Search

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

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.

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

This study aims to explain the relationships between secondary school students' digital literacy, computer programming self-efficacy and computational thinking self-efficacy. The study group consists of 204 secondary school students. A relational survey model was used in the research method and three different data collection tools were used to collect data. The structural equation model was used in data analysis to reveal a model that explains and predicts the relationships between variables. According to the results of the research, it was determined that digital literacy of secondary school students affected their computer programming self-efficacy, digital literacy affected their computational thinking self-efficacy, and computer programming self-efficacy affected their computational thinking self-efficacy. It was also found that digital literacy skills have an indirect effect on secondary students' computational thinking self-efficacy on computational thinking self-efficacy.

The paper discusses an alternative method of assessing the difficulty of pupils' programming tasks to determine their age appropriateness. Building a program takes the form of its successive iterations. Thus, it is possible to monitor the number of times such a program was built by the solver. The variance of the number of program builds can be considered as a criterion of the difficulty of the task. We seek to verify whether this variance is the greatest in the age group for which the task is most suitable. We created several series of programming tasks and offered them to 87000 pupils from 4th to 13th grade. For each task, we compared the optimal age group determined by the variance of the number of program builds method with the group determined by the correct answer ratio method. A strong correlation was observed in traditional microworlds Karel the Robot and Turtle. A moderate correlation was achieved in the new microworld Movie.

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

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

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

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

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

Cybersecurity has traditionally been perceived as a highly technical field, centered around hacking, programming, and network defense. However, this article contends that the scope of cybersecurity must transcend its technical confines to embrace a more inclusive approach. By incorporating various concepts such as privacy, data sharing, and ethics, cybersecurity can foster diversity among audiences with varying educational backgrounds, thereby cultivating a richer and more resilient security landscape. A more diverse cybersecurity workforce can provide a broader range of perspectives, experiences, and skills to address the complex and ever-evolving threats of the digital age. The research focuses on enhancing cybersecurity education to attract a diverse audience through the development and testing of a virtual platform on Haaukins (a cybersecurity training platform) designed with features resembling social media for capture-the-flag exercises. The results show that the cyber training platform effectively engages a diverse group of learners, bridging the gap between traditional technical boundaries and the urgent demand for comprehensive cybersecurity competence.

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

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

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

Python programming is widely employed in educational institutions worldwide. Within the "Merdeka Belajar" curriculum context, this programming is recognized as a suitable vehicle for mathematics instruction, significantly influencing students' motivation and learning outcomes, particularly following periods of educational hiatus. This study examines the effectiveness of Python programming in promoting heightened learning outcomes by examining the intricate relationship between student motivation and learning. The study uses quantitative research methodologies to evaluate student learning facilitated through Python programming, encompassing problem-solving assessments and the administration of motivation questionnaires. By engaging in coding practices, students understand the symbols they manipulate, facilitating their ability to juxtapose data derived from mathematical modeling with the resultant programming output. When disparities arise, students are empowered to reassess their work, fostering a more profound comprehension of the subject matter. These exercises serve to augment students' capacity to retain and process information within memory. Furthermore, students demonstrate a favorable disposition, exhibiting persistence in resolving programming challenges by meticulously analyzing error outputs, particularly those pertaining to TypeErrors. Encouraging students to confront errors through thoroughly examining error output manifestations engenders an efficacious learning paradigm. This research proffers invaluable insights for educational institutions contemplating the integration of Python programming as an instructional adjunct.

Abolitionist dreamers have called for justice-oriented teachers to teach K-12 students with marginalized identities through methods of critical and culturally responsive/sustaining pedagogies (Freire, 1970/2018; Gay, 2018; Ladson-Billings, 1995/2021; Paris & Alim, 2014); acts of love, care, hope, and healing (Ginwright, 2016; Love, 2019); and the humanization of students in STEM spaces (Benjamin, 2019). The dehumanization and silencing of students with marginalized identities in math, science, and computing spaces have resulted in significant harm to their STEM identities (Matthews, 2018; Vakil, 2018; Yeh et al., 2021). Critical and humanizing pedagogy seeks to move away from the practice of having students divest from their culture and assimilate to survive in schools (Salazar, 2013) and computing spaces. Teachers can transform schools and serve to liberate students through acts of love and care, the intentional humanization of students, and the development of social agency toward cultural, social, political, and institutional action for justice.

