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4th Conference on Computing Education Practice (CEP)

作者： Johnson, Fionnuala McQuistin, Stephen O'Donnell, John Univ Glasgow Glasgow Lanark Scotland

ISBN: (纸本)9781450377294

Python has become a popular language for the delivery of introductory programming courses. Two reasons for this are Python's convenience and syntactic simplicity, giving a low entry barrier for beginners and the ability to solve complex problems with short snippets of code. However, students exhibit widespread misconceptions about the meaning of basic language constructs, inhibiting their ability to solve problems and damaging their understanding of fundamental concepts. In this paper, we document our observations of level 1 university students over several years, as well as surveys probing the nature of their misconceptions. We analyze the misconceptions in relation to a notional machine model for Python, and show that many students form inadequate and brittle mental models of the language. Our results indicate that one of the major sources of misunderstanding is the heavy use of overloading in Python. Overloading hides the complexity of algorithms and data structures, often leading students to write code that involves mutability, sharing, copying, side effects, coroutines, concurrency, and lazy evaluation - and none of those topics are accessible to students who haven't yet mastered basic assignments, conditionals, and looping. We suggest that Python, when taught alone, is insufficient as an introductory language: students can gain a firmer grasp of programming fundamentals when Python is presented alongside a complementary low level language that makes a notional machine clear and explicit.

关键词： introductory programming course overloading misconceptions notional machine model

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A learning analytics dashboard for data-driven recommendations on influences of non-cognitive factors in introductory programming

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EDUCATION AND INFORMATION TECHNOLOGIES 2024年第8期29卷 9221-9256页

作者： Kaur, Amanpreet Chahal, Kuljit Kaur Guru Nanak Dev Univ Dept Comp Sci Amritsar Punjab India

Research so far has overlooked the contribution of students' noncognitive factors to their performance in introductory programming in the context of personalized learning support. This study uses learning analytics to design and implement a Dashboard to understand the contribution of introductory programming students' learning motivation, personality, and class participation factors to their programming performance and provide adaptive support to improve their motivation and persistence. Moreover, this study evaluates the effectiveness and usefulness of the dashboard in two phases: committee evaluation and laypersons evaluation. The evaluation results indicate dashboard's effectiveness in students' better-informed decision-making related to their learning approach. In both phases, the dashboard has been perceived as useful and easy-to-use tool. These findings indicate that the dashboard is perceived as a useful tool for providing timely feedback about non-cognitive factors contributing to students' programming performance. The study provides insights into the noncognitive side of novice programmers and encourages further exploration of other aspects influencing their performance.

关键词： introductory programming course Learning Analytics Dashboard Personality traits Motivational factors Class participation Performance prediction Data-driven recommendations

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Work-in-Progress: Closing the Gaps: Diversity in programming Education

Work-in-Progress: Closing the Gaps: Diversity in Programming...

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IEEE Global Engineering Education Conference (IEEE EDUCON)

作者： Groher, Iris Sabitzer, Barbara Demarle-Meusel, Heike Kuka, Lisa Hofer, Alexander Johannes Kepler Univ Linz Inst Business Informat Software Engn Linz Austria Johannes Kepler Univ Linz Sch Educ STEM Didact Linz Austria

ISBN: (纸本)9781728184784

In programming education for non-computer science students, high diversity exists among our students, including gender, culture, age, educational background, and work experience. introductory programming courses traditionally face high drop-out rates and poor performance, and students often perceive learning to program as difficult. Current research on diversity in programming education has primarily focused on gender differences, thus neglecting the influence of other diversity dimensions on students' performance. The project Diversity in programming Education (DIPE) aims to fill this gap by identifying how heterogeneous groups of students can be best supported. Based on our findings, we develop a didactic concept with accompanying teaching and learning material to support different diversity dimensions actively in programming education. The concept includes competence models for measuring competences and individual learning paths. To support a flat learning curve further in university programming courses in the future, we closely work with schools and educational centres for teachers to integrate algorithmic thinking into school curricula. We empirically evaluate our concept using a mixed methods approach. In particular, we will explore the effects of our concept in our programming course and will investigate the value of these effects as perceived by lecturers and students.

关键词： introductory programming course diversity gender didactic concept teaching material learning material

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Modula-2 versus Java as the first programming language: Evaluation of students' performance 16

Modula-2 versus Java as the first programming language: Eval...

