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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 paper reflects on the process of designing and implementing an update to the "Introduction to Information Technology" course at Bentley University, a business university in New England. Driven by a university-wide curriculum reform initiative and following best practices from digital literacy frameworks and IS model curricula, the new CS 100 course, entitled "Solving Business Problems with Information Technology," shifted emphasis from a personal computing context to exploring small business applications of technology. Students networked with university alumni at "Topics in Tech" presentations, where they learned about career options as information technology professionals. The authors describe the pilot courses that led to proposing a new CS 100 course and reflect its challenges and successes. Challenges included meeting the university's goal of transfer friendliness, scaling from a few pilots to multiple sections, consistency of course delivery, and evaluating assessment. Successes include placing course topics in a business context, introducing current technologies, and providing opportunities for students to learn about career opportunities and experiences from university alumni. The paper also describes how CS 100 aligns with the university's student learning goals for the new curriculum. These include developing future-focused skills, encouraging curiosity and critical analysis, communicating and collaborating in different settings, and demonstrating understanding of issues related to ethical use of technology.

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

Interest in the study of computer technology in schools (often in the form of Computer Science subjects or coding initiatives) is seen to have undergone two waves of interest, one in the late 1970s/early 1980s and the other more recently since about 2010. Through a historical exploration of the rationales for the study of computer technology in schools in both the first wave and the second wave, this paper aimed to explore the various curriculum ideologies underpinning their justification. In doing so, the paper highlights the chameleon-like nature of this change agenda in aligning with contemporary trends to advance the study of computer technology on the curriculum. The paper argues that current manifestations of the study of computer technology in schools are at odds with broader curriculum reform goals and international policies and raises questions about students' access to such knowledge.

This paper is an experiential report describing the faculty and student experiences and knowledge gained from developing an Internet of Things (IoT) course within an Information Technology (IT) Department at a large southeastern United States public university. The paper contributes insights gained developing the introductory IoT course, and it recommends the inclusion of IoT technology within an IT curriculum due to its disruptive and transformative nature. Many industries are deploying IoT applications making it imperative that IT graduates have the knowledge and skills needed to work with this emerging technology. The faculty leveraged the use of the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) Instructional Design Model to develop a pilot course in the summer of 2022 and then, based on the pilot feedback and analysis using ADDIE, refined the course design for spring 2023. The continuing course development draws on IT undergraduate curricula guidelines in "Information Technology Curricula 2017 (IT2017)," research into how industry uses IoT applications, and the use of hands-on educational technologies to identify and apply IoT knowledge and skills needed for IT graduates. The pervasive nature of IoT requires IoT faculty to have knowledge of each of the IT Pillars (programming, networking, human-computer interaction, databases, and web systems) as well as the IT Supplemental IoT Domain guidelines developed in "IT2017."

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.

The goal of this paper is to bring new insights to the study of social learning processes by designing measures of collaboration using high-frequency sensor data. More specifically, we are interested in understanding the interplay between moments of collaboration and cooperation, which is an understudied area of research. We collected a multimodal dataset during a collaborative learning activity typical of makerspaces: learning how to program a robot. Pairs of participants were introduced to computational thinking concepts using a block-based environment. Mobile eye-trackers, physiological wristbands, and motion sensors captured their behavior and social interactions. In this paper, we analyze the eye-tracking data to capture participants' tendency to synchronize their visual attention. This paper provides three contributions: (1) we use an emerging methodology (mobile dual eye-tracking) to capture joint visual attention in a co-located setting and replicate findings that show how levels of joint visual attention are positively correlated with collaboration quality; (2) we qualitatively analyzed the co-occurrence of verbal activity and joint visual attention in low and high performing groups to better understand moments of collaboration and cooperation; (3) inspired by the qualitative observations and theories of collaborative learning, we designed a new quantitative measure that captures cycles of collaborative and cooperative work. Compared to simple measures of joint visual attention, we found it to increase correlation coefficients with learning and collaboration scores. We discuss those results and describe how advances in analyzing sensor data can contribute to theories of collaboration. We conclude with implications for capturing students' interactions in co-located spaces using Multimodal Learning Analytics (MMLA).

