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This contribution explores the teaching of graph theory in the context of engineering education. We examine how teachers use collectively designed resources in terms of their backgrounds. After a literature review, we justify the choice to develop an analytical framework for studying the materials in terms of connections to be established in the resources. We suggest developments around the theoretical concept of connectivity, which allows us to analyze the potential connectivity, characterized by the connections enabled by the resources for teaching; and the effective connectivity that develops in teachers' practices and during the implementation of resources. We study the effective connectivity in terms of each teacher's personal relationship with graph theory. Graph theory evolves at the interface of mathematics and computer science research. We consider the case of two teachers, a computer scientist and a mathematician in a French Engineering School in France. The data collected consists of resources for teaching graph theory in engineering courses and interviews with teachers. As for results, we characterize how forms of connectivity developed in the use of resources for teaching graph theory are largely determined by the teachers' backgrounds.

Gender disparities persist in postsecondary computing fields, despite improvements in postsecondary equity overall and STEM fields as an aggregate. The entrenchment of this issue requires a comprehensive, longitudinal lens. Building on expectancy-value theory, the present study examines the relationships among students' gender-ability stereotypes, attainment values, course-taking, and major choices. Using data from the High School Longitudinal Study of 2009 (HSLS: 2009), we applied weighted t-tests and multiple-group structural equation modeling to investigate how motivational beliefs (i.e., gender-ability stereotypes, attainment values) and course-taking patterns in math and science may predict major choice in computing. Overall, we find gender differences in identity-based mathematics and science motivational beliefs have long-term effects. Gender-ability stereotypes in math and science shape attainment values in each domain, whereby stereotypes suppress girls' attainment values and enhance boys? attainment values (p < 0.001), in turn shaping course-taking and major decisions. Math- and science-related motivational and curricular factors affect "other" STEM more than computing major outcomes. Specifically, computer science course-taking is completed more by boys (d = 0.21), but girls' chances of declaring computing majors are especially enhanced by completing these courses in high school. Advanced science course-taking and science attainment value positively predict boys' but not girls' likelihood of declaring computing majors. We discuss the implications of these findings for research, policy, and practice.

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

Background: Computational thinking is derived from arguments that the underlying practices in computer science augment problem-solving. Most studies investigated computational thinking development as a function of learners' factors, instructional strategies and learning environment. However, the influence of the wider community such as educational policies on computational thinking remains unclear. Objectives: This study examines the impact of basic and technology-related educational policies on the development of computational thinking. Methods: Using supervised machine learning, the computational thinking achievements of 31,823 eighth graders across nine countries were analysed. Seven rule-based and tree-based classification models were generated and triangulated to determine how educational policies predicted students' computational thinking. Results and conclusions: Predictions show that students have a higher propensity to develop computational thinking skills when schools exercise full autonomy in governance and explicitly embed computational thinking in their curriculum. Plans to support students, teachers and schools with technology or introduce 1:1 computing have no discernible predicted influence on students' computational thinking achievement. Implications: Although predictions deduced from these attributes are not generalizable, traces of how educational policies affect computational thinking exist to articulate more fronts for future research on the influence of educational policies on computational thinking.

Brightpoint's HyFlex Pilot Program was a year-long project funded by the Online Virginia Network (OVN) to support faculty in the design and delivery of an actual HyFlex course. The cohort consisted of faculty who teach in the biology, architecture, early childhood, and web design disciplines. Participants worked in collaboration with one another and the Center for Teaching and Learning. In the following essay, members of the 2021-22 pilot cohort will reflect on their experiences learning and implementing a HyFlex approach in their courses. Each author will share challenges and successes as well as conclusions with implications for practice by colleagues statewide.

Reflective practice is an essential catalyst through which the benefits of teaching and learning can be reaped. Through it, weaknesses and strengths can be identified in a way that helps raise the level of addressing challenges that may arise as well as overcome them. This paper presents the critical reflective practices among computer science secondary school teachers from their point of view in Riyadh, Saudi Arabia. To this extent, the study aims to determine the degree of critical reflective practices among computer science secondary school teachers in Riyadh from their perspective. The paper also seeks to investigate the effects of variables such as gender, qualifications and experience on the perceptions of the aforementioned teachers, towards the critical reflective practices among computer science secondary school teachers. The study tool is a questionnaire which consisted of two dimensions and was distributed to a population of 739 participants. From this, the study sample comprised (223) computer science teachers working in secondary school in Riyadh. The findings revealed that there is no significant difference in the estimation degree concerning the critical reflective practices due to the gender. From the results, it was also established that there is no significant difference in the degree of estimation in relation to the critical reflective practices due to educational qualification variables. On the contrary however, there is a significant difference in the degree of estimation in regard to the critical reflective practices due to the years of experience variable. These differences were evident in a group of those with more than 10 years of experience. The other findings produced by the study highlight that the participants are in agreement about the importance of critical reflective practices. The degree of reflective practice, which is from the participants' point of view, is considered to be of a high value. The majority of the subjects opted to agree with the practice of reflection after a training session. It was determined from the results that some of the most common strategies favored by practitioners involved the communal practice of mind reflection with individuals from outside the school.

