Digital Group Intervention to Boost Resilience in Adolescents With Chronic Medical Conditions: Findings From the RESICO Randomized Controlled Trial.

Virtual Reality (VR) presents opportunities for Human-Centered Computing (HCC) education by facilitating immersive and experiential learning that may help students engage with complex user needs and real-world design contexts. Despite long-term interest in VR in HCC education, its integration into regular teaching practices across higher education remains limited, with a notable gap in understanding educators’ perspectives on the practical considerations that influence the integration of VR into their teaching. We conducted semi-structured interviews with 15 HCC educators following a brief VR simulation using a head-mounted display and a questionnaire on demographics and prior VR experience. Using thematic analysis, our qualitative findings reveal 21 practical considerations relating to three key themes: a) technology access and provisioning, b) pedagogical considerations and c) VR design and sourcing considerations. Educators identified diverse practical considerations for effective VR integration, including equipment and tech support, health and safety, cost, instructional design, training needs, and VR content development. Our research findings offer actionable implications for computing educators and administrators to inform the planning and integration of VR-supported learning experiences, and VR designers to design pedagogically-suitable VR experiences. Our study also identified seven future research directions including the need to examine disciplinary-specific VR needs, develop decision-making frameworks for content sourcing, explore pedagogical facilitation strategies, and investigate real-world implementation dynamics in authentic classroom settings.

This study addresses the challenges of traditional vocational education systems, which often fail to meet learners’ diverse needs in dynamic and emotionally complex environments. To bridge this gap, we propose an intelligent learning system that integrates multimodal affective computing with deep Q-network algorithms for personalized learning path optimization. By leveraging multimodal data fusion, the system enhances the accuracy of emotion recognition and dynamically adjusts learning paths in real time. Experimental results show a 27% improvement in learning efficiency, an 86% accuracy rate in personalized recommendations, and an 8% increase in student performance compared to conventional methods. Furthermore, a privacy-preserving architecture utilizing federated learning ensures secure large-scale applications. This study highlights the transformative potential of integrating affective computing and reinforcement learning in vocational education and sets the stage for broader applications in personalized learning systems.

Digital Teaching-Learning Platforms and Computer Education in Traditional Sanskrit Education in Uttarakhand A Survey

As information systems (IS), including data-driven solutions, become increasingly prevalent across sectors, the need for ethical data-driven decision-making (DDDM) is gaining recognition. This scoping literature review explores how agency and ethics are addressed in scientific discussions on DDDM. It examines the ethical terminology used, its definitions, and the forms of agency identified in 79 peer-reviewed articles from disciplines such as computer science, education, and engineering. Agency is central to ethical judgment, decision-making, and action. The review reveals a wide range of ethics-related concepts, many of which are loosely defined, incoherent, and problem-oriented. Drawing on a sociomaterial perspective, the study identifies three forms of agency relevant to DDDM: human, technological, and distributed. These forms reflect the complex interplay between individuals, technologies, and institutional contexts. The review concludes that ethics and agency must be considered together to clarify responsibility at different levels of decision-making. Although ethical concerns are increasingly discussed, the debate remains fragmented. In the broader context of IS and the rapid development of generative AI, cross-disciplinary research is essential to address ethical challenges comprehensively. This study contributes both practical insights and theoretical understanding to the evolving discourse on ethical DDDM.

Artificial intelligence (AI) has recently been recognized as playing an increasingly strategic role in computational physics education, notably with respect to the current strong focus on developing students’ ability to comprehend and apply computational modeling, data simulation, and problem solving within modern physics curriculums. This paper presents a state- of-the-art narrative review on the integration of AI in computational physics and related educational contexts, considering research from 2019 to date. The review discusses prominent AI technologies, pedagogical integration strategies and their documented effects on computational thinking, numerical modeling and student engagement. The synthesis also suggests that AI enhanced learning environments can support personalized feedback, adaptive assessment and flexible learning pathways in order to promote students‟ engagement and computational reasoning of physics learning. Meanwhile, the literature also shows ongoing challenges: ethical and equity issues; algorithmic bias; lack of instructor preparation for OAI pedagogies; and shortage of longitudinal empirical evidence in computational physics education. Through summarizing prevailing research trends and framing a set of important constraints, this review is intended to serve as a conceptual blueprint for an expanded research agenda towards the systematic, responsible, and pedagogically informed inclusion of AI in computational physics education

This study presents the development of immersive applications using augmented reality (AR) technologies to enhance the teaching and learning process of computer- aided design (CAD) and mechanism simulation in the engineering education program. The research focuses on increasing student engagement, improving spatial understanding, and providing blueprint manufacturing interpretation. AR tools were designed and implemented in university-level courses, with an emphasis on interactivity, technical precision, and real-time feedback. Results indicate significant improvements in spatial reasoning, blueprints interpretation, and assembly’s manipulation, confirming the value of immersive learning environments in science, technology, engineering and mathematics (STEM) education.

This review examines the implementation of computer science (CS) unplugged activities in K–12 education, focusing on their impact on educational outcomes, student motivation, and teacher perceptions. A total of 32 relevant studies published between 2009 and 2025 were analysed, including journal articles, conference reports, and book chapters. The findings suggest that CS unplugged generally improves computational thinking (CT) skills, particularly among younger learners and those who have not yet experienced programming concepts. Students often report greater engagement and less anxiety about coding, while teachers appreciate the cost-effective and flexible nature of unplugged lessons. However, inconsistencies are evident in the long-term retention of concepts and the degree of transfer to more advanced or “plug-in” programming tasks. The effective integration of CS unplugged activities often depends on sound teacher training and alignment with broader curriculum objectives. These findings highlight the potential of CS unplugged to improve early computer education, but also highlight the need for longitudinal studies, standardised assessments and systematic transitions from unplugged to digital practice. Given the substantial heterogeneity of study designs and outcomes—and, critically, the inconsistent operationalisation of CT alongside non-standardised testing metrics across studies—we did not aggregate effect sizes; consequently, a formal meta-analysis was not methodologically feasible.

