Identifying Struggling Teams in Software Engineering Courses Through Weekly Surveys

Presler-Marshall, Kai; Heckman, Sarah; Stolee, Kathryn T.

doi:10.1145/3478431.3499367

Cited by 10 publications

(2 citation statements)

References 18 publications

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“…Improving Team Work Manually analyzed weekly surveys to monitor software engineering students to identify and help struggling teams have proven useful (Presler-Marshall, Heckman, and Stolee 2022).…”

Section: Use Cases Of Sentiment Analysis In Educationmentioning

confidence: 99%

“…Periodically collecting student feedback in the form of free-form text or Likert-style survey questions is one approach to bridge this feedback gap. Assessing this feedback in a timely manner allows instructors to learn about course materials students struggle with, gain awareness of teams that have issues, or even identify students that fall behind (Ahadi et al 2015;Presler-Marshall, Heckman, and Stolee 2022;Gitinabard et al 2022;Neumann and Linzmayer 2021). Likert-type survey questions are easy to analyze but need to be carefully tailored to specific contexts and the responses tend to be unreliable (Holzbach 1978;Leising et al 2016;Murphy 1993).…”

Section: Introductionmentioning

confidence: 99%

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Practical Sentiment Analysis for Education: The Power of Student Crowdsourcing

Kasumba,

Neumman

2024

AAAI

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Sentiment analysis provides a promising tool to automatically assess the emotions voiced in written student feedback such as periodically collected unit-of-study reflections. The commonly used dictionary-based approaches are limited to major languages and fail to capture contextual differences. Pretrained large language models have been shown to be biased and online versions raise privacy concerns. Hence, we resort to traditional supervised machine learning (ML) approaches which are designed to overcome these issues by learning from domain-specific labeled data. However, these labels are hard to come by -- in our case manually annotating student feedback is prone to bias and time-consuming, especially in high-enrollment courses. In this work, we investigate the use of student crowdsourced labels for supervised sentiment analysis for education. Specifically, we compare crowdsourced and student self-reported labels with human expert annotations and use them in various ML approaches to evaluate the performance on predicting emotions of written student feedback collected from large computer science classes. We find that the random forest model trained with student-crowdsourced labels tremendously improves the identification of reflections with negative sentiment. In addition to our quantitative study, we describe our crowdsourcing experiment which was intentionally designed to be an educational activity in an introduction to data science course.

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Section: Use Cases Of Sentiment Analysis In Educationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Practical Sentiment Analysis for Education: The Power of Student Crowdsourcing

Kasumba,

Neumman

2024

AAAI

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Improving Grading Outcomes in Software Engineering Projects Through Automated Contributions Summaries

Presler-Marshall

Heckman

Stolee

2023

2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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Measuring and Modeling Group Dynamics in Open-Source Software Development: A Tensor Decomposition Approach

Böck

SchmidAngelika²,

ApelSven

2021

ACM Trans. Softw. Eng. Methodol.

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Many open-source software projects depend on a few core developers, who take over both the bulk of coordination and programming tasks. They are supported by peripheral developers, who contribute either via discussions or programming tasks, often for a limited time. It is unclear what role these peripheral developers play in the programming and communication efforts, as well as the temporary task-related sub-groups in the projects. We mine code-repository data and mailing-list discussions to model the relationships and contributions of developers in a social network and devise a method to analyze the temporal collaboration structures in communication and programming, learning about the strength and stability of social sub-groups in open-source software projects. Our method uses multi-modal social networks on a series of time windows. Previous work has reduced the network structure representing developer collaboration to networks with only one type of interaction, which impedes the simultaneous analysis of more than one type of interaction. We use both communication and version-control data of open-source software projects and model different types of interaction over time. To demonstrate the practicability of our measurement and analysis method, we investigate 10 substantial and popular open-source software projects and show that, if sub-groups evolve, modeling these sub-groups helps predict the future evolution of interaction levels of programmers and groups of developers. Our method allows maintainers and other stakeholders of open-source software projects to assess instabilities and organizational changes in developer interaction and can be applied to different use cases in organizational analysis, such as understanding the dynamics of a specific incident or discussion.

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Identifying Struggling Teams in Software Engineering Courses Through Weekly Surveys

Cited by 10 publications

References 18 publications

Practical Sentiment Analysis for Education: The Power of Student Crowdsourcing

Practical Sentiment Analysis for Education: The Power of Student Crowdsourcing

Improving Grading Outcomes in Software Engineering Projects Through Automated Contributions Summaries

Measuring and Modeling Group Dynamics in Open-Source Software Development: A Tensor Decomposition Approach

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