This pilot study aims to assess the acceptability of Open University's training platform called Gamified Intelligent Cyber Aptitude and Skills Training course (GICAST), as a means of improving cybersecurity knowledge, attitudes, and behaviours in undergraduate students using both quantitative and qualitative methods. A mixed-methods, pre-post experimental design was employed. 43 self-selected participants were recruited via an online register and posters at the university (excluding IT related courses). Participants completed the Human Aspects of Information Security Questionnaire (HAIS-Q) and Fear of Missing Out (FoMO) Scale. They then completed all games and quizzes in the GICAST course before repeating the HAIS-Q and FoMO scales as well as several open-ended questions. Pre-training HAIS-Q Knowledge, Attitude and Behaviour all improved from 'reasonable' pre-training levels to become 'very high' following training with large effect sizes estimated. FoMO improved to a lesser degree but also predicted the degree of HAIS-Q improvement suggesting it is relevant to the impact of this training course. Qualitatively, five key themes were generated: enjoyment, engagement, usability of GICAST, content relevance, and perceived educational efficacy. Overall, sentiment towards training was very positive as an enjoyable engaging and usable course. GICAST was found to be a feasible course for a wide range of students at a UK university: overall the training improved cyber-security awareness on a well validated measure with outcomes comparable to information-security-trained employees of a secure workplace. Despite a diversity of views about content, the course appears to be well suited to the non-IT undergraduate sector and may suit wide uptake to enhance students' employability in a wide range of cybersecurity relevant contexts.

The humanities have the potential to enhance STEM curriculum in career-oriented professional degree programs. As part of a larger college-wide initiative to develop interdisciplinary curricula, the National Endowment for the Humanities (NEH) awarded a grant to New York City College of Technology to explore the relationship between the humanities and career-focused degree programs in STEM departments. This article describes the design and implementation of coursework that bridges the curricular divides between humanities disciplines and technical or career-focused disciplines by bringing the humanities to bear on science and technology-oriented courses. Specifically, the humanities served to improve technical skills for students in STEM-oriented professional degree programs.

The research utilized the descriptive survey model, a quantitative research method, with a study group consisting of 172 social studies teacher candidates (131 females, 41 males). As data collection tools in the study, the Cyberculture Adoption/Development Level Scale developed by Koçak (2019) and the global citizenship scale (Global Citizenship Scale--GCS) developed by Morais and Ogden in 2010 were used. The Turkish adaptation, validity, and reliability study of the global citizenship scale was conducted by Tutkun (2019). In addition, a personal information form prepared by the researcher was used. The data analysis involved the use of the Independent Samples t-test, to determine the relationship between participants' scores on the cyber culture adoption level scale and the global citizenship level scale concerning the gender variable. For situations where differentiation was observed based on class, social media usage purpose and internet usage frequency, the One-Way ANOVA was employed for unrelated Samples. The findings of the research indicate that there is no statistically significant difference in the levels of cyberculture adoption and global citizenship among teacher candidates concerning gender and class variables. When examining the variable of social media usage purpose, a statistically significant difference is observed in the global citizenship levels of teacher candidates. In contrast, no statistically significant difference is found in the levels of cyberculture adoption. Regarding the daily internet usage duration variable, no impact is observed on the global citizenship levels of teacher candidates. In contrast, a statistically significant difference is found in the levels of cyberculture adoption.

While increasing emphasis has been placed on computer science (CS) and computational thinking (CT) little is known about these topics in elementary classrooms. Significant equity gaps exist within CS/CT at the elementary level, with a major contributor being the lack of highly qualified CS/CT elementary teachers. Professional development (PD) for inservice teachers who already teach in high need CS schools or for preservice teachers planning to teach in high need schools is a viable solution. The research presented was part of an ongoing university/elementary school Teacher-Researcher Partnership designed to address the CS/CT PD needs of elementary educators. An exploratory, descriptive case study was conducted to better understand the experiences of 4th grade inservice teacher partners co-designing and implementing a robotics event serving over 100 4th grade students, along with the experiences of preservice teachers facilitating the event. Inservice teacher partners (n=5) were participants generating data through co-design session recordings, co-designed artifacts, and a final reflective interview. Data from preservice teacher facilitators (n=14) were anonymous reflections. Thematic analysis found inservice teachers gained increased confidence and ownership over CS/CT activities. Moreover, inservice and preservice teachers both reported student benefits such as growth in Technology and Engineering Education (T&EE) problem-solving, critical thinking, and collaboration skills. The emergence of joyfulness from CS/CT engagement was an important finding, particularly given T&EE intentionally capitalizes on the benefits of appealing, minds-on/hands-on experiences for young learners. This research provides insights for other T&EE researchers who are exploring PD approaches that help build CS, CT, problem-solving, and other related T&EE skills and dispositions.

