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Contribution: This article analyzes the learning and motivational impact of teacher-authored educational video games on computer science education and compares its effectiveness in both face-to-face and online (remote) formats. This work presents comparative data and findings obtained from 217 students who played the game in a face-to-face format (control group) and 104 students who played the game in an online format (experimental group). Background: Serious video games have been proven effective at computer science education, however, it is still unknown whether the effectiveness of these games is the same regardless of their format, face-to-face or online. Moreover, the usage of games created through authoring tools has barely been explored. Research Questions: Are teacher-authored educational video games effective in terms of learning and motivation for computer science students? Does the effectiveness of teacher-authored educational video games depend on whether they are used in a face-to-face or online format? Methodology: A quasi-experiment has been conducted by using three instruments (pre-test, post-test, and questionnaire) with the purpose of comparing the effectiveness of game-based learning in face-to-face and online formats. A total of 321 computer science students played a teacher-authored educational video game aimed to learn about software design. Findings: The results reveal that teacher-authored educational video games are highly effective in terms of knowledge acquisition and motivation both in face-to-face and online formats. The results also show that some students' perceptions were more positive when a face-to-face format was used.

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.

Using games in education has the potential to increase students' motivation and engagement in the learning process, gathering long-lasting practical knowledge. Expanding interest in implementing a game-based approach in computer science education highlights the need for a comprehensive overview of the literature research. This scoping review aims to provide insight into current trends and identify research gaps and potential research topics concerning game-based learning in computer science. Using standard methodology for scoping review, we identified 113 articles from four digital libraries published between 2017 and 2021. Those articles were analyzed concerning the educational level, type of the game, computer science topic covered by the game, pedagogical strategies, and purpose for implementing this approach in different educational levels. The results show that the number of research articles has increased through the years, confirming the importance of implementing a game-based approach in computer science. Different kinds of games, using different technology, concerning different computer science topics are presented in the research. The obtained results indicate that there is no standardized game or standardized methodology that can be used for the creation of an educational game for computer science education. Analyzed articles mainly implement a game-based approach using learning by playing, and no significant focus is given to the effectiveness of learning by designing a game as a pedagogical strategy. Moreover, the approach is mainly implemented for developing computational thinking or programming skills, highlighting the need for its implementation in other topics beyond programming.

Novice programmers, who have yet to form effective mental models of the domain, often experience high cognitive load, low confidence, and high anxiety, negatively affecting learning and retention rates. These cognitive and affective limitations pose an instructional challenge. This study aimed to investigate the effectiveness of a whole-task instructional approach compared with a part-task instructional approach for novices learning to program from a cognitive and affective perspective. A fully randomized between-subjects controlled experiment was designed, including two online instructional conditions (whole-task vs. part-task). The "whole-task condition" followed the Task-Centered Instructional Strategy and included explicit instruction in the context of whole tasks. The "part-task condition" followed a part-task instructional strategy and included the same explicit instruction, yet in the context of objectives and topic-related tasks. Based on Bandura's triadic model (Bandura, Social foundations of thought and action: A social cognitive theory, Prentice-Hall, 1986), we propose a conceptual model, which we used to hypothesize that the Task-Centered Instructional Strategy may be more effective for novices learning to program. Sixty-five students with no programming experience volunteered to participate in the study and were randomly assigned to one of the conditions. Participants in the whole-task condition performed significantly better on the near and far transfer posttests. In accordance with our model, confidence and cognitive load during learning were found to be significant partial mediators of the effect of instructional strategy on performance. Overall, we found that the task-centered instructional strategy, combining explicit instruction with whole-tasks, is effective for addressing the cognitive and affective considerations relevant to novices in computer science education.

This research investigates the comparative effectiveness of the ChatGPT and the Google search engine in facilitating the self-learning of JavaScript functions among undergraduate open and distance learning students. The study employed a quasi-experimental post-test control group design to analyze the variables of disorientation, satisfaction, cognitive load, navigation performance, and test scores among 471 learners. Results indicated a marked advantage for ChatGPT users in reducing cognitive load and disorientation, improving navigation performance and increasing test scores. The study also explores the evolving dynamics between conventional keyword-based search engines and AI-driven conversational models, reflecting on their mutual influence in the domain of information retrieval. These findings suggest that AI chatbots like ChatGPT not only enhance the learning experience but also signify a paradigm shift in how information is accessed and processed in educational settings. The research contributes to understanding AI applications in education, particularly in self-directed learning scenarios in open and distance learning.

