Learning with Comments: An Analysis of Comments and Community on Stack Overflow

Sengupta, Subhasree; Haythornthwaite, Caroline

doi:10.24251/hicss.2020.354

Cited by 32 publications

(17 citation statements)

References 38 publications

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“…The purpose of their study was the provision of insights into commenting in Stack Overflow. They mention the requirement of further research on comment usage [1]. Identification of inadequate comments in Wikipedia's talk page edits and classification of the same into different categories was performed by Sulke and Varude.…”

Section: Related Workmentioning

confidence: 99%

“…The software engineering community considers Stack Overflow as a learning site and a learning community for software developers and practitioners [1], [2]. Comments in Stack Overflow (SO) are temporary "Post-It" notes relevant to a particular question or an answer which has already been posted [3].…”

Section: Introductionmentioning

confidence: 99%

“…Comments related to relevant but transient information guide the users in retrieving more information that is relevant to a certain post in Stack Overflow [7]. Nevertheless, Stack Overflow does not recommend comments related to suggesting corrections, compliments, answering a question, criticisms, secondary discussions, and discussions of site policies and community behavior to be posted on the site 1 . However, it is observed that most of the developers tend to respond to posts with comments which are not adhering to Stack Overflow's guidelines on comments [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Systematic Exploration and Classification of Useful Comments in Stack Overflow

Ranasinghe¹,

Chandimali²,

Wijesiriwardana³

2022

IJACSA

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Stack Overflow is a public platform for developers to share their knowledge on programming with an engaged community. Crowdsourced programming knowledge is not only generated through questions and answers but also through comments which are commonly known as developer discussions. Despite the availability of standard commenting guidelines on Stack Overflow, some users tend to post comments not adhering to those guidelines. This practice affects the quality of the developer discussion, thus adversely affecting the knowledgesharing process. Literature reveals that analyzing the comments could facilitate the process of learning and knowledge sharing. Therefore, this study intends to extract and classify useful comments into three categories: request clarification, constructive criticism, and relevant information. In this study, the classification of useful comments was performed using the Support Vector Machine (SVM) algorithm with five different kernels. Feature engineering was conducted to identify the possibility of concatenating ten external features with textual features. During the feature evaluation, it was identified that only TF-IDF and N-grams scores help classify useful comments. The evaluation results confirm Radial Basis Function (RBF) kernel of the SVM classification algorithm performs best in classifying useful comments in Stack Overflow regardless of the usage of the optimal combinations of hyperparameters.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Systematic Exploration and Classification of Useful Comments in Stack Overflow

Ranasinghe¹,

Chandimali²,

Wijesiriwardana³

2022

IJACSA

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“…MOOCs have evolved to fill the gap for skill development in non‐formal contexts (Reich & Ruipérez‐Valiente, 2019). Informal learning through technology is readily available in online learning communities with opportunities for incidental learning (Sengupta & Haythornthwaite, 2020).…”

Section: How Technology Data and Artificial Intelligence Affect Lifelong Learningmentioning

confidence: 99%

Developing capabilities: Lifelong learning in the age of AI

Poquet

Laat

2021

Brit J Educational Tech

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Due to the ongoing digitalisation of workplaces and educational settings, human activity underpinning learning and work is increasingly mediated by technology. The advancement of artificial intelligence (AI) and its integration into everyday technologies influences how people are exposed to information, interact, learn and make decisions. We argue that technology, data and evolving AI applications affect how humans enact and experience life and work, changing the context for learning. Hence, as this paper argues, the current notion of lifelong learning needs a revisit to embrace technology at its foundation. To bring freely chosen goals and ownership in one's learning to the fore, in the context of the coming AI age, we argue for the telos of learning to shift from human capital to human development, with the spotlight on capabilities. The paper draws on the capability approach to inform individuals and organisations of how they can support human development throughout lifelong learning. We then move to provide examples of how technologies underpinning workplace practices can be seen with the focus on capabilities as individuals learn to create value. What is known about the topic? The primary notion of lifelong learning refers to adult learning processes. The policy perspective that dominates organisation of lifelong learning opportunities focuses on human capital development. Technologies mediate learning and work. What this paper adds Technology is not explicitly addressed in meanings associated with lifelong learning. AI‐based technologies dynamically interact with human cognitive and social practices. The paper argues for a stronger focus on human development instead of human capital in the telos of lifelong learning opportunities. Capability approach is a viable alternative to human capital perspective on LLL. Data used to support learning can focus on learner agency and systemic factors that enable and constrain lifelong learning. Implications for practice and/or policy LLL interventions should promote systemic support for learner agency and ownership. LLL interventions should focus on negotiated value creation. Workplaces should embrace human‐machine integration but in ways that support capability and human development, not human capital.

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“…This research explores how interaction in online forums focused on computer programming supports both learning and community development. Prior work has investigated two main dimensions-(a) Understanding the learning practices embedded in online community discourse (Anderson, Huttenlocher, Kleinberg, & Leskovec, 2012;Sengupta & Haythornthwaite, 2020) and (b) Understanding user participation and engagement in online forums (O'Brien & Toms, 2008). A key factor that drives engagement and discourse in online collectives is the implicit culture of the community.…”

Section: Background and Project Motivationmentioning

confidence: 99%

“A tale of two virtual communities”: A preliminary analysis of discourse in two online programming support communities

Sengupta

2020

Proc Assoc Inf Sci Technol

Self Cite

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Software programming is increasingly becoming a collaborative and community driven effort, with online discussions becoming vital resources for learning and knowledge sharing. This study explores differences in the discourse patterns in two popular online programming communities to provide preliminary insights for the question of how virtual learning communities should be designed and structured. A content analysis of a random sample of 15 discussion threads from each of r/Askprogramming (236 contributions) and Stack Overflow (SO; 224 contributions) was used to explore the observed interaction patterns. Differences between sites emerge in the scope of topics and the nature of responses the community provides. While Stack Overflow is more task‐specific, r/Askprogramming supports a greater sense of bonding and camaraderie among community members in addition to task‐specific discussions. These findings suggest key normative structures that regulate the nature of discourse in these communities which may in turn have design implications for such online learning initiatives.

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Learning with Comments: An Analysis of Comments and Community on Stack Overflow

Cited by 32 publications

References 38 publications

Systematic Exploration and Classification of Useful Comments in Stack Overflow

Systematic Exploration and Classification of Useful Comments in Stack Overflow

Developing capabilities: Lifelong learning in the age of AI

“A tale of two virtual communities”: A preliminary analysis of discourse in two online programming support communities

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