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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.

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 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 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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

Although we are witnessing the accelerated development of computer science, and the opening of new fields of study, compiler construction is still a very important field that is taught at most world universities. Because of a large number of algorithms and complex theoretical constructions, these topics represent a difficult and complex domain for teachers to teach and for students to gain a better understanding. Educational software systems play an increasingly important role in the engineering sciences. These systems aim to help students learn by turning abstract theoretical concepts into tangible objects that students can interact with. In this article, we present the web-based simulation system ComVis, which represents a set of tools for learning and teaching topics in the field of compilers. The original version of the ComVis system is written in Java and is available as a desktop application. For greater accessibility and better visual representation, we have developed a web-based simulation system. In addition to new functionalities, this system also includes a large number of topics from the field of compilers. This article provides an overview of the tools that comprise the web-based ComVis system, with an emphasis on the interactivity that students achieve with the system in the learning process. The results of using the web-based ComVis system were verified by a quantitative evaluation of the system's effectiveness through a controlled experiment and a qualitative evaluation of usability by student survey and heuristic tests by experts.

In recent years, subject content such as programming and digital literacy has become an integral part of technology education. However, research shows that many programming teachers lack sufficient formal education to teach programming and show disparate educational and professional experiences. This study investigates how three teachers' knowledge and beliefs about their teaching practices are enacted in their classroom practice. The data for the study consist of videoed classroom observations and subsequent episodic narrative interviews with the teachers. Metaphor analysis have been used to uncover central relations between the teachers' knowledge and beliefs about their practice and their classroom actions. The result of the study reveals that the teachers describe their roles as teachers differently. Despite the differences, the teachers still share the idea that programming is an activity where small pieces of code is intertwined so that they can achieve a purpose. However, none of the teachers speak about code as being essential for learning programming. The teachers all seem to view themselves as assets for the students' achievements, nevertheless, they do not share the idea of why. Altogether, the result of the study suggests that even though the curriculum is the same, teachers' knowledge and beliefs about their teaching plays a big role in students' education. Also, the study show, that it is reasonable to suggest that conceptual metaphors affect not only our language, but also our actions in the classroom.

Recently, researchers have proposed to leverage technology-supported data (log files) to investigate temporal and sequential patterns of interaction behaviors in learning processes. There are two major challenges to be addressed: clarifying the positioning of interaction levels and identifying the evolution of the interaction action patterns in learning processes, particularly for students with differing achievements. This paper explores the use of sequential pattern mining to address the evolution of student action patterns in Massive Private Online Courses (MPOCs) and compare these patterns between different achievement groups. The study was conducted with first-year undergraduate computer science students enrolled in a computer application course at a traditional open university in one of the Chinese provinces (N = 1375). The results showed the development of various action patterns in each phase of the course and the distinct action patterns for high-achieving and low-achieving students. The findings of study provide a new perspective for instructors and students to understand interaction patterns at the fine-grained level, and can help instructional designers develop learner-cantered courses and platforms to improve online learning.

The gender imbalance in computer science (CS) is one of the most challenging issues in American education. CS is the only science, technology, engineering, and mathematics (STEM) field in which women's representation has steadily declined in recent decades. In this study, we explored one potential approach that could be effective in increasing college women's participation in CS: participation in Women-in-Computing (WiC) groups. Through participant observation and individual interviews in a WiC group at a major research university, we investigated how students engage in WiC, the impacts of the WiC on identity and belonging, and the challenge of sustainability. The results were coded using a hybrid of grounded and deductive coding and indicate that WiC groups offer various programs and events that enable women in CS to fully participate, learn, and grow. WiC represents an identity, a community, a safe space, and a journey. The results also suggest that the WiC has had positive impacts on students' identity and belonging, as evidenced by increased self-efficacy, reduced imposter syndrome, and enhanced sense of belonging and community. Furthermore, we outline three strategies employed by the WiC to ensure the group's sustainability. Our study sheds light on how WiC can encourage women to enter and persist in CS, and on some of the characteristics of a successful WiC. We demonstrate that WiC may be potentially effective in diversifying CS through identity-based participation. Moreover, student leaders design both the structure of the group and the leadership continuity process to ensure sustainability.

In our post-pandemic world, where the majority of higher education institutions have transitioned back to in-person classes, this paper argues that we must not return to pre-COVID teaching practices. Instead, we have the obligation and opportunity to create an educational experience and environment that better facilitates learning and instruction. This paper presents post-COVID best practices for employing technology in higher education based on an original survey and follow-up interviews of seventeen computing instructors at our institution. After a literature review, we describe four general categories of practices that enhance the post-COVID classroom: online student activities, digital instructor notes, remote classroom participation and collaboration, and a paperless classroom. For each of these categories, we provide vignettes to illustrate scope and intent. We also offer recommendations for addressing digital dishonesty, required infrastructure, institutional support, and being prepared to seamlessly return to a blended or fully remote environment in the event of another crisis. Finally, we identify additional emerging technological challenges and opportunities that require further effort. Overall, this paper emphasizes the need for a shift towards improved practices in the classroom rather than just a return to pre-pandemic norms. We believe implementing these recommendations will result in a more flexible, accessible, and robust post-COVID educational experience.