Lately, there has been a rising trend towards integrating sustainability issues and implementing sustainable development (SD) goals into higher education, especially in the Information and Communication Technology (ICT) field. This paper presents the challenges of the introduction of education for sustainability in higher education institutions, analyzes how these challenges could be addressed, and records the essential principles that can guide the incorporation of SD education in higher education. At the same time, this paper advocates the concept of sustainability integration into undergraduate ICT curricula and provides indications for developing ICT curricula that integrate sustainability issues. This paper also examines the contribution of education to the environmental awareness of the students at a Greek University while it captures their perceptions regarding the necessity for the inclusion of sustainability in the ICT curricula. The results are based on the processing of two questionnaires (pre-test and post-test data) distributed during two lectures regarding sustainability at a two-week interval. The findings of the study prove that the direct effect of the educational intervention on the intention of the students to engage in sustainability is significant. Furthermore, it is observed that students show a positive attitude towards integrating sustainability issues into their ICT education. In addition, although students had a deficient level of understanding of the concept of SD goals before the intervention, they showed a significant increment in their previous knowledge regarding sustainability and the environmental impact of ICT studies. The research also reveals that the students are not satisfied with the engagement of the university towards sustainability, and they propose various awareness activities to support their engagement with sustainability issues in an inclusive manner.

We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, approach, language paradigm, degree of automation, and evaluation techniques. Most papers assess the correctness of assignments in object-oriented languages. Typically, these tools use a dynamic technique, primarily unit testing, to provide grades and feedback to the students or static analysis techniques to compare a submission with a reference solution or with a set of correct student submissions. However, these techniques' feedback is often limited to whether the unit tests have passed or failed, the expected and actual output, or how they differ from the reference solution. Furthermore, few tools assess the maintainability, readability, or documentation of the source code, with most using static analysis techniques, such as code quality metrics, in conjunction with grading correctness. Additionally, we found that most tools offered fully automated assessment to allow for near-instantaneous feedback and multiple resubmissions, which can increase student satisfaction and provide them with more opportunities to succeed. In terms of techniques used to evaluate the tools' performance, most papers primarily use student surveys or compare the automatic assessment tools to grades or feedback provided by human graders. However, because the evaluation dataset is frequently unavailable, it is more difficult to reproduce results and compare tools to a collection of common assignments.

The computing education research community now has at least 40 years of published research on teaching ethics in higher education. To examine the state of our field, we present a systematic literature review of papers in the "Association for Computing Machinery" computing education venues that describe teaching ethics in higher-education computing courses. Our review spans all papers published to SIGCSE, ICER, ITiCSE, CompEd, Koli Calling, and TOCE venues through 2022, with 100 papers fulfilling our inclusion criteria. Overall, we found a wide variety in content, teaching strategies, challenges, and recommendations. The majority of the papers did not articulate a conception of "ethics," and those that did used many different conceptions, from broadly applicable ethical theories to social impact to specific computing application areas (e.g., data privacy and hacking). Instructors used many different pedagogical strategies (e.g., discussions, lectures, assignments) and formats (e.g., stand-alone courses, incorporated within a technical course). Many papers identified measuring student knowledge as a particular challenge, and 59% of papers included mention of assessments or grading. Of the 69% of papers that evaluated their ethics instruction, most used student self-report surveys, course evaluations, and instructor reflections. While many papers included calls for more ethics content in computing, specific recommendations were rarely broadly applicable, preventing a synthesis of guidelines. To continue building on the last 40 years of research and move toward a set of best practices for teaching ethics in computing, our community should delineate our varied conceptions of ethics, examine which teaching strategies are best suited for each, and explore how to measure student learning.

The HCS12 microcontroller and DRAGON12-Light Trainer boards are extensively utilized in microprocessor system design education. This paper details the rationale, approach, and outcomes from implementing the DRAGON12-Light Trainer board in teaching an upper-level undergraduate microprocessors course at Oakland University. The course's primary goal is for students to acquire the skills necessary to design both hardware and software for microprocessor-based systems, with applications across various industries. The paper assesses the effectiveness of employing the Motorola HCS12 microcontroller for its real-world relevance and integrates the Freescale CodeWarrior IDE v5.1 environment for software development. This research uniquely contributes by measuring the improvement in students' practical skills, specifically in hardware and software design using assembly and C programming, through hands-on lab assignments. It reports on the development of students' abilities to engage in microprocessor-based system design and critically evaluates the applied aspects of pedagogy by incorporating the DRAGON12-Light Trainer in lab exercises, such as those involving 'Measuring Human Reaction Time' and 'Servo motor interfacing.' Quantitative results, derived from student surveys and assessments, indicate significant improvements in students' programming competencies. The paper provides statistical results showcasing the increase in students' self-rated confidence levels in assembly and C language programming before and after course completion. Additionally, qualitative insights are discussed, reflecting students' experiences and the perceived applicability of the skills they acquired. These results underscore the pedagogical value of integrating practical training devices like the DRAGON12-Light Trainer board into microprocessors curricula.