Microlearning is a modern learning modality that has been adopted in recent years for student education. This study aimed to reveal the effect of video-based microlearning on the development of programming skills and technology acceptance among intermediate school students. The study used a quasi-experimental design for two groups. A technology acceptance scale and a performance observation card for programming skills were prepared in order to collect data for the study. A cluster random sample of intermediate school students participated in video-based micro learning. The students were divided into two groups: the experimental group (consisting of 31 students) and the control group (consisting of 32 students). The results of the study showed that there were statistically significant differences between the mean scores of both study groups in favor of the experimental group in the post-application of the performance observation card for programming skills. However, there were statistically significant differences between the mean scores of the students in the experimental group in the pre- and post-application of the technology acceptance scale in favor of the post application. The study suggested that computer teachers might benefit from employing video-based microlearning to teach students programming skills at different stages based on these results. Suggestions related to the research findings are also provided.

This study investigated the impact of problematization-oriented scaffolding and structuring-oriented scaffolding, incorporated within instructional videos, on students' computational thinking and their performance in programming education. We recruited 86 participants from three senior classes at a high school. Each of the three classes was assigned to one of the experimental conditions: the problematization-oriented scaffolding group, the structuring-oriented scaffolding group, and the control group, which received no scaffolding. Pre- and post-tests were conducted to assess students' computational thinking and programming performance during a three-week period. We observed significant improvements in the computational thinking skills of the problematization-oriented scaffolding group, particularly in the algorithmic thinking and cooperativity subdimensions, when compared to the control group. Structuring-oriented scaffoldings in videos also helped enhance students' computational thinking, especially in the algorithmic thinking subdimension. In contrast, students in the control group primarily improved their problem-solving skills, although the difference across the three groups was not significant. Furthermore, this study revealed that the use of learning scaffolds, whether problematization-oriented or structured, significantly contributes to students' learning achievements in comparison to the control group. These findings emphasize the importance of selecting the appropriate scaffolding approach to enhance specific dimensions of computational thinking.

This study aims to develop a predictive model for predicting gifted students' engagement levels and to investigate the features that are important in such predictions. Features reflecting students' emotions, social-emotional learning skills, learning approaches and video-watching behaviours were used in the prediction models. The study group consisted of 36 gifted students between the ages of 12 and 14 who attended an information technologies course, where students engaged with Arduino-based learning tasks. Data related to only one task were analysed. Prediction models were developed using different machine learning algorithms, and information gain scores were used to investigate important features in the prediction models. The results show that all prediction models achieved a higher classification accuracy than the base model. The highest classification accuracy (83%) was achieved with the Support Vector Machine (SVM) algorithm. Students' self-reported emotions while performing the task were found to be the most important feature in predicting their level of engagement. Other important features in predicting engagement level were the students' social-emotional learning skills, deep and surface learning approaches and their video-watching behaviours. As a result, it was found that the engagement level of gifted students could be predicted with high accuracy. These results can be used to predict students' engagement levels and to develop interventions for low-engaged students. The obtained results are discussed within the scope of emotion-aware learning design and social-emotional learning.

The use of a pedagogical approach mediated transfer with the bridging method has been successful in facilitating the transitions from block-based to text-based programming languages. Nevertheless, there is a lack of research addressing the impact of this transfer on programming misconceptions during the transition. The way programming concepts are taught to K-12 learners can later result in misconceptions for adult learners. The main objective was to examine the impact of mediated transfer using the bridging method pedagogical approach on the prevalence of programming misconceptions. We conducted a quasi-experimental study in school settings during informatics (computer science) classes among 163 sixth-grade students. The control group received traditional programming lectures using the text-based programming language, Python. Conversely, the experimental group utilized a mediated transfer pedagogical approach by starting with the block-based programming language MakeCode for micro:bit before transitioning to the text-based Python. Our findings indicate that the experimental group significantly reduced programming misconceptions in fundamental programming concepts: variables, sequencing, selection, and loops - compared to the control group. This suggests that the use of block-based programming language as an initial step in programming education, followed by a structured transition to text-based programming language, can effectively mitigate common misconceptions among K-12 learners.

