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2022 opened with World War C, the first major cyber world war. Wanting to capitalize on history in the making, Information Systems faculty are integrating real time events to increase student engagement, comprehension, and application of IS concepts. This paper outlines a successful midterm evaluation pivot that leverages current events. We discuss two different actions taken and objectives, outcomes, and implications for teaching and educational research. We find that incorporating extreme current events motivates students towards self-learning and creative knowledge outlets, which in turn stimulate greater comprehension, application, and retention across the entire class.

Modern digital technologies are advancing rapidly, driving transformative changes. For Information Systems (IS) educators, maintaining curricula that prepare graduates for the digital economy is imperative. This study developed a knowledge pool of 121 common keywords in digital transformation through literature review and investigated their application in the titles and descriptions of 4,093 IS courses across 315 undergraduate programs in the United States. The findings illustrate how IS educators conceptualize the digital transformation within the IS discipline and incorporate it into curricula. Additionally, the results offer insights for educators aiming to develop course content on digital transformation and update IS undergraduate curricula to align with the demands of modern enterprises in the era of digital transformation.

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.

Implementing ICT education in elementary schools in the industrial era 4.0 is urgent. Several studies have explored ICT education in elementary schools, but few studies with systematic literature reviews remain. This article presents a literature review for 2019-2023 regarding ICT education in elementary schools. The Systematic Review Literature method and PRISMA protocol use Publish or Perish 7, VOSviewer, and NVIVO 12 Plus. The search results for Scopus articles contained 812, which were then filtered according to the theme to 61 for the study. The 61 articles were analyzed according to topic via NVIVO 12 Plus, and the results were described according to the research questions. ICT education in elementary school is a form of technology in learning to send, process, create, share, display, store, and exchange information. The characteristics of ICT education in elementary schools are ICT for online learning, communication interaction, digital media, software, inclusive, real-time, TPACK-based, and making students independent. The implementation of ICT education in elementary schools is carried out in learning, digital-based projects, curriculum, tools, and learning materials for Mathematics and language in elementary schools, which are supported by teachers' digital competence. Future researchers must study ICT education in elementary schools according to technological developments.

The objectives of this research were to: 1) develop online lessons with the Google Classroom application on computer system operation for secondary 2 (Grade 8) students; 2) study pre-test and post-test academic achievement; and 3) study the students' satisfaction with the online lessons with the Google Classroom application on computer system operation for secondary 2 (Grade 8) students. The sample consisted of 20 students from secondary 2 (Grade 8) studying computational science at Sappasamit Bamrung Municipal School. They were selected for purposive sampling. The research instruments consisted of online lessons with the Google Classroom application, a quality evaluation guide towards media and content achievement tests, and evaluation forms of students' satisfaction. The data analysis statistics were the standard deviation and dependent sample t-tests. The research results indicated that: 1) the online lessons with the Google Classroom application on computer system operations had the quality of media at an excellent level with an average of 4.58, quality of contents with an average of 4.55, and efficiency criterion of 82/81. 2) The students had an average pre-test score of 17.40, while the satisfaction of students towards online lessons with Google Classroom application is at a high level." post-test was 24.35. The t-test analysis during and after learning was different at .05 statistically significant levels, and 3) students' satisfaction toward lessons with Google Classroom location at a high level of 4.49.

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.

The metaverse which is considered as the digital big bang of humanity and the next evolution of the internet is anticipated to offer a living environment for civilization 5.0 and beyond in various domains including education. Each novice technology that enters our daily lives generates also fear, anxiety, and problems as well as hope, benefit, and attraction. The purpose of this research is to investigate what ethical problems the metaverse may cause in higher education institutions and how healthy metaverse universities can be created within the framework of ethics. Interpretative phenomenological research design was employed in the study and data was collected from the field experts and lecturers in higher education. The study indicated thirteen VR/AR/XR experts' and lecturers' visions on the metaverse-based ethical issues and practical implications that could be applied within the scope of five thematic nodes comprising metaverse awareness, security and data, safety and wellness, social equality and diversity, and accessibility. It is expected that the study will contribute to the design of a healthy and good metaverse in the future of higher education institutions pursuant to ethical values.