This paper presents field-tested improvements over an 11-year period of a large-scale "Introduction to Information Systems" core business school course and provides a framework for implementation. Engagement and learning in large-scale courses can prove challenging, especially when the class is a requirement within a business school's core curriculum where most students taking the class are not majors in the course's subject matter. The course sections typically have 100 or more students each. The non-IS major student population attends this core course because they must, rather than want to, attend which in most cases leads to indifference and disengagement. A primary need and challenge of a required core course are to create a compelling 14-week "Introduction to Information Systems" course that captures this student population's interest in an engaging manner. This paper identifies an innovative approach to create and foster an enhanced learning environment organized around active learning, dynamic content, and a narrative structure. Major changes discussed in the paper are the abandonment of textbooks, the introduction of in-class activities and JavaScript coding, and the establishment of a narrative.

Objective: Community college transfer pathways are critical for advancing gender equity in STEM. Yet, community college students are often ignored within studies of women's participation in undergraduate computing. In a first effort to address this gap in the literature, this paper explores the composition of transfer-intending computing students over time (Study One) and gender differences in the characteristics and experiences of transfer-intending computing students (Study Two). Methods: This descriptive paper uses Center for Community College Student Engagement (CCCSE) survey data. Study One relies on a sample of nearly 30,000 transfer aspirants in computing across the United States between 2011 and 2019, allowing us to explore trends over time. Study Two examines a subset of over 9,000 students from the most recent survey cohort, providing a more nuanced snapshot of transfer-intending computing students. Descriptive statistics were used to examine how student experiences differ by gender and race/ethnicity. Results: Study One findings show that women's representation among transfer-intending computing students has declined over time. Study Two results reveal that, relative to men, women spend more time caregiving, commuting, studying, in student groups, and utilizing advising services, pointing to unique demands on women's time. We also identify significant differences in how Black and Indigenous women financed their college, relative to other women. Conclusions: While women are underrepresented among transfer-intending computing students, they represent a diverse group to support. We point to recommendations for policy and practice to support transfer-intending women in STEM and future research that considers intersectional identities among this diverse group.

Purpose: In today's digital world, data-driven digital artefacts pose challenges for education, as many students lack an understanding of data and feel powerless when interacting with them. This paper aims to address these challenges and introduces the data awareness framework. It focuses on understanding data-driven technologies and reflecting on the role of data in everyday life. The paper also presents an empirical study on young school students' data awareness. Design/methodology/approach: The study involves a teaching unit on data awareness framed by a pre- and post-test design using a questionnaire on students' awareness and understanding of and reflection on data practices of data-driven digital artefacts. Findings: The study's findings indicate that the data awareness framework supports students in understanding data practices of data-driven digital artefacts. The findings also suggest that the framework encourages students to reflect on these data practices and think about their daily behaviour. Originality/value: Students learn a model about interactions with data-driven digital artefacts and use it to analyse data-driven applications. This approach appears to enable students to understand these artefacts from everyday life and reflect on these interactions. The work contributes to research on data and artificial intelligence literacies and suggests a way to support students in developing self-determination and agency during interactions with data-driven digital artefacts.

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.

There can be many reasons why students fail to answer correctly to summative tests in advanced computer science courses: often the cause is a lack of prerequisites or misconceptions about topics presented in previous courses. One of the ITiCSE 2020 working groups investigated the possibility of designing assessments suitable for differentiating between fragilities in prerequisites (in particular, knowledge and skills related to introductory programming courses) and advanced topics. This paper reports on an empirical evaluation of an instrument focusing on data structures, among those proposed by the ITiCSE working group. The evaluation aimed at understanding what fragile knowledge and skills the instrument is actually able to detect and to what extent it is able to differentiate them. Our results support that the instrument is able to distinguish between some specific fragilities (e.g., value vs. reference semantics), but not all of those claimed in the original report. In addition, our findings highlight the role of relevant skills at a level between prerequisite and advanced skills, such as program comprehension and reasoning about constraints. We also suggest ways to improve the questions in the instrument, both by improving the distractors of the multiple-choice questions, and by slightly changing the content or phrasing of the questions. We argue that these improvements will increase the effectiveness of the instrument in assessing prerequisites as a whole, but also to pinpoint specific fragilities.

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.

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.

The United States faces persistent threats conducting malicious cyber campaigns that threaten critical infrastructure, companies and their intellectual property, and the privacy of its citizens. Additionally, there are millions of unfilled cybersecurity positions, and the cybersecurity skills gap continues to widen. Most companies believe that this problem has not improved and nearly 44% believe it has gotten worse over the past 10 years. Threat actors are continuing to evolve their tactics, techniques, and procedures for conducting attacks on public and private targets. Education institutions and companies must adopt emerging technologies to develop security professionals and to increase cybersecurity awareness holistically. Leveraging Virtual/Augmented/Mixed/Extended Reality technologies for education, training, and awareness can augment traditional learning methodologies and improve the nation's cybersecurity posture. This paper reviews previous research to identify how distance and remote education are conducted generally, and how Virtual/Augmented/Extended/Mixed reality technologies are used to conduct cybersecurity awareness training, cybersecurity training, and conduct operations. Finally, barriers to adopting these technologies will be discussed. Understanding how these technologies can be developed and implemented provides one potential way of overcoming the cybersecurity workforce gap and increasing the competencies and capabilities of cybersecurity professionals.

