The integration of artificial intelligence into educational tools is transforming learning environments. In computer science, students frequently encounter challenges with complex concepts and practical applications. ...
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ISBN:
(数字)9798331542788
ISBN:
(纸本)9798331542795
The integration of artificial intelligence into educational tools is transforming learning environments. In computer science, students frequently encounter challenges with complex concepts and practical applications. While LLMs offer valuable support, their responses can sometimes lack precision or rely on outdated information. Addressing these limitations is essential to enhance the effectiveness of AI-based tools in educational support. The main objective of this paper is to propose a tool-based RAG as automated assistant in an OOP Course. The tool was tested using common student queries and its responses were compared with those generated by ChatGPT. Initial observations suggest that RAG consistently generated contextually relevant responses due to its ability to access pertinent information from a structured knowledge base, resulting in more precise and applicable answers for students. The introduction of a RAG-based tool in classrooms has the potential to enhance student learning by providing instant, tailored responses to specific queries.
Novice students often report difficulties applying abstract concepts of object-orientedprogramming (OOP). Several studies highlight the potential of Immersive Virtual Reality (VR) as a valuable tool for supporting cl...
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ISBN:
(数字)9798331514846
ISBN:
(纸本)9798331525637
Novice students often report difficulties applying abstract concepts of object-orientedprogramming (OOP). Several studies highlight the potential of Immersive Virtual Reality (VR) as a valuable tool for supporting classroom teaching through novel and engaging learning environments. However, combining interactive learning in VR with concepts from OOP is challenging. We present VRLOOP (VR Learning of OOP), an immersive system to learn OOP concepts in a collaborative and embodied environment. The design of VRLOOP combines principles from HCI and educational psychology. VRLOOP supports enactive rather than symbolic learning, i.e. the use of textual code. We conducted an initial pilot study with 14 students to evaluate VRLOOP’s usability and gather subjective feedback. 93% reported that VRLOOP made the content from the class easier to understand. Finally, we discuss limitations and directions for future research on VR environments for learning programming concepts.
Addressing issues such as a lack of learning motivation due to unclear ability cultivation goals and the failure of conceptual and theoretical understanding to guide practical engineering skill training, this paper pr...
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ISBN:
(数字)9798350356670
ISBN:
(纸本)9798350356687
Addressing issues such as a lack of learning motivation due to unclear ability cultivation goals and the failure of conceptual and theoretical understanding to guide practical engineering skill training, this paper proposes a hybrid teaching design scheme combining online and offline methods. This proposal is based on the teaching concept of "OBE+ Curriculum Ideology and Politics" and is oriented towards meeting students' graduation engineering ability requirements. By reconstructing course teaching design and optimizing implementation methods, a comprehensive practice is outlined, covering teaching modes, content, implementation, and evaluation. This approach aims to provide a reference for reforming programming language courses within the context of new engineering education.
The problems in our teaching on object-orientedprogramming are analyzed, and the basic ideas, causes and methods of the reform are discussed on the curriculum, theoretical teaching and practical classes. Our practice...
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object-orientedprogramming (OOP) is a modern model of programming languages and an important module for many programming courses in academics. Not only do educators have trouble teaching OOP concepts but students are...
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ISBN:
(纸本)9789869721479
object-orientedprogramming (OOP) is a modern model of programming languages and an important module for many programming courses in academics. Not only do educators have trouble teaching OOP concepts but students are also reported to having trouble comprehending those concepts. The difficulty lies in dealing with abstract concepts and finding a relationship between the textbook explanations and the application of these concepts. Several works try to approach this problem, but they lack connecting the OOP concepts with its implementation in the source-code. In this research, we propose a new visualization form using concept maps to combine the OOP concepts with its' source-code to promote OOP concept comprehension. The proposed visualization is called the conceptual representation of the source-code (CRS). CRS unites the source-code statements and the OOP concepts into one comprehensible diagram. A concept map recomposition activity with Kit-Build is used to implement the CRS. We have conducted an experiment on university students to verify the learning effects and use of the proposed method. The results show a significant improvement in immediate learning by comparing before/after activity test-scores. In addition, students showed a positive impression and intention about using the tool during their studying of OOP by answering a questionnaire. The research findings shed light on a promising aspect of teaching OOP concepts in programming courses.
Large Language Models (LLMs) have emerged as promising tools to assist students while solving programming assignments. However, object-orientedprogramming (OOP), with its inherent complexity involving the identificat...
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ISBN:
(数字)9798400704987
ISBN:
(纸本)9798350352115
Large Language Models (LLMs) have emerged as promising tools to assist students while solving programming assignments. However, object-orientedprogramming (OOP), with its inherent complexity involving the identification of entities, relationships, and responsibilities, is not yet mastered by these tools. Contrary to introductory programming exercises, there exists a research gap with regard to the behavior of LLMs in OOP contexts. In this study, we experimented with three prominent LLMs - GPT-3.5, GPT-4, and Bard - to solve real-world OOP exercises used in educational settings, subsequently validating their solutions using an Automatic Assessment Tool (AAT). The findings revealed that while the models frequently achieved mostly working solutions to the exercises, they often overlooked the best practices of OOP. GPT-4 stood out as the most proficient, followed by GPT-3.5, with Bard trailing last. We advocate for a renewed emphasis on code quality when employing these models and explore the potential of pairing LLMs with AATs in pedagogical settings. In conclusion, while GPT-4 show-cases promise, the deployment of these models in OOP education still mandates supervision.
In the area of code generation research[21], the emphasis has transitioned from crafting individual functions to developing class-level method code that integrates contextual information. This shift has brought severa...
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In the area of code generation research[21], the emphasis has transitioned from crafting individual functions to developing class-level method code that integrates contextual information. This shift has brought several benchmarks such as ClassEval[5] and CoderEval[28], which consider class-level contexts. Nevertheless, the influence of specific contextual factors at the method level remains less explored. These factors include the method's own description, its interactions with other modules, and the broader project context. The varied impacts of these factors on code generation outcomes-such as pass rates, error type distributions, and developer support-could also have economic implications, notably in terms of the token consumption required for API calls. Furthermore, the extent of a method's external module interactions, or"coupling", significantly influences code generation, yet quantitative analyses of this impact are sparse. Our research focused on method-level code generation within the object-orientedprogramming (OOP) framework. We devised experiments that varied the extent of contextual information in the prompts, ranging from method-specific to project-wide details. We introduced the innovative metric of "Prompt-Token Cost-Effectiveness" to evaluate the economic viability of incorporating additional contextual layers. Our findings indicate that prompts enriched with method invocation details yield the highest cost-effectiveness. Additionally, our study revealed disparities among Large Language Models (LLMs) regarding error type distributions and the level of assistance they provide to developers. Notably, larger LLMs do not invariably perform better. We also observed that tasks with higher degrees of coupling present more substantial challenges, suggesting that the choice of LLM should be tailored to the task's coupling degree. For example, GPT-4 exhibited improved performance in low-coupling scenarios, whereas GPT-3.5 seemed better suited for tasks with
object-orientedprogramming (OOP) has become a crucial paradigm for managingthe growing complexity of modern software systems, particularly in fields likemachine learning, deep learning, large language models (LLM), a...
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Large Language Models (LLMs) have emerged as promising tools to assist students while solving programming assignments. However, object-orientedprogramming (OOP), with its inherent complexity involving the identificat...
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