A First Look at Novice Compilation Behaviour Using BlueJ

Jadud, Matthew C.

doi:10.1080/08993400500056530

Cited by 115 publications

(119 citation statements)

References 15 publications

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“…This strikes a good balance between removing compiler IDs with very low activity and retaining those which are the result of a Decaf reinstall, but which generated a representative and useful amount of data. Other studies such as Jadud (2006), have filtered their data in similar ways.…”

Section: Results and Analysismentioning

confidence: 99%

“…Jadud (2006) investigated the link between student performance and the error quotient (EQ), a metric influenced heavily by repeated errors. Although some correlations were found to exist they were weak, and the overall conclusion was that EQ and academic performance are related, but exactly how remained to be seen.…”

Section: Compiler Error Messagesmentioning

confidence: 99%

“…• Total number of errors in each group • Number of errors per student, including those generating specific CEMs • Number of repeated errors, Jadud's error quotient (Jadud, 2005(Jadud, , 2006, and the repeated error density (Becker, 2016b) The aim of this research is to discover if enhancing compiler error messages is effective in helping students learn to program. We seek to answer the following questions:…”

Section: Introductionmentioning

confidence: 99%

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Effective compiler error message enhancement for novice programming students

Becker

Glanville

Iwashima

et al. 2016

Computer Science Education

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Programming is an essential skill that all computing students must master. However programming can be difficult to learn. Compiler error messages are crucial for correcting errors, but are often difficult to understand and pose a barrier to progress for many novices. High frequencies of errors, particularly repeated errors, have been shown to be indicators of students who are struggling with learning to program. This study involves a custom IDE that enhances Java compiler error messages, intended to be more useful to novices than those supplied by the compiler. The effectiveness of this approach was tested in an empirical control/intervention study of approximately 200 students generating almost 50,000 errors. The design allows for direct comparisons between enhanced and non-enhanced error messages. Results show that the intervention group experienced reductions in the number of overall errors, errors per student, and several repeated error metrics. This work is important for two reasons. First, the effects of error message enhancement have been recently debated in the literature. This study provides substantial evidence that it can be effective. Second, these results should be generalizable at least in part, to other programming languages, students and institutions, as we show that the control group of this study is comparable to several others using Java and other languages.

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Section: Results and Analysismentioning

confidence: 99%

Section: Compiler Error Messagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effective compiler error message enhancement for novice programming students

Becker

Glanville

Iwashima

et al. 2016

Computer Science Education

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“…The long history of work on syntax error messages is often motivated by the need to better serve novice programmers (Tabanao et al, 2008;Jadud, 2005;Tabanao et al, 2011;Jadud, 2006;Jackson et al, 2005;Garner et al, 2005;McIver, 2000;Kummerfeld and Kay, 2003;Hristova et al, 2003). More recently, Denny et al (Denny et al, 2012) categorized common syntax errors that novices make by the error messages the compiler generates and how long it takes for the programmer to fix them.…”

Section: Prior Workmentioning

confidence: 99%

“…Plain text is a simple, flexible medium that has been preferred by programming languages for decades. Yet, plain text can be a major hurdle for novices learning how to code (Tabanao et al, 2008;Jadud, 2005;Tabanao et al, 2011). Not only do novices have to learn the semantics of a programming language, but they also have to learn how to place arcane symbols in the right order for the computer to understand their intent.…”

Section: Introductionmentioning

confidence: 99%

Finding and correcting syntax errors using recurrent neural networks

Santos

Campbell

Hindle

et al. 2017

Preprint

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Minor syntax errors are made by novice and experienced programmers alike; however, novice programmers lack the years of intuition that help them resolve these tiny errors. Standard LR parsers typically resolve syntax errors and their precise location poorly. We propose a methodology that helps locate where syntax errors occur, but also suggests possible changes to the token stream that can fix the error identified. This methodology finds syntax errors by checking if two language models "agree" on each token. If the models disagree, it indicates a possible syntax error; the methodology tries to suggest a fix by finding an alternative token sequence obtained from the models. We trained two LSTM (Long short-term memory) language models on a large corpus of JavaScript code collected from GitHub. The dual LSTM neural network model predicts the correct location of the syntax error 54.74% in its top 4 suggestions and produces an exact fix up to 35.50% of the time. The results show that this tool and methodology can locate and suggest corrections for syntax errors. Our methodology is of practical use to all programmers, but will be especially useful to novices frustrated with incomprehensible syntax errors.

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Utilizing programming traces to explore and model the dimensions of novices' code‐writing skill

Zhang

Paquette

Pinto

et al. 2023

Comp Applic In Engineering

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Studies have found that most novice programmers have low proficiency in writing code. However, it is unclear what subskills compose code writing and which subskills novice programmers struggle with. This study utilizes programming traces to identify latent subskills that constitute code writing so that teachers can offer specific instruction on the weak subskills. Data were collected from an undergraduate course teaching introductory computer science in Java. Six hundred and fourteen students made submissions to homework programming questions in a web‐based learning system. Based on the submission traces, we computed 11 features related to correctness and time students spent on their submissions. We conducted an exploratory factor analysis on two‐thirds of students selected randomly and identified four factors. The first factor, code style proficiency, was mainly related to code style errors. The second, syntactic proficiency, concerned compiler errors. The third is semantic proficiency, which concerns runtime and logic errors. The fourth, syntactic debugging proficiency, concerned the success rate and time required for fixing compiler and code style errors. A confirmatory factor analysis conducted on the remaining one‐third of the data supported the four‐factor structure. The factor model showed measurement invariance between the data set where the model was developed and two new datasets, one from the same sample but collected at a different time point and another from a different sample and context (onsite course vs. online course). The factors were related to prior programming abilities, programming language familiarity, and future exam performance. These associations provided validity evidence for the factor model.

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A First Look at Novice Compilation Behaviour Using BlueJ

Cited by 115 publications

References 15 publications

Effective compiler error message enhancement for novice programming students

Effective compiler error message enhancement for novice programming students

Finding and correcting syntax errors using recurrent neural networks

Utilizing programming traces to explore and model the dimensions of novices' code‐writing skill

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