An exploration of error quotient in multiple contexts

Petersen, Andrew; Spacco, Jaime; Vihavainen, Arto

doi:10.1145/2828959.2828966

Cited by 39 publications

(34 citation statements)

References 16 publications

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“…Segundo [Peterson et al 2015], métricas de software 1 baseadas no progresso do estudante, código ou comportamento são consideradas um caminho para revolucionar a educação. Mesmo uma heurística simples como a recorrência de um mesmo erro subsequente de compilação tem o potencial para identificar estudantes com dificuldade.…”

Section: Trabalhos Relacionadosunclassified

Predição de Zona de Aprendizagem de Alunos de Introdução à Programação em Ambientes de Correção Automática de Código

Dwan

Oliveira

Fernandes

2017

Anais Do XXVIII Simpósio Brasileiro De Informática Na Educação (SBIE 2017)

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Abstract. The present work proposes and validates a method to infer the learning zone of CS1 students in an online judge. To do so, the student's grade will be predicted, using a programming profile based on the data left by them as they solve exercises in that system. Students who scored lower than 5 were classified in a difficulty zone, otherwise in an expertise zone. Machine learning algorithms were used to make the prediction. The proposed predictive model obtained an accuracy of 78.3 % at the first two weeks of class, which overcomes research results that were conducted in similar scenarios.

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Section: Trabalhos Relacionadosunclassified

Predição de Zona de Aprendizagem de Alunos de Introdução à Programação em Ambientes de Correção Automática de Código

Dwan

Oliveira

Fernandes

2017

Anais Do XXVIII Simpósio Brasileiro De Informática Na Educação (SBIE 2017)

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“…In addition, repeating errors is common, and has been observed and studied for at least the last four decades, and across languages such as: assembly [8], C [15,19], Java [3,11,15], LISP [14], Pascal [13] and Python [15]. In [10] it was found that when a student encounters certain high frequency errors in Java, there were often double-digit chances that the next error was the same error.…”

Section: Related Workmentioning

confidence: 99%

“…In [15] the authors explored both versions of the EQ with data sets from four CS1 contexts involving different working practices, student backgrounds and languages: C, Python and Java. Using linear models the authors also explored a range of parameter values.…”

Section: Related Workmentioning

confidence: 99%

“…There are also few datadriven metrics to measure programming performance [15], and fewer for measuring repeated errors. This is despite the fact that the number and frequency of repeated errors has been cited as the best indicator of how well a programming student is progressing [11].…”

Section: Introductionmentioning

confidence: 99%

“…We also demonstrate that this metric is less context dependent, useful for short sequences, and is significantly reduced by an editor that has previously been shown to result in significantly fewer compiler errors (including repeated errors). Being less context dependent is seen as important since being able to generalize on study results is a fundamental requirement for putting them into effective use [15]. The authors are not aware of any other study empirically comparing the number/frequency of repeated errors between control and intervention groups.…”

Section: Introductionmentioning

confidence: 99%

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A New Metric to Quantify Repeated Compiler Errors for Novice Programmers

Becker

2016

Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education

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Encountering the same compiler error repeatedly, particularly several times consecutively, has been cited as a strong indicator that a student is struggling with important programming concepts. Despite this, there are relatively few studies which investigate repeated errors in isolation or in much depth. There are also few data-driven metrics for measuring programming performance, and fewer for measuring repeated errors. This paper makes two contributions. First we introduce a new metric to quantify repeated errors, the repeated error density (RED). We compare this to Jadud's Error Quotient (EQ), the most studied metric, and show that RED has advantages over EQ including being less context dependent, and being useful for short sessions. This allows us to answer two questions posited by Jadud in 2006 that have until now been unanswered. Second, we compare the EQ and RED scores using data from an empirical control/intervention group study involving an editor which enhances compiler error messages. This intervention group has been previously shown to have a reduced overall number of student errors, number of errors per student, and number of repeated student errors per compiler error message. In this research we find a reduction in EQ, providing further evidence that error message enhancement has positive effects. In addition we find a significant reduction in RED providing evidence that this metric is valid.

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Collection and Analysis of Source Code Metrics for Composition of Programming Learning Profiles

Santos

Fonseca

2019

2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)

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An exploration of error quotient in multiple contexts

Cited by 39 publications

References 16 publications

Predição de Zona de Aprendizagem de Alunos de Introdução à Programação em Ambientes de Correção Automática de Código

Predição de Zona de Aprendizagem de Alunos de Introdução à Programação em Ambientes de Correção Automática de Código

A New Metric to Quantify Repeated Compiler Errors for Novice Programmers

Collection and Analysis of Source Code Metrics for Composition of Programming Learning Profiles

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