A global concern in recent years has been the formal incorporation of computing and digital technology subjects into high school curricula. Despite many initiatives to make these subjects accessible to students, significant barriers to uptake remain in many countries. In this study we convened a panel of 32 academic, research, and other experts from several countries, to participate in a three round Delphi study with the goal of identifying ways to improve student participation for high school computer science and programming subjects in New Zealand. The study explored barriers and challenges, potential solutions and future scenarios, as well as implementation ideas for the most important solutions across five domains: Computer Science and Programming Standards, Resources, Support, Assessment, and Student-centred factors. Together these three Delphi rounds were synthesized into a framework summarizing challenges and potential solutions, including suggestions relating to the implementation of those solutions, alongside key aspects of a likely and a desirable future scenario regarding high school computing in New Zealand. The framework, which is the major result of this study, was designed with the intention to summarise and present potential ways to increase student participation and engagement for computer science and programming standards. We hope that this framework will be of interest to government, teachers, schools, industry, universities and other key stakeholders.

As computational methods are widely used in science disciplines, integrating computational thinking (CT) into classroom materials can create authentic science learning experiences for students. In this study, we classroom-tested a CT-integrated geoscience curriculum module designed for secondary students. The module consisted of three inquiry investigations that were age-appropriately translated from the computational practices of volcanologists who study tephra hazards and risks. We developed a domain-specific, block-code-based computational modeling environment where students carried out 1 of the 3 science practices in each inquiry investigation: experimentation, data visualization and interpretation, and modeling. We examined (1) student learning outcomes using pre- and post-tests and (2) student reflections on the computationally supported science practices surveyed at the end of each inquiry investigation during the module. Results indicate that students made statistically significant gains in science content as well as in computationally supported experimentation, data visualization and interpretation, and modeling practices. Over 80% of students identified different ways in which block coding supported their practices during inquiry investigations. Based on these findings, we discuss implications for the future development of computationally supported inquiry investigations in science.

Molecules and Computer: Chemistry Calculations in Class (MC[superscript 4]) is a computational laboratory intended for final-year high school or undergraduate students. The topic is the antioxidant potential of anthocyanidins, which is chemically related to their radical scavenging action via the mechanism of hydrogen atom transfer (HAT). This laboratory combines (bio)chemical and nutraceutical concepts with organic chemical reactions involving radical species. It allows students to apply important physicochemical (thermodynamic) concepts, such as Gibbs free energy of reaction and solvation. Finally, the procedure can easily be tailored to the resources at hand as well as the knowledge of the students. In fact, when computing facilities are not available, the whole set of molecular structures and energy data are provided as well as a simple datasheet required for their analysis. Alternatively, the whole protocol and useful scripts are provided so that students can generate their own results by experiencing the approach to computational chemistry.

Background: With the development of computational literacy, there has been a surge in both research and practice application of text-based and block-based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major programming modalities, especially in the context of secondary education settings. Objectives: To further compare programming effects between and within text-based and block-based modalities, this research conducted a quasi-experimental research in China's secondary school. Methods: An online programming platform, Code4all, was developed to allow learners to program in text-based and block-based modalities. This research collected multimodal data sources, including programming platform data, process data, and performance data. This research further utilized multiple learning analytics approaches (i.e., clustering analysis, click stream analysis, lag-sequential analysis and statistics) to compare learners' programming features, behavioural patterns and knowledge gains under two modalities. Results and Conclusions: The results indicated that learners in text-based modality tended to write longer lines of code, encountered more syntactical errors, and took longer to attempt debugging. In contrast, learners in block-based modality spent more time operating blocks and attempt debugging, achieving better programming knowledge performances compared to their counterparts. Further analysis of five clusters from the two modalities revealed discrepancies in programming behavioural patterns. Implications: Three major pedagogical implications were proposed based on empirical research results. Furthermore, this research contributed to the learning analytics literature by integrating process-oriented and summative analysis to reveal learners' programming learning quality.