A trained workforce is needed to protect operational technology (OT) and industrial control systems (ICS) within national critical infrastructure and critical industries. However, what knowledge, skills, and credentials are employers looking for in OT cybersecurity professionals? To best train the next generation of OT cybersecurity professionals, an understanding of current OT cybersecurity position requirements is needed. Thus, this work analyzes 100 OT cybersecurity positions to provide insights on key prerequisite requirements such as prior professional experience, education, industry certifications, security clearances, programming expertise, soft verbal and written communication skills, knowledge of OT frameworks, standards, and network communication protocols, and position travel. We found that OT cybersecurity roles are typically nonentry level, as experience was the most common requirement, and was required on 95% of analyzed positions. Possession of a bachelor's degree or higher was required for 82% of positions, while industry certifications such as the Certified Information Systems Security Professional (CISSP) or the Global Information Assurance Certification (GIAC) Global Industrial Cyber Security Professional (GICSP) were listed on 64% of positions. Knowledge of OT or IT frameworks and standards and strong communication skills were listed on 48% of positions, while programming expertise, possession of a United States security clearance, and knowledge of OT or IT networking protocols were required for 18%, 24%, and 27% of positions, respectively. A work travel requirement was listed on 29% of positions. Individuals seeking to enter the OT cybersecurity field, and educational programs focusing on training OT cybersecurity professionals should prioritize obtaining experience, education, and certification, possessing strong communication skills, and knowledge of relevant OT and IT industry standards and frameworks.

This paper presents an analysis of the current research landscape around hackathons and hackathon-like events as opportunities and means for learning. In particular, we aimed to explore how hackathon-like events are utilized to promote learning, what systematic approaches are used to orchestrate learning in such events, and to what extent knowledge is systematized and standardized in this context. To that end, we conducted a Systematic Literature Review following established guidelines. In this review we studied 39 research papers published over a 10-year span. Our findings indicate that no standardized setup for hackathons to promote learning and to assess learning outcomes has been investigated exhaustively. We did not find a systematic approach to reliably produce or measure learning effects in hackathons. Most of the reviewed papers relied on perceived learning for the assessment of learning. Based on the consulted literature, this does not provide comprehensive proof of positive learning outcomes from hackathons. We, therefore, conclude that additional research is needed to gather insights and consequently work toward solidifying the role of hackathons as learning tools. This work contributes to exploring the landscape and proposing research directions for capitalizing on the potential of hackathons and similar events for learning.

The problem of gender disparity in computer science and engineering has persisted despite longstanding efforts made in higher education. This study drew data from ten female computer science and engineering faculty in China and Italy to demonstrate the gender-specific challenges they face. The researcher applied positioning theory to analyse female faculty's motherhood experiences. Findings suggest that the dual mother-and-educator role conflict in the intersectional space of work and family can be both a burden and an asset to female faculty. As a burden, the sociocultural expectations of the mother's role drive females to take on primary childcare responsibilities. It leads to significant barriers to female faculty's career advancements. As an asset, the study finds that the female faculty's motherhood experience allowed them to generate new insights into their identities as educators and teaching practices. Their learning from motherhood prompts them to reflect and gain an appreciation of their educator identity. Such realisation enables them to improve their teaching practices and interactions with students. The study offers policy recommendations for higher education institutions to support female faculty navigating the dual roles of mother and educator. Finally, the researcher discusses the limitations and directions for future study.

It has been shown that active learning strategies are effective in teaching complex STEM concepts. In this study, we developed and implemented a laboratory experiment for teaching the concepts of Boolean logic gates, molecular beacon probes, molecular computing, DNA logic gates, microRNA, and molecular diagnosis of hepatocellular carcinoma, which are related to DNA molecular computing, an interdisciplinary cutting-edge research technology in biochemistry, synthetic biology, computer science, and medicine. The laboratory experience takes about110-140 min and consists of a multiple-choice pretest (15 min), introductory lecture (20 min), wet laboratory experiment (60-90min), and a post-test (15 min). Students are tasked to experimentally construct three molecular logic circuits made of DNA oligonucleotides and use them for the fluorescence-based detection of microRNA markers related to diagnostics of hepatocellular carcinoma. The class was taught to undergraduate students from freshman to senior academic levels majoring in chemistry, biochemistry, biotechnology, and biomedical sciences. Students were engaged during the session and motivated to learn more about the research technology. A comparison of students' scores on the pretest and post-test demonstrated improvement in knowledge of the concepts taught. Visual observation of the fluorescence readout led to a straightforward interpretation of the results. The laboratory experiment is portable; it uses inexpensive nontoxic reagents and thus can be employed outside a laboratory room for outreach and science popularization purposes.

