Open collaboration between universities and enterprises: a case study on GitHub

Abstract

PurposeSocial coding platforms (SCPs) have been adopted by scores of developers in building, testing and managing their codes collaboratively. Accordingly, this type of platform (site) enables collaboration between enterprises and universities (c-EU) at a lower cost in the form of online team-building projects (repositories). This paper investigates the open collaboration patterns between these two parties on GitHub by measuring their online behaviours. The purpose of this investigation is to identify the most attractive collaboration features that enterprises can offer to increase university students' participation intentions.Design/methodology/approachThe research process is divided into four steps. First, the authors crawled for numerical data for each interactive repository feature created by employees of Alibaba on GitHub and identified the student accounts associated with these repositories. Second, a categorisation schema of feature classification was proposed on a behavioural basis. Third, the authors clustered the aforementioned repositories based on feature data and recognised four types of repositories (popular, formal, normal and obsolete) to represent four open collaboration patterns. The effects of the four repository types on university students' collaboration behaviour were measured using a multiple linear regression model. An ANOVA test was implemented to examine the robustness of research results. Finally, the authors proposed some practical suggestions to enhance collaboration between both sides of SCPs.FindingsSeveral counterintuitive but reasonable findings were revealed, for example, those based on the “star” repository feature. The actual coding contribution of the repositories had a negative correlation with student attention. This result indicates that students were inclined to imitate rather than innovate.Originality/valueThis research explores the open collaboration patterns between enterprises and universities on GitHub and their impact on student coding behaviour. According to the research analysis, both parties benefit from open collaboration on SCPs, and the allocation or customisation of online repository features may affect students' participation in coding. This research brings a new perspective to the measurement of users' collaboration behaviour with output rates on SCPs.

Evaluating a programming topic using GitHub data: what we can learn about machine learning

“…Collaboration from other perspectives is investigated also by: Zöller et al (2020), who studied the collaboration patterns of open source projects on GitHub starting from the pull request submissions and acceptances of repositories. Cheng et al (2020), using data from GitHub, a particular type of collaboration: between companies and universities is examined to identify the characteristics of the collaboration that companies must focus on to increase the involvement of university students in their projects. Yezhou et al (2017) built the users’ social network to analyze its structure, to study the “commit” activities of programmers, to identify the most important users …”

“…Cheng et al (2020), using data from GitHub, a particular type of collaboration: between companies and universities is examined to identify the characteristics of the collaboration that companies must focus on to increase the involvement of university students in their projects.…”

Use of the Tensorflow Framework to Support Educational Problems: A Systematic Mapping

“…After a brief reading in the 32 abstracts, we classified them in six main categories as shown in Table 3. (Malekian et al, 2020), (Vijayalakshmi & Venkatachalapathy, 2019), , (Boncea et al, 2019), (Pablo, 2020), (Cheng & Zhang, 2020), (Aveleyra et al, 2018), (Tsiakmaki & Kostopoulos, 2020), (Czibula et al, 2019), , (Monllaó Olivé, et al, 2020), (Chunqiao et al, 2018), (Preuveneers et al, 2020), (Kőrösi & Farkas, 2020), (Injadat et al, 2020), (Qiu et al, 2019) Representation Learning (Zhang et al, 2019) Knowledge (Lee & Yeung, 2019), (Yang & Cheung, 2018), (Sha & Hong, 2017) Tracing Pedagogical Data Analytics (Guo & Zeng, 2020), (Hernández-Blanco, 2019), (Gudivada, et al, 2016) Decision Support System (Gutu-Robu et al, 2018), (Stoica, et al, 2019), (Holmes, 2020), (Moore et al, 2019), (Pensel & Kramer, 2020) Evaluation (Doleck et al, 2020) As expected, most of the papers (20) published belong to the Student Modeling category. Basically, the general focus represents cognitive aspects of student activities, such as analyzing students' performance, isolating underlying misconceptions, representing students' goals and plans, identifying prior and acquired knowledge, maintaining an episodic memory, and describing personality characteristics (Bakhshinategh et al, 2017).…”

“…In two papers there was no specific approach because one presents a common architecture and another shows a systematic review; for those papers the approach was listed as "General". To identify the educational data mining tasks that have benefited from deep learning and those that are pending to be explored Neural network (Vijayalakshmi & Venkatachalapathy, 2019) To compare the prediction of the student performance using different algorithms Decision tree, naive Bayes, random forest, support vector machine, K-Nearest neighbor, and deep neural network To develop a predictive model for effective learning feature extracting, learning performance predicting and result reasoning Convolutional GRU and neural network (Gutu-Robu et al, 2018) To introduce an updated version or our open-source NLP framework, ReaderBench, designed to support both students and tutors in multiple learning scenarios Natural language processing (Boncea et al, 2019) To improve scholar performance by a continuous Assessment reusable learning and intelligent monitoring and assessing process of daily knowledge gains object and neural network (Pablo, 2020) To detect students who are at risk of failing the course or need special support, providing teachers with a useful mechanism for predicting and improving student outcomes Learning experience (academic performance and online activity) (Cheng & Zhang, 2020) To identify the most attractive collaboration features that enterprises can offer to increase university students' participation intentions Online behaviours (Aveleyra et al, 2018) To predict which students may have problems in the second exam and help them with the aim of increasing the number of students who pass the exam.…”

About the life cycle of information systems based on open Web-technologies

“…Refinement of high science-intensive software to the level of completed software products with their subsequent inclusion in global software repositories would significantly increase the number of its potential users. It would also increase the return on the financial and time expenditures of researchers spent on its development, provide additional opportunities for testing and upgrading such software by third-party developers and users [6][7].…”