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.

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

Primary school children frequently use digital devices, which can be infected by computer viruses. In this mixed methods paper with two studies (N = 278 + 114), we examined 8-year-olds' preconceptions about computer viruses and protection against them; how to teach these children about said topics using three different, 30-min-long, content-equivalent lessons; and what knowledge the children can acquire. We found that participants had limited prior knowledge of computer viruses and almost no knowledge about protection against them. However, they rarely had misconceptions. They learnt, and retained over a month, key general points and a few specific points about this domain. Acquired knowledge was still somewhat patchy, most likely represented in 'pieces' rather than as complex, theory-like chunks. Nevertheless, all three approaches produced notable learning gains (d > 1.78). A lesson organized around a narrative 5-min video and six < 1 min video snippets was the most effective: compared to a lesson organized around two 5-min videos (d = 0.89) and a teacher-led lesson without videos (d = 0.54). The findings are consistent with contemporary instructional design theories and 'knowledge in pieces' conceptual change frameworks. They imply that the topic of computer viruses should be included in second-graders' curricula.

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

Children's preschool years are crucial for the development of computational thinking (CT) skills. However, debate continues regarding whether CT should be developed through plugged-in or unplugged activities. This study investigated the similarities and differences between plugged-in and unplugged activities with similar learning content and assessment methods for cultivating computational thinking (CT) in young children. Twenty-four young children (aged 5-6 years) from a kindergarten in Foshan, China, were randomly assigned to either the plugged-in or unplugged group to participate in a five-week study. In the plugged-in group, Dodobot was used in the classroom, while in the unplugged group, unplugged materials such as paper, pencil and tangram puzzles were used. Research results indicate that 1) both plugged-in and unplugged activities significantly improved the young children's CT skills after a short-term educational intervention, but there were no significant differences between the two groups; 2) the extent to which the plugged-in and unplugged activities promoted subdimensions of CT was different, with the plugged-in group demonstrating significant improvements in hardware, algorithm, and modularity and the unplugged group demonstrating significant improvements in terms of representation; 3) the children from both the plugged-in and unplugged groups showed high motivation; And 4) the children in both the plugged-in and unplugged groups showed cooperative behaviors. The frequency of cooperative behavior was more related to materials, and cooperation quality was more related to teacher guidance.

The skills and competencies of IT professionals are often described using employer-led skills frameworks. They express competencies as technical knowledge and skills combined with a range of personal qualities. Employers have indicated the importance of developing such qualities for new graduates. In response, recent ACM/IEEE curricular recommendations have shifted their emphases from bodies of knowledge to the development of competencies. The IT2017 ACM/IEEE Curriculum Guidelines for Baccalaureate Degree Programs proposed a model of IT competency comprising three interrelated components: content knowledge, skills, and dispositions, where dispositions represent personal qualities desirable in the workplace. The ACM/IEEE Computing Curricula 2020 (CC2020) report enriched the IT2017 disposition concept by identifying eleven dispositions that all computing programs should include for the career preparation of their graduates. However, developing and assessing dispositions in a degree program remain challenges, often involving internships, work placements and similar student opportunities. A recent mapping of the eleven CC2020 dispositions to the responsibility characteristics of the Skills Framework for the Information Age (SFIA), a widely used professional skills framework, suggested a promising approach to addressing this challenge. Inspired by this mapping, this paper aims to help educators assess students' achievement of CC2020 dispositions by mapping real-world experiences they have recorded in individual portfolios against the SFIA responsibility characteristics. First, the selection of SFIA to operationalize the CC2020 dispositions is validated by demonstrating that alternative frameworks pose significant challenges for any assessment approach that needs to be independent of particular technical skills. A tool is described that maps demonstration of SFIA responsibility characteristics to CC2020 dispositions, applying a simple, consistent assessment algorithm. Finally, the assessment process and outcomes are illustrated using a fictional student portfolio, constructed to reflect one author's experience of work placement students' achievements.