Contribution: Gender mainstreaming in university teaching should be covered in all the knowledge areas. This work successfully introduces the gender perspective as part of the methodological approach to teaching and learning in Computer Science. Background: This study describes how gender mainstreaming has been introduced and matured during six academic years, from 2016-2017 to 2021-2022, in Software Engineering I course in the Degree of Computer Science at the University of Salamanca. Intended Outcomes: The aim that has been pursued is to raise awareness among students of Computer Science about equality, equity, inclusion, and respect for diversity to build better professional ethics and advance in eliminating any gender-related gap in Computer Science. Application Design: The introduction of gender mainstreaming in the Software Engineering I course has been done in six stages to advance in the gender-gap reduction improving in each academic year with the experience and voluntary feedback from the students of the previous year, using anonymized questionnaires. Findings: Gender mainstreaming requires special attention in careers with a visible gender gap, such as Computer Science. Incorporating the gender perspective as part of the teaching-learning process does not have a measurable impact in a short period but instead aims to make software engineers reflect in such a way that they reason about the need to promote diversity in software development contexts.

The use of digital media among student community has increased their level of distraction during classroom teaching. Capturing their attention in classrooms, has become a cumbersome task for teaching faculty. Therefore, most of the academic institutions have resorted to the use of activity-based learning for science and engineering students that were originally used for teaching school students. This paper presents a comparative study on the effectiveness of different teaching learning methods applied to three groups of engineering students in Computer Science and Bio-Technology, evaluated by conducting tests and feedbacks. Also, a comparative study was performed to assess the effectiveness of conventional and modern evaluation tools. The results show that the subject understanding level of the students increased by 67-76% when any of the activity-based learning method was used. The study confirms the potential use of activity-based method for teaching learning and ICT tools for performance evaluation, for engineering students.

This article focuses on the practical implementation of Universal Design for Learning (UDL) in an online computer science course, articulating the collaborative efforts between the instructional designer (the first author) and the faculty member to redesign the course using UDL principles. Specific instances of redesigned learning modules and artifacts in adherence to UDL principles and results and insights gained from the implementation are reported. The article concludes with recommendations for higher education faculty and instructional designers to consider when applying UDL in their online courses.

Predicting academic performance for students majoring in computer science has long been a significant field of research in computing education. Previous studies described that accurate prediction of students' early-stage performance could identify low-performing students and take corrective action to improve performance. Besides, adopting machine learning algorithms with predictive analytics has proven possible and meaningful. The traditional approach of looking after students without uncovering the root causes of poor performance has shifted dramatically into improving the quality of the educational processes of students, teachers, and stakeholders. Thus, this study employed predictive analytics to develop an early warning prediction model using computing science degree performance data at a public institution. Predictive models based on our data analysis revealed that low, medium, and high-performing students could be predicted with an accuracy of 88% using only the grades of the courses they took in the second year. Moreover, 96% accuracy was achieved when all course grades were used in predictive models. The courses that are important in determining the overall performance of the students were also analyzed. By employing a multi-method approach, utilizing a large dataset spanning four academic years, and including a diverse sample of 430 students, our study offers a robust foundation to researchers, designers, and computer science educators for understanding and predicting student performance. The enhanced generalizability and implications for educational practice position our study as a valuable contribution to the field, paving the way for further advancements in predictive analytics.

This study puts forth the counternarrative of the first author Gislaine, a first-generation undergraduate student, Latina, and computer science major. Gislaine participated in a research internship and STEM mentorship program led by the second author, Meredith. Through this program, Gislaine designed and taught CS lessons to predominantly low-income, African-American middle school students over an 8-day urban summer school program. By analyzing Gislaine's written account of her journey to CS, as well as planning documents and written reflections on her daily experiences mentoring youth, we explore how Gislaine's intersecting social identities shape her experiences both as a CS learner and as a mentor to youth. Gislaine's counternarrative illuminates the systems of oppression present in schools and the CS field, while also highlighting Gislaine's transformative approaches to mentoring students in CS. Gislaine's narratives emphasize the importance of humanizing CS and leveraging students' strengths to promote equitable access to CS education. Through her experiences, she underscores the significance of recognizing and addressing systemic barriers, while also advocating for inclusive and empowering educational practices.

This study analyzed collaborative invention projects by teams of lower-secondary (13-14-year-old) Finnish students. In invention projects, student teams design and make materially embodied collaborative inventions using traditional and digital fabrication technologies. This investigation focused on the student teams' knowledge creation processes by examining how they applied maker practices (i.e., design process, computer engineering, product design, and science practices) in their co-invention projects and the effects of teacher and peer support. In our investigations, we relied on video data and on-site observations, utilizing and further developing visual data analysis methods. Our findings assist in expanding the scope of computer-supported collaborative learning (CSCL) research toward sociomaterially mediated knowledge creation, revealing the open-ended, nonlinear, and self-organized flow of the co-invention projects that take place around digital devices. Our findings demonstrate the practice-based, knowledge-creating nature of these processes, where computer engineering, product design, and science are deeply entangled with design practices. Furthermore, embodied design practices of sketching, practical experimenting, and working with concrete materials were found to be of the essence to inspire and deepen knowledge creation and advancement of epistemic objects. Our findings also reveal how teachers and peer tutor students can support knowledge creation through co-invention.

