Predicting success of a beginning computer course using logistic regression (abstract only)

Chowdhury, Ashraful A.; Nelson, C. Van; Fuelling, Clinton P.; McCormick, Roy L.

doi:10.1145/322917.323110

Cited by 7 publications

(7 citation statements)

References 3 publications

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“…Could these problems experienced in understanding worded problems be directly related to language performance? According to [9] and [10] performance in mathematics courses is a good indicator of performance in computer science. However, according to [11] Mathematics may often serve a symbolic rather than practical purpose, reference is made to researchers who questions the effectiveness of using mathematics as an entry requirement for computer science.…”

Section: Mathematics and Englishmentioning

confidence: 99%

Predicting Academic Performance

Golding

Donaldson

2006

Proceedings. Frontiers in Education. 36th Annual Conference

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The importance placed on matriculation increases at each level of academia, with the greatest significance placed at the tertiary level. The task of standardizing entry-level requirements at the tertiary level has lead to the implementation of assessments such as SAT GMAT and GRE. Prior research conducted at the University of Technology, Jamaica (UTECH) indicated that the task of finding effective predictors of academic performance remains incomplete. This study examines the relationship between students' overall academic performance (GPA) and matriculation requirements performance in first year courses in the Bachelor of Science and Information Technology (BSCIT) program at UTECH. The study evaluates undergraduate students that completed the BSCIT program in 2005. The files for all BSCIT undergraduate students of 2005 were surveyed and specific data was collected. The findings pointed out that performance in first year gateway courses had some level of significance in predicting performance. The findings from this study will be instrumental in restructuring the admissions policy for the program.

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Section: Mathematics and Englishmentioning

confidence: 99%

Predicting Academic Performance

Golding

Donaldson

2006

Proceedings. Frontiers in Education. 36th Annual Conference

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“…An interest in factors which could predict success in a CS1 course was increased in the 1980s due to the rapid growth in popularity of first year programming courses, the varying level of student ability, and the consequent demand placed on faculty resources (Leeper et al 1982;Barker et al 1983;Chowdhury et al 1987). Previously, research interest had largely been focused on occupational aptitude teststhe selection and evaluation of personnel most likely to have a successful and fulfilling career in the new computing industry (Mayer et al 1968;Cross 1970;Wolfe 1971).…”

Section: Introductionmentioning

confidence: 99%

Interacting factors that predict success and failure in a CS1 course

Rountree¹,

Rountree²,

Robins³

et al. 2004

Working Group Reports From ITiCSE on Innovation and Technology in Computer Science Education

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The factors that contribute to success and failure in introductory programming courses continue to be a topic of lively debate, with recent conference panels and papers devoted to the subject (e.g. Rountree et al. 2004, Ventura et al., 2004, Gal-Ezer et al., 2003. Most work in this area has concentrated on the ability of single factors (e.g. gender, math background, etc.) to predict success, with the exception of Wilson et al. (2001), which used a general linear model to gauge the effect of combined factors. In Rountree et al. (2002) we presented the results of a survey of our introductory programming class that considered factors (such as student expectations of success, among other things) in isolation. In this paper, we reassess the data from that survey by using a decision tree classifier to identify combinations of factors that interact to predict success or failure more strongly than single, isolated factors.

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“…One factor that will be considered in this study is prior knowledge in information technology both at the ordinary and advanced level. Several of the reviewed studies showed that success in Mathematics was a good predictor of success in computer science [9], [10], [3], [11], [12]. In fact this is a generally accepted notion, and some colleges have specific mathematics requirements for those students doing computer science.…”

Section: Review Of Literaturementioning

confidence: 99%

“…"There is a belief that the concepts which a student has to comprehend in order to master mathematics problems are similar to those for programming" [11]. In [10] and [13] specific reference is made to calculus. They indicate that the best predictor in their study, for success in computer science is success in a calculus course.…”

Section: Review Of Literaturementioning

confidence: 99%

Predicting Academic Performance in the School of Computing & Information Technology (SCIT)

Golding

McNamarah

Proceedings Frontiers in Education 35th Annual Conference

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What determines academic performance? Prior research shows that standardized measures such as aptitude (e.g. SAT scores), prior academic performance, effort and motivation explain a significant portion of the variation in class performance. When universities in the United States determine which students to admit, typical criteria include SAT, ACT or other achievement scores and high school GPA. At the University of Technology, Jamaica in the School of Computing & Information Technology, the main admission criteria are appropriate score in an aptitude test and passes in at least five Caribbean Examination Council subjects including Mathematics and English. This study examines the relationship between students' demographic attributes, qualification on entry, aptitude test scores, performance in first year courses and their overall performance in the program. The study has implications for the School's admission policy. The results should help us to identify an optimal set of admission indicators, which have the potential of predicting students' performance.

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Predicting success of a beginning computer course using logistic regression (abstract only)

Cited by 7 publications

References 3 publications

Predicting Academic Performance

Predicting Academic Performance

Interacting factors that predict success and failure in a CS1 course

Predicting Academic Performance in the School of Computing & Information Technology (SCIT)

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Predicting success of a beginning computer course using logistic regression (abstract only)

Cited by 7 publications

References 3 publications

Predicting Academic Performance

Predicting Academic Performance

Interacting factors that predict success and failure in a CS1 course

Predicting Academic Performance in the School of Computing &amp; Information Technology (SCIT)

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Predicting Academic Performance in the School of Computing & Information Technology (SCIT)