Search

In this paper, we describe a teaching scenario using a virtual environment (known also in the context of the 'metaverse'). This is motivated by the challenges that arise during the pandemic. More and more teaching scenarios are transferred to online learning settings, which allow learning at any time and at any time. One of the possibilities are virtual 3D environment. These allow more intensive immersion than for example video conferences. Furthermore, they offer new didactic concepts, for example, for group activities. The benefit of using virtual 3D environments we demonstrate by a concrete learning scenario: the simulation of robot programming. A further advantage when using virtual 3D environments are personal assistants (conversational/ pedagogical agents), for example, to the ease the work load borne by teachers; meanwhile, this works well also with natural language due to advantage stage of artificial intelligence implementations.

This paper discusses the design of interactive gamified assessments for an introductory programming course based on the multimedia segmenting principle and gamification. The objective is to develop more engaging online programming assessments for low-achieving students. The general design follows Nielsen's design guidelines and incorporates Zaharias' usability evaluation framework with the motivation to learn. The methodology employed the Successive Approximation Model Version 2 (SAM2), comprising two key phases: preparation and iterative design. In the initial phase, a comparative analysis was performed to determine the design principles. The iterative design phase encompassed the application's design via storyboards, the development of the high-fidelity prototype, and users' reviews. A qualitative approach was adopted, involving a user-centred design (UCD) session through focus group discussions with 12 first-year students from the Diploma of Computer Science program, all of whom were low achievers in programming. The participants need to review and rate the prototype based on the scales of the usability recommendations, which are visual design, content design, navigation, interaction, gamification design, and multimedia design. The results from the UCD session revealed that all participants agreed with the usability recommendations integrated into the interactive gamified programming assessments, with the highest mean score of 5.00.

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where creating assessments has long been a challenging task due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT's ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, this position paper investigates the potential opportunities and threats of ChatGPT for programming education, guided by the question: What could the potential consequences of ChatGPT be for programming education? This paper applies a methodological approach inspired by analytic autoethnography to investigate, experiment, and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. The study finds that there are several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be "lying" in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT have potential for a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast. [Note: The publication year (2023) shown in the citation on the PDF is incorrect. The correct publication year is 2024.]

Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first student pass-fail prediction approach proposed by Van Petegem and colleagues (2022) in a different CS1 course. Using student submission and self-report data, we investigated the reproducibility of the original approach, the effect of adding self-reports to the model, and the interpretability of the model features. The results showed that the original approach for student dropout prediction could be successfully reproduced in a different course context and that adding self-report data to the prediction model improved accuracy for the first four weeks. Wealso identified relevant features associated with dropout in the CS1 course, such as timely submission of tasks and iterative problem solving. When analyzing student behaviour, submission data and self-report data were found to complement each other. The results highlight the importance of transparency and generalizability in learning analytics and the need for future research to identify other factors beyond self-reported aptitude measures and student behaviour that can enhance dropout prediction.

As our society has advanced in the era of digital transformation, education has been transformed from knowledge-centered to competency-centered to solve future problems in the light of unpredictable changes and events in our lives. Programming education provides the basic knowledge needed, and fosters higher-order thinking skills in the process of generating and converging ideas to solve problems. However, in Korean elementary schools, it is mostly based on a lecture-based instructional design and focuses on knowledge delivery, which has limited the educational effects of programming. However, productive failure (PF) focuses on learning concepts in authentic problems, and lets the students generate different solutions and discuss them in an acceptable environment, with the result that they fail to solve the problem. Therefore, this study developed a PF-based educational program and tested it on sixth-grade students in a Korean elementary school. The results showed that the computational thinking (CT) and creative problem-solving (CPS) skills of the experimental group were significantly greater than those of the control group, with a medium effect size for CT and a high effect size for CPS skills. To generalize the results and increase the applicability, follow-up studies should expand the subject of the study, develop specific teaching guidelines for teachers, and invent various learning problems appropriate to the students' level and different domains of learning.

Teaching programming is a complex process requiring learning to develop different skills. To minimize the challenges faced in the classroom, instructors have been adopting active methodologies in teaching computer programming. This article presents a Systematic Mapping Study (SMS) to identify and categorize the types of methodologies that instructors have adopted for teaching programming. We evaluated 3,850 papers published from 2000 to 2022. The results provide an overview and comprehensive view of active learning methodologies employed in teaching programming, technologies, programming languages, and the metrics used to observe student learning in this context. In the results, we identified thirty-seven different ALMs adopted by instructors. We realized that seventeen publications describe teaching approaches that combine more than one ALM, and the most reported methodologies in the studies are Flipped Classroom and Gamification-Based Learning. In addition, we are proposing an educational and collaborative tool called CollabProg, which summarizes the primary active learning methodologies identified in this SMS. CollabProg will assist instructors in selecting appropriate ALMs that align with their pedagogical requirements and teaching programming context.

This research explores how metacognitive strategies influence the metacognitive awareness of undergraduate students enrolled in an online flipped programming course. It specifically focuses on regulatory actions crucial for success in programming instruction and distance education settings. The primary objective is to contribute to the existing literature by investigating the implementation of online flipped programming courses that integrate metacognitive-oriented approaches to support students' metacognitive regulatory actions. The study employed an explanatory sequential mixed methods design. A total of 29 university students enrolled in programming courses participated in the study, engaging with instructional videos provided before each 10-week lesson. They were administered the Metacognitive Awareness Scale and supplementary forms designed to assess their metacognitive awareness and regulatory actions. A detailed coding scheme was developed to analyze students' metacognitive regulation activities during programming lessons. The study also evaluated the impact of supportive activities on students' metacognitive awareness. While no statistically significant difference was found in the students' metacognitive awareness through quantitative analysis, qualitative data revealed that activities supporting metacognition significantly enhanced students' comprehension of the programming content.