The contents of calculus, known for their complexity, present significant challenges for students, particularly in mastering multiple integrals and effectively visualizing related concepts. The transition to distance learning prompted by the COVID-19 pandemic has further complicated the learning process in multiple integrals. In line with this and considering the potential of computer-supported collaborative learning (CSCL), in this research we explore the impact of teaching in a CSCL environment on student achievements, focusing on students from the computer science study program. Through data analysis using ANOVA and Bonferroni post-hoc tests, it was found that students exposed to collaborative learning in GeoGebra environment demonstrated higher levels of theoretical and practical knowledge compared to peers who acquired knowledge without using GeoGebra. Additionally, this group of students achieved results comparable to those of students who attended traditional in-person teaching, showing noticeable improvements in solving complex tasks. Our findings show the effectiveness of CSCL approach in context of distance learning and highlight potential of collaborative environments enhanced with technology in facilitating student understanding and achievements in calculus education.

Within the field of K-2 computer science (CS) education, unplugged computational thinking (CT) activities have been suggested as beneficial for younger students and shown to impact young students' skills and motivation to learn about CS. This study sought to examine how children demonstrate CT competencies in unplugged sequencing tasks and how children use manipulatives to solve unplugged sequencing tasks. This case study approach examined two unplugged sequencing tasks for six children ranging from ages five to eight (pre-kindergarten to 2nd grade). Children showed evidence of several CT competencies during the sequencing tasks: (1) pattern recognition, (2) algorithms and procedures, (3) problem decomposition, and (4) debugging. The strategies and use of manipulatives to showcase CT competencies seemed to evolve in complexity based on age and developmental levels. Taking into account children's abilities to demonstrate CT competencies, this study suggests that sequencing is a developmentally appropriate entry point for young children to begin engaging in other CT competencies. In addition, these unplugged sequencing tasks can also be easily integrated into other activities commonly experienced in early childhood classrooms.

This study examined the effects of embodied learning experiences on students' understanding of computational thinking (CT) concepts and their ability to solve CT problems. In a mixed-reality learning environment, students mapped CT concepts, such as sequencing and loops, onto their bodily movements. These movements were later applied to robot programming tasks, where students used the same CT concepts in a different modality. By explicitly connecting embodied actions with programming tasks, the intervention aimed to enhance students' comprehension and transfer of CT skills. Forty-four first- and second-grade students participated in the study. The results showed significant improvements in students' CT competency and positive attitudes toward CT. Additionally, an analysis of robot programming performance identified common errors and revealed how students employed embodied strategies to overcome challenges. The effects of embodied learning and the impact of embodied learning strategies were discussed.

The academic success of first-year students' learning in science faculties is by no means assured, especially in an Open Distance Learning setting with its limited number of face-to-face encounters between students and lecturers or tutors. Therefore, such encounters should be highly efficient in view of the considerable amount of knowledge transfer to students. The University of South Africa (Unisa) makes provision for contact sessions of 15 hours per semester for selected modules in an attempt to elevate the pedagogical efficiency of these sessions by focusing on the threshold concepts as an innovative way of learning. This paper shows that tutorials adopting the threshold concepts approach have the potential to make students academically more successful. The focus of this study is an introductory information systems module that teaches the Python programming language. Our statistical analysis demonstrated that the year marks and final exam scores of the participating students were frequently higher than those of the students in the control group.

As universities shifted to online education with the onset of the Coronavirus Disease 2019 (COVID-19) pandemic, both pedagogy and assessment patterns across disciplines underwent a change, with a shift towards collaborative digital assessments. In this context, using qualitative and quantitative methods for data collection from an assessment conducted for 55 students who worked in groups to create YouTube videos on pre-selected modules, our analysis reveals that this assessment tool had a significant impact on peer learning, motivation and group dynamics. Students were more motivated to learn and share their learning, and they could also successfully engage in recognising and addressing group dynamics, thereby cultivating more than just subject knowledge. This finding aligns with existing scholarship and the paper adds to research about pedagogy and assessment via digital education in the Indian context, which is still upcoming.

Introduction to Computer Science is traditionally the first course that all computer science and software engineering majors take. The course introduces many problem-solving techniques which can be challenging for many freshman students. In order to mitigate some of the issues of this course, we, at the Higher Education Institute, introduced a new prerequisite course, Introduction to Programming Logic, which is a required course for all students who have not taken any previous computer science course. In the Summer Session of 2022, we included prize-based learning in one of the sections of the course. Prize-based learning is similar to both problem-based learning and project-based learning in many aspects, including the principle of student-centred learning. However, it differs with respect to the motivation for student success. This approach utilises the students' ambition to win, to encourage students to work harder and learn more both inside as well as outside the classroom.