This study explored factors impacting the decision for gifted females to pursue a career in computer science. The study used a phenomenological approach to delve into the experiences of ten gifted alumnae of The Gatton Academy of Mathematics and Science (Gatton), a residential STEM program, aiming to reveal nuanced insights into factors shaping their career development. Participants were selected based on their initial intentions to pursue careers other than CS before attending Gatton but changing to a CS focus afterward. Semi-structured interviews conducted via Zoom and coded using thematic analysis provided rich data on lived experiences and perspectives of participants. Findings shed light on the multifaceted factors influencing gifted women's career intentions before attending Gatton, the transformative effects of the residential STEM program on their decisions to pursue CS careers, and the evolution of their perceptions of CS over time.

The rapid growth of technology and computer science, which has led to a surge in demand for skilled professionals in this field. The skill set required for computer science jobs has evolved rapidly, creating challenges for those already in the workforce who need to adapt their skills quickly to meet industry demands. To stay ahead of the curve, it is essential to understand the hottest skills needed in the field. The article introduces a new method for analyzing job advertisements using social network analysis to identify the most critical skills required by employers in the market. In this research, to form the communication network of skills, first 5763 skills were collected from the LinkedIn social network, then the relationship between skills was collected and searched in 7777 computer science job advertisements, and finally, the balanced communication network of skills was formed. The study analyzes the formed communication network of skills in the computer science job market and identifies four distinct communities of skills: Generalists, Infrastructure and Security, Software Development, and Embedded Systems. The findings reveal that employers value both hard and soft skills, such as programming languages and teamwork. Communication skills were found to be the most important skill in the labor market. Additionally, certain skills were highlighted based on their centrality indices, including communication, English, SQL, Git, and business skills, among others. The study provides valuable insights into the current state of the computer science job market and can help guide individuals and organizations in making informed decisions about skills acquisition and hiring practices.

Recital 33 GDPR has often been interpreted as referring to 'broad consent'. This version of informed consent was intended to allow data subjects to provide their consent for certain areas of research, or parts of research projects, conditional to the research being in line with 'recognised ethical standards'. In this article, we argue that broad consent is applicable in the emerging field of Computational Social Science (CSS), which lies at the intersection of data science and social science. However, the lack of recognised ethical standards specific to CSS poses a practical barrier to the use of broad consent in this field and other fields that lack recognised ethical standards. Upon examining existing research ethics standards in social science and data science, we argue that they are insufficient for CSS. We further contend that the fragmentation of European Union (EU) law and research ethics sources makes it challenging to establish universally recognised ethical standards for scientific research. As a result, CSS researchers and other researchers in emerging fields that lack recognised ethical standards are left without sufficient guidance on the use of broad consent as provided for in the GDPR. We conclude that responsible EU bodies should provide additional guidance to facilitate the use of broad consent in CSS research.

We propose that psychology graduates are uniquely placed to work in the cybersecurity industry due to their understanding of human behavior and the possession of skills needed to address cybersecurity issues. However, there are challenges in attracting psychology graduates to the cybersecurity industry as they may not be fully aware of how skills developed in a psychology degree can be applied to a cybersecurity career. This small-scale evaluation explored psychology students' understanding of what working in cybersecurity entailed and what psychology skills and knowledge (their psychological literacies) they thought were needed for working in psychology and cybersecurity roles. Undergraduate psychology students (N = 66) answered two open-ended questions about skills needed to work in cybersecurity and then judged the importance of nine psychological literacy items for working in cybersecurity and psychology. A content analysis revealed that students recognized not just the technical aspects of cybersecurity work but also aspects relating to human behavior. Seven of the nine psychological literacy items were perceived as significantly more important for working in psychology than cybersecurity. This study is the first to link psychological literacy to working in cybersecurity. The implications of the results are discussed in terms of supporting students to recognize their psychological literacies for cybersecurity careers and suggestions are made for awareness-raising initiatives.

Improving equity and inclusion for underrepresented groups in the field of Computer Science (CS) has garnered much attention. In particular, there is a long-standing need for diversity efforts that center on the experiences of Black women, and specific actions to increase their representation--especially given the biases that they often encounter in the field. There is limited research concerning Black women in CS, specifically their conceptions of the field and their overarching CS identity development. More research in this area is especially important given the marginalization that Black women often experience at the intersections of their race and gender. Guided by a combination of critical theoretical lenses, this qualitative study examines Black women's conceptions of what it means to be a computer scientist and the degree to which those conceptions map onto how they see themselves in the field. Moreover, we explore experiences that help to bolster Black women's CS identity. The findings highlight key aspects of what it means to be a computer scientist for the Black women in this study--notably the ability to use computing to make societal contributions. Also, the results accentuate key nuances in the participants' personal CS identification, particularly as it relates to the resilience required to overcome unique barriers that many Black women encounter when engaging within the field. Moreover, the findings highlight the importance of social support systems to facilitate Black women's CS identity development. Implications for policy and practice within education and industry are discussed.