Concurrency is a complex to learn topic that is becoming more and more relevant, such that many undergraduate Computer Science curricula are introducing it in introductory programming courses. This paper investigates the combined use of Sonic Pi and Team-Based Learning to mitigate the difficulties in early exposure to concurrency. Sonic Pi, a domain-specific music language, provides great support for "playing" with concurrency, and "hearing" common problems such as data races and lack of synchronization among different concurrent threads. More specifically, the paper focuses on students' misconceptions regarding concurrency in Sonic Pi and compares them to those arising in traditional concurrent programming languages. In addition, it preliminarily explores knowledge transfer from Sonic Pi to C/C++. The approach has been applied in two teaching experiments with undergraduate students in our University involving 184 participants. Our investigations bring out the need to address misconceptions through targeted interventions for a clear understanding of concurrent programming concepts. Sonic Pi's simplified abstraction and domain-specific flavor has demonstrated to be effective, especially for first-year students.

This study aims to explain the relationships between secondary school students' digital literacy, computer programming self-efficacy and computational thinking self-efficacy. The study group consists of 204 secondary school students. A relational survey model was used in the research method and three different data collection tools were used to collect data. The structural equation model was used in data analysis to reveal a model that explains and predicts the relationships between variables. According to the results of the research, it was determined that digital literacy of secondary school students affected their computer programming self-efficacy, digital literacy affected their computational thinking self-efficacy, and computer programming self-efficacy affected their computational thinking self-efficacy. It was also found that digital literacy skills have an indirect effect on secondary students' computational thinking self-efficacy on computational thinking self-efficacy.

The paper discusses an alternative method of assessing the difficulty of pupils' programming tasks to determine their age appropriateness. Building a program takes the form of its successive iterations. Thus, it is possible to monitor the number of times such a program was built by the solver. The variance of the number of program builds can be considered as a criterion of the difficulty of the task. We seek to verify whether this variance is the greatest in the age group for which the task is most suitable. We created several series of programming tasks and offered them to 87000 pupils from 4th to 13th grade. For each task, we compared the optimal age group determined by the variance of the number of program builds method with the group determined by the correct answer ratio method. A strong correlation was observed in traditional microworlds Karel the Robot and Turtle. A moderate correlation was achieved in the new microworld Movie.

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.

This study investigated the impact of problematization-oriented scaffolding and structuring-oriented scaffolding, incorporated within instructional videos, on students' computational thinking and their performance in programming education. We recruited 86 participants from three senior classes at a high school. Each of the three classes was assigned to one of the experimental conditions: the problematization-oriented scaffolding group, the structuring-oriented scaffolding group, and the control group, which received no scaffolding. Pre- and post-tests were conducted to assess students' computational thinking and programming performance during a three-week period. We observed significant improvements in the computational thinking skills of the problematization-oriented scaffolding group, particularly in the algorithmic thinking and cooperativity subdimensions, when compared to the control group. Structuring-oriented scaffoldings in videos also helped enhance students' computational thinking, especially in the algorithmic thinking subdimension. In contrast, students in the control group primarily improved their problem-solving skills, although the difference across the three groups was not significant. Furthermore, this study revealed that the use of learning scaffolds, whether problematization-oriented or structured, significantly contributes to students' learning achievements in comparison to the control group. These findings emphasize the importance of selecting the appropriate scaffolding approach to enhance specific dimensions of computational thinking.

Contribution: Reporting of students' view on the use of preparatory sprint and virtual meetings, as well as on the workload effort in combination with coding artifacts in a Scrum-variant project-based course. Background: Scrum has been adopted to a large extent in Software Engineering (SE) courses. Relevant aspects have been examined in the literature, such as the overall course experience from the side of the instructors and the students, the challenges of adapting Scrum for the classroom and training on specific roles. Intended Outcomes: To design a course that allows students to come in contact with developing projects from the industry as would be performed in a company context using Scrum. The main aim was to document aspects of the students' experience in this context (e.g., workload view in the framework of the team), which--together with the use of objective data, i.e., development artifacts--can assist in understanding students better. Application Design: Drawing upon the previous teaching experience, a Scrum-variant in a project-based SE course was designed, examining how students perceive it in relation to the following aspects: Scrum adaptations in the classroom, workload in the team. How these compare with the actual development work performed by students is also investigated. Findings: The results from two consecutive years of teaching the course (2020 and 2021), applying quantitative analysis on the data, show that students need some time to become productive and estimate user stories correctly but are aware of their development effort within the team.

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.

