An Analysis of Java Programming Behaviors, Affect, Perceptions, and Syntax Errors among Low-Achieving, Average, and High-Achieving Novice Programmers

Rodrigo, Ma. Mercedes T.; Andallaza, Thor Collin S.; Castro, Francisco Enrique Vicente; Armenta, Marc Lester V.; Dy, Thomas; Jadud, Matthew C.

doi:10.2190/ec.49.3.b

Cited by 14 publications

(12 citation statements)

References 38 publications

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“…Significant interaction was found between academic achievement and error detection, error correction attempt and completion of error correction supporting the previous studies in debugging (Bednarik, 2012;Fitzgerald et al, 2008;Lin et al, 2016;Rodrigo, Andallaza, Castro, Armenta, & Jadud, 2013) Similar to other studies' findings, student characteristics (expert/novice, experienced/inexperienced, successful/unsuccessful etc.) have an essential role in debugging.…”

Section: Discussionsupporting

confidence: 88%

The Investigation of Novice Programmers’ Debugging Behaviors to Inform Intelligent E-Learning Environments: A Case Study

Türkmen¹,

Caner²

2020

Turkish Online Journal of Distance Education

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This study aims to provide a comprehensive and in-depth investigation of the debugging process in programming teaching in terms of cognitive and metacognitive aspects, based on programming students who demonstrate low, medium, and high programming performance and to propose instructional strategies for scaffolding novice learners in an effective way. Data were collected from 41 novice programming students from C++ and Python programming language courses of the same instructor in the scope of case study methodology, and data instruments included paper-based programming questions. The questions were framed under three categories as questions' difficulty levels (low, moderate and high), error types (syntax and logic), and question types (if-else and while). As having three categories, a total of 12 different data (3x2x2) were taken from each student, which means 492 data rows were evaluated in the study. Chi-square test results revealed that while error detection and correction are significantly high in low difficulty level questions, error detection and error correction attempts for logic errors were substantially higher compared to syntax errors. Further analysis conducted for paper-based markings that were used by students throughout their error detection, correction, and completion attempts. Chi-square test results revealed significant relationships between marking availability and error types, as well as difficulty levels. Results were discussed for both traditional learning and e-learning environments in terms of what kind of educational implications and strategies can be outlined by data for increasing the effectiveness of programming education for novice learners.

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Section: Discussionsupporting

confidence: 88%

The Investigation of Novice Programmers’ Debugging Behaviors to Inform Intelligent E-Learning Environments: A Case Study

Türkmen¹,

Caner²

2020

Turkish Online Journal of Distance Education

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“…Replications of this work similarly correlated the EQ from a well-defined population to traditional measures [16,17,18] as well as other observational data (eg. student affect) [13,14].…”

Section: Underlying Assumptionsmentioning

confidence: 99%

Aggregate Compilation Behavior

Jadud

Dorn

2015

Proceedings of the Eleventh Annual International Conference on International Computing Education Research

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The error quotient (EQ) was first reported in 2006 as a behavioral measure of novice programmers. The EQ scores how well students deal with correcting syntax errors (or not) in their programs. The original studies were carried out on data collected using BlueJ, a pedagogic Java programming environment; today, newly installed instances of BlueJ capture data similar to these early studies automatically, meaning data regarding nearly 2 million programmers is captured every year by the Blackbox project. In this paper, we apply Jadud's original error quotient algorithm to this new, massive data set, and discuss our results and analysis in light of related work. Further, we consider the implications of our findings for researchers and educators in applying the EQ to 27,698 users in 10 different countries during the fall term of 2013.

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“…Programming is known for its complexity and difficulty, and thus many programming students have difficulties with acquiring necessary programming competencies (Yukselturk & Altiok, 2017). Insomuch that, students with high, moderate, and low programming success tend to make similar mistakes (Rodrigo, Andallaza, Castro, Armenta, Dy, & Jadud, 2013) and students who are competent in other areas may be inadequate for success in programming (Byrne & Lyons, 2001). Because, computer programming requires abstract thinking, logical thinking, and program solving skills (Lin, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the department variable, the personal traits of programming (e.g., self-efficacy, attitude) can be differentiated (de Raadt et al, 2005;Askar & Davenport, 2009;Korkmaz & Altun, 2013;Sebetci & Aksu, 2014;Yagci, 2016). Similarly, the effort (Cetin, 2016;Hawi, 2010;Rodrigo, Andallaza, Castro, Armenta, Dy, & Jadud, 2013) and past experience for programming (Askar & Davenport, 2009;Bergin & Reilly, 2005;Lin, 2016;Sharma, & Shen, 2018;Wilson & Shrock, 2001) can vary in terms of personal traits. Thus, within the scope of this study, department, weekly study hour (time), and programing experience variables appear to be the other important independent variables.…”

Section: Introductionmentioning

confidence: 99%

Examination of Undergraduate and Associate Degree Students’ Computer Programming Attitude and Self-Efficacy According to Thinking Style, Gender and Experience

Günbatar¹

2018

CONT ED TECHNOLOGY

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This study aimed to determine undergraduate and associate degree students' computer programming attitude and self-efficacy levels, and compare them according to thinking style, gender, department, weekly study time, and programming experience variables. The study employed the correlational research model. The researcher attempted to reach all associate and undergraduate students who had received the computer programming course at a state university. The computer programming self-efficacy scale, the computer programming attitude scale, and the holistic and analytic thinking in problem-solving scale were used to collect research data. Results suggested that the participants with different thinking styles showed significant differences regarding programming attitude and programming self-efficacy. Programming attitude and thinking style were significant predictors of programming self-efficacy. No difference was observed between genders in terms of the common effect and the partial effect of programming attitude and programming self-efficacy. However; differences were observed between participants from different departments and with different weekly study time. There was also a significant difference between the participants with different programming experience levels in terms of the common effect of programming attitude and self-efficacy, whereas no difference was found in terms of attitude alone.

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An Analysis of Java Programming Behaviors, Affect, Perceptions, and Syntax Errors among Low-Achieving, Average, and High-Achieving Novice Programmers

Cited by 14 publications

References 38 publications

The Investigation of Novice Programmers’ Debugging Behaviors to Inform Intelligent E-Learning Environments: A Case Study

The Investigation of Novice Programmers’ Debugging Behaviors to Inform Intelligent E-Learning Environments: A Case Study

Aggregate Compilation Behavior

Examination of Undergraduate and Associate Degree Students’ Computer Programming Attitude and Self-Efficacy According to Thinking Style, Gender and Experience

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