Exploring student success factors in a remedial mathematics course

Abstract The current research explores links between university productivity and student success in Australia. Interviews were conducted with 15 stakeholders and experts on the topic of higher education productivity. The research uses qualitative methods to identify instances when participants discussed institutional productivity in conjunction with student success factors. Four common themes emerged that linked institutional productivity to student success: “Student experience and engagement,” “attrition, retention and progression,” “cross-subsidies,” and “teaching-research effort.” Findings reveal two feasible options for improving productivity estimation for the education function of universities. Findings also reveal leverage points for intervention to improve student success and productivity. The research highlights where mutual interests lie for managing resources and facilitating better student outcomes.

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Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors

Journal of Applied Research in Higher Education ◽

10.1108/jarhe-10-2017-0120 ◽

2019 ◽

Vol 11 (2) ◽

pp. 178-198 ◽

Cited By ~ 6

Author(s):

Bothaina A. Al-Sheeb ◽

A.M. Hamouda ◽

Galal M. Abdella

Keyword(s):

Student Success ◽

Student Performance ◽

Success Factors ◽

Comprehensive Approach ◽

First Year ◽

Valuable Insight ◽

Data Set ◽

Grade Point ◽

Content Type ◽

Second Year

Purpose The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction. Design/methodology/approach The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper. Findings The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter. Research limitations/implications First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study. Practical implications The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering. Originality/value This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.

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Assessing the Effectiveness of Quantway®: A Multilevel Model With Propensity Score Matching

Community College Review ◽

10.1177/0091552118771754 ◽

2018 ◽

Vol 46 (3) ◽

pp. 257-287 ◽

Cited By ~ 2

Author(s):

Hiroyuki Yamada ◽

Angel X. Bohannon ◽

Alicia Grunow ◽

Christopher A. Thorn

Keyword(s):

Propensity Score ◽

Student Success ◽

Propensity Score Matching ◽

Hierarchical Linear Modeling ◽

Developmental Mathematics ◽

College Level ◽

Linear Modeling ◽

Mathematics Courses ◽

Matched Comparison ◽

Mathematics Course

Objective: Quantway is a Carnegie Math Pathways initiative, which redesigns the content, pedagogy, and structure of traditional developmental mathematics courses to simultaneously tackle traditional barriers to student success and support a broad range of developmental students in achieving their mathematics potential. Specifically, Quantway is a quantitative reasoning sequence that is comprised of a single term accelerated developmental mathematics course called Quantway 1 and a college-level mathematics course called Quantway 2. This study assesses the effectiveness of the developmental mathematics course, Quantway 1, during its first six semesters of implementation. Method: We used a hierarchical linear modeling technique to conduct propensity score matching across 37 student characteristics to compare the course performance of Quantway 1 students with matched comparison students from traditional developmental mathematics courses. Results: Quantway 1 students demonstrated significantly higher odds of success in fulfilling developmental mathematics course requirements and enrolling in college mathematics courses in the following year than matched comparison students. In addition, Quantway 1 effects were positive across all sex and race/ethnicity subgroups as well as in nearly all classrooms and colleges. Contributions: This study provides robust evidence that Quantway 1 increases student success in fulfilling developmental mathematics requirements and advances equity in student outcomes. Implications of and future directions for the Pathways are discussed.

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The Short-Term Effectiveness of a Remedial Mathematics Course: Evidence from a UK University

SSRN Electronic Journal ◽

10.2139/ssrn.2010953 ◽

2012 ◽

Author(s):

Giorgio Di Pietro

Keyword(s):

Remedial Mathematics ◽

Short Term ◽

Mathematics Course

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Students’ Reactions to Reform Mathematics Pedagogy in a Postsecondary Remedial Mathematics Course

The Mathematics Enthusiast ◽

10.54870/1551-3440.1399 ◽

2017 ◽

Vol 14 (1-3) ◽

pp. 289-308

Author(s):

Luke Smith ◽

W. Gary Martin ◽

Anna Wan ◽

Gilbert Duenas

Keyword(s):

Remedial Mathematics ◽

Reform Mathematics ◽

Mathematics Pedagogy ◽

Mathematics Course

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School Effectiveness in Multilingual Education: A Review of Success Factors

Education Sciences ◽

10.3390/educsci11050193 ◽

2021 ◽

Vol 11 (5) ◽

pp. 193

Author(s):

Laura Kirss ◽

Ülle Säälik ◽

Äli Leijen ◽

Margus Pedaste

Keyword(s):

Education Research ◽

Student Success ◽

Success Factors ◽

School Effectiveness ◽

Critical Role ◽

School Level ◽

Current Evidence ◽

Individual Level ◽

Multilingual Education ◽

School Effectiveness Research

School effectiveness research (SER) and bi/multilingual education research have been largely developed as separate research paradigms. Hence, SER research does not facilitate clear conclusions on bi/multilingual (the term ‘multilingual’ is used henceforth) education and its effectiveness. Despite the intensification of multilingual education research over the last four decades, only a few authors and studies have focused on offering a compact overview of what factors need to be in place for the programs to be effective. These works are neither recent nor systematic. In this article, we aim to contribute to this research gap by systematically reviewing the research evidence on specific factors explaining multilingual student success in multilingual education programs. The findings of this systematic literature review integrate the current evidence regarding the critical factors conducive to student success in multilingual education. The results reveal that the reviewed studies mostly discussed school level factors and only occasionally talked about state/regional or individual level factors. We also underscore the critical role of leadership in making multilingual education successful. The implications of this review are twofold: by using a conceptual framework to discuss the success factors, the interdependence of the variables shaping multilingual education is highlighted, while the results collect the latest evidence for decision makers in multilingual education.

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