The European Higher Education Area (EHEA) proposes to enhance active learning and student protagonism in order to improve academic performance. In this sense, different methodologies are emerging to create scenarios for self-regulation of their learning. In this study the cooperative, collaborative and interdisciplinary learning methodologies were compared in Spanish universities. The main objectives were to evaluate their effects in higher education and to explore the relationship between perceived group cooperation and self-perceived ability to work in a group, differences between educational Spanish contexts, educational methodologies and gender. To this end, a quasi-experimental design was carried out. Data analysis included the descriptive metrics, correlations and analysis of variance to evaluate the differences among pedagogical methods, their effects on cooperative learning, teamwork outcomes and gender differences, comprising a total of 229 students in Spain from Psychology, Early Childhood Education, Primary Education and Computer Engineering completed the two questionnaires. Results showed that the highest correlation between perceived cooperative activity and self-perceived ability to work in a group was found among computer science students, especially among women, suggesting that the interdisciplinary learning focused on software tool development may be the most effective methodology to improve teamwork and cooperative learning outcomes. Despite these findings concern only to Spanish universities, limiting the generalizability of results, the interdisciplinary methodology seems promising for improving both teaching quality and teamwork skills. The learning methodologies of interdisciplinary projects may therefore need to be implemented within the framework of cooperative and collaborative methodologies.

The goal of the present study was to explore the relations among perceived psychosocial learning environments, instructional modality, motivation, self-regulated learning, and academic achievement in blended computer science education. The participants were 207 undergraduate students enrolled in a blended online and face-to-face design course. We employed exploratory structural equation modeling to analyze the data. Our findings indicated that within the perceived psychosocial learning environments, dimensions such as personal relevance, authentic learning, and active learning significantly predicted various motivational beliefs. Furthermore, active learning and expectancy were strong predictors of effective time management, while instructional modality and emotional cost were closely linked to academic procrastination. Notably, academic procrastination emerged as the sole significant predictor of academic achievement, measured by course grades. These results suggest that traditional curricula emphasizing textbook reading and code memorization may be ineffective in teaching computer science. Additionally, our study highlights a higher tendency for procrastination in online settings. We recommend a curriculum focused on personal relevance, authentic learning, and active learning to better motivate students and enhance their ability to manage their learning effectively. To improve academic achievement in computer science education, it is crucial to address maladaptive self-regulatory processes and motivational beliefs, which primarily arise from active learning and instructional modality. We will conclude with specific recommendations for designing learning environments that better support computer science education.

This paper offers a synthesis on computing identity and the experiences of women and minoritized populations in computing and computer science in higher education. Examining computing identity and its role in the computing experiences of minoritized undergraduates can help us better understand ways to engage and support women and minoritized individuals in the computing field thus increasing participation and persistence of these groups in computing. In this article, we present a review of research literature on computing identity within the experiences of women and minoritized undergraduate students in computing. The research questions guiding this work are: (1) What research literature exists about computing identity?, (2) According to existing literature, what are the experiences of women and racially minoritized undergraduates in computing (e.g., persons who identify as Black, Hispanic/Latinx, Indigenous people)?, and (3) What theoretical frameworks are used to guide existing computing identity literature? Emergent themes include the need to focus on exposure to computing, persistence, career expectations, and engagement. While there is no consensus on a computer identity framework, there is research guided by social cognitive career theory, social identity theory, and practice theory. We conclude that computing identity, including its multiple conceptualizations, offers multiple opportunities for informing and broadening participation of minoritized populations in computing. Future research should include further conceptual exploration of computing identity.

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 presents an inclusive K-12 AI curriculum for elementary schools, focusing on six design principles to address gender disparities. The curriculum, designed by the researchers and an elementary teacher, uses tangible tools, and emphasizes collaboration in solving daily problems. The MANOVA results revealed initial gender differences in AI knowledge. Following MANCOVA analysis showed significant improvements in AI knowledge, as well as perceptions and behavioral intentions toward AI, effectively bridging the observed knowledge gaps without any significant differences attributed to gender or initial knowledge levels. This evidence underscores the success of tangible and collaborative AI interventions in uniformly enhancing AI knowledge and positively changing perceptions and behavioral intentions among all participants, regardless of gender. Both female and non-binary students felt increased engagement and reduced anxiety toward AI, with improved comprehension and a preference for collaborative learning. This study contributes to the design of inclusive AI interventions, highlighting gender differences and including non-binary students as an essential part of the analysis.

