Background and Context As automation and digitalization transform industries globally, educational systems must cultivate students with competencies in Robotics, Artificial Intelligence, and Coding (RAC) to drive technological innovation. This study focuses on talented secondary students as the unit of analysis, examining how integrated learning approaches can effectively develop RAC competencies within Thailand's educational ecosystem through industry-academic partnerships.Objective This study examined the effectiveness of four pedagogical models: Work-Integrated Learning (WIL), Project-Based Learning (PBL), Blended Learning (BLN), and Accelerated Learning (ACL). It also investigated how intrapersonal and environmental factors influence these models and identified the main elements supporting students' perceived RAC competency development.Method A sequential explanatory mixed-methods design was applied with 3,359 applicants, from which 86 high-achieving students were selected through a multi-tiered process. Quantitative analyses using repeated measures ANOVA and multiple regression assessed factor influences, while qualitative data from interviews, reflective journals, and projects were analyzed thematically.Findings The results demonstrated that WIL was the most effective approach, followed by PBL, BLN, and ACL. Intrapersonal factors exerted stronger effects on WIL, PBL, and BLN, whereas environmental factors were more influential for ACL. Five drivers of students' perceived success in RAC learning emerged: skill acquisition, hands-on learning, industry exposure, confidence and motivation, and collaboration.Implications The study highlights the importance of prioritizing WIL and PBL in RAC education. For the CS Education community, the findings provide insights into how pedagogical approaches interact with learner characteristics and contexts, informing the design of effective frameworks for technology talent development.
Background and Context: Digital technologies have become part of our lives. Meanwhile, computer programming, the activity that mediates the communication between humans and technology, has expanded to fields and practitioners beyond Computer Science. Programmers who give instructions to digital artifacts should have proper awareness of the artifacts' states and environments. This often requires them to change their point of view in order to better interact with those systems. Notably, this skill resembles a fundamental human ability that can be learned and developed over time: empathy. Objective: In this study, we probed potential connections between computer programming activities (object-oriented programming, educational coding games, educational robotics) and programmers' level of empathy, to gain further insights into human-technology interactions. Method: Through a quantitative approach, an online survey was implemented and data were collected from 104 adults in Greece with prior experience in computer programming. The majority of the participants work in education or education-related fields (58%). Findings: According to the results, statistically valid relationships were observed between ease of programming and empathy, either directly with it or with at least one of its components (cognitive empathy, emotional empathy and social skills). Implications: The indicated correlations between computer programming and empathy could represent a paradigm shift in the field of Education and lead to a deeper understanding of Computer Science Education and Didactics.
Background and contextIntegrating CT is vital for students to become efficient and creative problem solvers in an increasingly computational world. However, while CT is central to CS education, there is a gap in the literature on how CT emerges and develops in early childhood with limited research on how CT is taught in early childhood classroomsObjectiveThe purpose of this study was to uncover what early childhood teachers learned about CT and how they integrated CT in their classrooms after participating in a yearlong professional development program.MethodUsing a qualitative methodological approach and constant comparative analysis we collected and analyzed interviews, classroom observations, teaching videos and field notes from 12 early childhood teachers who participated in a yearlong professional learning project.FindingsTeachers reported learning that CT already existed in their classrooms and that teaching CT is a collaborative and creative process. Teachers' generative learning and implementation of CT was mediated by noticing and naming CT, becoming aware of students' engagement and growing CT abilities, reflections on their identities as learners and teachers, and their ability to integrate CT with English language arts and reading curricula.ImplicationsThis study emphasized teachers' autonomy and agency in the design, implementation, and integration of CT activities that aligned with their students' developmental needs and curricular demands.
Mutation testing (MT) is a powerful technique that evaluates test suite quality by introducing intentional faults (mutations) into code. In junior-level Data Structures and Algorithms (DSA) courses, students are tasked with developing programming projects with multi-week life cycles where MT could improve their test suites and coding skills. However, teaching students to effectively use MT is challenging. This study introduces a modified version of MT feedback combined with training content as an alternative to traditional code coverage for improving student test suites. We conducted a comparative analysis across three semesters that used different testing approaches: code coverage, MT with standard feedback from a widely used MT implementation (PIT), and MT with feedback modified and training to better support student understanding. We support our findings using 38 semi-structured interviews and quantitative analysis of IDE activity data and students' code submissions. Initial implementation of standard PIT MT with a reduced set of mutation operators showed mixed results, as the stock feedback failed to meet student needs. We developed an improved approach incorporating additional documentation and training with practical examples, client-side support for MT, and improved feedback messages. The revised feedback and training was associated with positive results in student understanding and application of MT principles. This research provides a framework for effectively integrating MT in programming courses, contributing to improved student test suite development, better student code, and better project outcomes. The findings offer practical guidelines for instructors teaching testing in CS courses, bridging the gap between industry tools and educational needs.