Hollywood screenwriters worry about Artificial Intelligence (AI) replacements taking over their jobs. Famous museums litigate to protect their art from AI infringement. A major retailer scraps a machine-learning based recruitment program that was biased against women. These are just a few examples of how AI is affecting the world of work, learning, and living. MIS and computer science students are among the professional groups who are embarking into careers with nebulous frontiers obscured by the outcroppings brought on by AI. Computer Science and Information System curriculum task forces have recognized the increasing ethical and professional implications developers' work can have beyond the scope of the programmers' code. In this article, the authors examine the professional, legal, and ethical implications of copyrights and algorithmic bias resulting from development of AI-enhanced applications and offer suggestions for addressing these topics in courses considering changes to the CC2020 and IS2020 Model Curriculum frameworks.

Learning computer programming is typically difficult for newcomers. Demotivation and learned helplessness have received much attention. Besides the subject's intricacy, low in-class participation has been associated with poor student achievement. This paper presents a follow-up, stage 2 study on the novel instructional technique, Student-Driven Probe Instruction (SDPI), to address low in-class participation in programming courses. Instead of the teacher lecturing/explaining content to the class and asking questions, students were shown a snippet of code or other relevant material and given the option to ask questions beforehand. The study was conducted in two stages: stage 1 pilot and stage 2. This paper presents the results of stage 2, while stage 1 operations and results are discussed briefly. The number of questions asked in class, real-time Trello board postings, and emails/Slack conversations with the instructor were used to track participation. In-class participation showed significant improvement. Average quiz and in-class activity scores showed marginal gains. Results from the end-of-course survey show that students preferred SDPI over the traditional lecture style since it stirred their interest in the content and provided them the confidence to ask questions in class. The study is purely exploratory in nature, and no conclusions can be drawn due to the extremely small sample size of the student population.

Microlearning is a modern learning modality that has been adopted in recent years for student education. This study aimed to reveal the effect of video-based microlearning on the development of programming skills and technology acceptance among intermediate school students. The study used a quasi-experimental design for two groups. A technology acceptance scale and a performance observation card for programming skills were prepared in order to collect data for the study. A cluster random sample of intermediate school students participated in video-based micro learning. The students were divided into two groups: the experimental group (consisting of 31 students) and the control group (consisting of 32 students). The results of the study showed that there were statistically significant differences between the mean scores of both study groups in favor of the experimental group in the post-application of the performance observation card for programming skills. However, there were statistically significant differences between the mean scores of the students in the experimental group in the pre- and post-application of the technology acceptance scale in favor of the post application. The study suggested that computer teachers might benefit from employing video-based microlearning to teach students programming skills at different stages based on these results. Suggestions related to the research findings are also provided.

The purpose of this study is to derive a correlation between the technological thinking disposition and the computational thinking ability of gifted students in Korea. The correlation between each element was analyzed by looking at the sub-elements of computational thinking according to the components of technological thinking disposition. The experiment was conducted from September 2019 to February 2021 with 217 students of I Gifted School in Incheon, South Korea. The collected data were analyzed with Pearson's correlation coefficient using the statistical program R using Google COLAB. A summary of the study results is as follows. First, regarding the correlation between technological thinking ability, among the 6 components of technological thinking disposition, technological creativity and expression disposition and technological manipulation disposition show the highest correlation at 0.851. This shows that students who have an excellent ability to implement algorithms with new ideas or express them in various other attempts when implementing programs for gifted students also tend to enjoy program coding or have a tendency to like coding. Second, concerning the correlation between the technological thinking disposition and the sub-factors of computational thinking, some elements showed negative correlations and some had almost no correlation index. Students with high technological curiosity, however, tended to show a 0.287 in the parallelism factor compared to other factors. This showed a generally high trend. It can be said that students who want to know the functions, uses, forms, and characteristics of functions while implementing programs tend to have a better ability to divide large tasks into smaller tasks and process them simultaneously compared with other sub-elements of computational thinking. Third, regarding the correlation between computational thinking skills, the correlation between data analysis and pattern recognition was the highest at 0.637. This indicates that students who have an excellent ability to analyze a given coding problem also can find rules in data, showing that students at gifted schools in Korea tend to enjoy problem-solving.

The use of a pedagogical approach mediated transfer with the bridging method has been successful in facilitating the transitions from block-based to text-based programming languages. Nevertheless, there is a lack of research addressing the impact of this transfer on programming misconceptions during the transition. The way programming concepts are taught to K-12 learners can later result in misconceptions for adult learners. The main objective was to examine the impact of mediated transfer using the bridging method pedagogical approach on the prevalence of programming misconceptions. We conducted a quasi-experimental study in school settings during informatics (computer science) classes among 163 sixth-grade students. The control group received traditional programming lectures using the text-based programming language, Python. Conversely, the experimental group utilized a mediated transfer pedagogical approach by starting with the block-based programming language MakeCode for micro:bit before transitioning to the text-based Python. Our findings indicate that the experimental group significantly reduced programming misconceptions in fundamental programming concepts: variables, sequencing, selection, and loops - compared to the control group. This suggests that the use of block-based programming language as an initial step in programming education, followed by a structured transition to text-based programming language, can effectively mitigate common misconceptions among K-12 learners.

Pair Programming is considered an effective approach to programming education, but the synchronous collaboration of two programmers involves complex coordination, making this method difficult to be widely adopted in educational settings. Artificial Intelligence (AI) code-generation tools have outstanding capabilities in program generation and natural language understanding, creating conducive conditions for pairing with humans in programming. Now some more mature tools are gradually being implemented. This review summarizes the current status of educational applications and research on AI-assisted programming technology. Through thematic coding of literature, existing research focuses on five aspects: underlying technology and tool introduction, performance evaluation, the potential impacts and coping strategies, exploration of behavioral patterns in technological application, and ethical and safety issues. A systematic analysis of current literature provides the following insights for future academic research related to the practice of "human-machine pairing" in programming: (1) Affirming the value of AI code-generation tools while also clearly defining their technical limitations and ethical risks; (2) Developing adaptive teaching ecosystems and educational models, conducting comprehensive empirical research to explore the efficiency mechanisms of AI-human paired programming; (3) Further enriching the application of research methods by integrating speculative research with empirical research, combining traditional methods with emerging technologies.