Given a myriad of recent contemporary challenges graduate students are facing as well as long-standing issues with student attrition, there is a pressing need to reexamine models of doctoral student progress. While existing research commonly examines departure or failure to meet milestones as the outcome of interest, by the time students leave their programs, it is too late for faculty or departments to offer meaningful interventions. Drawing from Girves and Wemmerus's conceptual framework for doctoral student degree progress, we leverage more recent literature to propose and test an adapted conceptual framework among a recent nationwide sample of computing doctoral students in the United States. Findings illuminate the importance of psychosocial factors (e.g., sense of belonging, researcher self-efficacy), positive perceptions of departmental community, and the role of faculty advisors in reducing doctoral students' consideration of departure. Implications for departmental efforts and faculty advising practices that can honor students' agency and identity in the computing Ph.D. process and that may affect change in doctoral education are discussed.

Background: Project-based collaborative learning (PBCL) is a technique that supports knowledge and skill development through complex, real-world projects. Understanding factors that influence group performance in PBCL, such as gender composition, is crucial. Objectives: This study investigates the impact of gender composition on group performance in PBCL, specifically focusing on the critical thinking, creativity, communication, and collaboration (4Cs) 21st-century learning skills. Methods: A total of 312 students were divided into distinct Python programming groups for the experiment. Group performance was assessed using an agglomerative clustering algorithm guided by the ontological group learner model, which examined gender composition, communication patterns, collaboration strategies and project assessment outcomes. Results and Conclusion: Gender-balanced groups demonstrated positive outcomes in the 4Cs skills, with groups containing a greater proportion of women exhibiting superior performance in collaboration and communication, whereas majority-male groups performed relatively poorer across all skills. Notably, in the context of PBCL, the presence of more women in small groups enhanced the 4Cs skills project assessment outcomes. Takeaways: Future research should focus on providing effective support for male online learners in developing the 4Cs skills. The findings offer insights and recommendations for group formation in collaborative learning, facilitating the design of inclusive and impactful learning environments.

Background: Gender stereotypes about women and men are prevalent in computer science (CS). The study's goal was to investigate the role of gender bias in computer-supported collaborative learning (CSCL) in a CS context by elaborating on gendered experiences in the perception of individual and team performance in mixed-gender teams in a hackathon. Dataset: The dataset of this study was collected at a 3-day CSCL hackathon aimed at gaining knowledge on designing educational games. We assigned the 28 participants of the hackathon to mixed-gender groups and asked them to fill out a questionnaire, including collective self-esteem scales, before the start. During the hackathon, we again asked the participants to complete team progress evaluation surveys individually after each workday. Lastly, we interviewed 11 participants to elaborate on the quantitative findings with qualitative data. Methodology: We applied an exploratory mixed-method approach using quantitative survey data at several time points during the hackathon, which was analysed with clustering and descriptive statistics and complemented with qualitative coding of interviews with participants. Results: The results demonstrate that social and psychological aspects of gender are important for understanding the outcomes and perceptions of gender in a CS hackathon. The analysis further suggests that collective self-esteem can be used as a key variable to assess gender differences in CSCL studies, providing explanatory benefits. More broadly, results gave reason to believe that CSCL in the CS domain currently severely fails to account for gender representation. Interviewed participants raised substantial concerns about the underlying gender stereotypes prevalent in communication, team roles, and work division. We provide recommendations for practitioners seeking to create gender-inclusive and counter-stereotypical CSCL and wider, critical proposals for how we, as researchers, can assess gender with appropriate methodologies and interventions in computer science education.

This study investigated the computational thinking (CT) practices of eight pre-service teachers through their Scratch and Python programs. Conducted within an undergraduate-level computer science education course, students learned CT concepts via parallel instruction in block-based programming (Scratch) and text-based programming (Python). The research focused on the synergistic effects of this parallel instruction, investigating how novices' CT practice evolved while encountering unique features of each environment. Findings indicated that students' CT practices in one programming environment positively influenced their practices in the other, particularly where syntactic similarities existed. Overall, their strategies for controlling program flow progressed from time-based controls to message-passing controls, and finally to condition-based controls. However, the students demonstrated a lack of abstracting and modularizing skills in their computational problem-solving across both programming environments.