Cultural heritage is no longer something that can only be experienced in a museum exhibition. Digital tools have facilitated the distribution of material relating to artefacts, both in its representation and in presenting its context. This paper describes how digital modelling techniques can be synthesised with 3D scanning to digitally restore artefacts and create authentic replicas of their original states. The digital artefacts can then be used to assist the process of interpreting these artefacts in diverse forms, both in the museum and outside the museum. The study looks at Pictish sculpture as a case-study, restoring 3D models of two stones, and creating varying opportunities for their interpretation. As part of this study, new interactive tools, a virtual reality environment, and a virtual tour are built to assist immersive interpretation of the Pictish sculpture. The application of these digitised objects serves as an opportunity for informal learning. These applications were evaluated during a drop-in session. Findings show that all participants enjoyed the immersive mode of learning with 89% also showing a willingness to learn more about the topic.

Collaborative learning has numerous benefits such as enhancing learners' critical thinking, developing social skills, and improving learning gains. While engaging in this interactive process, learners' satisfaction toward their partners plays a crucial role in defining the success of the collaboration. However, detecting learners' satisfaction during an ongoing collaboration remains challenging, and there are no automatic techniques to predict learners' satisfaction. In this paper, we propose a multimodal approach to automatically predict peer satisfaction for co-located collaboration with features extracted from 44 middle school learners' collaborative dialogues. We investigated three types of features extracted from learners' dialogues: 1) linguistic features indicating semantics and sentiment; 2) acoustic-prosodic features including energy and pitch; and 3) visual features including eye gaze, head pose, facial action units, and body pose. We then trained several regression models with each of those features to predict the peer satisfaction scores that learners received from their partners. The results revealed that head position and body location were significant indicators of peer satisfaction: lower head and body distances between partners were associated with more positive peer satisfaction. Next, we investigated the influence of multimodal feature fusion methods on peer satisfaction prediction accuracy: early fusion versus late fusion. We report the comparison results between models trained with (1) best-performing unimodal features, (2) multimodal features combined by early fusion, and (3) multimodal features combined by late fusion. This line of research reveals how multimodal features from collaborative dialogues are associated with peer satisfaction, and represents a step toward the development of real-time intelligent systems that support collaborative learning.

In today's world, the ability to think computationally is essential. The skillset expected of a computer scientist is no longer solely based on the old stereotype but also a crucial skill for adapting to the future. This perspective presents a new educational challenge for society. Everyone must have a positive attitude toward understanding and using these skills daily. One thousand two hundred seven documents about computational thinking (CT) may be found while searching the Scopus database from 1987 to 2023. Data from Scopus were analyzed using VOSviewer software. This study educates academics by delving into the fundamentals of what is known about the CT of visual and quantitative research skills. This approach allows for a more in-depth look at the literature and a better understanding of the research gap in CT. This bibliometrics analysis demonstrates that (1) research on CT is common to all sciences and will develop in the future; (2) the majority of articles on CT are published in journals in the fields of education, engineering, science and technology, computing and the social sciences; (3) the United States is the most dominant country in CT publications with a variety of collaborations; (4) keywords that often appear are CT, engineering, education, and mathematics, and (5) research on CT has developed significantly since 2013. Our investigation reveals the beginnings and progression of the academic field of research into CT. Furthermore, it offers a road map indicating how this study area will expand in the coming years.

The calls for reasoning and proof to have a prominent place in secondary mathematics classrooms raise the question of knowledge that teachers need to support student learning of reasoning and proof. Several conceptualizations of Mathematical Knowledge for Teaching Proof (MKT-P) have been proposed over the years, yet some key questions about the nature of MKT-P remain unclear, such as whether MKT-P is a special kind of knowledge specific to teaching or whether it is just common mathematics knowledge. Another question is whether MKT-P can be improved through targeted intervention. Our study attempts to respond to both questions. An MKT-P questionnaire was administered to in-service secondary teachers, undergraduate mathematics and computer science (M&CS) majors, and pre-service secondary teachers before and after taking a capstone course Mathematical Reasoning and Proving for Secondary Teachers. The results suggest that MKT-P is indeed a specialized type of knowledge, which can be improved through targeted intervention.

For adults engaged in formal learning, self-efficacy judgements may have substantial impact on key learning attitudes and outcomes. A complex systems/person-centric perspective emphasises the importance of contextual features of adult life, yet extant study of self-efficacy is largely constrained to judgements of competence in course activities. This approach may omit critical information about adult learners' motivational beliefs. We developed a multidimensional measure of adult learning self-efficacy and provided initial validation data using a sample of 1200 working adults in an online Masters of Computer Science degree program. Our results suggest that adult learning self-efficacy (SE) is a multidimensional construct assessing self-perceived competence in 1) integrating learning and non-learning demands (Adaptive SE), 2) achieving mastery of course content (Academic SE), and 3) leveraging learning for professional growth (Transfer SE). Academic SE was further distinguishable by efficacy for memory and problem solving (Academic-MP) and for maintaining attentional focus (Academic-F). Our measure contributes to the literature on adult learning ecosystems by expanding research on motivational beliefs to include self-efficacy for managing critical contextual features of adult learning.