Providing consistent, individualized feedback to teachers is essential for improving instruction but can be prohibitively resource-intensive in most educational contexts. We develop M-Powering Teachers, an automated tool based on natural language processing to give teachers feedback on their uptake of student contributions, a high-leverage dialogic teaching practice that makes students feel heard. We conduct a randomized controlled trial in an online computer science course (N = 1,136 instructors), to evaluate the effectiveness of our tool. We find that M-Powering Teachers improves instructors' uptake of student contributions by 13% and present suggestive evidence that it also improves students' satisfaction with the course and assignment completion. These results demonstrate the promise of M-Powering Teachers to complement existing efforts in teachers' professional development.

Whereas hackathons are widespread within and outside academia and have been argued to be a valid pedagogical method for teaching interdisciplinarity, no detailed frameworks or methods are available for conceptualizing and organizing educational hackathons, i.e., hackathons dedicated to best achieving pedagogic objectives. This paper is dedicated to introducing EDUCational Hackathons for learning how to solve Interdisciplinary Challenges (EDUCHIC) through: (1) defining the fundamental principles for framing an activity as an EDUCHIC, integrating principles from pedagogical methods, hackathon organization, and interdisciplinarity processes; (2) describing general properties that EDUCHIC possess as a consequence of the interaction of the fundamental principles; (3) developing operational guidelines for streamlining the practical organization of EDUCHIC, including an exhaustive end-to-end process covering all the steps for organizing EDUCHIC and practical frames for carrying the key decisions to be made in this process; and (4) a demonstration of these guidelines through illustrating their application for organizing a concrete EDUCHIC.

A global concern in recent years has been the formal incorporation of computing and digital technology subjects into high school curricula. Despite many initiatives to make these subjects accessible to students, significant barriers to uptake remain in many countries. In this study we convened a panel of 32 academic, research, and other experts from several countries, to participate in a three round Delphi study with the goal of identifying ways to improve student participation for high school computer science and programming subjects in New Zealand. The study explored barriers and challenges, potential solutions and future scenarios, as well as implementation ideas for the most important solutions across five domains: Computer Science and Programming Standards, Resources, Support, Assessment, and Student-centred factors. Together these three Delphi rounds were synthesized into a framework summarizing challenges and potential solutions, including suggestions relating to the implementation of those solutions, alongside key aspects of a likely and a desirable future scenario regarding high school computing in New Zealand. The framework, which is the major result of this study, was designed with the intention to summarise and present potential ways to increase student participation and engagement for computer science and programming standards. We hope that this framework will be of interest to government, teachers, schools, industry, universities and other key stakeholders.

There has been a renewed interest in creativity as a twenty-first century skill in K-12 mathematics education. However, previous research has paid less attention to creative actions than to other learning outcomes, which are often product- instead of process-based, especially in a programming context. Thus, situated in the context of mathematical learning in a block-based programming environment, Scratch, this study seeks to investigate how in-service mathematics teachers develop mathematical concepts and programming skills to demonstrate their creative actions as a form of professional development. By conducting task-based interviews and thematic analysis, we found that testing and iterative practices of reusing and remixing are two important kinds of creative actions inspired by the programming environment, which give rise to new possibilities for doing mathematics in terms of generating new ways to engage in mathematical processes and to understand mathematics from a computational perspective. Our findings will inform teacher education and professional development programs addressing creativity in technology-enhanced mathematics classrooms, with particular attention to the role of mathematics, programming, and their interplay in inspiring teachers' (and students') creative actions and new possibilities for doing mathematics.

The COVID-19 pandemic has forced a significant increase in the utilization of video-based e-learning platforms for programming education. These platforms never considered the essential attributes of student characteristics and learning preferences while designing such a problematic subject having high dropout and failure rates. The traditional e-learning environments deliver instructional videos to the learners by assuming all learners have a single learning preference. Moreover, existing learning style models need to address the recent requirements of e-learning paradigms. To address this issue, this paper presents a novel learning style model tailored for instructional video-based programming e-learning environments that map individual learning preferences with various video design patterns. An adaptive e-learning environment was employed to assess the effectiveness of the proposed model that leveraged a decision tree classifier to divide learners into four preferences. In a paired experimental design, 195 first-year undergraduate students were randomly assigned to one of three groups where learner scores and feedback were taken as evaluation metrics. The control group partook without instructional videos for the entire semester of six months. During the same period, experimental group-1 learned with a traditional video-based e-learning environment, and experimental group-2, with the proposed learning style model, enabled an adaptive e-learning environment. Based on the proposed decision tree learning model, it is understood that the intervention group showed significant improvements in knowledge acquisition, grade, and positive feedback compared to the other groups. Hence, the proposed model is highly recommended for traditional programming e-learning environments to deliver instructional videos based on learners' learning preferences.

Computer programming is a skill of increasing importance in scientific and technological fields. However, in introductory computer science (CS1) courses in higher education, approximately one in every three students fails. A common reason is that students are overwhelmed by an accelerated and inflexible pace of learning that jeopardizes success. Accordingly, in the computer science education literature it has been suggested that the pedagogical philosophy of 'mastery learning,' which supports students progressing at their own pace, can improve academic outcomes of CS1 courses. Nevertheless, few extended mastery learning implementations in CS1 have been documented in the literature, and there is a lack of guidance and best practices to foster its adoption. In this paper, we present a four-year action research study in which a modular mastery-based CS1 course was designed, evaluated and improved in successive iterations with cohorts of engineering freshmen in a Latin American research university (N = 959). In the first year of the intervention, only 19.3% of students passed the course in their first semester attempting it. In successive iterations, the instructional design, teaching and learning activities, course content, and course management were iteratively improved such that by the fourth year of offering 77.1% of students passed the course in their first semester. Over this period, course attrition was reduced from 25.0% to 3.8% of the cohort, and students' mean time spent in the course decreased from 23.2 weeks (SD = 7.38) to 14.9 (SD = 3.64). Results indicate that modularization for mastery learning is a viable approach for improving academic results in a CS1 course. Practical considerations towards successful implementation of this approach are presented and discussed.

