An open source software （OSS） ecosystem refers to an OSS development community composed of many software projects and developers contributing to these projects. The projects and developers co-evolve in an ecosystem....An open source software （OSS） ecosystem refers to an OSS development community composed of many software projects and developers contributing to these projects. The projects and developers co-evolve in an ecosystem. To keep healthy evolution of such OSS ecosystems, there is a need of attracting and retaining developers, particularly project leaders and core developers who have major impact on the project and the whole team. Therefore, it is important to figure out the factors that influence developers＇ chance to evolve into project leaders and core developers. To identify such factors, we conducted a case study on the GNOME ecosystem. First, we collected indicators reflecting developers＇ subjective willingness to contribute to the project and the project environment that they stay in. Second, we calculated such indicators based on the GNOME dataset. Then, we fitted logistic regression models by taking as independent variables the resulting indicators after eliminating the most collinear ones, and taking as a dependent variable the future developer role （the core developer or project leader）. The results showed that part of such indicators （e.g., the total number of projects that a developer joined） of subjective willingness and project environment significantly influenced the developers＇ chance to evolve into core developers and project leaders. With different validation methods, our obtained model performs well on predicting developmental core developers, resulting in stable prediction performance （0.770, F-value）.展开更多
The integration of artificial intelligence(AI)in education has revolutionized teaching and learning methodologies,offering personalized experiences and efficient resource management.However,this technological advancem...The integration of artificial intelligence(AI)in education has revolutionized teaching and learning methodologies,offering personalized experiences and efficient resource management.However,this technological advancement has also surfaced a plethora of ethical concerns that necessitate careful consideration.This paper delves into the ethical issues arising from AI applications in education,such as data privacy,algorithmic bias,educational equity,and the evolving role of teachers.Through a comprehensive analysis,we identify the challenges and propose strategic countermeasures to mitigate these ethical dilemmas.Case studies from both domestic and international contexts are employed to illustrate real-world applications and the associated ethical decision-making processes.The paper concludes with a summary of findings,policy recommendations,and an outlook on future research directions,emphasizing the need for a balanced approach that respects both technological innovation and ethical standards in educational AI deployment.展开更多
基金This work is supported by the National Key Research and Development Program of China under Grant No. 2016YFB0800400, the National Basic Research 973 Program of China under Grant No. 2014CB340404, the National Natural Science Foundation of China under Grant Nos. 61572371, 61273216, and 61272111, the China Postdoctoral Science Foundation （CPSF） under Grant No. 2015M582272, the Natural Science Foundation of Hubei Province of China under Grant No. 2016CFB158, and the Fundamental Research Funds for the Central Universities of China under Grant No. 2042016kf0033.
文摘An open source software （OSS） ecosystem refers to an OSS development community composed of many software projects and developers contributing to these projects. The projects and developers co-evolve in an ecosystem. To keep healthy evolution of such OSS ecosystems, there is a need of attracting and retaining developers, particularly project leaders and core developers who have major impact on the project and the whole team. Therefore, it is important to figure out the factors that influence developers＇ chance to evolve into project leaders and core developers. To identify such factors, we conducted a case study on the GNOME ecosystem. First, we collected indicators reflecting developers＇ subjective willingness to contribute to the project and the project environment that they stay in. Second, we calculated such indicators based on the GNOME dataset. Then, we fitted logistic regression models by taking as independent variables the resulting indicators after eliminating the most collinear ones, and taking as a dependent variable the future developer role （the core developer or project leader）. The results showed that part of such indicators （e.g., the total number of projects that a developer joined） of subjective willingness and project environment significantly influenced the developers＇ chance to evolve into core developers and project leaders. With different validation methods, our obtained model performs well on predicting developmental core developers, resulting in stable prediction performance （0.770, F-value）.
文摘The integration of artificial intelligence(AI)in education has revolutionized teaching and learning methodologies,offering personalized experiences and efficient resource management.However,this technological advancement has also surfaced a plethora of ethical concerns that necessitate careful consideration.This paper delves into the ethical issues arising from AI applications in education,such as data privacy,algorithmic bias,educational equity,and the evolving role of teachers.Through a comprehensive analysis,we identify the challenges and propose strategic countermeasures to mitigate these ethical dilemmas.Case studies from both domestic and international contexts are employed to illustrate real-world applications and the associated ethical decision-making processes.The paper concludes with a summary of findings,policy recommendations,and an outlook on future research directions,emphasizing the need for a balanced approach that respects both technological innovation and ethical standards in educational AI deployment.