Integrated computing curricula combine learning objectives in computing with those in another discipline, like literacy, math, or science, to give all students experience with computing, typically before they must decide whether to take standalone CS courses. One goal of integrated computing curricula is to provide an accessible path to an introductory computing course by introducing computing concepts and practices in required courses. This study analyzed integrated computing curricula to determine which CS practices and concepts are taught, how extensively the curricula are taught, and, by extension, how they might prepare students for later computing courses. The authors conducted a content analysis to examine primary and lower secondary (i.e., K-8) curricula that are taught in non-CS classrooms, have explicit CS learning objectives (i.e., CS+X), and that took 5+ hours to complete. Lesson plans, descriptions, and resources were scored based on frameworks developed from the K-12 CS Framework, including programming concepts, non-programming CS concepts, and CS practices. The results found that curricula most extensively taught introductory concepts and practices, such as sequences, and rarely taught more advanced content, such as conditionals. Students who engage with most of these curricula would have no experience working with fundamental concepts, like variables, operators, data collection or storage, or abstraction in the context of a program. While this focus might be appropriate for integrated curricula, it has implications for the prior knowledge that students should be expected to have when starting standalone computing courses.

Structural Equation Modeling (SEM) continues to grow in popularity with numerous articles, books, courses, and workshops available to help researchers become proficient with SEM quickly. However, few resources are available to help users gain a deep understanding of the analytic steps involved in SEM, with even fewer providing reproducible syntax for those learning the technique. This work builds off of the original work by Ferron and Hess to provide computer syntax, written in python, for the specification, estimation, and numerical optimization steps necessary for SEM. The goal is to provide readers with many of the numerical and analytic details of SEM that may not be regularly taught in workshops and courses. This work extends the original demonstration by Ferron and Hess to incorporate the reticular action model notation for specification as well as the estimation of variable means. All of the code listed is provided in the appendix.

In this paper, we continue describing the project and the experimentation of "Ricerca Operativa Applicazioni Reali" (ROAR; in English, Real Applications of Operations Research), a three-year project for higher secondary schools, introduced. ROAR is composed of three teaching units, addressed to Grades 10, 11, and 12, respectively, having the main aim to improve students' interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. In a previous paper, we reported on the design and implementation of the first unit, started in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy), in a Grade-10 class. Here, we focus on the second unit, carried out in Winter/Spring 2022 with the same students, now in a Grade-11 class. In particular, we describe objectives, prerequisites, topics and methods, the organization of the lectures, digital technologies used, and a challenging final project. Moreover, we analyze the feedback from students and teachers involved in the experimentation.

We present a new highly interdisciplinary project-based course in computer aided drug discovery (CADD). This course was developed in response to a call for alternative pedagogical approaches during the COVID-19 pandemic, which caused the cancellation of a face-to-face summer research program sponsored by the Louisiana Biomedical Research Network (LBRN). The course integrates guided research and educational experiences for chemistry, biology, and computer science students. We implement research-based methods with publicly available tools in bioinformatics and molecular modeling to identify and prioritize promising antiviral drug candidates for COVID-19. The purpose of this course is three-fold: I. Implement an active learning and inclusive pedagogy that fosters student engagement and research mindset; II. Develop student interdisciplinary research skills that are highly beneficial in a broader scientific context; III. Demonstrate that pedagogical shifts (initially incurred during the COVID-19 pandemic) can furnish longer-term instructional benefits. The course, which has now been successfully taught a total of five times, incorporates four modules, including lectures/discussions, live demos, inquiry-based assignments, and science communication.

We report the results of a 4800-respondent survey among students at a technical university regarding their usage of artificial intelligence tools, as well as their expectations and attitudes about these tools. We find that many students have come to differentiated and thoughtful views and decisions regarding the use of artificial intelligence. The majority of students wishes AI to be integrated into their studies, and several wish that the university would provide tools that are based on reliable, university-level materials. We find that acceptance of and attitudes about artificial intelligence vary across academic disciplines. We also find gender differences in the responses, which however are smaller the closer the student's major is to informatics (computer science).

Purpose: The purpose of this study is to investigate the barriers that prevent workers in the software industry from experiencing flow in their work. Design/methodology/approach: This study was conducted by using a qualitative critical incident technique-inspired questionnaire. Findings: The findings suggest that workers in the software industry perceive that the most obvious obstacles to experiencing flow are related to work not presenting enough cognitive challenges and situational barriers related to the characteristics of the job (e.g. workdays having too many interruptions and distractions, timetables often being considered too tight for creative exploration and problem solving and having negative user experiences with development tools). Originality/value: The findings provide insights into flow barriers, specifically barriers that prevent workers in the software industry from experiencing flow.