Background and Context: Informatics Education research has often called for including the coverage of debugging skills in education, whose acquisition also fosters the learning of programming. Programming and debugging alike are complex tasks, whose mastering requires operating on multiple interconnected parts simultaneously. Cognitive Load Theory suggests that reducing the complexity of how new information is presented improves educational outcomes. Complex concepts may be better understood when encountered in isolated contexts first, before being combined into complex scenarios. Focusing on isolated elements is highlighted as a research gap by a recent review of the literature on programming education. Objective: To fill this research gap by designing a programming-oriented intervention focused on isolated elements. The intervention is based on drill-type exercises, delivered through an adaptive system, which focus on debugging and further scaffold an established educational approach based on Code.org, in the intent of reducing its cognitive load. Method: A pre-test/post-test study with 89 fourth-grade learners, comparing the impact on programming skills of our experimental intervention against approaches based on Code.org and standard STEM activities. Findings: Our results show a significant difference in post-test performance across the experimental conditions, with the experimental adaptive drill-type intervention leading to better overall performance. Implications: This work showcases the potential of our approach as an effective educational strategy. We also highlight the benefits that focus on isolated elements and adaptive drill-type exercises may have even on environments specifically designed for novices, such as Code.org.
Background and Context: Integrating computational thinking (CT) into K-12 education is hindered by insufficient teacher training, gender biases in STEM, and challenges in evaluating professional development (PD) programs due to limited assessment tools. Most research overlooks affective and contextual variables, focusing only on CT skills. Objective: The study aims to understand the factors that enable an effective integration of CT teaching practices and gender affirmative actions, after teachers participate in a PD program. Method: We used a sequential explanatory mixed methods design to examine a nationwide PD program for middle and high school teachers in a middle-income country. Pretest-posttest surveys (n = 3790) and classroom observations (n = 383) assessed teachers' CT knowledge, technological self-efficacy, practices, and gender bias, while three focus groups involving 17 teachers explored the mechanisms influencing these outcomes. Findings: The PD program significantly improved teachers' content knowledge in CT, technological self-efficacy, and reduced gender bias in STEM. However, classroom observations revealed incomplete implementation of CT practices and gender equity strategies. Qualitative findings suggest key mechanisms shaping teacher implementation practices: teachers' cognitive shifts toward recognizing CT's relevance across disciplinary domains and school subjects; perceived capacity in managing technological tools and technological complexity; the importance of institutional support; and, reflective processes triggered by observing increased engagement among female students. Implications: Teachers' cognitive shifts, self-efficacy, and institutional support are pivotal for effective CT integration into school practices. Our findings underscore the necessity of PD programs to address these aspects, fostering the intentional application of CT practices and gender equity strategies.
Background and Context: Despite increasing momentum to integrate computer science (CS) into K-12 education, elementary teachers often report low confidence in teaching CS due to limited experience, resources, and pedagogical knowledge. Teaching self-efficacy, which refers to the belief in one's ability to teach effectively, plays a critical role in teachers' willingness to adopt CS instruction. There remains limited research on how professional development (PD) programs improve elementary teachers' CS teaching self-efficacy, particularly regarding the specific sources that contribute to their confidence. Objective: This study aimed to investigate the impact of an integrated CS PD program on in-service elementary teachers' CS teaching self-efficacy. It also sought to identify which aspects of the PD and learning experiences served as key sources of efficacy information and how teachers perceived their relative importance. Method: Using a mixed-methods approach, we collected data from 33 in-service elementary teachers. Quantitative data were gathered through pre-post administration of the CSTEBI, analyzed using paired-samples t-tests. Qualitative data from teacher reflections were coded by two researchers to identify themes related to sources of teaching self-efficacy. Findings: The PD significantly improved teachers' CS teaching self-efficacy. Qualitative analysis identified simulated modeling as the most influential source of efficacy, followed by cognitive pedagogical mastery, cognitive content mastery, cognitive self-modeling, and effective actual modeling. We also identified the integration of disciplines as a novel and distinct source of efficacy. Implications: Our findings suggest that effective CS PD should prioritize experiential learning approaches where teachers engage as learners while simultaneously developing both content knowledge and pedagogical content knowledge, particularly through cross-disciplinary applications.
