Identifying predictors of success for an objects-first CS1

Ventura, Philip R.

doi:10.1080/08993400500224419

Cited by 116 publications

(80 citation statements)

References 26 publications

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“…A t-test revealed significant differences in the performance those students who had prior programming experience prior to enrolling on the course (n = 15, M = 71.76, SD = 10.47) and those students who did not (n = 24, M = 64.34, SD = 10.70), (t(37) = 2.12, p < .05). These findings are consistent with previous research such as [25], but contradict research such as [2,18]. Further analysis showed more interesting relations between prior experience and performance; however none of the following measures were significant, (p > .05).…”

Section: Previous Programming Experiencesupporting

confidence: 77%

“…To date only 3 studies [8,11,18] have explored relationships between attributions and performance. Significant (p < .05), but weak, correlations were found between performance and attributions of success to task difficulty (r = -.10), and attributions to effort (r = .07).…”

Section: Attributions Of Successmentioning

confidence: 99%

“…Although early work focused upon using standardized aptitude testing [5] over the past fifty years, various predictors have been proposed. These include a range of demographic, psychological, academic, and cognitive factors, such as: previous programming experience [2,18,25], math background [17,24], science background [2,6], behavioral traits [2], self-esteem [4], learning styles [7,9,10], learning strategies [3], and attributions of success [8,11,18].…”

Section: Introductionmentioning

confidence: 99%

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Watson

Godwin

2014

Proceedings of the 45th ACM Technical Symposium on Computer Science Education

150

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACTResearch over the past fifty years into predictors of programming performance has yielded little improvement in the identification of at-risk students. This is possibly because research to date is based upon using static tests, which fail to reflect changes in a student's learning progress over time. In this paper, the effectiveness of 38 traditional predictors of programming performance are compared to 12 new data-driven predictors, that are based upon analyzing directly logged data, describing the programming behavior of students. Whilst few strong correlations were found between the traditional predictors and performance, an abundance of strong significant correlations based upon programming behavior were found. A model based upon two of these metrics (Watwin score and percentage of lab time spent resolving errors) could explain 56.3% of the variance in coursework results. The implication of this study is that a student's programming behavior is one of the strongest indicators of their performance, and future work should continue to explore such predictors in different teaching contexts.

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Section: Previous Programming Experiencesupporting

confidence: 77%

Section: Attributions Of Successmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Watson

Godwin

2014

Proceedings of the 45th ACM Technical Symposium on Computer Science Education

150

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“…Even in the area of introductory object-oriented programming there has been research trying to establish general factors to predict success or failure of particular students. Especially the work of Phil Venture [27] focuses on a systematic evaluation of hypothesis related to the factors for success of an introductory programming course using an objects-first approach [14]. The results are documented in [27,28].…”

Section: Introductionmentioning

confidence: 99%

An investigation of potential success factors for an introductory model-driven programming course

Bennedsen

Caspersen

2005

Proceedings of the 2005 International Workshop on Computing Education Research - ICER '05

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In order to improve the course design of a CS1 model-driven programming course we study potential indicators of success for such a course. We explain our specific interpretation of objects-first.Of eight potential indicators of success, we have found only two to be significant at a 95% confidence interval: math grade from high school and course work. The two significant indicators explain 24.2% of the variation of the exam grade. The result concerning math grade contradicts earlier findings.We discuss four aspects of our research: the explanation power of the potential success indicators, the impact of our findings on teaching, limits of what to conclude from the available data, and the variety of the notion "objects-first".Because of the variety of interpretations of "objects-first", the present research is necessary as a supplement to earlier research in order to make generalizable results on the success factors for objectsfirst programming.

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“…In addition, Rountree et al (2002) found that the grade a student expected to achieve in an introductory module was the most important indicator of performance. Ventura (2005) examined predictors of a graphical design-centric objects-first Java course and found that student effort (as measured by the number of hours spent using the labs) and comfort level were the strongest (positive) predictors of success. Newsted (1975) in a study of introductory Fortran, students found that two of the most important predictors of performance were perceived ability and time spent working on the course and working with other students.…”

Section: Review Of Literaturementioning

confidence: 99%

Predicting introductory programming performance: A multi-institutional multivariate study

Bergin

Reilly

2006

Computer Science Education

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A model for predicting student performance on introductory programming modules is presented. The model uses attributes identified in a study carried out at four third-level institutions in the Republic of Ireland. Four instruments were used to collect the data and over 25 attributes were examined. A data reduction technique was applied and a logistic regression model using 10-fold stratified cross validation was developed. The model used three attributes: Leaving Certificate Mathematics result (final mathematics examination at second level), number of hours playing computer games while taking the module and programming self-esteem. Prediction success was significant with 80% of students correctly classified. The model also works well on a per-institution level. A discussion on the implications of the model is provided and future work is outlined.

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Identifying predictors of success for an objects-first CS1

Cited by 116 publications

References 26 publications

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An investigation of potential success factors for an introductory model-driven programming course

Predicting introductory programming performance: A multi-institutional multivariate study

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