Towards Deriving Programming Competencies from Student Errors

Berges, Marc; Striewe, Michael; Shah, Philipp; Goedicke, Michael; Hubwieser, Peter

doi:10.1109/latice.2016.6

Cited by 11 publications

(15 citation statements)

References 14 publications

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“…33 Chaweewan et al, 2018;Santana, Figueredo & Bittencourt, 2018;Ahmad et al, 2017;al-Rifaie, Yee-King & d'Inverno, 2017;Delev & Gjorgjevikj, 2017;Berges et al, 2016;Funabiki, Ishihara & Kao, 2016;Doshi, Christian & Trivedi, 2014;Chung & Hsiao, 2020;Effenberger, Pelánek & Čechák, 2020 Greater than 2 and less than 3…”

Section: Year-wise Publication Trendsmentioning

confidence: 99%

“…Types StudiesModelLiao et al, 2019;Bhatia, Kohli & Singh, 2018;Ninrutsirikun et al, 2020;Effenberger, Pelánek & Čechák, 2020;Iqbal Malik & Coldwell- Neilson, 2017;Ashenafi, Riccardi & Ronchetti, 2015; Carter, Hundhausen & Adesope, 2015 Framework Yeomans, Zschaler & Coate, 2019; Chaweewan et al, 2018; Omer, Farooq & Abid, 2020; Premchaiswadi, Porouhan & Premchaiswadi, 2018 Experiment McCall & Kölling, 2019; Ullah et al, 2019; Ureel & Wallace, 2019; Pereira et al, 2020; Esteero et al, 2018; Gomes & Correia, 2018;King, 2018;Lin et al, 2018;Turner, Pérez-Quiñones & Edwards, 2018;Ahadi, Hellas & Lister, 2017;Ahmad et al, 2017;Delev & Gjorgjevikj, 2017;Dorodchi, Dehbozorgi & Frevert, 2017;Echeverría et al, 2017;España-Boquera et al, 2017;Estey, Keuning & Coady, 2017;Chung & Hsiao, 2020;Fu et al, 2017;Hsiao, Huang & Murphy, 2017;Kumar, 2017;Seanosky et al, 2017;Berges et al…”

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confidence: 99%

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Introductory programming course: review and future implications

Omer

Farooq

Abid

2021

PeerJ Computer Science

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The introductory programming course (IPC) holds a special significance in computing disciplines as this course serves as a prerequisite for studying the higher level courses. Students generally face difficulties during their initial stages of learning how to program. Continuous efforts are being made to examine this course for identifying potential improvements. This article presents the review of the state-of-the-art research exploring various components of IPC by examining sixty-six articles published between 2014 and 2020 in well-reputed research venues. The results reveal that several useful methods have been proposed to support teaching and learning in IPC. Moreover, the research in IPC presented useful ways to conduct assessments, and also demonstrated different techniques to examine improvements in the IPC contents. In addition, a variety of tools are evaluated to support the related course processes. Apart from the aforementioned facets, this research explores other interesting dimensions of IPC, such as collaborative learning, cognitive assessments, and performance predictions. In addition to reviewing the recent advancements in IPC, this study proposes a new taxonomy of IPC research dimensions. Furthermore, based on the successful practices that are listed in the literature, some useful guidelines and advices for instructors have also been reported in this article. Lastly, this review presents some pertinent open research issues to highlight the future dimensions for IPC researchers.

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Section: Year-wise Publication Trendsmentioning

confidence: 99%

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confidence: 99%

Introductory programming course: review and future implications

Omer

Farooq

Abid

2021

PeerJ Computer Science

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“…• Legal issues, which are often interrelated with all other aspects [8] • Technological bases for electronic exams, e. g. [10], [12] • Automatic assessment of programming assignments, an issue to which a significant amount of effort is dedicated by [7], [13], [14] Berges et al analyze students' solutions of exercises, in order to detect missing competences [15]. To achieve this, they use an approach based on static and dynamic program analysis.…”

Section: Related Workmentioning

confidence: 99%

“…However, due to limitations of the current testing infrastructure, is does not allow to detect unrolled loops or unnecessary typecasts. Here, a more sophisticated code analysis infrastructure is required, that helps to do semantic checks as described by [15].…”

Section: Automated Assessmentmentioning

confidence: 99%

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Alignment of Teaching and Electronic Exams and Empirical Classification of Errors for an Introductory Programming Class

Böttcher

Thurner

Zehetmeier

2020

2020 IEEE 32nd Conference on Software Engineering Education and Training (CSEE&T)

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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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Towards Deriving Programming Competencies from Student Errors

Cited by 11 publications

References 14 publications

Introductory programming course: review and future implications

Introductory programming course: review and future implications

Alignment of Teaching and Electronic Exams and Empirical Classification of Errors for an Introductory Programming Class

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

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