Background and ContextRecursion represents a powerful programming technique, important for computer science education, yet students consistently struggle with recursive concepts. Despite more than four decades of research, little attention has been paid to how different national education systems structure recursion instruction through actual teaching materials.ObjectiveThis study examines how four countries (Croatia, the United Kingdom, Singapore and Indonesia) approach recursion instruction in secondary education (ages 13-18) through analysis of teaching materials, identifying common elements and significant variations.MethodWe conducted systematic qualitative document analysis of teaching materials at equivalent granularity: curriculum-aligned textbooks, official curriculum guides and government-published instructional resources. This ensures valid comparison by examining teaching materials rather than comparing curriculum specifications to instructional resources. Documents were coded according to five dimensions: placement, pedagogy, examples, difficulty strategies and assessment methods.FindingsAll four countries introduce recursion at upper secondary level (ages 16-18) using factorial and Fibonacci as primary examples. Significant variations exist in pedagogical frameworks and efficiency instruction. Croatia provides early conceptual introduction at age 13-14 and explicit efficiency instruction including memoisation. The United Kingdom emphasises recursion-iteration comparison through trace tables. Singapore mandates explicit stack understanding. Indonesia employs formal mathematical notation. The near-universal omission of explicit efficiency instruction represents a significant gap.ImplicationsEffective recursion pedagogy benefits from multi-modal instruction combining mathematical formalism, visual representation, mechanistic understanding and performance considerations. These elements are rarely found together. Examining teaching materials rather than curriculum specifications alone is essential for understanding pedagogical approaches.
Background and ContextAs more K-12 teachers are required to teach computing, there is a need to understand how it is enacted in classrooms. In addition, many non-specialists are teaching computing for the first time.ObjectiveWe set out to better understand how computing is currently being taught across five jurisdictions in the UK and Ireland. The aim of the study was to understand CS teaching practices, including the use of certain approaches and resources in the computing classroom.MethodWe analysed survey data from 475 primary and secondary teachers across a number of relevant categories: teachers' use of CS resources and tools, CS-specific teaching and assessment approaches, and self-reported motivations for choosing specific resources. We also analysed qualitative responses using Ertmer's Ertmer's (2012) notion of first- and second-order barriers to identify internal/external challenges facing computing teachers.FindingsData revealed "signature" CS teaching practices common across teaching levels including research-informed practices, unplugged activities, and teacher modelling/demonstration. Teachers also predominantly used Scratch and Python when teaching with considerable overlap across teaching levels. We also found significant differences in computing resources available to teachers, such as primary teachers having fewer dedicated computer rooms or individual devices for students than secondary teachers. Conversely, secondary teachers were more constrained by the need to prepare students for exams and assessments.ImplicationsThese results have implications for how computing is enacted in classrooms and how teachers can be better supported.
Background and ContextComputational thinking and early programming skills are essential for digital literacy. This study investigates the code literacy (reading and explaining code) of first-grade students (ages 6-7) in a mandatory Early Childhood Robotics (ECR) program. The research focuses on students' confidence and actual ability to interpret sequences, conditions, and loops, with specific attention to gender-related differences.ObjectiveThe study aims to investigate the relationship between students' self-perceived comfort with their own and others' code and their actual performance. It also explores whether participation in the ECR program is associated with early differences in coding comprehension between girls and boys.MethodA mixed-methods approach was used to collect data from 89 first graders. Quantitative measures included surveys assessing self-perception and expert-based evaluations of coding performance. Qualitative data were gathered through interviews to capture students' attitudes and engagement. Objective assessments examined students' ability to read and explain block-based code involving sequences, conditions, and loops.FindingsResults revealed a significant gap between self-perception and actual ability, particularly for more complex structures. While two-thirds of students could handle simple code, only 42% understood loop structures. Girls outperformed boys in all coding types. Despite differences in performance, 85% of students expressed a strong willingness to participate again, suggesting high levels of interest and positive attitudes toward the program.ImplicationsThese findings highlight the educational value of introducing structured robotics and programming programs in early primary education. Mandatory ECR programs can promote widespread engagement and may support balanced early participation in computational learning.