Although previous research has provided some insights into the effects of block-based and text-based programming modalities, there is a dearth of a detailed, multi-dimensional analysis of the transition process from different introductory programming modalities to professional programming learning. This study employed a quasi-experimental design to address this gap, involving 64 secondary school students in two groups. For the beginning five weeks, the first group used an introductory block-based programming environment, while the second group used an introductory text-based programming environment. Then, both groups transitioned to professional text-based programming for the subsequent eight weeks. The results showed that participants who transitioned from introductory text-based programming to professional text-based programming (1) significantly outperformed in computational thinking skills; (2) had more code-writing and debugging behaviors and fewer irrelevant behaviors, and (3) had more interactions with the instructor. No significant differences were observed between the two groups regarding enjoyment, confidence, and interest in programming. Drawing on these findings, this study proposes pedagogical implications that could facilitate the adoption of programming modalities within the broader context of STEM education.

Objectives: At the K-12 level, "CS for All" initiatives across the country strive to increase equitable access to and participation in computer science (CS). However, there are many open questions about the implementation and effectiveness of these initiatives, including the extent to which exposing young people to CS early on can shape their longer-term CS interest and engagement. In this article, we examine CS participation among 6th-8th-grade Black girls and assess whether CS participation during middle school shapes CS interest and engagement during their first year of high school. We focus on Black girls in the hopes of developing a more nuanced and rigorous understanding of computing experiences at the intersection of racism and sexism in this field. Participants: The focal group of students in this study are 6th-8th-grade Black girls from New York City. We employ a comparative lens in this article, contextualizing the CS experiences and outcomes of Black girls to Latinas, Asian, and White girls, as well as Black boys. Study Method: We primarily rely on quantitative data for this article, applying a critical lens to our analyses and interpretation. More specifically, we conduct descriptive analyses of course-taking patterns as well as survey data that focus on student attitudes and beliefs about CS. We then carry out inferential analyses of students' administrative records examining how, if at all, middle school CS participation is related to high school outcomes for Black girls. We employ a comparative lens and rely on qualitative data to make sense of our results. Findings: We find that, troublingly, Black girls in the district are disproportionately less likely to receive CS instruction in middle school. Black girls are also less likely than Black boys, Latinas, and White girls to feel that they belong in CS. However, Black girls in CS courses report similar levels of engagement, family, and peer support, as well as value for CS relative to other students in the district. Finally, we find that participation in CS courses in middle school does not increase the likelihood that Black girls will select high schools that offer CS courses or take a CS course during their first year of high school. Conclusions: Our findings suggest that to increase equitable access and participation in CS, it is not enough to simply expose students to CS coursework. We call for sustained attention to the experiences that Black girls have in their CS classes as well as broader structural barriers that might shape CS course-taking.

While a growing emphasis has been placed on broadening participation in computer science (CS) education, an enduring gender gap exists. One reason for this is gender-based CS stereotypes, which serve as gatekeepers and act in exclusionary ways. However, some high schools in the U.S. have still built gender-inclusive CS programs. We conducted a 3-month long case study at one such school (Forest View High School, FVHS, pseudonym) to better understand existing stereotypes. Data sources included student and alumnae interviews, student reflections, and teacher interviews. Constant comparative analysis was used to uncover emergent themes. Overall, gender-based stereotypes did not appear to be present, potentially as a result of mitigating strategies like access to women role models and recognition of the importance of a growth mindset. However, nerd-genius stereotypes were common. Despite a focus on nerd-genius stereotypes, students also held perceptions that computer scientists could be any type of person.

This research aims to examine the effect of coding education on the analytical thinking skills of gifted students. The participants are 18 students, 11 to 12 years old. An embedded experimental mixed design was used in the research. The data collection was carried out with the Analytical Thinking Skill Scale to determine the difference in the analytical thinking skills of the study group before and after the coding training; the Analytical Thinking Skill Observation Form to determine the analytical thinking skill levels during the implementation process; and the semi-structured interview form to get their opinions on the coding training. The data were analyzed with a pre-test-post-test quasi-experimental design, descriptive analysis, and content analysis. The results show that coding education developed the participants' analytical thinking skills. The difference in the analytical thinking skills of the study group was not statistically significant in terms of gender. According to the students' views on the coding education application process, there was an improvement in the sub-dimensions of sorting, classification, comparison, and evaluation in analytical thinking skills; coding education developed problem-solving and thinking skills, was useful, encouraged students to choose a profession, and was entertaining, as well as negative opinions such as being difficult, boring, and requiring a lack of time.