Recently, creativity has been acknowledged for its importance in improving computer science learning and attitudes. However, very little is known about how to integrate creativity in preservice teacher computing education. Toward this end, we developed a creativity-enhancement intervention in an online Creative Computing instructional module to introduce preservice elementary teachers (N = 76) to the ideas of creativity in the context of computing education. Using a pre-/posttest quasi-experimental design, we examined the effects of the creativity-enhancement intervention on participants' person-, process-, and Scratch product-related creativity compared to the same curriculum that did not include the creativity-enhancement intervention. Preservice elementary teachers who received the creativity-enhancement intervention reported significantly higher person- and process-related creativity and produced more creative Scratch projects than their peers who did not receive the intervention. When looking at the three creativity strands holistically, the study findings provide strong evidence that the intervention had a positive effect on preservice elementary teachers' creativity in the context of computing education.

Student outcomes are of great importance in higher education institutions. Accreditation bodies focus on them as an indicator to measure the performance and effectiveness of the institution. Forecasting students' academic performance is crucial for every educational establishment seeking to enhance performance and perseverance of its students and reduce the failure rate in the future. The main goal of this study is to predict the performance of undergraduate first-level students in the Computer Department during the years 2016 to 2021 to enhance their performance in future by discovering the best algorithm use to analyze the educational data to identify the students' academic performance. The secondary data was collected by reviewing the Student Affairs Department at the Faculty of Specific Education at Damietta University, in addition to the Statistics Department at the university. The dataset contained 830 instances after excluding 139 instances of missing values, irrelevant rows, and outliers. The dataset was divided into train (577 instances (70%)), test (253 instances (30%)) and involved six features such year, midterm, practical exam, writing exam, final total degree, and grade. This paper use five machine learning (ML) algorithms which was selected according to the literature review and high accuracy in predicting educational data mining: For the purpose of comparison, a number of different machine learning algorithms, such as Random Forest, Decision Tree, Naive Bayes, Neural Network, and K-Nearest Neighbours, were utilized and evaluated with evaluation metrics such as confusion matrix, accuracy, precision, recall, and F-measure. The Random Forest and Decision Tree classifiers emerged as the top-performing algorithms, accurately categorizing 250 instances when predicting students' performance in the statistics course. This was determined based on the findings of the study. Out of a total of 253 instances that were included in the testing set, they only made three incorrect classifications.

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 explores the changing landscape of technology teacher education, in relation to the increasing integration of digital content, especially programming, in teacher education for grades 4-6 (pupils 10-12 years old) and how student teachers in Sweden perceive this content. Limited research exists on student teachers in technology, particularly focusing on programming. This study therefore investigates student teachers' perceptions of teaching programming in technology education, after completing their technology course in teacher education. We answer the following research questions: What are the student teachers' perceptions of teaching programming in technology education? and How is potential subject didactics knowledge for teaching programming manifested in student teachers' perceptions of technology teaching? Using a phenomenographic approach, 25 student teachers' perceptions of programming in technology education were investigated through semi-structured individual and group interviews. Different perceptions were revealed and presented in four categories: (1) following instructions in a logical order, (2) learning a programming language, (3) solving technological problems, and (4) understanding and describing a technological environment. The results show that student teachers' perceptions of the subject of technology predominantly focuses on following instructions and the learning of a programming language. The identified potential subject didactics knowledge is constituted of an awareness of three critical aspects: understanding programming language, understanding programming as a way of solving problems, and the relationships of technological problems to everyday life and society. This study offers valuable insight into the development of competencies required to teach programming in technology, informing educational strategies and future research in this emerging field.

Over the last decade, there has been an explosion of national interest in computer science (CS) education. In response to this, several organizations and initiatives have emerged in recent years to expand the CS pipeline. However, within these broad and laudable efforts, one important area has been largely overlooked--the instruction of CS to multilingual students, including the large and growing number of students designated as English learners in K-12 schools. These are one of the most underserved and understudied groups in CS education. In this article, we draw on existing research, as well as our own and others' theoretical and empirical work to date, to put forth both a framework and curriculum for teaching CS to multilingual students.