With ongoing efforts in the United States to further develop the availability of computer science education in the public schools, federal, state, and local educational agencies are increasing efforts to encourage and promote the inclusion of computer science and programming skills in the middle school curriculum (Grover, Pea, & Cooper, 2015). The goal for the Online Content Modules: Computer Science in the Middle Grades project was the development of five online content modules with a focus on computer science instruction in three public school districts in West Tennessee, and disseminated through a week-long professional development summer institute.

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.

Computational thinking (CT), an essential competency for comprehending and addressing intricate issues in the digital world, has been incorporated into curriculum planning as a goal for programming education. This study introduced flow design into programming curricula to investigate its impact on undergraduates 'CT skills during pair work. Two types of flow design approaches, construct-by-self flow design (CBS-FD) and construct-on-scaffold flow design (COS-FD), were proposed to determine which approach better enhances students' CT skills. Seventy-six first-year undergraduates participated, including thirty in the CBS group and thirty-six in the COS group. Evaluations made from the results and processes of programming tasks were employed to describe computational performance and computational practices, respectively. Data gathered from CT skill surveys were thoroughly analyzed to gain a deeper understanding of computational perspectives. Our findings highlighted that COS-FD significantly improved participants' computational performance compared with CBS-FD. The COS groups fostered an engaging, sharing atmosphere, while CBS groups spent more energy on negotiating the manipulation of flow design. Moreover, both COS-FD and CBS-FD proved beneficial in enhancing participants' computational perspectives, with the COS groups better improving their algorithm thinking. The study presents valuable perspectives on the design and implementation of collaborative programming activities within curriculum education.

This study investigated how a computer science (CS) problem-based curriculum impacted elementary students' CS learning and attitudes. Four sixth-grade teachers and 200 of their students participated in the study. Researchers developed a CS curriculum in collaboration with the teachers, which consisted of two main units: (1) an introduction to block-based coding and (2) a problem-based learning (PBL) applied coding project. Overall, students significantly improved their knowledge of CT concepts after the introductory block-based coding lessons and retained that knowledge after completing the PBL activities approximately three months later. Results suggest that "Event" and "Parallelism" were challenging concepts for most of the students, whereas "Loop" and "Sequence" were easily grasped by most of the students. Further analysis based on prior knowledge levels revealed that the high-prior knowledge (HK) group outperformed the low-prior knowledge (LK) group on every measure. However, LK narrowed the gap of CT concepts after the introductory block-based coding lessons. Students also communicated relatively positive attitudes towards CS at the conclusion of the PBL unit. These results provide support for further exploring the integration of inquiry-oriented instructional strategies such as PBL to support CS instruction.

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.

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.

We recommend the conceptualization of computing as a critical literacy, and ground this conceptualization in considerations of historical and current realities in computing. The frameworks of connected learning and computational participation are recommended as guides for "doing" critical computing literacy. We present the findings from an interpretive case study of the "SciGirls" Code (SGC) program. This program was piloted with 16 after-school programs for middle school girls that engaged them in computer science (NSF #1543209). This research investigated girls' learning, attitudes, and participation involving 84 participants across 11 sites aged 10-14 and in 5th-8th grades. Through sharing a vignette from one site and highlighting findings from this study, we illustrate ways to "do" critical computing literacy and examine the potential of using connected learning and computational participation in designing more equitable computer science education offerings.

With ongoing efforts in the United States to further develop the availability of computer science education in the public schools, federal, state, and local educational agencies are increasing efforts to encourage and promote the inclusion of computer science and programming skills in the middle school curriculum (Grover, Pea, & Cooper, 2015). The goal for the Online Content Modules: Computer Science in the Middle Grades project was the development of five online content modules with a focus on computer science instruction in three public school districts in West Tennessee, and disseminated through a week-long professional development summer institute.

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.

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.

The purpose of the current study was to explore the effect of modality (constructionist mBlock, Scratch, and Python interventions) on six-grade students' computational thinking, programming attitude, and achievement. The pre-test and post-test quasi-experimental design was used to explore the research questions. The study group consisted of 105 six grade students from three different classes. A constructionist learning environment was formed for Scratch, mBlock, and Python groups. All groups were given 8 week-instruction. Instruction included two forty-minute sessions each week. The data were collected through the programming achievement test, computational thinking test, and computer programming attitude scale. The results of the study showed that mBlock group outperformed the Scratch and Python groups with respect to computer programming attitude. Students who attended mBlock and Scratch groups had higher levels of programming achievement than those of the students who attended the Python group. No significant differences with respect to computational thinking were observed between the groups. This study has implications for educators who are teaching computational thinking and programming. Further research was recommended to explore the effect of modality.

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.