Utilizing Traditional Cognitive Measures of Academic Preparation to Predict First-Year Science, Technology, Engineering, and Mathematics (STEM) Majors' Success in Math and Science Courses

Andrews, Charles K

doi:10.25148/etd.fi14110703

Cited by 1 publication

(2 citation statements)

References 31 publications

(121 reference statements)

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“…Through a thorough examination of the literature related to predicting student academic performance in STEM majors, three streams of research can be found. Several studies relied on cognitive factors such as; high school exam scores (De Winter and Dodou, 2011), HSGPA and admission test scores (Jiang and Freeman, 2011; Andrews, 2014) and scores on standardized tests such as the ACT and the SAT (Kauffmann et al , 2007; French et al , 2005). Though high school grades and other admission tests might be good predictors for first-year GPA, Ackerman et al (2013) argued that a great deal of individual difference variance remains unaccounted for.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The majority of such studies have used the classical linear regression or other types of regression. These approaches include logistic regression (Lin and Imbrie, 2014), multiple linear regression (Ting and Man, 2001; Virtanen et al , 2013), hierarchical logistic regression (French et al , 2005; Andrews, 2014), hierarchical multiple regression (Whitaker, 2014) and multinomial logistic regression (Hall et al , 2015). Other statistical methods include path analysis (French et al , 2003) and correlational analysis (Bernadin and McKendrick, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Al-Sheeb

Abdella

2019

JARHE

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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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