This study identifies the components contributing to resilience among undergraduate computer science students in introductory programming courses. It presents an interpretive qualitative study's initial findings t...
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ISBN:
(纸本)9780998133171
This study identifies the components contributing to resilience among undergraduate computer science students in introductory programming courses. It presents an interpretive qualitative study's initial findings through 20 student interviews. The study reveals the significance of self-efficacy, including self-talk, autonomy, self-management, self-regulation, and intrinsic motivation, as influential factors in understanding and fostering resilience among students in computer science contexts. These findings contribute to the existing literature on resilience and offer valuable insights for educators and researchers seeking to support student's academic success and well-being. This study's unique contributions include exploring self-talk and self-management components, which are yet to be extensively studied with resilience in previous research. Future research can build upon these findings to develop interventions and educational practices that foster resilience among undergraduate computer science students.
Considerable attention has been devoted to the use of automated assessment tools in introductory programming instruction. However, the efficacy of automated feedback hinges on the quality of the content it provides an...
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ISBN:
(纸本)9798400710117
Considerable attention has been devoted to the use of automated assessment tools in introductory programming instruction. However, the efficacy of automated feedback hinges on the quality of the content it provides and the student's ability to apply this information to their learning. The process of learners' continuous debugging of their code exemplifies the principles of self-regulated learning (SRL). This study aims to investigate the effectiveness of automated feedback using SRL theory. The findings indicate that there was no difference in code improvement rates between students with different SRL abilities when using automated feedback. However, automated feedback was effective in helping students debug common compilation errors, which has significant implications for improving the efficiency of programming education. We recommend that researchers further design the feedback content and the presentation to investigate the connection between SRL, feedback usage, and the programming learning process.
programming courses are increasingly attended around the world by students majoring in different fields, as learning a programming language is often a necessity for graduates of higher education. Social sciences stude...
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ISBN:
(纸本)9798350394023;9798350394030
programming courses are increasingly attended around the world by students majoring in different fields, as learning a programming language is often a necessity for graduates of higher education. Social sciences students are among the groups of learners for whom introductory programming courses are offered. Whereas this group of learners might be highly motivated to learn how to write code for the first time, they are often pessimistic regarding their own abilities, and they start the course with a low level of self-confidence. This work in progress describes the author's experience with holding such a course geared towards humanities students. For the first few years, before the Covid-19 pandemic, the course was held in the traditional manner, i.e. in a classroom. In 2020, it had to suddenly be switched to an online format, until, finally, in 2023, it reverted to its face-to-face format. The paper describes the changes in class modality, the ways in which the teacher dealt with the sudden online format, as well as how the students perceived the level of difficulty and the level of enjoyment of taking part in the classes. Empirical observations, along with data gathered through questionnaires administered to students, highlight some of the challenges and the lessons learned during these switches in class modality.
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 experi...
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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.
This pilot study explores how visualization strategies, grounded in multiple representations theory, impact novice students' engagement, and cognitive load during program tracing tasks. Students were were shown a ...
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ISBN:
(纸本)9798400710384
This pilot study explores how visualization strategies, grounded in multiple representations theory, impact novice students' engagement, and cognitive load during program tracing tasks. Students were were shown a visualization of the three-variable swap problem at the start of an introductory programming course (CS1) at a large public North American research-intensive university. We compared three conditions: interactive multiple representations, Python Tutor (a single-representation tool), and text-only methods. Preliminary results indicate that interactive multiple representations increase engagement for students with prior programming experience, while no significant differences were observed for students without prior experience. These findings suggest that while multiple representations may boost engagement, identifying how to effectively support students of all experience levels and reduce cognitive load requires further study.
Artificial Intelligence (AI) can foster education but can also be misused to breach academic integrity. Large language models like ChatGPT are able to generate solutions for individual assessments that are expected to...
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Artificial Intelligence (AI) can foster education but can also be misused to breach academic integrity. Large language models like ChatGPT are able to generate solutions for individual assessments that are expected to be completed independently. There are a number of automated detectors for AI assisted work. However, most of them are not dedicated to programming and/or they rely on existing student submissions (i.e., the learning approach). This paper presents a straightforward detector for AI assisted code, relying on code anomaly. No existing student submissions are needed. The detector employs 34 features covering constants, data structures, branches, loops, functions, and others. According to our evaluation on three data sets, the detector and its normalized variation are effective with 89% top-K precision. However, allowing discussion among colleagues and access to the internet might reduce the effectiveness by 25%. The effectiveness is further reduced by about the same amount when AI assistance is only used on some tasks, not the whole submissions. Although our detectors should be used with caution due to the limitations, it sufficiently shows that code anomaly can be distinctive for identifying AI assisted work. Instructors can start looking for the code anomaly among the submissions for such identification.
introductory programming courses form the foundation of all programming qualifications, which are in turn essential to a well-educated information technology workforce. Sadly these courses cannot boast high retention ...