The landscape of education is dynamically evolving with the infusion of technology into classrooms, but it is not just math or science disciplines that are getting a digital makeover. Social studies education has entered a transformative phase as well. The "Computer Science for Social Studies" project, funded by the National Science Foundation, proposes a groundbreaking model tailored for rural middle schools. This initiative aims to weave computer science into the fabric of social studies, rather than teaching it as an isolated skill, making both subjects more relevant, engaging, and beneficial for 21st-century students. This interdisciplinary venture not only enriches the curriculum but also equips students with a blend of technological literacy and historical knowledge, essential for navigating today's digitally driven world. By merging the analytical prowess of computer science with the depth of social studies, the authors are preparing their students to be critical thinkers and adept problem solvers.

Artificial intelligence (AI) has breached creativity research. The advancements of creative AI systems dispute the common definitions of creativity that have traditionally focused on five elements: actor, process, outcome, domain, and space. Moreover, creative workers, such as scientists and artists, increasingly use AI in their creative processes, and the concept of co-creativity has emerged to describe blended human-AI creativity. These issues evoke the question of whether creativity requires redefinition in the era of AI. Currently, "co-creativity" is mostly studied within the framework of computer science in pre-organized laboratory settings. This study contributes from a human scientific perspective with 52 interviews of Finland-based computer scientists and new media artists who use AI in their work. The results suggest scientists and artists use similar elements to define creativity. However, the role of AI differs between the scientific and artistic creative processes. Scientists need AI to produce accurate and trustworthy outcomes, whereas artists use AI to explore and play. Unlike the scientists, some artists also considered their work with AI co-creative. We suggest that co-creativity can explain the contemporary creative processes in the era of AI and should be the focal point of future creativity research.

Purpose: Supporting community college transfer students represents a critical strategy for broadening participation in STEM. In addition to being a racially diverse group, students who pursue STEM degrees by way of community college report frequent interests in graduate study and academic careers. Thus, supporting and expanding transfer students' PhD interests can help to diversify the STEM professoriate. This study aims to identify the experiences that predict PhD interests among students who transferred into the computer science major from a community college. Design/methodology/approach: Relying on longitudinal survey data from over 150 community college transfer students throughout their first year at their receiving four-year university, we used regression analysis to identify the post-transfer college experiences that predict early interest in PhDs. Findings: We found that receiving information about PhDs from a professor strongly predicted PhD interest among transfer students. Relationships with other variables indicate that the provision of information about graduate school was more likely to occur for students who participated in undergraduate research experiences than for those participating in internships. Descriptive data document inequities in who has access to these types of experiences. Originality/value: This paper provides new insight into how STEM departments can develop targeted efforts to ensure that information about PhD training is equitably available to all transfer students. Working to ensure that faculty equitably communicate with students about PhD opportunities may go a long way in countering potential deterrents among transfer students who may be interested in such pathways.

The COVID-19 pandemic has introduced challenges to delivering laboratory-based STEM curricula. This study evaluates students' learning experience in a synchronous team design activity within the laboratory component of an undergraduate Computer Engineering course during the pandemic. Using the EduPad digital platform, students collaborated in small groups to develop a working design for a software simulator. The researchers measured students' social, cognitive, and teaching presences during the remote team design activities and gathered their feedback on the learning experience. The findings demonstrate that students exhibited high levels of cognitive presence during the remote team design activities, suggesting effective strategies for promoting cognitive presence in synchronous engineering laboratory instruction. This study contributes to the community of inquiry framework and expands the existing repertoire of techniques for enhancing cognitive presence in online synchronous instruction. The implications for further research are also discussed.

In this paper we discuss the types of knowledge used by six middle school students as they engaged with a debugging task designed to integrate ideas from computer science, mathematics and science. Our findings show that the computational thinking practice of debugging is a rich source of opportunities to integrate these different disciplines. The analysis illustrates how the types of knowledge the students did and did not use at each step of the debugging process were related to their ability to succeed at each step. Our work contributes to theory and practice by uncovering implications for studying debugging through two refined frameworks and for designing debugging tasks to support transdisciplinary learning.

Educational escape rooms aims to motivate students, to strengthen knowledge and evaluate learning. Pre-service teachers enrolled in "Computer Science and Digital Competency" course shows lack of motivation and difficulties to realise its usefulness in everyday practice, becoming an ideal context to apply this strategy. 157 students belonging to a European university participated in the experience as case study. The educational escape room was conducted following a hybrid model, mixing a physical organization of props with a virtual organization of the narrative, tests and achievements. The experiment was designed to answer?two hypotheses, first if applying escape room as an educational strategy fosters pre-primary and primary students' motivation, since this method address complex concepts in a practical way, and second, if the application of this strategy as teaching strategy makes students perceive the learning process as a game.