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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.

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.

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.

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.

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.

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.

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.

Knowing the factors affecting fear of large carnivores is important for wildlife management and conservation. In this regard, the effect of worldviews of human-wildlife and human-nature relationships on the fear of large carnivores needs to be investigated in different cultures. In this study, the influence of wildlife value orientations (WVOs), connectedness with nature, religiosity, fear of dogs, and gender on the fear of wolves and wild boars were examined. Quantitative research was conducted via a questionnaire on a convenience sample of 656 university students from state universities in various regions of Turkey. Domination orientation did not influence the fear of either animal. Mutualism negatively influenced the fear of wolves but did not significantly influence the fear of wild boar. Connectedness to nature is related negatively to the fear of both animals. More religious students feared wild boars more. Female students had a greater fear of both animals than male students. In addition, students with a fear of dogs had a greater fear of both animals. The relationship between worldviews and fear of large carnivores can vary across different species and needs to be investigated in different cultures

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.

The future of university learning in Sub-Saharan Africa has become increasingly digitally transformed by both e-Learning, and learning analytics, post-COVID-19 pandemic. Learning analytics intervention is critical for effective support of socially-shared regulated learning skills, which are crucial for twenty-first-century e-Learners. Socially-shared regulation is the major determinant of successful collaborative e-learning. However, most e-learners lack such skills thereby facing socio-cognitive challenges, due to the unavailability of intelligent support during learning. This research aims to investigate and understand the effect of Learning Analytics instructional support using feedback and prompts, on e-learners' SSRL indicators. A theoretical model was derived from these factors and built from selected features. Both survey data and behavioral trace data were employed in the Learning analytics-based intervention. In this paper, only a segment of the data is discussed. The e-learners' perceptions and feedback confirmed that Learning Analytics-based interventions using prompts and feedback are effective in promoting SSRL in collaborative e-learning contexts. The findings indicated that the success of SSRLA-based intervention be tied to support from instructors and academic counselors, particularly feedback on previous problems and quizzes. This will improve e-learners' SSRL skills for quality educational experience, hence motivate e-learners, and help lecturers to identify at-risk learners in web programming problem-based courses. In conclusion, without adequate utilization of the Learning Analytics interventional trace data, critical information about learners' behavior patterns in terms of their online interactivity with the course activities and their SSRL profiles and strategies cannot be disclosed leading to little improvement of e-Learning interventions.

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.

In the United States, school curricula are often created and taught with distinct boundaries between disciplines. This division between curricular areas may serve as a hindrance to students' long-term learning and their ability to generalize. In contrast, cross-curricular pedagogy provides a way for students to think beyond the classroom walls and make important connections across disciplines. The purpose of this paper is a theoretical reflection on our use of Expansive Framing in our design of lessons across learning environments within the school. We provide a narrative account of our early work in using this theoretical framework to co-plan and enact interdisciplinary mathematics and computer science (CS) tasks with a team of elementary school educators and school district personnel. The unit focuses on the concepts of exponents in mathematics and repeat loops as a control structure in computer science. Using a narrative approach, we describe what occurred during the collaborative planning of lessons and subsequent enactments in two fifth-grade classrooms and one computer lab and provide a practitioner-oriented account of our experience.

This study aims to map the landscape of the importance of previous research on computational thinking (CT) in mathematics learning over the last decade. This research is a literature review research, there are 113 publications collected from the Scopus database which are then analyzed using the bibliometric analysis method assisted by the Vosviewer application. The data taken from the Scopus database was refined so that it became 113 publications. The results of the study show that publications are spread across various continents, including America, Asia, Africa, and Europe. The United States and the United Kingdom are the most influential countries and have high cooperation with other countries in this field. The main conclusion from this study is that computational thinking has an important role in learning mathematics and STEM. The integration of computational thinking in the mathematics curriculum can improve students' understanding of mathematics and help them face the challenges of the digital age. In addition, the development of effective training methods for mathematics teachers as well as the assessment of good computational thinking learning tools and platforms can help improve the effectiveness of computational thinking teaching in mathematics contexts. These innovations will help better understand how computational thinking can be effectively integrated in mathematics learning and prepare students for a future filled with digital technologies.