Programming is a major subject in various Information Systems (IS) programs, with students often finding it a challenging skill to acquire. While there is extensive literature on factors helping students learn to program, most of which focuses on non-IS students. Due to the increasing demand for professionals with programming skills, there is a pressing need for further research on factors that could enhance learning programming skills at the higher education level. One promising approach to address this issue involves examining students' internal characteristics, their programming self-efficacy, and its connection to instructional methods that can enhance it. This study adopted a quantitative research design to evaluate students' programming self-efficacy. A survey was conducted to measure students' beliefs in their programming competence and engagement in various instructional activities, including the value they attributed to learning programming, the time spent practicing, and the frequency they sought guidance from teaching assistants (TA). Through a hierarchical multiple regression analysis, this work investigated how these mentioned variables could predict student-programming self-efficacy. The results indicated that the value students placed on learning programming emerged as the most significant predictor for programming self-efficacy. On the other hand, there was no substantial evidence that the practice time or consulting TA predict programming self-efficacy and the practice time or consulting TA. These findings suggest that educators and instructional designers need to emphasize the practicality and importance of learning how to program to enhance students' perceived value.

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.

The purpose of this study was to investigate whether taking a course online or face-to-face matters in terms of student engagement and achievement. Gender differences were also examined. The level of student engagement in an information technology course in a freshman sample from a school of education was surveyed and compared in two consecutive years where the course was taught online in the first year and face-to-face in the second year. There were a total of 129 students, 62 in the online mode and 67 in the face-to-face mode. Data were collected using a survey that included a student engagement scale, as well as midterm and final exams. Nonparametric analyses were used due to data with non-normal distributions for some of the dependent variables, with the Mann-Whitney U test being the main form of analysis for group comparisons. Regarding gender, female students who received face-to-face instruction lost interest in the course toward the final exams, as evidenced by significantly lower engagement scores for both active learning and paying attention. The results show that online versus face-to-face delivery of the computer science course can be more efficient in terms of student engagement, and a similar performance score as in the face-to-face mode can be achieved with less effort to be present at school. In addition, it is evident that female students need more support for greater engagement when the mode of delivery must be face-to-face.

Satellite laboratories, designed to ignite interest in technical and computer science topics, employ cross-age peer tutoring and physical computing platforms. Catering to students from 5th grade onwards, these laboratories are led by tutors from 9th grade onwards. Employing a design-based research approach, the project aims to comprehensively understand teaching and learning dynamics and address well-founded problems. This study investigates the impact of satellite laboratories on (situational) interest and subsequent course choices, recognizing the decline in STEM interest as students age, particularly in computer science. To combat this trend, early stimulation of STEM interest is deemed crucial. Thirteen students were interviewed, revealing that many already held STEM interests before participating. The data, analyzed through structured content analysis, demonstrates alignment with the four-phase model of interest genesis, highlighting situational epistemic and emotional interest. Students view satellite laboratories as valuable introductions influencing future course selections. This research underscores the significance of initiatives fostering early STEM interest to sustain engagement and combat declining interest in computer science.

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.

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.

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.

In K-12 computing education, there is a need to identify and teach concepts that are relevant to understanding machine learning technologies. Studies of teaching approaches often evaluate whether students have learned the concepts. However, scant research has examined whether such concepts support understanding digital artefacts from everyday life and developing agency in a digital world. This paper presents a qualitative study that explores students' perspectives on the relevance of learning concepts of data-driven technologies for navigating the digital world. The underlying approach of the study is data awareness, which aims to support students in understanding and reflecting on such technologies to develop agency in a data-driven world. This approach teaches students an explanatory model encompassing several concepts of the role of data in data-driven technologies. We developed an intervention and conducted retrospective interviews with students. Findings from the analysis of the interviews indicate that students can analyse and understand data-driven technologies from their everyday lives according to the central role of data. In addition, students' answers revealed four areas of how learning about data-driven technologies becomes relevant to them. The paper concludes with a preliminary model suggesting how computing education can make concepts of data-driven technologies meaningful for students to understand and navigate the digital world.

