1 Introduction Program retrieval aims to enable the flexible retrieval of program snippets based on a natural language query,significantly accelerating software development *** shows that over 60%of developers perform...
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1 Introduction Program retrieval aims to enable the flexible retrieval of program snippets based on a natural language query,significantly accelerating software development *** shows that over 60%of developers perform program retrieval daily[1].Recent years have witnessed an increasing interest in deep learning-based program retrieval,which aims to construct embedding representations for program snippets and ***,the distribution of natural languages and programs is inconsistent,resulting in a semantic *** critical challenge is to bridge the semantic gap between the programming language and natural language,and accurately measure their similarity.
A branch of computerscience known as genetic programming has been given a boost with the application of large language models that are trained on the combined intuition of the world's programmers.
A branch of computerscience known as genetic programming has been given a boost with the application of large language models that are trained on the combined intuition of the world's programmers.
The global shortage of skilled programmers remains a persistent challenge. High dropout rates in introductory programming courses pose a significant obstacle to graduation. Previous studies highlighted learning diffic...
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The global shortage of skilled programmers remains a persistent challenge. High dropout rates in introductory programming courses pose a significant obstacle to graduation. Previous studies highlighted learning difficulties in programming students, but their specific weaknesses remained unclear. This gap exists due to the predominant focus on the overall academic performance evaluation. To address this gap, this study employed cognitive diagnostic modeling (CDM) to profile the skill mastery of programming students. An empirical analysis was conducted to select the most appropriate model for the data, and the linear logistic model (LLM) was determined to be the best fit. Final examination results from 308 information technology (IT) and 279 computerscience (CS) students were analyzed using the LLM. Unfortunately, findings revealed that programming students exhibited proficiency primarily in code tracing and language proficiency but displayed deficits in theoretical understanding, logical reasoning, and algorithmic thinking. From a practical standpoint, this deficiency in fundamental skills sheds light on the factors contributing to academic failures and potentially eventual dropout in programming education. When comparing the student population by academic program, CS students demonstrated superior mastery compared to their IT counterparts, although both groups exhibited a lack of mastery in code tracing. These deviations underscore the pressing need for tailored educational strategies that address the unique strengths and weaknesses of each student group. Overall, this study offers valuable insights into programming education literature and contributes to the expanding application of CDM in educational research.
While abstraction is critical for the transferability of automated laboratory science in (bio)chemical and materials sciences, its improper implementation is a technical debt taken against the reproducibility of exper...
While abstraction is critical for the transferability of automated laboratory science in (bio)chemical and materials sciences, its improper implementation is a technical debt taken against the reproducibility of experimental results. Over the decades, computerscience has developed guidelines and strategies for how abstractions are captured in programming languages-particularly concerning the substitutability of implementations of abstracted ideas and the clear definition of the contexts in which abstractions are used. However, few programming languages developed for automated experiments fully leverage the wisdom learned in computerscience. To achieve collaborative sharing of scientific knowledge via automated laboratories, the way that experimental protocols are codified and interpreted by machine agents must use abstractions responsibly and with reproducibility, rather than solely transferability, at its core. This Review discusses how computerscience principles of abstraction can be translated to create more reproducible automation as an enabler for the acceleration of collaborative research with self-driving laboratories. Digital workflow representations in automated and autonomous chemistry laboratories can achieve transferability by using abstract concepts. However, such abstractions must abide by certain rules to ensure reproducibility. Lessons learned from computerscience for responsible abstraction are translated into an automated chemistry laboratory context to guide digital workflow development towards reproducibility.
We present Rhyme, a declarative multi-paradigm query language designed for querying and transforming nested structures such as JSON, tensors, and beyond. Rhyme is designed to be multi-paradigm from ground-up allowing ...
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ISBN:
(纸本)9789819722990;9789819723003
We present Rhyme, a declarative multi-paradigm query language designed for querying and transforming nested structures such as JSON, tensors, and beyond. Rhyme is designed to be multi-paradigm from ground-up allowing it to seamlessly accommodate typical data processing operations-ranging from aggregations and group-bys to joins-while also having the versatility to express diverse computations like tensor expressions (a la einops) and declaratively express visualizations (e.g., visualizing query outputs with tables, charts, and so on). Rhyme generates optimized JavaScript code for queries by constructing an intermediate representation that implicitly captures the program structure via dependencies. This paper presents a system description of Rhyme implementation while highlighting key design decisions and various use cases covered by Rhyme.
Computational thinking, and by extension, computerprogramming, is notoriously challenging to learn. Conversational agents and generative artificial intelligence (genAI) have the potential to facilitate this learning ...
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ISBN:
(纸本)9783031549748;9783031549755
Computational thinking, and by extension, computerprogramming, is notoriously challenging to learn. Conversational agents and generative artificial intelligence (genAI) have the potential to facilitate this learning process by offering personalized guidance, interactive learning experiences, and code generation. However, current genAI-based chatbots focus on professional developers and may not adequately consider educational needs. Involving educators in conceiving educational tools is critical for ensuring usefulness and usability. We enlisted nine instructors to engage in design fiction sessions in which we elicited abilities such a conversational agent supported by genAI should display. Participants envisioned a conversational agent that guides students stepwise through exercises, tuning its method of guidance with an awareness of the educational background, skills and deficits, and learning preferences. The insights obtained in this paper can guide future implementations of tutoring conversational agents oriented toward teaching computational thinking and computerprogramming.
The aim of the paper is to present two virtual machines (VMs) placement models that minimize energy consumption in a data center. Both placement models use binary programming. The first model aims to find an allocatio...
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Students' prior knowledge and self-regulated learning are important predictors of academic success. A growing body of literature studies these predictors with respect to introductory programming courses. Especiall...
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ISBN:
(数字)9783031426827
ISBN:
(纸本)9783031426810;9783031426827
Students' prior knowledge and self-regulated learning are important predictors of academic success. A growing body of literature studies these predictors with respect to introductory programming courses. Especially in the first semester, cohorts exhibit a wide range of backgrounds with many students having no previous programming experience at all. Furthermore, many first semester students lack self-regulated learning capabilities. In the light of high drop-out rates in introductory programming courses, it is crucial to consider student characteristics, such as previously acquired programming skills or self-regulated learning capabilities. In this work, we collected data on such student characteristics via surveys and investigated the relation between survey data and students' use of a version control system during a first semester programming course at a European university. We also related the survey data to the number of test cases students pass in their assignments. Using random forests, we investigated, how version control data can be used to predict student performance in an assignment and to what extent additional survey data can improve such predictions. Our results show that especially in an early phase of an assignment, data on prior knowledge and self-regulated learning can help predict student success.
In a lab work session, students may spend an inordinate amount of time attempting to correct simple errors, repeatedly revisiting and repeating the same errors due to the limited access to the tutor. Instructors, on t...
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ISBN:
(纸本)9783031328824;9783031328831
In a lab work session, students may spend an inordinate amount of time attempting to correct simple errors, repeatedly revisiting and repeating the same errors due to the limited access to the tutor. Instructors, on the other hand, frequently find themselves explaining the same errors. This tool is designed to improve the educational environment in the computer lab for both students and instructors. The first experiment did not show that the tool improves learning programming, but it showed some insights about students that use the tool only in face-to-face sessions and students that use it only with the training mode compared to a control group.
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