Suchergebnisse

AbstractChatGPT, an AI-based chatbot with automatic code generation abilities, has shown its promise in improving the quality of programming education by providing learners with opportunities to better understand the principles of programming. However, limited empirical studies have explored the impact of ChatGPT on learners' programming processes. This study employed a quasi-experimental design to explore the possible impact of ChatGPT-facilitated programming mode on college students' programming behaviors, performances, and perceptions. 82 college students were randomly divided into two classes. One class employed ChatGPT-facilitated programming (CFP) practice and the other class utilized self-directed programming (SDP) mode. Mixed methods were utilized to collect multidimensional data. Data analysis uncovered some intriguing results. Firstly, students in the CFP mode had more frequent behaviors of debugging and receiving error messages, as well as pasting console messages on the website and reading feedback. At the same time, students in the CFP mode had more frequent behaviors of copying and pasting codes from ChatGPT and debugging, as well as pasting codes to ChatGPT and reading feedback from ChatGPT. Secondly, CFP practice would improve college students' programming performance, while the results indicated that there was no statistically significant difference between the students in CFP mode and the SDP mode. Thirdly, student interviews revealed three highly concerned themes from students' user experience about ChatGPT: the services offered by ChatGPT, the stages of ChatGPT usage, and experience with ChatGPT. Finally, college students' perceptions toward ChatGPT significantly changed after CFP practice, including its perceived usefulness, perceived ease of use, and intention to use. Based on these findings, the study proposes implications for future instructional design and the development of AI-powered tools like ChatGPT.

Aramid fiber reinforced plastic (AFRP) composites have been widely used in aerospace, military, and automotive industries. The common drilling process deployed for AFRP manufacturing can induce delamination that drastically deteriorate the mechanical performance and fatigue lives of the drilled AFRP components, therefore, establishing an accurate delamination model is desirable for delamination suppression and hole quality optimization. However, existing delamination models sum up all loads of the chisel edge and cutting lips act on the uncut plies under the chisel edge algebraically, which does not represent the true contact conditions. In this study, a new delamination regime is proposed where delamination caused by thrust forces exerted by both the chisel edge and cutting lips have been considered. On this basis, a novel analytical model in the context of AFRP drilling is proposed for the critical thrust force (CTF) prediction. Double cantilever beam (DCB) and delamination tests have been performed to validate the new model and results show that our proposed model agrees highly with the experimental results where the thrust force exerted by the chisel edge accounts for 24% of the total load during drilling of AFRP.