As online learning has become an inevitable trend in the post-peak era of the COVID-19 pandemic, distributed pair programming (DPP) is gaining momentum in both education and industry. DDP serves as a collaborative programming approach and also benefits the development of computational thinking, a fundamental skill in today's world. This study conducted a systematic review of studies on DPP published after 2010 to understand the themes and factors that impact the team effectiveness of DPP and thus inform future research and practices on how to better leverage this approach for teaching and learning. The results showed that individual characteristics attracted major investigations in the selected 23 studies, including prior programming experience, actual skill, perceived skill, gender, personality, time management, confidence, and self-esteem, with pair compatibility identified as a critical team design factor that significantly affects programmers' satisfaction. Although the feel-good factor in the team process was investigated, no significant impact was found. Under the team environment theme, we compared different opinions on the orientation (e.g., scripted roles) and the use of technology (e.g., integrated development environment tools). Future research should investigate how task structure influences team effectiveness of DPP and relates to computational thinking education. Additionally, because most studies were conducted in higher education contexts, more research in primary and secondary educational contexts is also needed.

This study investigated the effect of project-based Arduino robot application on students' computational thinking skills development in robotics programming course using the quasi-experimental research design. The participants consist of 73 students in tertiary institutions in South-East Nigeria that offer Computer and Robotics Education programme. There was no sampling as the entire second-year trainee teachers of computer and robotics education in the selected institutions participated in the study. The instrument used for data collection was the Robotics Programming Computational Thinking Scale (RPCTS). Mean and standard deviation were used to answer the research questions, while Analysis of Covariance (ANCOVA) was used to test the hypotheses at an alpha level of 0.05. The result of the study showed that students who were taught using Project-based Arduino Robot Application (PARA) had higher mean computational thinking scores than those taught using the conventional lecture method, and there was a significant difference in the mean computational thinking scores of students based on method of instruction. Also, the male students taught robotics programming using PARA had higher mean computational thinking scores compared to their female counterparts, and there was a significant difference in the mean computational thinking scores of male and female students taught robotics programming using PARA. Schools and other stakeholders in education should support the training of teachers on how to develop and used project-based approaches in teaching/learning robotics programming-related courses.

Hour of Code is a widely recognized global event that aims to introduce programming to novice users and integrate computer science into education. This paper presents an analysis of the effectiveness of the support system and user interface of Minecraft Adventurer, a serious game designed for the Hour of Code global event. Although previous studies have primarily focused on the educational benefits of Hour of Code games, there has been limited research on their support methods. Therefore, this paper aims to address this gap with an empirical study of the experience of 104 students who played the game for one hour. Student progress was tracked by an administering teacher and after the game session, a questionnaire was administered to collect data on the participant's perceptions of the support system, interface efficiency, and overall experience with Hour of Code. The results of the study reveal significant problems with the aforementioned systems, which apply not only to Minecraft Adventurer but also to several other similar serious games. Additionally, the findings showed a correlation between the utilization of the support system and student performance, indicating that student's comprehension of the support system significantly influences their learning outcomes. This paper concludes by providing potential solutions to address the identified insufficiencies, offering valuable insights for future researchers and game developers on the design and evaluation of serious games for educational purposes.

Computational thinking (CT), an essential 21st century skill, incorporates key computer science concepts such as abstraction, algorithms, and debugging. Debugging is particularly underrepresented in the CT training literature. This multi-level meta-analysis focused on debugging as a core CT skill, and investigated the effects of various debugging interventions. Moderator analyses revealed which interventions were effective, in which situations, and for what kind of learner. A significant overall mean effect of debugging interventions ([g-bar] = 0.64, CI = (0.32, 0.96), p < 0.001), was found based on 62 effect sizes from 18 source articles. Significant between-studies variation indicated that true effects could range from -0.54 to 1.82. In addition, sensitivity analyses and checks on confounding provided further understandings of intervention features and their impacts. Interventions using enhanced debuggers and systematic instruction were particularly effective in fostering debugging skills. Debugging intervention effects varied by participant population and potentially by publication type. Moreover, debugging interventions had impact regardless of how debugging skills were measured, programming medium used, control-group type, and whether the study was randomized. Future studies should investigate the best practices for improving debugging abilities for whom and under what circumstances.

In recent years, computing education researchers have investigated the impact of problem context on students' learning and programming performance. This work continues the investigation motivated, in part, by cognitive load theory and educational research in computer science and other disciplines. The results of this study could help inform computing assessment design. If the context and authenticity of a programming problem aid student performance then, instructors' time in creating appropriately contextualized programming problem descriptions is time well-spent. On the other hand, if the context of a programming problem hinders performance, then instructors should leave it out of programming problems. Recent studies investigating the impact of programming problem context on student success have arrived at different conclusions. Presented here is a series of experiments, conducted over 3 years, investigating the impact of context on novice programmers' success in algorithmic programming assignments using three contextualized tasks and their generic counterparts. This experiment series also looked into the possibility of "authenticity" as a factor affecting performance. Common sense would suggest that a student would perform better on a problem if they understood or cared about it. Contextualization could provide authenticity and authenticity could provide interest. Research suggests that perceiving a problem as authentic has a positive effect on engagement and learning. Alternatively, if a problem is "just an abstract set of numbers", it may be harder to make sense of the details and the lack of context could consequently contribute additional cognitive load to an already challenging assignment. The results of this study show that assignment context and problem context authenticity have no effect on the performance of novice programmers. We think, however, that contextualization could be worth investing in to support students' interest in computing. Additional implications of the results suggest that instructors can assign equivalent versions of the same problem in varied contexts to suit their students' interests without worrying if the context will hinder performance.