In this study, the Computer Science (CS) education in K-12 of the countries in the "Asia-Pacific region", which are among the top 10 in PISA, and CS education of Türkiye were investigated. PISA is conducted to assess science, mathematics, and reading skills. PISA, which is carried out every 3 years by the OECD, evaluates 15-yearold students. When the countries that have been successful in PISA are examined, it has been observed that the number of countries in the "Asia-Pacific region" in the top 10 has increased in recent years. In this study, data analysis was done with document analysis, which included PISA results and reports, OECD reports, computer science curriculum of countries, and academic studies on PISA and computer science education. As a result of the data obtained, the countries' CS education was compared to identify successful practices. Also, the countries' practices in computer science education were compared to Türkiye, and implications were made about the effects of such works on PISA results.

Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as submission, compilation, edit, and keystroke events, with keystroke-level logs being the most fine-grained of commonly used dataset types. However, the lack of open datasets, especially at the keystroke level, is notable. There are several reasons for this failing, with the most prominent being the challenges of deidentification that are peculiar to keystroke log data. In this paper, we present the public release of two fully deidentified keystroke datasets that are the first of their kind in terms of both event and metadata richness. We describe our collection technique and properties of the data along with deidentification techniques that, while not fully relieving researchers of significant effort, at least reduce and streamline manual work in hopes that researchers will release similar datasets in the future.

To date, very limited research interest has been given to the strategies English medium instruction (EMI) teachers or lecturers deploy to provide corrective feedback (CF) on the language use to their students during class interaction. In other words, when EMI teachers incidentally focus on students' problematic language use, how do they correct it -- providing explicit correction or using recast or elicitation? This article reports on a study that examined CF types EMI teachers and lecturers used during classroom discourse, drawing on data collected from classroom observations and recordings of six different EMI classes in high school and university settings in Korea. The frequency and types of CF used in reactive language-related episodes (LREs) were identified in the EMI classes and compared between the two settings and across disciplines (social science, mathematics, and computer science). Findings showed that all the EMI teachers and lecturers offered CF to their students but with different frequency; the schoolteachers offered CF more frequently than the university lecturers. Also, the schoolteachers used more various types of CF than the lecturers. In both settings, CF occurred most frequently in mathematics compared to the other two disciplines. This article ends with suggestions for ways the findings of this study can be used to raise EMI teachers' awareness of various options for providing CF on students' linguistic errors during their incidental teaching practices.

The aim of this study is to examine the effect of block-based coding education on middle school students' computational thinking skills and attitudes towards coding with Blocky example. The training phase of the implementation process was planned and implemented by subject experts in 4 weeks and 2 hours a week. In the research, both quantitative and qualitative research methods and exploratory sequential design were used as mixed methods. In this study, quantitative research method and pretest- post-test control group semi-experimental pattern model was used as the research model. The universe of the study consists of 5th grade students studying at a secondary school in Aksaray province Eskil district of Türkiye in the 2019-2020 academic year. Within the scope of the research, the research participant group was determined considering that 5th grade students will receive coding training for the first time. In addition to the total number of students to be reached is 38, including 19 control and 19 experimental groups. While the "blocky" application used in block-based coding education was determined as the independent variable in the research, the students' computational thinking skills and their attitudes towards learning to code were determined as dependent variables. As a result of the applications and analysis, the difference between the values of students' computational thinking skills self-efficacy and attitudes towards learning coding before and after education is statistically significant. In order to determine the effect size of the Blocky block-based coding environment on the Information Computational Thinking Skills Self-Efficacy Scale (CTSES) and the Attitudes Toward Learning Coding Scale (ATLCS), the eta squared value was examined. It can be said that the Blocky block-based coding environment has a "large" effect size on computational thinking skills, self-efficacy perception and on the attitude towards learning coding.

In a previous publication we examined the connections between high-school computer science (CS) and computing higher education. The results were promising -- students who were exposed to computing in high school were more likely to take one of the computing disciplines. However, these correlations were not necessarily causal. Possibly those students who took CS courses, and especially high-level CS courses in high school, were already a priori inclined to pursue computing education. This uncertainty led us to pursue the current research. We aimed at finding those factors that induced students to choose CS at high school and later at higher-education institutes. We present quantitative findings obtained from analyzing freshmen computing students' responses to a designated questionnaire. The findings show that not only did high-school CS studies have a major impact on students' choice whether to study computing in higher education -- it may have also improved their view of the discipline.