For effective teamwork, especially in demanding learning situations like a hackathon, coordination is crucial as it contributes to mutual trust and shared mental models of team members. However, teams experience challenges that mar team coordination. Research has shown that interpersonal skills such as socially-shared emotion regulation (SSER) can be key in dealing with such challenges. We examined the relationship between SSER and mutual trust, and SSER and shared mental models in the context of a hackathon. Adapted from a small SSER scale, we built a 27-item questionnaire to examine SSER and its relationship with mutual trust and shared mental models in programming teams. We also used heat maps to provide an overview of individual team members' perceptions of their teams' SSER strategy application, mutual trust, and strength of shared mental model bonds within the team. Regarding the relationship between SSER and shared mental models, our analyses revealed signification association (correlation) between: (1) SSER situation modification and shared mental model: task and communication skills and (2) SSER situation modification and shared mental model: team dynamics and interaction. For the relationship between SSER and mutual trust, our analyses revealed significant relationships between: (1) SSER situation modification and perceived trustworthiness, (2) SSER cognitive change and perceived trustworthiness, (3) SSER response modulation and cooperative behaviors, and (4) SSER response modulation and monitoring behaviors. These relationships highlight the power of SSER in building key team coordination mechanisms and strengthening team performance.

Computer science development, especially machine learning, is a thriving innovation essential for education. It makes the process of teaching and learning more accessible and manageable and also promotes equality. The positive influence of machine learning can also be felt in Islamic studies, particularly in Hadith studies. This literature review highlights the role of machine learning in managing research regarding Hadith studies that have been published and categorizing it by their research topics, language & corpus, and the machine-learning algorithms. This article review has been conducted on 48 previously published hadith study journals. Then, we summarize existing trends, including trending topics, common language & corpus, and general algorithms often used in previous hadith-related reviews. This article aims to give new insight to help the broad community of researchers interested in these narrations to create fresh and further research with the uncommon topic, language & corpus, and algorithms. Furthermore, this article is also expected to contribute to academics and practitioners as a guide for conducting future research on the application of computer science in Hadith studies. We conclude that the most frequently discussed topic is Hadith Classification at 33.33%, the most widely used language is Arabic at 43.75%, and the most commonly used algorithm is SVM at 12.5%. In addition, the dataset mainly used is a public dataset by Al-Bukhari at 30.53%.

Learning computer science (CS) is increasingly becoming a necessary component of K-12 education, but in most cases, teachers do not have either the essential knowledge to teach or a curriculum to follow. In this article, we analyze the outcomes from a yearlong, blended professional development (PD) program to teach teachers game design and coding skills, and we codevelop a middle school curriculum. Using a mixed-methods design, this study presents the findings as we investigate how teachers experienced the PD program. Our findings show that the teachers' level of engagement differed for various reasons, such as challenges related to time, motivation, and interest. We identified a high-engagement group who developed a deep understanding of coding knowledge and engaged in the PD activities more. We also identified a low-engagement group who failed to attend most activities despite mentioning generic interests. We provide an explanation why these teachers might have varied in terms of their engagement and offer how "data" can be used to predict engagement levels and as a diagnostic tool (e.g., lacked specificity in explaining CS concepts in reflections). We recommend adding motivational strategies and better tracking and monitoring mechanisms for better engagement and PD design considerations for online and blended learning for teachers.

This article offers the construct "unitizing predicates" to name mental actions important for students' reasoning about logic. To unitize a predicate is to conceptualize (possibly complex or multipart) conditions as a single property that every example has or does not have, thereby partitioning a universal set into examples and nonexamples. This explains the cognitive work that supports students to unify various statements with the same logical form, which is conventionally represented by replacing parts of statements with logical variables p or P(x). Using data from a constructivist teaching experiment with two undergraduate students, we document barriers to unitizing predicates and demonstrate how this activity influences students' ability to render mathematical statements and proofs as having the same logical structure.

Competencies (knowledge, skills, and dispositions) enable employers and educators to speak a common language regarding what computing graduates are expected to demonstrate on the job. This study focuses on competencies required by managers in the computing industry, based on semi-structured interviews of ten individuals in managerial roles, such as directors, project managers, and product managers with prior experience in computing-related roles. Constant Comparative for Naturalistic Inquiry was used to analyze the data. The most frequently discussed managerial skills included leadership, project management, hiring and evaluating candidates, and mentorship. In addition, professional skills such as communication, problem-solving, and lifelong learning were mentioned, along with essential dispositions that support the development of these skills, e.g., collaborative mindset, lifelong learning orientation, and self-regulation. Participants also emphasized the need to make judgments, build relationships, and collaborate within or outside their team. Career readiness in the computing industry is not limited to entry-level jobs; professionals should have the opportunity to navigate their preferred career path--whether they aspire to move down a technical or managerial path. This study can contribute to both students' and educators' understanding of the managerial career path and what types of competencies and experiences should be included in computing education programs to set them up for success across their career path. Implications for pedagogical approaches will also be discussed.