Background: Learning analytics (LA) research often aggregates learning process data to extract measurements indicating constructs of interest. However, the warranty that such aggregation will produce reliable measurements has not been explicitly examined. The reliability evidence of aggregate measurements has rarely been reported, leaving an implicit assumption that such measurements are free of errors. Objectives: This study addresses these gaps by investigating the psychometric pros and cons of aggregate measurements. Methods: This study proposes a framework for aggregating process data, which includes the conditions where aggregation is appropriate, and a guideline for selecting the proper reliability evidence and the computing procedure. We support and demonstrate the framework by analysing undergraduates' academic procrastination and programming proficiency in an introductory computer science course. Results and Conclusion: Aggregation over a period is acceptable and may improve measurement reliability only if the construct of interest is stable during the period. Otherwise, aggregation may mask meaningful changes in behaviours and should be avoided. While selecting the type of reliability evidence, a critical question is whether process data can be regarded as repeated measurements. Another question is whether the lengths of processes are unequal and individual events are unreliable. If the answer to the second question is no, segmenting each process into a fixed number of bins assists in computing the reliability coefficient. Major Takeaways: The proposed framework can be a general guideline for aggregating process data in LA research. Researchers should check and report the reliability evidence for aggregate measurements before the ensuing interpretation.

Background: With the development of computational literacy, there has been a surge in both research and practice application of text-based and block-based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major programming modalities, especially in the context of secondary education settings. Objectives: To further compare programming effects between and within text-based and block-based modalities, this research conducted a quasi-experimental research in China's secondary school. Methods: An online programming platform, Code4all, was developed to allow learners to program in text-based and block-based modalities. This research collected multimodal data sources, including programming platform data, process data, and performance data. This research further utilized multiple learning analytics approaches (i.e., clustering analysis, click stream analysis, lag-sequential analysis and statistics) to compare learners' programming features, behavioural patterns and knowledge gains under two modalities. Results and Conclusions: The results indicated that learners in text-based modality tended to write longer lines of code, encountered more syntactical errors, and took longer to attempt debugging. In contrast, learners in block-based modality spent more time operating blocks and attempt debugging, achieving better programming knowledge performances compared to their counterparts. Further analysis of five clusters from the two modalities revealed discrepancies in programming behavioural patterns. Implications: Three major pedagogical implications were proposed based on empirical research results. Furthermore, this research contributed to the learning analytics literature by integrating process-oriented and summative analysis to reveal learners' programming learning quality.

Although previous research has provided some insights into the effects of block-based and text-based programming modalities, there is a dearth of a detailed, multi-dimensional analysis of the transition process from different introductory programming modalities to professional programming learning. This study employed a quasi-experimental design to address this gap, involving 64 secondary school students in two groups. For the beginning five weeks, the first group used an introductory block-based programming environment, while the second group used an introductory text-based programming environment. Then, both groups transitioned to professional text-based programming for the subsequent eight weeks. The results showed that participants who transitioned from introductory text-based programming to professional text-based programming (1) significantly outperformed in computational thinking skills; (2) had more code-writing and debugging behaviors and fewer irrelevant behaviors, and (3) had more interactions with the instructor. No significant differences were observed between the two groups regarding enjoyment, confidence, and interest in programming. Drawing on these findings, this study proposes pedagogical implications that could facilitate the adoption of programming modalities within the broader context of STEM education.

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.

Computer science is often integrated within early childhood education (ECE) through the use of educational robots. This requires adequate preparation of ECE teachers to program and debug, which can be done with scaffolding. In this paper, we use a QUANT + qual mixed method approach incorporating lag sequential analysis, ICAP framework coding, sentiment analysis, and supplementary qualitative analysis. Students tended to engage more with tasks in the revision and evaluation phases than in the exploration phase, and became less dependent on scaffolding prompts and more competent in debugging, as debugging task complexity increased. This study addresses a key gap in the scaffolding literature in that it examined learners' interactions with scaffolding for evidence of decreased reliance on scaffolding.

Web programming courses are practical courses that can only run with the help of computer devices. The content or learning content in web programming courses is in program code directly created with a computer. The models developed include conceptual models, procedural models, and physical models. The research method used is Research and Development (R&D) using the Dick & Carey instructional system design model combined with the Hannafin & Peck model. The learning model was tested for feasibility and effectiveness before being implemented. The learning model developed has four components: syntax, reaction principle, social system, and support system. The feasibility of the model is seen from the percentage of the results of the one-to-one evaluation, which is 91.47%, the small group evaluation is 94.88% and the field trial evaluation is 92%. The effectiveness test results can be seen from the increase in the post-test score with the pre-test reaching 68.7% and the percentage of the success rate of student studies who have carried out learning with the tutorial learning model reaching 83%. The result of this research is a web programming learning model computer-based learning tutorial that is effective for improving student learning outcomes in web programming courses.

Teaching computer programming language remotely presents particular difficulties due to its requirement for abstract and logical thinking. There is a dearth of research specifically examining the potential factors that determine student performance when distance self-learning is conducted for programming language education. This study aims to determine the influencing factors to conduct successful programming education on distance self-learning to help novice students effectively. The study is underlined by a concept map-based approach. A quasi-experimental approach was used to collect data from seventy-eight students. By utilizing factor analysis, this study proposes nine determinant factors of distance self-learning performance, namely educational architecture, knowledgeability, expectation insight, engagement tools, good organization, technological accessibility, assignment familiarity, advantageous tool, and assistance feature. This work suggested determinant factors that can predict student learning performance to motivate meaningful distance self-learning and construct their knowledge in programming education.

