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15th International Conference on Learning Analytics and Knowledge

作者： Hassany, Mohammad Brusilovsky, Peter Savelka, Jaromir Narayanan, Arun Balajiee Lekshmi Akhuseyinoglu, Kamil Agarwal, Arav Hendrawan, Rully Agus Univ Pittsburgh Pittsburgh PA 15260 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798400707018

As large language models (LLMs) show great promise in generating a wide spectrum of educational materials, robust yet costeffective assessment of the quality and effectiveness of such materials becomes an important challenge. Traditional approaches, including expert-based quality assessment and student-centered evaluation, are resource-consuming, and do not scale efficiently. In this work, we explored the use of pre-existing student learning data as a promising approach to evaluate LLM-generated learning materials. Specifically, we used a dataset where students were completing the program construction challenges by picking the correct answers among human-authored distractors to evaluate the quality of LLM-generated distractors for the same challenges. The dataset included responses from 1,071 students across 22 classes taught from Fall 2017 to Spring 2023. We evaluated five prominent LLMs (OpenAI-o1, GPT-4, GPT-4o, GPT-4o-mini, and Llama-3.1-8b) across three different prompts to see which combinations result in more effective distractors, i.e., those that are plausible (often picked by students), and potentially based on common misconceptions. Our results suggest that GPT-4o was the most effective model, matching close to 50% of the functional distractors originally authored by humans. At the same time, all of the evaluated LLMs generated many novel distractors, i.e., those that did not match the pre-existing human-authored ones. Our preliminary analysis shows that those appear to be promising. Establishing their effectiveness in real-world classroom settings is left for future work.

关键词： Large Language Models (LLMs) Distractor Generation and Evaluation Student Learning Data introductory programming GPT LLaMA

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You're (Not) My Type- Can LLMs Generate Feedback of Specific Types for introductory programming Tasks?

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JOURNAL OF COMPUTER ASSISTED LEARNING 2025年第1期41卷

作者： Lohr, Dominic Keuning, Hieke Kiesler, Natalie Friedrich Alexander Univ Erlangen Nurnberg Nurnberg Germany Univ Utrecht Utrecht Netherlands Nuremberg Tech Nurnberg Germany

BackgroundFeedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generative AI and especially large language models (LLMs), we expect feedback as part of learning systems to transform, especially for the context of programming. In the past, it was challenging to automate feedback for learners of programming. LLMs may create new possibilities to provide richer, and more individual feedback than ever *** article aims to generate specific types of feedback for introductory programming tasks using LLMs. We revisit existing feedback taxonomies to capture the specifics of the generated feedback, such as randomness, uncertainty and degrees of *** iteratively designed prompts for the generation of specific feedback types (as part of existing feedback taxonomies) in response to authentic student programs. We then evaluated the generated output and determined to what extent it reflected certain feedback *** and ConclusionThis study provides a better understanding of different feedback dimensions and characteristics. The results have implications for future feedback research with regard to, for example, feedback effects and learners' informational needs. It further provides a basis for the development of new tools and learning systems for novice programmers including feedback generated by AI.

关键词： ChatGPT feedback feedback classification feedback types GenAI generative AI GPT-4 introductory programming large language models

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Code Interviews: Design and Evaluation of a More Authentic Assessment for introductory programming Assignments 2025

Code Interviews: Design and Evaluation of a More Authentic A...

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56th Technical Symposium on Computer Science Education

作者： Kannam, Suhas Yang, Yuri Dharm, Aarya Lin, Kevin Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798400705328

Generative artificial intelligence poses new challenges around assessment, increasingly driving introductory programming educators to employ invigilated exams. But exams do not afford more authentic programming experiences that involve planning, implementing, and debugging programs with computer interaction. In this experience report, we describe code interviews: a more authentic assessment method for take-home programming assignments. Through action research, we experimented with the number and type of questions as well as whether interviews were conducted individually or with groups of students. To scale the program, we converted most of our weekly teaching assistant (TA) sections to conduct code interviews on 5 major weekly take-home programming assignments. By triangulating data from 5 sources, we identified 4 themes. Code interviews (1) pushed students to discuss their work, motivating more nuanced but sometimes repetitive insights;(2) enabled peer learning, reducing stress in some ways but increasing stress in other ways;(3) scaled with TA-led sections, replacing familiar practice with an unfamiliar assessment;(4) focused on student contributions, limiting opportunities for TAs to give guidance and feedback. We reflect on the design of code interviews for student experience, academic integrity, and teacher workload.