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ISBN:
(纸本)9783031485350;9783031485367
introductory programming courses form the foundation of all programming qualifications, which are in turn essential to a well-educated information technology workforce. Sadly these courses cannot boast high retention or success rates. The inverted (or "flipped") classroom approach has been touted as a way to engage large classes of mixed-ability students. To inform the design of a first-year university introductory programming course, the authors conducted a systematic literature review on existing flipped classroom implementations. Five electronic databases were searched with the keywords "flipped learning" and "introductory programming" for peer-reviewed academic literature published in the decade between 2013 and 2022 to answer the question "How may flipped learning be used to enhance student learning in an introductory programming module?". It was found that while the flipped learning approach poses challenges to both lecturers and students, it creates an increase in engagement during class and ensures that better use is made of limited face-to-face time between instructors and students. Meaningful future research would focus on seeking objective student feedback to understand the nature of the student experience.
In the wake of highly capable Large Language Models (LLM) like ChatGPT, educational institutions have been navigating how to position themselves within this Artificial Intelligence (AI) era. There have been various su...
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ISBN:
(纸本)9781905824731;9798350356595
In the wake of highly capable Large Language Models (LLM) like ChatGPT, educational institutions have been navigating how to position themselves within this Artificial Intelligence (AI) era. There have been various suggestions and attempts to exclude ChatGPT in the education sector due to its AI abilities to give accurate responses to students, plagiarism and over reliance on the tool. However, there are also attempts to formally incorporate ChatGPT in education, such as in the field of economics, computer sciences or Mathematics. Without proper guidelines on the uses of ChatGPT in education, these AI technologies can be disruptive, uncontrollable and pose a risk to academic integrity. Based on the synthesis of ideas in the literature and ChatGPT experimental tests, this paper presents relevant guidelines for effective use of ChatGPT in the introductory programming education.
Background and Context. Metacognitive skills are important for all students learning to program and interest in applying pedagogical approaches in early programming courses that focus on metacognitive aspects is growi...
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ISBN:
(纸本)9781450391948
Background and Context. Metacognitive skills are important for all students learning to program and interest in applying pedagogical approaches in early programming courses that focus on metacognitive aspects is growing. However, most studies of such approaches are not rigorously based in theory, and when they are, almost always utilize foundational education and psychology theories from as far back as the 1970s. More recent theory is less tested, and not all relevant metacognitive theories have been explored in the computing education research literature. Objectives. We present the first use in a programming education context of a newer metacognitive theory that explicitly examines the differences between self-regulation, co-regulation, and socially shared regulation. Our research questions are: 1) How do students express their learning strategies, both when working alone and when working in groups, and how do these align with existing models of self-regulation and co-regulation? and 2) To what extent do written expressions of self-regulation, co-regulation, and socially shared regulation relate to student performance? Methods. Grounded in the above mentioned theory, we collected qualitative self-reflection and quantitative course performance data from nearly 1,000 students in an introductory programming course. We use these data to explore students' self-regulation habits when studying alone and their co-regulation habits when studying in groups. Findings. Our findings indicate that higher self-regulation correlates with higher performance, but higher co-regulation had the opposite effect. We explore these differences through a qualitative analysis of the self-reflection statements and identify co-regulation strategies to build upon existing models of self-regulation. Implications. We identify emergent themes in our data that align with those in recent literature in self-regulated learning in computing education and present the first set of co-regulation themes in
Teaching an introductory programming course to first-year students has long been a challenge for many college instructors. The COVID-19 pandemic, which caused unprecedented shifts in learning modality across the globe...
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Teaching an introductory programming course to first-year students has long been a challenge for many college instructors. The COVID-19 pandemic, which caused unprecedented shifts in learning modality across the globe, has worsened the learning experience of novice programmers. Instructors have to find innovative ways to keep students engaged and learning. Blended or hybrid learning has become a new preferred way of learning during the COVID-19 pandemic. Blended learning is viewed as a combination of both in-person and online instructions. Such a learning environment offers instructors the flexibility to provide learners with an engaging face-to-face learning experience while promoting the well-being and safety of students. Starting Fall 2020, York College (and other CUNY colleges) has since offered several courses in hybrid mode. Two years have passed since the abrupt transition. There were several lessons learned from the experiences. In this paper, I discussed evidence-based pedagogical approaches that were used to teach students in an introductory computer programming class at York College, CUNY, where blended learning was used. Student perceptions of learning experience and obtaining coding skills in both online and in-person environments are also presented. The findings from the survey suggested that students benefited from face-to-face interactions and feedback, while those who preferred an online environment liked the flexibility that online components offer. Through careful design and implementation of pedagogical approaches used in the class, novice programmers could potentially benefit from both face-to-face and online components of blended learning.
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