In the current era where computational literacy holds significant relevance, a growing number of schools across the globe have placed emphasis on K-12 programming education. This field of education primarily comprises two distinct modalities--the block-based programming modality (BPM) and the text-based programming modality (TPM). Previous research may not have provided a complete understanding of the differences between these two modalities as it did not take into account both the learning process and learning outcomes. This study aimed to compare secondary students' programming behaviors, computational thinking skills, and attitudes toward programming between the two modalities through a quasi-experimental design in a Chinese secondary school. The findings showed that (1) learners in TPM encountered more syntactical errors and spent more time between two clicks of debugging, while learners in BPM had more code-changing behaviors by adjusting programming blocks, made more attempts of debugging, and had more irrelevant behaviors; (2) learners in BPM achieved a higher level of computational thinking skills; (3) learners in both modalities experienced a slight decrease in confidence and enjoyment, while learners in BPM had higher interest levels in programming. (4) Code Changer, Minimal Debugger, Maximal Debugger, Distracted Coder and Average Coder were identified through students' programming behavior in the two programming modalities, and differences in their CT skills and attitudinal data were revealed. Lastly, pedagogical implications based on the findings are also discussed.

Amidst the rapid advancement in the application of artificial intelligence learning, questions regarding the evaluation of students' learning status and how students without relevant learning foundation on this subject can be trained to familiarize themselves in the field of artificial intelligence are important research topics. This study employed the use of a self-built AI platform (Ladder) for students to systematically learn and apply AI learning model established by the partial least squares (PLS) method to investigate the influence between variables (learning attitudes, self-regulated learning, AI anxiety, individual impact, computational thinking abilities, cognitive styles). This study was particularly conducted in the Department of Computer Science and Information Engineering of a top national university in Southern Taiwan. The valid data were collected from 65 students (55 male students; 10 female students). Furthermore, this study demonstrated the relationship between cognitive style, self-regulated learning and computational thinking. For the first time, it explored the impact of AI anxiety and completed existing research on it. The results of this study show that interest in learning positively affects learning attitudes. In addition, learning attitudes have a positive influence on each individual's performance. Based on multiple theories and the artificial intelligence learning platform, the model proposed in this study effectively understood students' learning status.

This study presents a new blended learning model that combines a computer-assisted learning system called Cognitive Apprenticeship Programming Learning System (CAPLS) with instructor co-teaching in an introductory programming course. CAPLS, as its unique aspect, functions as a master in cognitive apprenticeship, guiding learners throughout their learning while also assessing their progress. In contrast, the instructor in physical class settings serves a supportive role, monitoring progress and articulating as needed to fill knowledge gaps. To investigate the impact of this learning model on students' motivation, we used the Motivated Strategies for Learning Questionnaire (MSLQ) at the beginning and end of the semester. College Entrance Math score, midterm and final exams were also used to assess student learning outcomes. The study was conducted with first-year students in the Department of Information and Computer Engineering, and two key findings emerged. First, students' programming proficiency was strongly correlated with their College Entrance Math scores. While math ability impacted programming learning, all students improved their final scores compared to their midterms, with high-scoring math students outperforming their peers. Second, the proposed blended cognitive teaching strategy significantly reduced students' extrinsic goal and self-efficacy levels, but their learning outcomes still significantly improved. This suggests that the proposed teaching model promotes more conscious learning. These results can be used as a reference for improving student learning outcomes and experiences with computer-assisted learning systems.

As computational thinking becomes increasingly essential, the challenge of designing effective teaching approaches to foster students' abilities in this area persists, especially for higher order thinking skills. This study addresses this challenge by proposing and implementing a peer assessment-based Scrum project (PA-SP) learning approach in university computer programming learning. The approach involves dividing 98 third-year college students into an experimental group, which employs the PA-SP approach, and a control group, which uses the conventional Scrum project (C-SP) approach. The study investigates the impact of the PA-SP approach on students' programming projects, learning motivation, project collaboration, communication, critical thinking, and cognitive load. The results demonstrate that the PA-SP approach significantly enhances learning achievement and motivation while also improving collaboration, communication, and critical thinking. By providing practical insights into effective teaching strategies to promote students' computational thinking, this research has significant implications for improving programming education in universities.

Tangible programming combines the advantages of object manipulation with programmable hardware, which plays an essential role in improving programming skills. As a tool for ensuring the quality of projects and improving learning outcomes, the PDCA cycle strategy is conducive to cultivating reflective thinking. However, there is still a lack of empirical research on the effect of introducing the PDCA cycle strategy into programming education. In this study, using a PDCA cycle strategy, in a four-pronged model of "(P)draw up a plan, (D)assemble and programming, (C)test and debug, display and reflect (A)," and its effects on students' programming skills and their reflective thinking were explored. There were 65 children between the ages of 7 and 8 years participated in this study. There were 31 students in each of the experimental group and the control group. A combination of qualitative and quantitative research methods was adopted in this research, and students' programming processes and results were observed and counted. The study results revealed that after attending the 'Magic Card Robot' course that applied the PDCA cycle strategy, the experimental group students outperformed their counterparts in programming skills (sequencing, repetitive and conditional structures). Meanwhile, the experimental group students' reflective thinking levels were higher than those of the control group students. These findings imply that tangible programming education using the PDCA cycle strategy in the course has potential.