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17th International Conference on Computer Systems and Technologies (CompSysTech)

作者： Savic, Milos Ivanovic, Mirjana Radovanovic, Milos Budimac, Zoran Univ Novi Sad Fac Sci Dept Math & Informat Novi Sad Serbia

ISBN: (纸本)9781450341820

The choice of the first programming language in computer science curricula is a complex multi-faceted issue that needs to be addressed from multiple points of view. In this paper we adopt the perspective of the teacher, and after surveying factors such as language features, programming paradigms, curricular concerns and students' perceptions, we present extended results of the empirical assessment of student performance at our introductory programming course, where a switch was recently made from Modula-2 to Java, keeping the course structurally unchanged. Our main aim is to investigate the impact of the first programming language to the acquisition of essential programming concepts, techniques and principles. New results involving students' high-school GPA and performance at other courses, backed up by thorough statistical analysis, remove some potential sources of bias regarding students' learning and cognitive abilities, reaffirming the overall conclusion that the programming language used in the introductory programming course does not have a deep impact on the acquisition of essential concepts, techniques and principles of the imperative programming style.

关键词： introductory programming course change of the first programming language students' performance empirical evaluation

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Why Do CS1 Students Become Repeaters?

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IEEE REVISTA IBEROAMERICANA DE TECNOLOGIAS DEL APRENDIZAJE-IEEE RITA 2022年第3期17卷 245-253页

作者： Marco-Galindo, Maria-Jesus Minguillon, Julia Garcia-Solorzano, David Sancho-Vinuesa, Teresa Univ Oberta Catalunya Comp Sci Multimedia & Telecommun Sch Barcelona 08018 Spain

Learning to program is hard for many students. As a result, CS1 courses have a significant percentage of repeaters. For this reason, the goal of this article is to analyze which factors affect repeaters so that a specific learning strategy for them can be performed. In this regard, a first analysis of a CS1 course shows there are two types of repeaters: (1) those who do (almost) nothing throughout the semester and drop out, and (2) those who work during the whole semester, but finally fail. According to repeaters' perceptions, they were motivated to learn to program, but it was difficult for them to keep up because of it was so hard to reconcile it with their personal context, so based on what they did the previous semester, they would prefer to continue from where they left off or change the pace of activities.

关键词： CS1 dropout factors introductory programming course performance analysis repeaters' perception

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Detecting ChatGPT-Generated Code Submissions in a CS1 course Using Machine Learning Models 2024

Detecting ChatGPT-Generated Code Submissions in a CS1 Course...

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55th ACM Technical Symposium on Computer Science Education (SIGCSE)

作者： Hoq, Muntasir Shi, Yang Leinonen, Juho Babalola, Damilola Lynch, Collin Price, Thomas Akram, Bita North Carolina State Univ Raleigh NC 27695 USA Univ Auckland Auckland New Zealand

ISBN: (纸本)9798400704239

The emergence of publicly accessible large language models (LLMs) such as ChatGPT poses unprecedented risks of new types of plagiarism and cheating where students use LLMs to solve exercises for them. Detecting this behavior will be a necessary component in introductory computer science (CS1) courses, and educators should be well-equipped with detection tools when the need arises. However, ChatGPT generates code non-deterministically, and thus, traditional similarity detectors might not suffice to detect AI-created code. In this work, we explore the affordances of Machine Learning (ML) models for the detection task. We used an openly available dataset of student programs for CS1 assignments and had ChatGPT generate code for the same assignments, and then evaluated the performance of both traditional machine learning models and Abstract Syntax Tree-based (AST-based) deep learning models in detecting ChatGPT code from student code submissions. Our results suggest that both traditional machine learning models and AST-based deep learning models are effective in identifying ChatGPT-generated code with accuracy above 90%. Since the deployment of such models requires ML knowledge and resources that are not always accessible to instructors, we also explore the patterns detected by deep learning models that indicate possible ChatGPT code signatures, which instructors could possibly use to detect LLM-based cheating manually. We also explore whether explicitly asking ChatGPT to impersonate a novice programmer affects the code produced. We further discuss the potential applications of our proposed models for enhancing introductory computer science instruction.

关键词： ChatGPT large language model artificial intelligence introductory programming course CS1 cheat detection plagiarism detection

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Java Projects Motivated by Student Interests

Java Projects Motivated by Student Interests

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13th Annual Conference on Innovation and Technology in Computer Science Education

作者： Djordjevic, Mirela Manhattanville Coll Purchase NY 10577 USA

ISBN: (纸本)9781605580784

We present four Java projects suggested by our student that were developed in Java CS1/CS2 course. What is in interest of our students who start programming? The answer might be different from teachers or textbook approaches. Some topics include drawing, games, colors, measuring time, etc. Question can be raised if we can just monitor a process of studying by watching the development of projects. Can we as teachers learn what major interests of our students are when learning to program. We share our finding of interesting examples.