Today computing is intricate in all aspects of our lives, beginning with communications and education to banking, information security, health, shopping, and social media. Development of the computing is proportional to the development of software which is becoming a serious part of all daily lives. This paper, therefore, assessed the impact of teaching Computer Programming Language in Tanzanian primary schools. To achieve this, a new tool for teaching and learning a computer programming language was developed. Furthermore, a two-tier approach was adopted where the training started on an algorithm first, then if, loops and case conditionals as the third concept inside the first tier. On the other hand, variables, lists, and functions are hooked on the second tier. Results indicate that students acquired together reasoning learning in a conceptual argument of view and skill performance. Students were able to accomplish the expertise to practice or apply the theories of programming knowledge in programming-connected tasks.

This paper describes the implementation of a STEM scholarship program which utilized a holistic approach to providing a multi-dimensional student support system. The program has been successful in encouraging and supporting women in Applied Mathematics and Computer Science by offering a diverse suite of extracurricular opportunities, actively engaging them in organized events, research projects, and participation in STEM communities, and helping them achieve higher GPAs and shorter times to graduation. The supported women also benefitted from close mentoring relationships with the faculty mentors. The program emphasized the development of empowering settings for women's engagement and achievement, which act to sustain and expand interest in mathematics and computing, and thereby help them to see themselves as future professionals in the field.

Within the field of social reintegration and re-education, this paper presents an educational experience carried out at the Iquitos Penitentiary Center, Lima, Peru, with the aim of providing an introduction to informatics to 25 inmates who volunteered to take part in the project. Twenty students and a teacher from the Scientific University of the South also in Peru, were responsible for initiating the transmission of knowledge from the university to inmates, with the collaboration and participation of the penitentiary coordinator. The main objectives of the case study were to validate both the suitability of the CS unplugged proposal and the adaptability of the L2T2L pedagogic strategy to the transmission of knowledge to adults, specifically penitentiary inmates. This strategy had been originally designed to transmit informatics knowledge from university to primary school. The validity and effectiveness of the experience was assessed using surveys. Results confirm that inmates achieved a good level of understanding when endeavoring to resolve most of the CS unplugged assignments designed for them. It was also seen that L2T2L is adaptable and valid for different scenarios other than those for which it was initially designed. Indeed, it was proven to be valid for transmitting knowledge to the prison population. Finally, it should be pointed out that the experience is easily replicable and that it brings an opportunity to introduce informatics into education programs in prisons, something which can contribute enormously to social reintegration and re-education, facilitating the subsequent reentry of inmates into the community once their period of imprisonment has ended.

Information technology is a significant action to promote education informatization. Currently, there are numerous literature studies on information technology education, but existing research lacks a comparative summary of the research status, research hot spots, and research trend of information technology in China and abroad from an overall perspective. Therefore, this paper selected the information technology education literature collected by China National Knowledge Infrastructure (CNKI) and Web of Science Core Collection (WoS) from 2000 to 2021 as the research object and used bibliometrics to analyze the literature characteristics, core forces, research hot spot, and frontier trend of information technology education in China and abroad. The results showed that: (1) The discipline literature of information technology education showed a fluctuating growth trend, in which the growth rate of foreign literature was faster, and the scale of Chinese literature was slightly smaller; Both Chinese and foreign literature involved pedagogy, computer, and other disciplines, with the characteristics of multi-disciplinary integration. (2) The United States was the leading country in this field, followed by China, with the total literature accounting for more than 50%, ranking the leading position in the world; China Audio-Visual Education and Computers & Education were representative journals in China and abroad. (3) Foreign research focused on information technology, education, science, attitude, model, perception, experience, self-efficacy, Internet, etc. China's hotspots included information literacy, computational thinking, curriculum standards, application contexts, teaching models, subject integration, etc. Foreign research focused on both theory and practice, while China focused on the theoretical nature of the subject, but the degree of practice was insufficient. (4) Student participation, technology integration, teaching innovation, Internet +, core literacy, and artificial intelligence (AI) have been popular until now, constituting the discipline frontier in information technology education.

Artificial intelligence is the unification of philosophy, cognitive science, mathematics, neurophysiology, psychology, computer science, information theory, cybernetics, and uncertainty theory. Therefore, it is feasible and necessary to utilize STEAM (Science, Technology, Engineering, Liberal Arts, and Mathematics) education to learn artificial intelligence courses. Computational thinking skills are of vital importance to high school students. This paper integrates artificial intelligence education with STEAM model with the aim of enhancing students' computational thinking skills. First, we investigate the feasibility of this model and set teaching objectives about artificial intelligence curricula. Second, artificial intelligence curricula with STEAM model is proposed to carry out interdisciplinary artificial intelligence knowledge acquisition. Finally, the effects of this model on students' computational thinking skills, learning motivation, and self-efficacy are evaluated. One hundred thirty-six participants are recruited from a high school in Beijing. The results reveal that the integration of artificial intelligence education with STEAM is able to promote computational thinking skills, learning motivation, and self-efficacy of the students in the experimental group. The main implication of this study is that artificial intelligence education in light of STEAM model can be used as a teaching guide for the combination of artificial intelligence curricula with multi-disciplinary knowledge at the primary and secondary levels.

