On the Quality of Crowdsourced Programming Assignments

Crowdsourcing has been used in computer science education to alleviate the teachers’ workload in creating course content, and as a learning and revision method for students through its use in educational systems. Tools that utilize crowdsourcing can act as a great way for students to further familiarize themselves with the course concepts, all while creating new content for their peers and future course iterations. In this study, student-created programming assignments from the second week of an introductory Java programming course are examined alongside the peer reviews these assignments received. The quality of the assignments and the peer reviews is inspected, for example, through comparing the peer reviews with expert reviews using inter-rater reliability. The purpose of this study is to inspect what kinds of programming assignments novice students create, and whether the same novice students can act as reliable reviewers. While it is not possible to draw definite conclusions from the results of this study due to limitations concerning the usability of the tool, the results seem to indicate that novice students are able to recognise differences in programming assignment quality, especially with sufficient guidance and well thought-out instructions

How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow

Context: The success of Stack Overflow and other community-based question-and-answer (Q&A) sites depends mainly on the will of their members to answer others' questions. In fact, when formulating requests on Q&A sites, we are not simply seeking for information. Instead, we are also asking for other people's help and feedback. Understanding the dynamics of the participation in Q&A communities is essential to improve the value of crowdsourced knowledge. Objective: In this paper, we investigate how information seekers can increase the chance of eliciting a successful answer to their questions on Stack Overflow by focusing on the following actionable factors: affect, presentation quality, and time. Method: We develop a conceptual framework of factors potentially influencing the success of questions in Stack Overflow. We quantitatively analyze a set of over 87K questions from the official Stack Overflow dump to assess the impact of actionable factors on the success of technical requests. The information seeker reputation is included as a control factor. Furthermore, to understand the role played by affective states in the success of questions, we qualitatively analyze questions containing positive and negative emotions. Finally, a survey is conducted to understand how Stack Overflow users perceive the guideline suggestions for writing questions. Results: We found that regardless of user reputation, successful questions are short, contain code snippets, and do not abuse with uppercase characters. As regards affect, successful questions adopt a neutral emotional style. Conclusion: We provide evidence-based guidelines for writing effective questions on Stack Overflow that software engineers can follow to increase the chance of getting technical help. As for the role of affect, we empirically confirmed community guidelines that suggest avoiding rudeness in question writing.Comment: Preprint, to appear in Information and Software Technolog

CAN LAYMEN OUTPERFORM EXPERTS? THE EFFECTS OF USER EXPERTISE AND TASK DESIGN IN CROWDSOURCED SOFTWARE TESTING

In recent years, crowdsourcing has increasingly gained attention as a powerful sourcing mechanism for problem-solving in organizations. Depending on the type of activity addressed by crowdsourcing, the complexity of the tasks and the role of the crowdworkers may differ substantially. It is crucial that the tasks are designed and allocated according to the capabilities of the targeted crowds. In this pa-per, we outline our research in progress which is concerned with the effects of task complexity and user expertise on performance in crowdsourced software testing. We conduct an experiment and gath-er empirical data from expert and novice crowds that perform different software testing tasks of vary-ing degrees of complexity. Our expected contribution is twofold. For crowdsourcing in general, we aim at providing valuable insights for the process of framing and allocating tasks to crowds in ways that increase the crowdworkers’ performance. Secondly, we intend to improve the configuration of crowdsourced software testing initiatives. More precisely, the results are expected to show practition-ers what types of testing tasks should be assigned to which group of dedicated crowdworkers. In this vein, we deliver valuable decision support for both crowdsourcers and intermediaries to enhance the performance of their crowdsourcing initiatives

Crowdsourcing Content Creation for SQL Practice

Crowdsourcing refers to the act of using the crowd to create content or to collect feedback on some particular tasks or ideas. Within computer science education, crowdsourcing has been used -- for example -- to create rehearsal questions and programming assignments. As a part of their computer science education, students often learn relational databases as well as working with the databases using SQL statements. In this article, we describe a system for practicing SQL statements. The system uses teacher-provided topics and assignments, augmented with crowdsourced assignments and reviews. We study how students use the system, what sort of feedback students provide to the teacher-generated and crowdsourced assignments, and how practice affects the feedback. Our results suggest that students rate assignments highly, and there are only minor differences between assignments generated by students and assignments generated by the instructor.Peer reviewe