This study aims to examine the impact of interdisciplinary computational thinking (CT) skills training on primary school teachers' perceptions of CT skills. The sample of the study consisted of 30 primary school teachers in Istanbul. In this study, where quantitative and qualitative methods were used together, qualitative data were obtained from the teacher identification form. Quantitative data were obtained from the scale for CT skills. After the pre-test was applied to the study group, "CT Skills Training" was applied. During the training, the basic concepts of CT skills and the subskills were covered theoretically and practically. From the quantitative data, the education applied was determined to have had a positive effect on the primary school teachers' perceptions of CT skills. From the qualitative data, it was determined that the participants had a positive opinion about the applied training and thought that they gained skills related to CT.

The purpose of this research was to analyze the confirmatory components of the computational thinking learning competency measurement model. The sample group consisted of 240 students in the Bachelor of Education Program in Computer and Digital Technology for Education in Years 2-4 of five of the Rattanakosin Rajabhat Universities. Data collection involved the use of a 5-level (Level 5 indicates that the student's awareness is at the highest level while Level 1 indicates that the student's awareness is at the lowest level) checklist questionnaire after which confirmatory factor analysis (CFA) was performed. The results of the research found that the model created by the researcher has 3 components: knowledge, skills, and attributes which are latent variables, and which are consistent with the empirical data with Chi-Square values. The results are statistically significant at the 0.01 level (Chi-Square=47.680, p=0.112, df=37, Relative Chi-Square Ratio=1.288, GFI=0.970, AGFI=0.930, CFI=1.000, SRMR=0.035, RMSEA=0.035), which meets the specified criteria.

Young people need digital competency and confidence to effectively harness computing power to solve problems and design solutions; a core component is improving young people's computational thinking. Unfortunately, access to computer science education is lacking for all youth, and in particular for youth who live in lower-income households, who are Black or Latino, or live in rural areas. With funding from Google, through the National 4-H Council, California 4-H embarked on a three-year project to build the capacity of 4-H professionals, volunteers, and teenagers to facilitate computer science education with youth. Our programming was effective as assessed with survey methodology. We offer recommendations to Extension professionals in replicating computer science education.

This study aims to determine the opinions of science teachers on robotics coding, which is one of the prominent applications in recent times and is expected to be integrated into education. The research was designed as a case study, one of the qualitative research methods. The study group of the research consists of 12 science teachers. Semi-structured interviews were conducted with the participating science teachers to determine their opinions on robotics coding, using a semi-structured interview form developed by the researchers. The interview form consists of two sections. The first section includes the demographic characteristics of the teachers. The second section includes open-ended questions aimed at determining the definitions of science teachers regarding robotics coding, the usability of robotics coding in science classes, teachers' experiences with robotics coding, and their suggestions. The collected data were analyzed using the descriptive analysis method and interpreted according to thematic order. As a result, it can be said that robotics coding applications are usable in science education, play an important role in developing various skills in students, and contribute to efficient and permanent learning in classes. However, it was concluded that support for teachers and students in terms of knowledge and equipment, provision of material support, or resources are necessary for its use in classes.

Facilitating Computer Science Unplugged (CS-unplugged) activities with reflective thinking activities lead students to in-depth reviews of their decisions and think of ways to solve the CS-unplugged problems. This study aims to evaluate the instruction enriched with reflective thinking activities to develop computational thinking skills. The study was carried out as a case study in a Computer Science course. The participants were 24, 5th-grade secondary school students. Students used two-column reflection notes and daily diaries to reflect their thoughts. Data were collected through Computational thinking skills evaluation form (CTEF), interviews, and observations to examine the role of reflective thinking activities during the instruction. Descriptive analysis, the thematic analysis was carried out through interviews was used to analyze data. The use of the two-column reflection notes and diaries positively contributed to the development of students' computational thinking skills. The students who engaged in reflective thinking activities outperformed in exhibiting their computational thinking skills than those who did not engage in. We hope that the findings of this study will assist in the future design and implementation of CS-unplugged activities to improve CT skills.

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.]