Owing to the predominant role of Machine Learning(ML) across domains, it is being introduced at multiple levels of education, including K-12. Researchers have leveraged games, augmented reality and other ways to make learning ML concepts interesting. However, most of the existing games to teach ML concepts either focus on use-cases and applications of ML instead of core concepts or directly introduce ML terminologies, which might be overwhelming to school students. Hence, in this paper, we propose "ML-Quest," a game to incrementally present a conceptual overview of three ML concepts: "Supervised Learning," "Gradient Descent" and "K-Nearest Neighbor (KNN) Classification." The game has been evaluated through a controlled experiment, for its usefulness and player experience using the TAM model, with 41 higher-secondary school students. Results show that students in the experimental group perform better in the test than students in the control group, with 5% of students in the experimental group scoring full marks. However, none of the students in the control group could score full marks. The survey results indicate that around 77% of the participants who played the game either agree or strongly agree that ML-Quest has made their learning interactive and is helpful in introducing them to ML concepts.

With the advent of technological advancement in learning, such as context-awareness, ubiquity and personalisation, various innovations in teaching and learning have led to improved learning. This research paper aims to develop a system that supports personalised learning through adaptive content, adaptive learning path and context awareness to meet individual learner's requirements and promote the effectiveness and performance of the learning process. Furthermore, the study used an experimental control group to ascertain the significance of learners' learning styles and preferences in improving learning performance. The model considers different modules such as personalisation, location support, learning plan and context awareness while relying on the proper classification of learners' learning styles. The system was experimented using 127 learners of a Computer Science Course. The result obtained using 10-fold cross-validation on various algorithms shows better performance in ROC, Kappa and Accuracy, which were all used to evaluate the quality of classification outcome and compared with learners' performances in assessments. During classification, Naïve Bayes Model has the highest average ROC value of 0.979, TP Rate of 0.951 and FP Rate of 0.045. The results obtained show an improvement in learning.

Traditional education systems do not provide students with much freedom to choose the right training of study that suits them, which leads on long-term to the negative effects not only on social, economic and mental' well-being of student, but also will have a negative effect on the quality of the work produced by this student in the future. In addition, skills and talents in a specific area refer to general skills and working life practices. This paper presents a pedagogical orientation system based on blockchain technology and machine learning that accurately predicts the future specialized training and profession in which the common core student will have more opportunity to succeed. Python programming language has been used to implement the suggested pedagogical orienting mechanism in two key steps. (1) In the profiles matching step we have used the incidence matrix to correlate the professional and training profiles. (2) The student ranking issue was resolved at the ranking profiles step using the learning to rank methodology. The data used to feed the various machine learning algorithms in our system are stored on the blockchain, representing the academic results of 320 students at the end of the common core training in the computer science department of 20th august 1955 university in Skikda, Algeria, which served as a database for subsequent experiments and served to confirm the system's feasibility and credibility. The proposed system has demonstrated its effectiveness by accurately predicting the future specialized training compatible with the student's scientific capabilities, allowing him to succeed and excel. Also, it has contributed to reducing the possibility of falsifying results and certificates while enhancing security and transparency through blockchain protocols. Finally, this system encourages all participants to trust the automatic orientation process, which makes it possible to improve the issues experienced in the fields of education by enhancing interactions between the student, the university, and the employing industries during the educational orientation process.

The adoption of enterprise-wide systems like Customer Relationship Management (CRM) systems continues to grow globally. Due to the prevalence of CRM software in businesses and CRM's expected growth, Information Systems (IS) graduates will likely interact with a CRM system in their careers. However, learning enterprise systems like CRM is challenging for students due to the complexities surrounding integrating people and processes around a technology platform. This paper illustrates how an information systems course employs a CRM project using an experiential learning approach to help IS students develop functional and technical knowledge of CRM and discover creative ways to solve business problems using Salesforce. Project details, exercises, and lessons learned from previous project implementations are provided. Results of students' assessments indicate the hands-on Salesforce CRM project can lead to improved functional and technical learning outcomes.