A survey of the use of crowdsourcing in software engineering

The term 'crowdsourcing' was initially introduced in 2006 to describe an emerging distributed problem-solving model by online workers. Since then it has been widely studied and practiced to support software engineering. In this paper we provide a comprehensive survey of the use of crowdsourcing in software engineering, seeking to cover all literature on this topic. We first review the definitions of crowdsourcing and derive our definition of Crowdsourcing Software Engineering together with its taxonomy. Then we summarise industrial crowdsourcing practice in software engineering and corresponding case studies. We further analyse the software engineering domains, tasks and applications for crowdsourcing and the platforms and stakeholders involved in realising Crowdsourced Software Engineering solutions. We conclude by exposing trends, open issues and opportunities for future research on Crowdsourced Software Engineering

The Example Guru: Suggesting Examples to Novice Programmers in an Artifact-Based Context

Programmers in artifact-based contexts could likely benefit from skills that they do not realize exist. We define artifact-based contexts as contexts where programmers have a goal project, like an application or game, which they must figure out how to accomplish and can change along the way. Artifact-based contexts do not have quantifiable goal states, like the solution to a puzzle or the resolution of a bug in task-based contexts. Currently, programmers in artifact-based contexts have to seek out information, but may be unaware of useful information or choose not to seek out new skills. This is especially problematic for young novice programmers in blocks programming environments. Blocks programming environments often lack even minimal in-context support, such as auto-complete or in-context documentation. Novices programming independently in these blocks-based programming environments often plateau in the programming skills and API methods they use. This work aims to encourage novices in artifact-based programming contexts to explore new API methods and skills. One way to support novices may be with examples, as examples are effective for learning and highly available. In order to better understand how to use examples for supporting novice programmers, I first ran two studies exploring novices\u27 use and focus on example code. I used those results to design a system called the Example Guru. The Example Guru suggests example snippets to novice programmers that contain previously unused API methods or code concepts. Finally, I present an approach for semi-automatically generating content for this type of suggestion system. This approach reduces the amount of expert effort required to create suggestions. This work contains three contributions: 1) a better understanding of difficulties novices have using example code, 2) a system that encourages exploration and use of new programming skills, and 3) an approach for generating content for a suggestion system with less expert effort

On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow

Abundant data is the key to successful machine learning. However, supervised learning requires annotated data that are often hard to obtain. In a classification task with limited resources, Active Learning (AL) promises to guide annotators to examples that bring the most value for a classifier. AL can be successfully combined with self-training, i.e., extending a training set with the unlabelled examples for which a classifier is the most certain. We report our experiences on using AL in a systematic manner to train an SVM classifier for Stack Overflow posts discussing performance of software components. We show that the training examples deemed as the most valuable to the classifier are also the most difficult for humans to annotate. Despite carefully evolved annotation criteria, we report low inter-rater agreement, but we also propose mitigation strategies. Finally, based on one annotator's work, we show that self-training can improve the classification accuracy. We conclude the paper by discussing implication for future text miners aspiring to use AL and self-training.Comment: Preprint of paper accepted for the Proc. of the 21st International Conference on Evaluation and Assessment in Software Engineering, 201