This article presents a systematic review of the literature with the aim of providing an updated framework for the scientific production developed in the field of digital competence and inclusive education in Higher Education, as indexed in the Web of Science (WoS) and SCOPUS databases. Twenty-one scientific articles published between 2016-2023 were included and analyzed, with no temporal cohort established. The search matches yielded a first document of the topic of study in 2016 in the Social Sciences Citation Index. The results obtained on the conceptual structure of the analyzed documents were carried out through a multiple correspondence analysis (MCA) observing the existence of two dimensions composed of three clusters. Cluster one was composed of studies on digital competence, virtual learning environments, digital divide, and functional diversity, among others. Cluster two comprised studies carried out on digital tools and competencies, inclusive education, educational processes, and digital strategies; while cluster three was made up of educational inclusion related to interculturality and digital competence. Among its limitations, there were aspects related to the heterogeneity of the studies, which make it difficult to compare the data, and the sample and size of the study, which makes the results obtained and the generated data have a lower percentage of generalization compare to studies that use larger simples. This study has implications for researchers and Higher Education institutions interested in research on digital competence for inclusive education, with the possibilities of digital competence for inclusive education being established from the studied variables, allowing teachers to adapt and personalize learning to meet individual student needs. All of this is in line with the goals of the 2030 Agenda, concerning the empowerment of citizens and the digitization of public services to ensure the population's access to such services via the internet.

Students from underrepresented populations--females, working class, and youth from marginalized racial/ethnic groups--are less likely than their middle-class Asian and White male peers to study computer science (CS) in college. The dearth of CS undergraduates from these groups contributes to projected labor force shortages. Sources of the dilemma include weak or absent inspiration and CS preparation in middle schools and negative stereotypes suggesting certain groups do not belong in CS. This case study describes three years of a community collaboration between a local university and a nearby middle school attended by primarily low-income students of color. The University of North Carolina Charlotte/Wilson STEM Academy Partnership focused on undergraduates majoring in CS teaching monthly workshops designed to inspire and academically prepare the middle schoolers for college and CS majors by teaching them coding and computational thinking while also challenging stereotypes about who belongs in CS. Post-workshop assessments, reflective essays, interviews, and administrative data were thematically coded. Findings suggest the workshops sparked interest in college and CS, undermined toxic stereotypes, and nurtured the academic self-confidence of middle schoolers. The Partnership provided the undergraduates with opportunities to meet their own academic goals while "paying it forward." Results suggest that the Partnership can serve as a model starting point for disrupting the disproportionalities in female and underrepresented minority students in CS.

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.

A computer science camp for pre-collegiate students was operated during the summers of 2022 and 2023. The effect the camp had on attitudes was quantitatively assessed using a survey instrument. However, enrollment at the summer camp was small, which meant the well-known Pearson's Chi-Squared to measure the significance of results was not applied. Thus, a quantitative analysis method using a multinomial probability distribution as a model of a multilevel Likert scale survey was used. Exact calculations of a multinomial probability model with likelihood ratio were performed to quantitatively analyze the results of questionnaires administered to participants in two cohort groups (combined N=17). Probabilities per Likert categories were determined from the data itself using Bayes theorem with a Dirichlet prior. Each cohort functioned as part of a homogenous sample, thus allowing cohorts to be pooled. Post-test revealed significant changes in participants' attitudes after camp completion. Using this technique has implications for studies with small sample sizes. Using exact calculation of the multinomial probability model with the use of likelihood ratio as a statistical test of evidence has advantages: a) it is an exact value that can be used on any size sample, although it offers a quantitative analysis option for small sample size studies; b) depends only on what was observed during a study; c) does not require advanced calculation; d) modern spreadsheet and statistical package programs can calculate the analysis; and e) likelihood ratio employed in Bayes theorem can update prior beliefs according to evidence. Utilizing small sample size quantitative analysis can strengthen insights into data trends and showcase the importance of this quantitative technique.

The study focuses on the identification of relationships and/or rules between computational thinking (CT) concepts among the undergraduate students of Applied Informatics due to their attitudes towards mathematics. We analyze three CT concepts -- decomposition, pattern recognition, and algorithmic thinking. We assume that students who have a closer relationship with mathematics, a positive attitude towards mathematics, have better developed CT. We conducted the experiment during the 2022/2023 academic year on the Information Coding and Displaying subject. The results indicate that those students who have no relationship to mathematics, negative attitude towards mathematics, have no problem with decomposition and pattern recognition, but without significant algorithmic thinking. On the other hand, students who have a close relationship with mathematics are also able to decompose or recognize patterns, but moreover they have shown algorithmic thinking. The contribution of the study comprises the identification of relationships and/or patterns of computational thinking concepts among students who have a relationship to mathematics, who cannot assess their relationship to mathematics, as well as among students who have no relationship to mathematics. Our results indicate a different occurrence of computational thinking concepts as well as links and/or relationships between them.