关键词： authentic assessment introductory programming oral exams

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Automating Autograding: Large Language Models as Test Suite Generators for introductory programming

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JOURNAL OF COMPUTER ASSISTED LEARNING 2025年第1期41卷

作者： Alkafaween, Umar Albluwi, Ibrahim Denny, Paul Al Hussein Tech Univ Dept Comp Sci Amman Jordan Princess Sumaya Univ Technol Dept Comp Sci Amman Jordan Univ Auckland Dept Comp Sci Auckland New Zealand

BackgroundAutomatically graded programming assignments provide instant feedback to students and significantly reduce manual grading time for instructors. However, creating comprehensive suites of test cases for programming problems within automatic graders can be time-consuming and complex. The effort needed to define test suites may deter some instructors from creating additional problems or lead to inadequate test coverage, potentially resulting in misleading feedback on student solutions. Such limitations may reduce student access to the well-documented benefits of timely feedback when learning *** evaluate the effectiveness of using Large Language Models (LLMs), as part of a larger workflow, to automatically generate test suites for CS1-level programming *** problem's statement and reference solution are provided to GPT-4 to produce a test suite that can be used by an autograder. We evaluate our proposed approach using a sample of 26 problems, and more than 25,000 attempted solutions to those problems, submitted by students in an introductory programming course. We compare the performance of the LLM-generated test suites against the instructor-created test suites for each *** and ConclusionsOur findings reveal that LLM-generated test suites can correctly identify most valid solutions, and for most problems are at least as comprehensive as the instructor test suites. Additionally, the LLM-generated test suites exposed ambiguities in some problem statements, underscoring their potential to improve both autograding and instructional design.

关键词： autograding computing education cs1 gpt introductory programming large language model LLM

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introductory programming course: review and future implications

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PEERJ COMPUTER SCIENCE 2021年 7卷 e647页

作者： Omer, Uzma Farooq, Muhammad Shoaib Abid, Adnan Univ Management & Technol Dept Comp Sci Lahore Punjab Pakistan Univ Educ Dept Informat Sci Lahore Punjab Pakistan

The introductory programming course (IPC) holds a special significance in computing disciplines as this course serves as a prerequisite for studying the higher level courses. Students generally face difficulties during their initial stages of learning how to program. Continuous efforts are being made to examine this course for identifying potential improvements. This article presents the review of the state-ofthe-art research exploring various components of IPC by examining sixty-six articles published between 2014 and 2020 in well-reputed research venues. The results reveal that several useful methods have been proposed to support teaching and learning in IPC. Moreover, the research in IPC presented useful ways to conduct assessments, and also demonstrated different techniques to examine improvements in the IPC contents. In addition, a variety of tools are evaluated to support the related course processes. Apart from the aforementioned facets, this research explores other interesting dimensions of IPC, such as collaborative learning, cognitive assessments, and performance predictions. In addition to reviewing the recent advancements in IPC, this study proposes a new taxonomy of IPC research dimensions. Furthermore, based on the successful practices that are listed in the literature, some useful guidelines and advices for instructors have also been reported in this article. Lastly, this review presents some pertinent open research issues to highlight the future dimensions for IPC researchers.

关键词： introductory programming Teaching Learning Assessment Content Tool Review

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introductory programming languages at Australian universities at the beginning of the twenty first century

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JOURNAL OF RESEARCH AND PRACTICE IN INFORMATION TECHNOLOGY 2003年第3期35卷 163-167页

作者： de Raadt, M Watson, R Toleman, M Univ So Queensland Dept Math & Comp Toowoomba Qld 4350 Australia Univ So Queensland Dept Informat Syst Toowoomba Qld 4350 Australia

introductory programming instructors in Australian universities are choosing the programming language they teach primarily based on a perception of industry demand. This paper examines if this perception is justifiable, and offers instructors of all programming languages an insight into the issue of academic and industry balance, now and into the future.

关键词： programming languages introductory programming industry demand ACM computing classification system: D.3.0

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introductory programming: A Systematic Literature Review 23

Introductory Programming: A Systematic Literature Review

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23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE)

作者： Luxton-Reilly, Andrew Simon Albluwi, Ibrahim Becker, Brett A. Giannakos, Michail Kumar, Amruth N. Ott, Linda Paterson, James Scott, Michael James Sheard, Judy Szabo, Claudia Univ Auckland Auckland New Zealand Univ Newcastle Callaghan NSW Australia Princeton Univ Princeton NJ 08544 USA Univ Coll Dublin Dublin Ireland Norwegian Univ Sci & Technol Trondheim Norway Ramapo Coll Mahwah NJ USA Michigan Technol Univ Houghton MI 49931 USA Glasgow Caledonian Univ Glasgow Lanark Scotland Falmouth Univ Falmouth England Monash Univ Clayton Vic Australia Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781450362238

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research.