We live in a digital age, not least accelerated by the COVID-19 pandemic. It is all the more important in our society that students learn and master the key competence of algorithmic thinking to understand the informatics concepts behind every digital phenomena and thus is able to actively shape the future. For this to be successful, concepts must be identified that can convey this key competence to all students in such a way that algorithmic thinking is integrated in the subject of informatics -beyond a pure programming course. Furthermore, based on the Legitimation Code Theory, semantic waves provide a way to develop and review lesson plans. Therefore, we planned a workshop, that follow the phases of a semantic wave addressing algorithmic problems using a block-based programming language. Considering this, we suggest the so-called SWAT concept (Semantic Wave Algorithmic Thinking concept), which is carried out and analyzed in a workshop with students. The workshop was carried out in online format in an 8th grade of a high school during a coronavirus lockdown. The level of algorithmic thinking was measured using a pretest and posttest both in the treatment group and in a control group and with the help of the approximate adjusted fractional Bayes factors for testing informative hypotheses statistically and through a reductive, qualitative content analysis of the students' work results (worksheets and created programs) evaluated. The semantic wave concept was measured using several cognitive load ratings of the students during the workshop and also statistically evaluated with the approximate adjusted fractional Bayes factors for testing informative hypotheses, as well as a qualitative content analysis of the worksheets. Results of this pilot study provide first insights, that the SWAT-concept can be used in combination of unplugged and plugged parts.

For schools to successfully deliver the computer studies curriculum, they should have, among other things, qualified teachers and computer laboratories. This study investigated the availability of qualified computer studies teachers and computer laboratories in public secondary schools in Lusaka City, Zambia. A mixed-methods approach was employed in which 61 schools out of 72 were randomly selected to take part in a survey, and this was followed by interviews, observations, interviews and documentary analysis. Among those surveyed, 50 schools completed the questionnaire, and the results show that 76% of computer studies teachers have qualifications in computer studies or information and communication technologies (ICTs) and the majority (55%) hold a diploma. However, there is a higher teacher-pupil ratio of as high as 1:276. The findings further show that 98% of the schools have computer laboratories and the majority have a lower computer-pupil ratio of 1:2 and 1:3. The results also show that data projectors and air conditioning units are available in many computer laboratories. However, many computer laboratories do not have servers and Internet connectivity. Given the above findings, it has been recommended that the Government of Zambia recruit more computer studies teachers and that school managers ensure the availability of facilities such as Internet connectivity in the computer laboratories.

Integrating computational thinking (CT) into various disciplines via computer science (CS) methods such as unplugged, block-based, text-based, and physical programming is a trending topic in educational sciences. This study presents a perspective on implementing an unplugged computer science activity to integrate CT into mathematics education. This study aims to examine the integration of CT into mathematics education, its classroom practice, and the opinions of students and pre-service teachers towards the unplugged CS activity. For this purpose, we developed an unplugged CS activity, which includes CT components, computer science and mathematics education achievements. The unplugged CS activity is a problem of transmitting data most efficiently and accurately between transmitter and receiver. The researchers trained twelve pre-service mathematics teachers to implement this activity in realclassroom environments. The pre-service teachers implemented the unplugged CS activity with 80 students in four classes. CT components were considered in the activity design. The students performed data collection, data analysis, decomposition, pattern recognition, algorithm design, and testing and debugging, which are components of CT, during the activity. Middle school students stated that they had enjoyed the activity and that this activity made the mathematics learning process satisfying. They established a relationship between the activity and mathematics education, besides, they needed help connecting it with CS concepts. Pre-service teachers stated that they had challenges in classroom management during the activity. Classroom management should be a part of teacher education in designing and implementing CT-integrated lessons. Courses containing classroom management, CS activity design and implementation should be developed for pre-service teachers based on theory and practice within their discipline.