Due to swift technological changes in society, programming tasks are proliferating in formal and informal education around the globe. However, challenges arise regarding the acquisition of programming skills. Many students are unequipped to develop programming skills due to limited instruction or background and therefore feel insecure when encountering programming in higher education. Some after-school initiatives focus on teaching younger students programming skills, however, not all students have the opportunity to attend. It can also be very challenging for teachers to teach programming--even more so due to significant differences in students' knowledge and interests. To alleviate these challenges, we designed and developed a digital personalized learning (DPL) track for programming in the first grade of secondary education (12-14 year-old students) with a threefold purpose: (a) to encourage students bridging the gap between visual and more general-purpose textual programming languages (b) to meet differences in students' programming knowledge by challenging them, albeit on their own pace, and subsequently (c) to support teachers in the delivery of programming education with relevant supportive learning materials. The design was tested by students and teachers, both of varying technical abilities. Assessments of the DPL-track were positive, with students identifying the tasks as challenging and the tools as motivating. Teachers praised the adaptivity, as well as the gradual transition from visual to textual programming. We present several suggestions for design improvement and dilemmas while reflecting on our design case.

Generative artificial intelligence (AI) tools were met with a mix of enthusiasm, skepticism, and fear. AI adoption soared as people discovered compelling use cases--developers wrote code, realtors generated narratives for their websites, students wrote essays, and much more. Calls for caution attempted to temper AI enthusiasm. Experts highlighted inaccuracies in some of the generated information. Artists, writers, programmers, and other professionals who create for a living questioned their job security. Educators worried that these tools would harm academic integrity. Nevertheless, the tools persist, and AI enthusiasm shows no sign of abating. The current study focuses on how AI tools can be leveraged to improve self-directed learning in the classroom. In a capstone cybersecurity course, the instructor encouraged students to use ChatGPT 3.5 on a self-directed learning assignment to select topics, find learning resources, and carry out their learning plans. Some students did not use ChatGPT. Those who used ChatGPT found it to be a helpful learning aid. All students reported that they plan to use ChatGPT for self-directed learning after graduating. No student violated academic integrity policies. The results demonstrate that AI tools can enhance self-directed learning, though students should be trained to use them effectively. Recommendations for developing assignments that support academic integrity and rigor in the age of AI are provided.

A significant gender gap continues to exist within computer science (CS) education, despite nationwide emphasis in the U.S. on improving CS education equity and access. To explore this issue, we conducted an ethnographic case study within a classroom at Forest View High School (FVHS, pseudonym) where girls' participation in CS was consistently higher than state averages over 12 years. We sought to understand teacher and student experiences within this gender-inclusive program. Data were collected over three months through observations, interviews, course documents, and reflections. Results indicate three strategies for supporting a more gender-inclusive classroom: (1) Providing personalized and relevant learning experiences; (2) focusing on growth mindset development; and (3) creating a welcoming environment. Implications for practice include providing assignment choice and personalized one-on-one support for students, modeling a growth mindset and providing opportunities to learn from failure, and building personal relationships with students and incorporating humor. Overall, teachers can act as agents of social change within the CS classroom, and play an essential, central role in broadening participation and equity initiatives. However, this work must also be supported by administrators, counselors, and other school stakeholders to be effective for enacting change.

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.

Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modelling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students' performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performances and identify the critical areas that need significant improvement.