Lessons Learned From Four Computing Education Crowdsourcing Systems

Crowdsourcing is a general term that describes the practice of many individuals working collectively to achieve a common goal or complete a task, often involving the generation of content. In an educational context, crowdsourcing of learning materials- where students create resources that can be used by other learners- offers several benefits. Students benefit from the act of producing resources as well as from using the resources. Despite benefits, instructors may be hesitant to adopt crowdsourcing for several reasons, such as concerns around the quality of content produced by students and the perceptions students may have of creating resources for their peers. While prior work has explored crowdsourcing concerns within the context of individual tools, lessons that are generalisable across multiple platforms and derived from practical use can provide considerably more robust insights. In this perspective article, we present four crowdsourcing tools that we have developed and used in computing classrooms. From our previous studies and experience, we derive lessons which shed new light on some of the concerns that are typical for instructors looking to adopt such tools. We find that across multiple contexts, students are capable of generating high quality learning content which provides good coverage of key concepts. Although students do appear hesitant to engage with new kinds of activities, various types of incentives have proven effective. Finally, although studies on learning effects have shown mixed results, no negative outcomes have been observed. In light of these lessons, we hope to see a greater uptake and use of crowdsourcing in computing education.Peer reviewe

Beyond Automated Assessment: Building Metacognitive Awareness in Novice Programmers in CS1

The primary task of learning to program in introductory computer science courses (CS1) cognitively overloads novices and must be better supported. Several recent studies have attempted to address this problem by understanding the role of metacognitive awareness in novices learning programming. These studies have focused on teaching metacognitive awareness to students by helping them understand the six stages of learning so students can know where they are in the problem-solving process, but these approaches are not scalable. One way to address scalability is to implement features in an automated assessment tool (AAT) that build metacognitive awareness in novice programmers. Currently, AATs that provide feedback messages to students can be said to implement the fifth and sixth learning stages integral to metacognitive awareness: implement solution (compilation) and evaluate implemented solution (test cases). The computer science education (CSed) community is actively engaged in research on the efficacy of compile error messages (CEMs) and how best to enhance them to maximize student learning and it is currently heavily disputed whether or not enhanced compile error messages (ECEMs) in AATs actually improve student learning. The discussion on the effectiveness of ECEMs in AATs remains focused on only one learning stage critical to metacognitive awareness in novices: implement solution. This research carries out an ethnomethodologically-informed study of CS1 students via think-aloud studies and interviews in order to propose a framework for designing an AAT that builds metacognitive awareness by supporting novices through all six stages of learning. The results of this study provide two important contributions. The first is the confirmation that ECEMs that are designed from a human-factors approach are more helpful for students than standard compiler error messages. The second important contribution is that the results from the observations and post-assessment interviews revealed the difficulties novice programmers often face to developing metacognitive awareness when using an AAT. Understanding these barriers revealed concrete ways to help novice programmers through all six stages of the problem-solving process. This was presented above as a framework of features, which when implemented properly, provides a scalable way to implicitly produce metacognitive awareness in novice programmers

Improving User Involvement Through Live Collaborative Creation

Creating an artifact - such as writing a book, developing software, or performing a piece of music - is often limited to those with domain-specific experience or training. As a consequence, effectively involving non-expert end users in such creative processes is challenging. This work explores how computational systems can facilitate collaboration, communication, and participation in the context of involving users in the process of creating artifacts while mitigating the challenges inherent to such processes. In particular, the interactive systems presented in this work support live collaborative creation, in which artifact users collaboratively participate in the artifact creation process with creators in real time. In the systems that I have created, I explored liveness, the extent to which the process of creating artifacts and the state of the artifacts are immediately and continuously perceptible, for applications such as programming, writing, music performance, and UI design. Liveness helps preserve natural expressivity, supports real-time communication, and facilitates participation in the creative process. Live collaboration is beneficial for users and creators alike: making the process of creation visible encourages users to engage in the process and better understand the final artifact. Additionally, creators can receive immediate feedback in a continuous, closed loop with users. Through these interactive systems, non-expert participants help create such artifacts as GUI prototypes, software, and musical performances. This dissertation explores three topics: (1) the challenges inherent to collaborative creation in live settings, and computational tools that address them; (2) methods for reducing the barriers of entry to live collaboration; and (3) approaches to preserving liveness in the creative process, affording creators more expressivity in making artifacts and affording users access to information traditionally only available in real-time processes. In this work, I showed that enabling collaborative, expressive, and live interactions in computational systems allow the broader population to take part in various creative practices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145810/1/snaglee_1.pd