In this paper, to handle the problem of the quick evolution of cyber-security attacks, we developed the iMonsters board game and proposed the attack and defense knowledge self-evolving algorithm. Three versions of the iMonsters were launched in 2013, 2017, and 2019, respectively. Accordingly, the cyber-security ontology can be refined by the ontology fusion-or-splitting procedure for the newly collected cyber-security incidents, as well as the roles and rules of the iMonsters can be refined by the gaming portfolio mining procedure for the collected portfolio. Furthermore, we conducted game-based learning (GBL), a quasi-experiment of pre/post-testing, and concept map testing using the iMonsters board game in a children's summer camp. The experimental results indicate that the students can acquire up-to-date cyber-security knowledge with the iMonsters better than students who learn in a traditional classroom setting, and the students' satisfaction with acquiring cyber-security knowledge with the instructional design of the iMonsters is better compared with learning in a traditional classroom setting. Satisfaction with the new version has continuously increased. Besides, the results of the in-depth interviews show that the new version was easier to learn. Thus, we may conclude that using the self-evolving iMonsters can improve learning effectiveness and participation in GBL.

Debugging (or troubleshooting) provides a rich context to foster problem-solving. Yet, while we know much about some problems and strategies that novices face in programming on-screen, we know far less about debugging and troubleshooting in the context of physical computing, where coding issues may overlap with materially embedded problems. In this paper, we study the thought processes novice students employed and the challenges they faced in debugging an electronic textile project with multiple overlapping problems that crossed physical, electronic, and computational domains. We employed a think-aloud protocol to develop an instrumental case study by video-recording 45 minutes of one pair of 9th-grade students debugging and fixing a buggy e-textile project. The problem space included the computational system's programmatic, electronic, and physical spatial aspects, which are more generally reflective of physical computing systems. We found that (1) students' troubleshooting was more recursive and less linear than traditional approaches that usually propose linear, procedural, step-wise activities, and (2) students coordinated their approach across multiple modalities, taking advantage of a distributed set of tools and people in order to tackle a complex set of problems. In the discussion, we address various pedagogical implications for improving teaching about troubleshooting.

This article analyses the developments in computer education in French-speaking Western Switzerland in the 1980s and 1990s. It investigates how computer education changed with the arrival of microcomputers, who the agents of these changes were, and with what sociopolitical and economic developments these pedagogical changes interacted. By analysing archival material from five cantonal departments of education and from Raymond Morel (at the time, Western Switzerland's most influential educational IT expert), it is argued that computer education in Western Switzerland's general education experienced three shifts. First, education "with" computers displaced education "about" computers in the first half of the 1980s. Second, from the mid-1980s onwards, office applications became central. Third, computer education morphed into telecommunications education around 1990. The paper contributes to the historiography of local school computers and computer education. It adds comment on the "glocal" histories of computers, which shed light on local computer cultures and economies across the globe.

Teaching and learning programming, and especially Object-Oriented Programming (OOP), is a complicated and challenging task. Students have to comprehend various OOP concepts and utilize them for designing object-oriented programs. Various types of educational programming environments, such as microworlds and educational games, have been devised for supporting novices mainly in comprehending OOP concepts. However, such environments do not usually support students in the conceptual design of object-oriented programs of a considerable length and complexity. In this paper, we focus on a systematic mapping study (SMS) of educational programming environments for enhancing the conceptual design in OOP, which relies on modularity, abstraction and encapsulation. The research questions investigate the intended learning outcomes, the empirical evidence on the effectiveness, and the teaching/learning technologies used by educational programming environments for enhancing the conceptual design in OOP. The findings can support instructors in selecting appropriate tools for their courses and researchers in the field of educational programming environments for OOP.

Computing technology is reshaping the way in which professionals in the architecture, engineering, and construction industries conduct their business. The execution of construction tasks is changing from traditional 2D to 3D building information modeling (BIM)-based concepts. The use of BIM is expanded and enriched by the introduction of advanced computing technologies. At the same time, the demands for graduates in construction management with basic computing technology skills are on the rise. One of the challenges of construction management (CM) programs is how to incorporate computing knowledge so that future graduates can gain more experience with it during their undergraduate education. This paper presents a case study on this topic from an undergraduate construction management course at Purdue University. The in-class exercises and project required the use of computing technologies (e.g., quantity takeoff software, scheduling software, and Python programming) in this case study. The performance of students was evaluated before and after the project and the exercises. The findings showed the benefits of incorporating computing into a construction management curriculum as follows: (1) introducing the students to the efficiency and accuracy of computing technologies in CM; and (2) improving students' knowledge and skills in computing technologies and professional software applications. Based on students' feedback, short-term (one-class-long) programming training during the semester and more detailed instructions for a project are needed improvements in this course. The practice in this study provides insights for other instructors seeking to include more computing technologies into CM courses.