As we move further into the digital age, the acquisition of digital literacy (DL) and computational thinking (CT) skills is emerging internationally as an essential goal for students in contemporary school curricula. As the world becomes more uncertain and volatile due to impacts of artificial intelligence (AI), international unrest, climate change, global economic instability and unforeseen catastrophes such as the Coronavirus (COVID-19) pandemic, it is necessary to review, revise and refine school education curricula and policies. The issue of what is essential for students to learn, and how they learn it, is of growing importance to international organisations such as the Organisation for Economic Co-operation and Development (OECD) and the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and is emerging as a significant driver for educational reform across the globe. The growing prominence of CT and DL skills across many industry sectors has prompted recent changes to international assessment frameworks such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). This paper will briefly discuss specific examples of alternative approaches to addressing CT in national curricula for the compulsory years of schooling and explain how CT has been adopted as a driver for mathematics curriculum change in Australia.

In our investigation of university students' knowledge about real numbers in relation to computer algebra systems (CAS) and how it could be developed in view of their future activity as teachers, we used a computer algorithm as a case to explore the relationship between CAS and the knowledge of real numbers as decimal representations. Our work was carried out in the context of a course for university students who aim to become mathematics teachers in high schools. The main data consists of students' written responses to an assignment of the course and interviews to clarify students' perspectives in relation to the responses. The analysis of students' work is based on the anthropological theory of the didactic (ATD). Our results indicate that simple CAS-routines have a potential to help university students (future teachers) to apply their university knowledge on certain problems related to the decimal representation of real number which are typically encountered but not well explained in high school.

Emotions play a crucial role in knowledge acquisition and can significantly impact motivation when studying a new field. Unfortunately, young people, especially girls, are often not drawn to Computer Science. To address this issue, we conducted an analysis of emotions among 8-9-year-old and 12-13-year-old students engaged in Computational Thinking activities, considering educational level, gender, and type of intervention. Our study sought to understand the lack of interest by examining the emotions present in primary and secondary school students. Hour-long in-person classes were conducted, focusing on Computational Thinking activities. We used the Developmental Channels Questionnaire, which includes 13 emotions rated on a Likert scale from 0 to 10, to measure emotions. The results showed that the predominant emotions were mostly positive and ambiguous, with low intensity negative emotions, particularly in primary education. Gender differences were observed only in secondary education, while in primary education, the differences were not significant. Girls demonstrated an emotional evolution when engaging in these activities, unlike boys. These findings provide valuable quantitative insights for primary and secondary school teachers. Understanding the emotions experienced can help guide effective teaching approaches. By addressing emotional factors, educators can enhance students' interest in computer science, thus fostering a more inclusive and engaging learning environment.

The purpose of the current study was to explore the effect of modality (constructionist mBlock, Scratch, and Python interventions) on six-grade students' computational thinking, programming attitude, and achievement. The pre-test and post-test quasi-experimental design was used to explore the research questions. The study group consisted of 105 six grade students from three different classes. A constructionist learning environment was formed for Scratch, mBlock, and Python groups. All groups were given 8 week-instruction. Instruction included two forty-minute sessions each week. The data were collected through the programming achievement test, computational thinking test, and computer programming attitude scale. The results of the study showed that mBlock group outperformed the Scratch and Python groups with respect to computer programming attitude. Students who attended mBlock and Scratch groups had higher levels of programming achievement than those of the students who attended the Python group. No significant differences with respect to computational thinking were observed between the groups. This study has implications for educators who are teaching computational thinking and programming. Further research was recommended to explore the effect of modality.

This paper reports on the findings of a Design-Based Research (DBR) study that investigated the transformative learning of six high school computer science teachers after they participated in a professional development (PD) training with a focus on Culturally Responsive Computing (CRC). Findings from the statistical analysis of pre-and post-surveys reveal ways in which teachers' understanding and enactment of CRC in their classrooms led to a reporting of increased student engagement, a deeper understanding of diverse learning needs, and improved access to cultural resources to specifically meet girls' needs. Findings from interviews and focus groups further reveal that after engaging in the PD, teachers qualitatively adapted their classroom strategies in order to uplift the cultural practices and gender identities of historically marginalized students. This study has implications for how teachers' professional development is designed and how they are guided to enact culturally responsive computing in ways that help recruit and retain racial and ethnic minority girls in CS courses.