Background and ContextCollaborative programming offers advantages for fostering novice students' computational thinking (CT) skills. Yet, there is a limited understanding of CT within a collaborative group and a lack of descriptive knowledge regarding CT in authentic learning processes through process-based approaches.ObjectiveIn this study, we applied the theoretical framework of collaborative problem-solving (CPS) to understand how CT emerged in group discourse. We investigated the association between CT skills and the social dimension of CPS skills, and its relation to the task performance.MethodThe context of the study was a robotics programming workshop organised for 15-16-year-old students. The data included videos and log files which students' activities were recorded. Through epistemic network analysis (ENA), we identified the co-occurrences of CT and CPS skills and compared the differences in discourse patterns between high- and low-performance groups.FindingsWe found that the high-performance groups discussed algorithm design and evaluation, while the discourse of the low-performance groups lacked examples of CT skills. Moreover, CT in two-way communication for building shared understanding was associated with the higher task performance.ImplicationsBased on the results, we recommend that teachers design tasks and facilitate social interaction to encourage students to verbalise and practice CT skills within collaborative groups. For future studies, we suggest considering students' background, analysing debugging processes where students build on outcomes of previous tasks to perform new ones, and examining how learning environments, such as robots, programming interfaces, and task design, influence the externalisation of CT skills in group discourse.
Background and Context: U.S. demand for computer science (CS) talent remains high, yet participation in CS education and jobs is uneven by gender and race/ethnicity. While traditional CS pathways dominate research on persistence, alternative pathways such as coding bootcamps and apprenticeship programs may address workforce demand and equity gaps. Yet, little is known about who persists in these pathways. Objectives: This study investigates persistence in LC101, a coding training and apprenticeship program offered by LaunchCode. LC101 is designed to expand access to tech careers for women and minoritized groups underrepresented in STEM. We examine how individual characteristics, prior experience, program supports, and participant motivations relate to persistence across course completion and apprenticeship placement. Method: We use a mixed-methods design with survey data from 524 LC101 participants, generalized ordered logistic regression, and 22 in-depth interviews. Quantitative analyses identify predictors of persistence, and qualitative interviews examine experiences, barriers, and motivations. Findings: Entry test performance and prior coding experience are positively associated with persistence across both coursework and apprenticeship stages, while educational attainment is not. Black participants exhibit higher odds of apprenticeship completion, highlighting the equity potential of alternative pathways. Mentorship is linked to higher odds of course completion. Time constraints and course difficulty are common barriers. Course completers more often cite intrinsic interest in coding; non-completers emphasize extrinsic goals. Implications: Results clarify who persists in alternative CS pathways. Strengthening mentorship, addressing time-related barriers, and cultivating intrinsic interests may improve persistence and success in alternative CS preparation pathways. Structured workforce connection components, like LC101's apprenticeship, may support equitable pathways into CS careers beyond traditional education systems.
Background and Context: Students in Computer Science (CS) tend to have high dropout rates. The variables related to dropout have been studied, but no consensus has been found regarding the most important influence factors including university admission requirements that can be variable for different countries and universities. Objective: This empirical study examines the variables which predict whether CS students graduate within the nominal study period, and whether variables in models differ for female and male students. Method: Data of 390 CS students' personal characteristics, academic preparation before the university and academic achievement during the first year were included in this quantitative study as potential variables predicting completion of CS studies within the nominal study period. The data were analysed using binary logistic regression. Findings: Graduation within the nominal study period was influenced by factors such as student age, gender, higher secondary education graduation with honours, and the percentage of curriculum completion in the second semester. For both female and male students, higher secondary education graduation with honours and the percentage of curriculum completion in the second semester emerged as the key predictors of timely graduation. Implications: The CS admissions processes should reconsider placing greater weight on honours graduation. Second-semester academic progress should be used to trigger early-alert systems and offer proactive academic support. Interventions should be customized to offer flexible schedules and personalized advisory options for older students and provide additional monitoring for male students.