We present a case study that demonstrates how instructors can intentionally design a positive motivational climate in online and hybrid courses. We also examine the extent to which students' perceptions of the motivational climate predict their effort and achievement across three different modalities (face-to-face [FTF], online, and hybrid) of the same course. We surveyed students in an undergraduate computer science course once a semester for three consecutive years (FTF in Year 1, online in Year 2, and hybrid in Year 3). Measures included motivation-related scales and final course grades. Our findings, based on survey responses from 981 students, demonstrate that it is possible to create a motivational climate in online and hybrid courses that is as good or better than the motivational climates in a FTF course. Across the FTF, online, and hybrid courses, students' perceptions of the motivational climate predicted their effort and achievement in similar ways, with perceptions of usefulness, interest, and success serving as the strongest predictors.

This paper presents the first experiences of the use of an online open-source repository with programming exercises. The repository is independent of any specific teaching approach. Students can search for and select an exercise that trains the programming concepts that they want to train and that only uses the programming concepts they already know, then submit their solutions, and get automatic feedback from the system. We analyzed quantitatively how students used the system by inspecting the logged actions of the students using the system. We also did a qualitative analysis by interviews, to find out how the students appreciated the use of the repository and to get feedback for improvements. We focused on how students select exercises as finding the exercise that fulfils the training needs of a student is the innovative part of our repository.

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.

The integration of artificial intelligence (AI) topics into K-12 school curricula is a relatively new but crucial challenge faced by education systems worldwide. Attempts to address this challenge are hindered by a serious lack of curriculum materials and tools to aid teachers in teaching AI. This article introduces the theoretical foundations and design principles for implementing co-design projects in AI education, empirically tested in 12 Finnish classrooms. The article describes a project where 4th- and 7th-graders (N = 213) explored the basics of AI by creating their own AI-driven applications. Additionally, a framework for distributed scaffolding is presented, aiming to foster children's agency, understanding, creativity, and ethical awareness in the age of AI.

When it comes to mastering the digital world, the education system is more and more facing the task of making students competent and self-determined agents when interacting with digital artefacts. This task often falls to computing education. In the traditional fields of computing education, a plethora of models, guidelines, and principles exist, which help scholars and teachers identify what the relevant aspects are and which of them one should cover in the classroom. When it comes to explaining the world of digital artefacts, however, there is hardly any such guiding model. The ARIadne model introduced in this paper provides a means of explanation and exploration of digital artefacts which help teachers and students to do a subject analysis of digital artefacts by scrutinizing them from several perspectives. Instead of artificially separating aspects which target the same phenomena within different areas of education (like computing, ICT or media education), the model integrates technological aspects of digital artefacts and the relevant societal discourses of their usage, their impacts and the reasons behind their development into a coherent explanation model.

Service-Learning (SL) is a powerful methodology to acquire competences and values in Higher Education. However, there is still no widespread use in Information and Computer Science (ICS) degrees where most of the subjects are focused on the development of theoretical and practical contents purely related to technical competences. In this paper, we show the structure of a SL methodology to develop Bachelor's Thesis: 1) definition of proposals considering all the competencies of the Bachelor's Thesis subject and the needs of entities; 2) development of applications using agile methodologies, and 3) assessment of the SL experience from students, entities and professors. We present an experience developed in the 2019/2020 academic year with two entities devoted to disfavoured people. The results show both the high technical and professional quality of the projects and the high satisfaction of entities and students. We hope that the development of applications with the collaboration of non-profit entities allows the acquisition of both specific and transversal competencies on ICS degrees at the same time enhancing the development of useful professional ones.

Artificial intelligence (AI) technology has made a significant impact on technological progress and has been integrated into various sectors and organizations. As a result, developing a workforce with knowledge and expertise in AI has become necessary. Skilled AI professionals will play a critical role in driving economic growth and competitiveness in the digital age. Therefore, it is essential to develop AI competency among various groups of people. Learning AI skill sets is necessary to facilitate effective collaboration between humans and machines in the learning process. Known for Life offers a range of knowledge, including technical skill sets, business skill sets, and skill sets for individuals that incorporate ethics, such as the ethical use of AI in education to enhance the learning experience and evaluate student performance. Understanding AI can help educators adopt modern teaching methods and prepare students for AI-related careers, but it is crucial to consider ethical implications.

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.