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. [ABSTRACT FROM AUTHOR]

Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying classification as a higher-order thinking process that connects the two. To achieve that, we designed and developed an online constructionist gaming tool called SorBET which integrates coding and database design enabling students to interpret, organize, and analyze data through game play and game design. The paper presents and discusses the results of a pilot study that aimed to investigate the data practices secondary students develop through playing and modifying SorBET games, and to determine the impact of game modding on student critical engagement with CT. According to the results, students developed and used certain data practices such as data interpretation and data model design to become better players or to design an interesting classification game. Moreover, game modding process motivated students to question the original games' content, leading them to develop a critical stance towards the game data model and representations. [ABSTRACT FROM AUTHOR]

Computer cooking is a task-related phenomenon where students (end-users) must blindly follow a long list of orders without any connection to the content of the problem, if there is any. Despite its low efficacy, this method is widely used and accepted in informatics both in the learning-teaching process and testing. The National Base Curriculum 2020 in Hungary is in complete accordance with the 'Informatics Reference Framework for Schools', but the course books hardly use the latest results of computer education research. The present paper provides examples of how the results of computer education research can be integrated into teaching-learning materials and classroom practices and discusses the effectiveness and consequences of the different solutions, where tool-centred approaches are compared to problem-focused solutions. [ABSTRACT FROM AUTHOR]

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

Anytime Valid Comparison of Sequential Forecasters How do we compare forecasters that each make a probabilistic prediction on a sequence of events (e.g., weather and sports)? In "Comparing Sequential Forecasters," Choe and Ramdas propose flexible approaches to sequential inference on the mean score difference between any two forecasters. To estimate this time-varying quantity, the authors propose a sequential analog of confidence intervals, called confidence sequences (CSs). These CSs correctly cover the score difference under continuous monitoring, and the evaluator can freely peek at the scores to stop the experiment ("anytime valid"). The authors further develop a complementary anytime valid approach called e-processes, which quantify the evidence against the claim that one forecaster is never better than the other in mean scores. The validity of these methods does not depend on stationarity or other assumptions on how the score differences evolve sequentially. In their paper, the authors showcase CSs and e-processes for comparing real-world baseball and weather forecasters. Consider two forecasters, each making a single prediction for a sequence of events over time. We ask a relatively basic question: how might we compare these forecasters, either online or post hoc, avoiding unverifiable assumptions on how the forecasts and outcomes were generated? In this paper, we present a rigorous answer to this question by designing novel sequential inference procedures for estimating the time-varying difference in forecast scores. To do this, we employ confidence sequences (CS), which are sequences of confidence intervals that can be continuously monitored and are valid at arbitrary data-dependent stopping times ("anytime-valid"). The widths of our CSs are adaptive to the underlying variance of the score differences. Underlying their construction is a game-theoretic statistical framework in which we further identify e-processes and p-processes for sequentially testing a weak null hypothesis—whether one forecaster outperforms another on average (rather than always). Our methods do not make distributional assumptions on the forecasts or outcomes; our main theorems apply to any bounded scores, and we later provide alternative methods for unbounded scores. We empirically validate our approaches by comparing real-world baseball and weather forecasters. Funding: A. Ramdas acknowledges funding from the National Science Foundation Division of Mathematical Sciences [Grant 1916320]. Research reported in this paper was sponsored in part by the DEVCOM Army Research Laboratory under Cooperative Agreement W911NF-17-2-0196 (ARL IoBT CRA). Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2021.0792. [ABSTRACT FROM AUTHOR]

Interest in the study of computer technology in schools (often in the form of Computer Science subjects or coding initiatives) is seen to have undergone two waves of interest, one in the late 1970s/early 1980s and the other more recently since about 2010. Through a historical exploration of the rationales for the study of computer technology in schools in both the first wave and the second wave, this paper aimed to explore the various curriculum ideologies underpinning their justification. In doing so, the paper highlights the chameleon‐like nature of this change agenda in aligning with contemporary trends to advance the study of computer technology on the curriculum. The paper argues that current manifestations of the study of computer technology in schools are at odds with broader curriculum reform goals and international policies and raises questions about students' access to such knowledge. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The advent of Version Control Systems (VCS) in computer science education has significantly improved the learning experience. The Learning Analytics community has started to analyze the interactions between students and VCSs to evaluate the behavioral and cognitive aspects of the learning process. Within the aforesaid scope, a promising research direction is the use of Artificial Intelligence (AI) to predict students' exam outcomes early based on VCS usage data. Previous AI-based solutions have two main drawbacks: (i) They rely on static models, which disregard temporal changes in the student–VCS interactions. (ii) AI reasoning is not transparent to end-users. This paper proposes a time-dependent approach to early predict student performance from VCS data. It applies and compares different classification models trained at various course stages. To gain insights into exam performance predictions it combines classification with explainable AI techniques. It visualizes the explanations of the time-varying performance predictors. The results of a real case study show that the effect of VCS-based features on the exam success rate is relevant much earlier than the end of the course, whereas the timely submission of the first lab assignment is a reliable predictor of the exam grade. [ABSTRACT FROM AUTHOR]