This research aims to examine the effect of coding education on the analytical thinking skills of gifted students. The participants are 18 students, 11 to 12 years old. An embedded experimental mixed design was used in the research. The data collection was carried out with the Analytical Thinking Skill Scale to determine the difference in the analytical thinking skills of the study group before and after the coding training; the Analytical Thinking Skill Observation Form to determine the analytical thinking skill levels during the implementation process; and the semi-structured interview form to get their opinions on the coding training. The data were analyzed with a pre-test-post-test quasi-experimental design, descriptive analysis, and content analysis. The results show that coding education developed the participants' analytical thinking skills. The difference in the analytical thinking skills of the study group was not statistically significant in terms of gender. According to the students' views on the coding education application process, there was an improvement in the sub-dimensions of sorting, classification, comparison, and evaluation in analytical thinking skills; coding education developed problem-solving and thinking skills, was useful, encouraged students to choose a profession, and was entertaining, as well as negative opinions such as being difficult, boring, and requiring a lack of time.

This study examines the methodological dimensions of programming education articles published in educational sciences journals indexed in SSCI by exploring their general trends. To do this, 162 articles published between January 2012 and February 2020 in 30 international journals indexed in SSCI were analyzed with a systematic review method using the "Educational Technology Publication Classification Form" as a data collection tool. The results revealed that most of the studies in this field were conducted in the United States and Turkiye. The number of these studies has increased since 2015, and those studies were carried out using quantitative research methodology. Mostly questionnaires and achievement tests were used as a data collection tool, a convenience sampling method was used, and descriptive analyses were adopted to analyze the data. As a result, the articles examined in this study showed that programming education positively contributes to learners' learning and success levels and the development of their computational thinking skills. We believe that these results will shed light on future studies related to programming education.

The second CSEDM data challenge aimed at finding innovative methods to use students' programming traces to model their learning. The main challenge of this task is how to decide which past problems are relevant for predicting performance on a future problem. This paper proposes a set of weighting schemes to address this challenge. Specifically, students' behaviors and performance on past problems were weighted in predicting performance on future problems. The weight of a past problem was proportional to its similarity with the future problem. Problem similarity was quantified in terms of source code, problem prompts, and struggling patterns. In addition, we considered another weighting scheme where past problems were weighted by the order in which students attempted them. Prior studies have used problem similarity and order information in learner modeling, but the proposed weighting schemes are more flexible in capturing problem similarity on various problem properties and weighting various behaviors and performance information on past problems. We systematically investigate the utility of the weighting schemes on performance prediction through two analyses. The first analysis found that the weighting schemes based on source code similarity, struggling pattern similarity, and problem order improved the prediction performance, but the weighting scheme based on problem prompts did not. The second analysis found that the weighting scheme allows a simple and interpretable model, such as logistic regression, to have performance comparable to state-of-the-art deep-learning models. We discussed the implications of the weighting schemes for learner modeling and suggested directions for further improvement.

Few studies have analyzed students' teamwork (pairwork) habits in programming projects due to the challenges and high cost of analyzing complex, long-term collaborative processes. In this work, we analyze student teamwork data collected from the GitHub platform with the goal of identifying specific pair teamwork styles. This analysis builds on an initial corpus of commit message data that was manually labeled by subject matter experts. We then extend this annotation through the use of self-supervised, semi-supervised learning to develop a large-scale annotated dataset that covers multiple course offerings from a second-semester CS2 course. Further, we develop a series of predictive models to automatically identify student teamwork styles. Finally, we compare trends in students' performance and team selection for each teamwork style to see if any of them reflected better student outcomes or different trends of help-seeking among students. Our analysis showed that applying self-supervised semi-supervised methods helps us to label larger subsets of data automatically and maintains and even sometimes improves the performance of the fully supervised models on a held-out validation set. Our analysis also showed that members of teams in which all members have significant contributions tend to have better performance in class, but their help-seeking behaviors are not significantly different.

Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as submission, compilation, edit, and keystroke events, with keystroke-level logs being the most fine-grained of commonly used dataset types. However, the lack of open datasets, especially at the keystroke level, is notable. There are several reasons for this failing, with the most prominent being the challenges of deidentification that are peculiar to keystroke log data. In this paper, we present the public release of two fully deidentified keystroke datasets that are the first of their kind in terms of both event and metadata richness. We describe our collection technique and properties of the data along with deidentification techniques that, while not fully relieving researchers of significant effort, at least reduce and streamline manual work in hopes that researchers will release similar datasets in the future.

This study is designed to reveal the research trends of graduate theses published in the field of computer programming in K-12 between 2018 and 2022. Document analysis was used for data collection in this study. The data was divided into 9 categories, and the results demonstrated that the scholars in the Departments of Computer Educational and Instructional Technologies (CEIT) at Atatürk University and Ondokuz Mayis University of Türkiye mainly published master's theses in this field in 2019. Another result was that though CEIT is also the main department for doctoral theses, no difference exists in terms of years or universities. "Teaching, learning, and training programming" and "computational thinking" are the most frequently used keywords in graduate programs, respectively. While quantitative and mixed method (parallel, convergent), convenience sampling, and psychometric scales are preferred in master's theses, mixed method (explanatory, mixed), purposive sampling, interviews/focus group interviews, and psychometric scales are preferred in doctoral theses, respectively. Observation and semi-structured interview forms are used as data collection tools in the theses. Although quantitative methods are mainly utilized in data analysis, parametric and non-parametric statistical tests are also used, and content analysis is resorted to as part of qualitative methods. Whilst validity and reliability studies have been carried out measurement tools are employed in master's theses, scales have been developed in doctoral theses. The present study also offers some suggestions for future research on teaching computer programming.

