Mining Code Submissions to Elucidate Disengagement in a Computer Science MOOC

Vinker, Efrat; Rubinstein, Amir

doi:10.1145/3506860.3506877

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…Twelve publications focused on learners disengaged and at risk of dropping out. Vinker and Rubinstein (2022) suggested visualizations of learners' submission trajectories to reveal disengaged learners and thus, alert instructors. Four publications specified their target cohort.…”

Section: Resultsmentioning

confidence: 99%

“…Dashboards were the main means of visual support (Eradze & Tammets, 2017; Konert et al, 2016; Rohloff et al, 2019; Ruipérez‐Valiente, Muñoz‐Merino, Gascon‐Pinedo & Kloos (2017); Ruipérez‐Valiente, Muñoz‐Merino, Pijeira Díaz, et al, 2017; Ruiz et al, 2014; Smith, 2015; Teusner et al, 2018; Yu et al, 2021). Four studies proposed different kinds of visualizations (Klusener & Fortenbacher, 2015; Sharma et al, 2016, 2020; Vinker & Rubinstein, 2022) for increasing awareness, motivating the learners and improving the learning experience. Klusener and Fortenbacher (2015) reported the use of scatterplots and Sankey diagrams for instructors' awareness and stimulation of engagement.…”

Section: Resultsmentioning

confidence: 99%

“…Yu et al, 2021). Four studies proposed different kinds of visualizations (Klusener & Fortenbacher, 2015;Sharma et al, 2016Vinker & Rubinstein, 2022) for increasing awareness, motivating the learners and improving the learning experience. Klusener and Fortenbacher (2015) reported the use of scatterplots and Sankey diagrams for instructors' awareness and stimulation of engagement.…”

Section: Type Of Contributionmentioning

confidence: 99%

“…Among them, two studies propose computer agents to deliver feedback, while the conditions that trigger the feedback are decided by the course instructors(Reza et al, 2021;Yılmaz et al, 2021).Attending I.Q.2.3, 22 publications focused on shaping feedback interventions for all participants without reporting a specific cohort as a target of the designed interventions. Twelve publications focused on learners disengaged and at risk of dropping out Vinker and Rubinstein (2022). suggested visualizations of learners' submission trajectories to reveal disengaged learners and thus, alert instructors.…”

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confidence: 99%

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Delving into instructor‐led feedback interventions informed by learning analytics in massive open online courses

Topali

Chounta

Martínez‐Monés

et al. 2023

Computer Assisted Learning

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Background: Providing feedback in massive open online courses (MOOCs) is challenging due to the massiveness and heterogeneity of learners' population. Learning analytics (LA) solutions aim at scaling up feedback interventions and supporting instructors in this endeavour. Paper Objectives: This paper focuses on instructor-led feedback mediated by LA tools in MOOCs. Our goal is to answer how, to what extent data-driven feedback is provided to learners, and what its impact is.Methods: We conducted a systematic literature review on the state-of-the-art LAinformed instructor-led feedback in MOOCs. From a pool of 227 publications, we selected 38 articles that address the topic of LA-informed feedback in MOOCs mediated by instructors. We applied etic content analysis to the collected data. Results and Conclusions:The results revealed a lack of empirical studies exploring LA to deliver feedback, and limited attention on pedagogy to inform feedback practices. Our findings suggest the need for systematization and evaluation of feedback. Additionally, there is a need for conceptual tools to guide instructors' in the design of LA-based feedback.Takeaways: We point out the need for systematization and evaluation of feedback. We envision that this research can support the design of LA-based feedback, thus contributing to bridge the gap between pedagogy and data-driven practice in MOOCs.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Type Of Contributionmentioning

confidence: 99%

mentioning

confidence: 99%

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Delving into instructor‐led feedback interventions informed by learning analytics in massive open online courses

Topali

Chounta

Martínez‐Monés

et al. 2023

Computer Assisted Learning

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show abstract

“…That is, they capture the process through which students develop the final solution [64]. Thus, programming trace analysis can contribute to the understanding of programming behaviors and learning and has attracted increasing interest [7,9,30,64,65]. Nevertheless, there is limited work on linking programming trace analysis and novices' codewriting skills.…”

Section: Introductionmentioning

confidence: 99%

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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Exploring the Connections Between the Use of an Automated Feedback System and Learning Behavior in a MOOC for Programming

Gabbay

Cohen²

2022

Lecture Notes in Computer Science

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Mining Code Submissions to Elucidate Disengagement in a Computer Science MOOC

Cited by 7 publications

References 26 publications

Delving into instructor‐led feedback interventions informed by learning analytics in massive open online courses

Delving into instructor‐led feedback interventions informed by learning analytics in massive open online courses

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

Exploring the Connections Between the Use of an Automated Feedback System and Learning Behavior in a MOOC for Programming

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