关键词： ITiCSE working group CS1 introductory programming novice programming systematic literature review systematic review literature review review SLR overview

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Exploring the Effectiveness of Data-Driven Learning Materials for Promoting Student Performance in introductory programming

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IEEE ACCESS 2024年 12卷 125170-125178页

作者： Qian, Yizhou Wu, Yifei Jiangnan Univ Dept Educ Technol Wuxi 214122 Peoples R China Jiangnan Univ Jiangsu Res Ctr Internet Plus Educ Wuxi 214122 Peoples R China

Cryptic and ambiguous error messages provided by programming environments are crucial barriers to beginners when learning to program. This study designed a coding manual to explain common Python programming errors to students using a data-driven approach, following guidelines suggested by prior studies. A quasi-experiment with two groups of middle school students was conducted to examine the effectiveness of the coding manual in reducing student errors, increasing confidence, and promoting learning performance. By analyzing 6015 erroneous student programs collected by the automated assessment tool (AAT) Mulberry, we found that the experimental group who used the coding manual during learning did not make fewer language specification errors (LSEs) in general. However, for LSEs indicating the actual errors in the program, the coding manual showed significant effects in reducing error frequencies. We also found that the coding manual failed to increase students' confidence in programming and promote learning performance. Possible causes of the ineffectiveness may include high cognitive loads during programming, the productivity of learning from the debugging process, and the incompleteness of the explanations and examples in the coding manual. We recommend that computer programming instructors use AATs or similar tools to collect learning data and identify students' common errors, directly explain identified common LSEs in class, and explicitly teach debugging methods and strategies. Future research should focus on students' self-regulation during programming, better methods of explaining common errors to novices, and the long-term effects of using a coding manual on students' learning in introductory programming.

关键词： Codes Encoding programming profession Python Manuals programming environments Syntactics Data models Data-driven learning materials introductory programming programming errors

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One step at a time: Parallelism in an introductory programming course

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2017年 105卷 4-17页

作者： Bogaerts, Steven A. Depauw Univ 602 S Coll Ave Greencastle IN 46135 USA

By introducing parallelism in introductory programming courses, all computer science students can receive a basic understanding of this crucial topic. Such an early introduction, however, has many challenges. This paper first presents a fall 2013 comparison of two Computer Science I (CSI) sections, leading to a conclusion emphasizing the importance of devoting sufficient time to a sufficiently small set of parallelism topics. Six additional CSI sections are then considered, offered from spring 2014 through spring 2016 by three different instructors. Five of these removed coverage of Java thread programming due to challenges found in fall 2013, only to show measurably reduced effectiveness of the parallelism module. Thus a new thread programming integration strategy is presented, as done in spring 2016. This strategy includes active out-of-class activities that split the disparate challenges of Java thread programming into distinct exercises. Results demonstrate improved student interest and learning. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Parallelism Multi-threading CS1 introductory programming Java Education

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Enhancing Practice and Achievement in introductory programming With a Robot Olympics

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IEEE TRANSACTIONS ON EDUCATION 2015年第4期58卷 249-254页

作者： Scott, Michael James Counsell, Steve Lauria, Stanislao Swift, Stephen Tucker, Allan Shepperd, Martin Ghinea, Gheorghita Brunel Univ London Coll Engn Design & Phys Sci Dept Comp Sci Uxbridge UB8 3PH Middx England

Computer programming is notoriously difficult to learn. To this end, regular practice in the form of application and reflection is an important enabler of student learning. However, educators often find that first-year ***. students do not readily engage in such activities. Providing each student with a programmable robot, however, could be used to facilitate application and reflection since, potentially, robots facilitate engaging learning experiences while providing immediate and intuitive feedback. This paper explores whether an introductory course centered upon programming personal robots in preparation for an end-of-course event day-a Robot Olympics-can help students to, first, engage in programming practice more frequently and, second, improve the quality of their code. A survey was conducted to examine the students' programming practice behavior, and students' final coursework submissions were also reviewed for aspects of program quality. The findings from this cohort were compared to a reference group from a previous cohort that shared similar learning objectives and entry requirements, but had focused on Web programming instead of using robots. The results reveal statistically significant increases in programming practice compared to the reference group. Furthermore, being enrolled in the course that culminated in the Robot Olympics was a significant predictor of two aspects of program quality: functional coherence and sophistication. This suggests that robot-centered courses can promote engagement with, and enhance some aspects of, programming practice.

关键词： Achievement CS1 introductory programming motivation personal robots practice quality

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