their original Year 4 performance even after taking regression to the mean into account. This triangulates well with work from the wider higher education literature exploring the impact of grade retention (Tafreschi and Thiemann, 2016) which finds that under such policies students generally boost their performance in subsequent years. Further work is underway to delineate the effects of remediation and the impact of undertaking a sequential (rather than testremediate-retest) model on students' self-regulated learning behaviours and resilient mindsets (Yeager and Dweck, 2012).…”

Section: Discussionsupporting

confidence: 78%

“…Students who do not meet the required standard on the full sequence in either Year 4 or Year 5 are required to repeat the full year, followed by resit assessment in the same sequential format, taken alongside their new peer group. This model uses the academic principle of grade retention, best described as the mandatory repetition of study/assessment as a result of insufficient performance (Tafreschi and Thiemann, 2016). The broader literature surrounding grade retention shows mixed effects (positive effects in primary education but not in high school) but has recently been associated with sustained improvements in Grade Point Average (GPA) in a major study tracking repeating students at a European University (Jacob and Lefgren, 2004;Jacob and Lefgren, 2009;Tafreschi and Thiemann, 2016).…”

Section: Table 1 Herementioning

confidence: 99%

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The benefits of sequential testing: Improved diagnostic accuracy and better outcomes for failing students

2017

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Section: Discussionsupporting

confidence: 78%

Section: Table 1 Herementioning

confidence: 99%

The benefits of sequential testing: Improved diagnostic accuracy and better outcomes for failing students

2017

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“…Conversely, academic momentum is hindered when a student does not successfully complete a course (Adelman, 2005;2006). Numerous studies have examined how course grades and pass/failure rates in specific courses influence the likelihood of degree attainment (Bailey, Jeong, Cho, 2010;Tafreschi & Thiemann, 2016;Zeidenberg, Jenkins, & Scott, 2012). However, there is another common course outcome that remains understudied in the literature: withdrawing from the course.…”

Section: Introductionmentioning

confidence: 99%

Giving Up on a Course: An Analysis of Course Dropping Behaviors Among Community College Students

et al. 2018

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Excessive course dropping is costly to students and institutions. Using longitudinal transcript data, this study investigated course withdrawal patterns among 5900 students at a large, racially/ethnically diverse community college district in Texas. Two-thirds of the students dropped at least one course, and 13.5% of the total course enrollments resulted in withdrawal. Course withdrawal rates were significantly higher among students who were: male, African American, age 20-24, GED holders, academically underprepared, enrolled part-time, and had a cumulative college GPA of less than 2.0. Science, mathematics, and writing courses had high drop rates, as did Second Start and fully online courses. Dropping 20% or more of attempted courses was associated with 44% lower odds of a successful enrollment outcome. We hypothesize that community college students are rational, act in their own selfinterest, and perform a cost-benefit analysis with the knowledge they possess when deciding whether to drop a particular course. However, students often overuse or misuse the course withdrawal function. Despite acting in self-interest at the moment, the decision to drop a course is often not in students' longterm best interest, and excessive course dropping can resemble a 'cooling out' mechanism by which students' reduce their aspirations toward degree completion. Recognizing community college structures and rules (e.g., withdrawal procedures, tuition refund policy) influence course dropping decisions, we describe academic policies and classroom practices that can help reduce course withdrawals. We believe that reducing course attrition is a foundational, yet often overlooked, mechanism for reducing community college attrition.

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“…Ref. [42] noted that students who retook classes is likely to drop out. Meanwhile, [43] found that students who repeated a course in economics attained lower course grades relative to their peers.…”

Section: Case Studymentioning

confidence: 99%

A Rule Induction Framework on the Effect of ‘Negative’ Attributes to Academic Performance

Gue

Sy²,

Nuñez³

et al. 2021

Int. J. Emerg. Technol. Learn.

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Attaining high retention rates among engineering institutions is a predominant is-sue. A significant portion of engineering students face challenges of retention. Academic advising was implemented to resolve the issue. Decision support sys-tems were developed to support the endeavor. Machine learning have been inte-grated among such systems in predicting student performance accurately. Most works, however, rely on a black box model approach. Rule induction generates simpler if-then rules, exhibiting clearer understanding. As most research works considered attributes for positive academic performance, there is the need to con-sider ‘negative’ attributes. ‘Negative’ attributes are critical indicators to possibility of failure. This work applied rule induction techniques for course grade predic-tion using ‘negative’ attributes. The dataset is the academic performance of 48 mechanical engineering students taking a machine design course. Students’ at-tributes on workload, course repetition, and incurred absences are the predictors. This work implemented two rule induction techniques, rough set theory (RST) and adaptive neuro fuzzy inference system (FIS). Both models attained a classifi-cation accuracy of 70.83% with better performance for course grades of ‘Pass’ and ‘High’. RST generated 16 crisp rules while ANFIS generated 27 fuzzy rules, yielding significant insights. Results of this study can be used for comparative analysis of student traits between institutions. The illustrated framework can be used in formulating linguistic rules of other institutions.

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Doing it twice, getting it right? The effects of grade retention and course repetition in higher education

Cited by 28 publications

References 25 publications

The benefits of sequential testing: Improved diagnostic accuracy and better outcomes for failing students

The benefits of sequential testing: Improved diagnostic accuracy and better outcomes for failing students

Giving Up on a Course: An Analysis of Course Dropping Behaviors Among Community College Students

A Rule Induction Framework on the Effect of ‘Negative’ Attributes to Academic Performance

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