关键词： Java programming introductory programming course CS2001 Java Task Force - CS for Liberal Arts

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Predicting Rough Error Causes in Novice Programmers Using Cognitive Level 20th

Predicting Rough Error Causes in Novice Programmers Using Co...

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20th International Conference on Intelligent Tutoring Systems (ITS)

作者： Kim, Deok Yeop Lee, Woo Jin Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea

ISBN: (纸本)9783031630279;9783031630286

Novice programmers face various errors during the learning of a programming language. Most of them need help from instructors since they lack error resolution skills. On the other side, instructors spend a lot of time figuring out students' error causes. Long error detection times result in delayed and failed feedback, leading to a loss of student motivation. To support instructor's fast feedback, a detection method of error cause is needed. Existing detection methods, which are code-based, detect common and specific errors that can be identified by analyzing source code. These methods do not cover the diverse error patterns of novice programmers sufficiently, such as logical defects. To resolve this issue, it may be beneficial to detect rough and correct error causes of diverse error patterns. In this paper, a prediction method of rough error cause is proposed by considering not only source code, but also problem information, execution results, and the cognitive level indicating programming skills. We assume that different programming skills lead to different error patterns, which can help roughly but precisely predict error causes of runtime and logic errors in novice programmers. For performance evaluation, data from two introductory programming courses are used to validate the effectiveness of the cognitive level. Additionally, the usability for fast feedback is validated by comparing the error causes detection times of the instructors in each case.

关键词： Error Detection Cognitive Level programming Error introductory programming course Learning Taxonomy

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Would Flipped Classroom be My Approach in Teaching Computing courses: Literature Review 9

Would Flipped Classroom be My Approach in Teaching Computing...

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9th International Conference on Information and Education Technology (ICIET)

作者： Bakheet, Eman Madani Gravell, Andrew M. Univ Southampton Sch Elect & Comp Sci Southampton Hants England Saudi Elect Univ Riyadh Saudi Arabia

ISBN: (纸本)9781665419338

Adopting technology in education has been introduced in different models. Flipped classroom as one of the blended learning approach was tested and applied in teaching computing courses. This approach shows promising evidence that it is efficient through research evaluating it. This paper aims to review the research conducted in the flipped classroom, and in particular from computer science education perspective. The study involved a review of 20 studies that applied the flipped classroom in teaching computing courses. These studies involved experiments varying from longitudinal to a single semester evaluation. Indeed, the majority of these reports concerned a CS1 course (introductory programming course). Therefore, this paper discusses and reviews the current literature reporting research conducted on flipped classroom from computer science perspectives.

关键词： Flipped classroom Inverted classroom literature review Computer science courses introductory programming course Students perspectives

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Using Software Reasoning to Determine Domain-law Violations and Provide Explanatory Feedback: Expressions Tutor Example 14

Using Software Reasoning to Determine Domain-law Violations ...

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14th International Conference on Computer Supported Education (CSEDU)

作者： Sychev, Oleg Penskoy, Nikita Terekhov, Grigory Volgograd State Tech Univ Software Engn Dept Lenin Ave 28 Volgograd Russia

ISBN: (纸本)9789897585623

Introducing students to a new subject domain involves getting them acquainted with many new concepts. Some of these students need a trial-and-error process to learn these concepts, but it is time-consuming for teachers. An intelligent tutor capable of detecting domain-law violations and providing explanatory feedback can allow training until learning without supervision. This is especially important when teaching software engineering because it requires learning a lot of new concepts and has well-defined laws. Our goal was to develop a tutor capable to explain to the student the cause of their errors: the subject-domain laws that they violated. We present an approach to modeling subject-domain concepts and laws that allows finding correct answers and determining law violations in students' answers. A web-based tool for learning the order of evaluation for programming-language expressions was developed to assess the viability of this approach. The experiments show that Apache Jena and Clingo inference engines work quickly enough to find domain-law violations after each error in middle-sized tasks. The developed tool was evaluated by volunteer undergraduate students and received positive feedback. After the initial evaluation, the tool was used in the learning process;the students' learning gains after using the system were statistically significant.

关键词： Constraint-based Intelligent Tutor Software Reasoning introductory programming course Expressions

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