The aim of this study is to examine the effect of block-based coding education on middle school students' computational thinking skills and attitudes towards coding with Blocky example. The training phase of the implementation process was planned and implemented by subject experts in 4 weeks and 2 hours a week. In the research, both quantitative and qualitative research methods and exploratory sequential design were used as mixed methods. In this study, quantitative research method and pretest- post-test control group semi-experimental pattern model was used as the research model. The universe of the study consists of 5th grade students studying at a secondary school in Aksaray province Eskil district of Türkiye in the 2019-2020 academic year. Within the scope of the research, the research participant group was determined considering that 5th grade students will receive coding training for the first time. In addition to the total number of students to be reached is 38, including 19 control and 19 experimental groups. While the "blocky" application used in block-based coding education was determined as the independent variable in the research, the students' computational thinking skills and their attitudes towards learning to code were determined as dependent variables. As a result of the applications and analysis, the difference between the values of students' computational thinking skills self-efficacy and attitudes towards learning coding before and after education is statistically significant. In order to determine the effect size of the Blocky block-based coding environment on the Information Computational Thinking Skills Self-Efficacy Scale (CTSES) and the Attitudes Toward Learning Coding Scale (ATLCS), the eta squared value was examined. It can be said that the Blocky block-based coding environment has a "large" effect size on computational thinking skills, self-efficacy perception and on the attitude towards learning coding.

This paper describes a pilot study that explores students learning how to program via a multi-disciplinary approach. The study participants were eleven 6th grade students who learned programming fundamentals via music activities in a Scratch 3.0 environment. These activities included the programming of familiar melodies and the development of suitable animations or computer games. For that matter, a study unit termed MelodyCode was developed in the spirit of the STEAM education approach and the spiral learning method and included exploration tasks based on individual learning. Via the programming of familiar melodies, they became acquainted with programming concepts such as functions, variables, repetition and control commands, parallel processes, and more. Competitions that win awards were held from time to time, which prompted students to invest efforts in their projects to reach first place and gain the teacher and classmates' appreciation. The study was conducted in the form of action research. The data analysis yielded references to the effect of MelodyCode on common stereotypes students hold regarding programming (masculine profession, necessitates good mathematics knowledge), cognitive aspects (cognitive load, linking music concrete use to abstract programming concepts), and affective aspects (joyful and relaxing class atmosphere, motivation, curiosity, self-efficacy).

Source code plagiarism is a common occurrence in undergraduate computer science education. Many source code plagiarism detection tools have been proposed to address this problem. However, most of these tools only measure the similarity between assignment submissions, and do not actually identify which are suspicious of plagiarism. This work presents a semi-automatic approach that enables the indication of suspicious assignment submissions by analysing source code similarity scores among the submissions. The proposed approach seeks the consensus of multiple source code plagiarism detection tools in order to identify program pairs that are consistently evaluated with high similarity. A case study is presented to demonstrate the use of the proposed approach. The results of this case study indicate that it can accurately identify assignment submissions that are suspicious of plagiarism.

We live in a digital age, not least accelerated by the COVID-19 pandemic. It is all the more important in our society that students learn and master the key competence of algorithmic thinking to understand the informatics concepts behind every digital phenomena and thus is able to actively shape the future. For this to be successful, concepts must be identified that can convey this key competence to all students in such a way that algorithmic thinking is integrated in the subject of informatics -beyond a pure programming course. Furthermore, based on the Legitimation Code Theory, semantic waves provide a way to develop and review lesson plans. Therefore, we planned a workshop, that follow the phases of a semantic wave addressing algorithmic problems using a block-based programming language. Considering this, we suggest the so-called SWAT concept (Semantic Wave Algorithmic Thinking concept), which is carried out and analyzed in a workshop with students. The workshop was carried out in online format in an 8th grade of a high school during a coronavirus lockdown. The level of algorithmic thinking was measured using a pretest and posttest both in the treatment group and in a control group and with the help of the approximate adjusted fractional Bayes factors for testing informative hypotheses statistically and through a reductive, qualitative content analysis of the students' work results (worksheets and created programs) evaluated. The semantic wave concept was measured using several cognitive load ratings of the students during the workshop and also statistically evaluated with the approximate adjusted fractional Bayes factors for testing informative hypotheses, as well as a qualitative content analysis of the worksheets. Results of this pilot study provide first insights, that the SWAT-concept can be used in combination of unplugged and plugged parts.

This study aims to explore how gamification elements influence the development of the Community of Inquiry (CoI) in an online project-based programming course conducted on Facebook. We formed student groups by using a quasi-experimental design from students studying in the computer science department. While both courses were project-based, the experimental group's project development process was enriched with gamification elements. We collected data from the CoI survey, transcript analysis of online discussions, and interviews with students. The results indicated that the use of gamification elements contributed significantly to students' social, cognitive, and teaching presence development. Besides, while a high level of CoI perception was created in both groups in the online project-based learning environment, the design and organization role of the instructor came to the fore in the gamified environment more.

This study aims to evaluate the trend and frequency of coding/programming activities in Information Technology (IT) courses, according to different parameters. The study was conducted through a survey of a private IT (Information Technology) teachers in different levels (like primary, secondary, and high schools), with questions focusing on the extent to which coding/programming activities were integrated into their courses, the frequency of these activities, and the factors that influenced their use. The study also explored the relationship between teacher characteristics, such as their level of programming knowledge and teaching experience, and the use of coding/programming activities in their courses. According to the t-test results of the study showed that most IT teachers believed that coding/programming activities were important and useful for their students, but their use varied depending on factors such as gender, education status, age range, teacher experience, school resources, participating a programming training course and student interest. The study also found that the frequency of coding/programming activities varied across different IT courses, with some courses integrating these activities more frequently than others. The study provides insights into the current state of coding/programming activities in IT courses and highlights the need for further research to explore ways to better integrate these activities into IT education.

In programming problem solving activities, sometimes, students need feedback to progress in the course, being positively affected by the received feedback. This paper presents an overview of the state of the art and practice of the feedback approaches on introductory programming. To this end, we have carried out a systematic literature mapping to understand and discuss the main approaches for providing and evaluating feedback used in the learning of novice programmers in the problem-solving activity. Thus, according to a formal protocol, an automatic search was performed for papers from 2016 to 2021. As a result, 39 studies were selected for the final analysis. As a result, we propose three different categorizations: the main approaches to providing feedback, the main methods used in the evaluation and the main aspects and effects of the evaluated feedback.

Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5.