The intersection of computational linguistics, affective computing and education innovation is the Lyric analysis through Natural Language Processing (NLP). Using deep learning networks CNNs to identify rhythmic patterns, Transformers to map the song lyrics to contextual emotions, and GANs to add metaphors to the lyrics the framework converts the lyrics of the songs into computational-affective products. The research combines quantitative modeling and qualitative pedagogy, making it possible to visualize emotions, track Valence–Arousal-Dominance (VAD) and detect metaphors, which can be used in support of language learning and emotional literacy. A multilingual collection of curated lyric corpus of pop, folk, and educative songs was evaluated through the use of BERT-based sentiment models and topic clustering. Empirical findings indicate that the F1-score of emotion classification is 0.87 and that there are significant pedagogical gains such as 2834% enhancement in student comprehension, empathy and engagement. There was high adoption (87%) and improved interpretive dialogue and inclusivity of teachers who used AI-assisted dashboards. These were supported by donut chart representations of emotional distribution, engagement and teacher satisfaction. The framework also extends linguistic and cultural knowledge as well as reinvents AI as a collaborative co-creator in education and enables reflective, empathetic and data-informed learning experiences. Future directions Multimodal lyric analysis (text and audio) Multimodal adaptive learning systems based on cognitive profiles Culturally balanced corpora that maintain regional diversity Future directions Multimodal lyric analysis (text and audio) Multimodal adaptive learning systems based on cognitive profiles Culturally balanced corpora that maintain regional diversity Lyric analysis using NLP therefore creates a platform of emotionally intelligent, culturally inclusive and AI augmented learning.

This study explores the factors influencing Saudi primary school students’ intention to use tablet personal computers (TPCs) in education, with a focus on gender differences. Data were collected from 279 students across 20 public schools and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and multi-group analysis (MGA). The results showed that performance expectancy significantly predicts behavioral intention in both male and female students, indicating a shared belief in the educational benefits of TPCs. Social influence had a significant negative effect only among females, suggesting possible societal or familial hesitation. Hedonic motivation had a stronger impact on females than males, indicating gender differences in how enjoyable use drives intention. In both groups, behavioral intention significantly predicted actual TPC usage. Other factors, such as effort expectancy, facilitating conditions, and habit, showed limited or no significant effects. These findings provide new insights into gendered patterns of technology adoption among young learners in a Middle Eastern context and highlight the importance of designing gender-responsive digital learning strategies.

This study investigated the impact of Information and Communication Technology (ICT) on students’ performance in Basic Science in secondary schools in Nasarawa State, Nigeria. Classroom Mini-Experimental research design was used to examine how the integration of ICT tools could impact on students’ performance in Basic Science. A total of 780 (male = 414; female = 366) Upper Basic II students sampled across secondary schools were used in the study. The instrument used for data collection was Information and Communication Technology Performance Test (ICTPT). Experts in the fields of science and computer education verified the content of the ICTPT. ICTPT's reliability coefficient was found to be 0.78 after the internal consistency reliability coefficient was calculated using Kuder-Richardson (K-R21). Descriptive statistics of mean and standard deviation were used to answer the research questions. Inferential statistics of ANCOVA was used to test the research hypotheses. The findings revealed that ICT has impact on students learning for it improved students’ performance in Basic Science. Findings also established gender sensitivity with similar performance of the students when ICT resources were used. The researcher recommends that regular training should be given to teachers on ICT appliances to guarantee maximum usage of the ICT tools available in schools.

This study investigates the integration of Cognitive Discourse Functions (CDFs) in English-Medium Instruction (EMI) classrooms across various disciplines in Turkish higher education, with a particular focus on teacher education programs. CDFs provide a systematic framework for analysing how language facilitates cognitive processes such as describing, explaining, evaluating, and categorising in disciplinary contexts. The data comprise 472 minutes of lesson recordings from five courses across three fields: Computer Education and Educational Technology, English Language Teaching, and Mathematics and Science Education. Lessons were analysed using CDF coding to examine field-specific variations in language use and their implications for disciplinary literacies. The study provides an initial exploration of the interconnectedness of CDFs with disciplinary literacies by delineating their role in building foundational knowledge, facilitating reasoning, and fostering critical thinking. These findings indicate that explicit integration of CDFs into EMI pedagogy has potential for addressing language and content challenges faced by students in EMI settings. The article contributes to the growing body of research on EMI by providing insights into the interplay between language and disciplinary knowledge construction.

Objectives : Interest plays a central role in learning by shaping what, how, when, where, and why learning occurs. In data science education, where complex concepts, lived experiences, and practical skills intersect, capturing and cultivating student interest can be especially generative. This work explores approaches for designing and evaluating interest-driven data science instructional materials. Methods : This article presents a participatory design study that informs the development of a data science curriculum for high school students. To assess how well learner interests and values are reflected in the resulting curriculum, we used the Integrated Interest Development for Computing Education framework [ 56 ], which provides a concrete operationalization of interest that captures its multifaceted nature. Findings : The article demonstrates and discusses how participatory design can be used to identify students’ interests and how those interests can be used to inform the creation of an interest-driven curriculum. Further, it highlights how different types of participatory design activities yield insight into different facets of students’ interests and identities, which can then be used to design learning experiences. This work shows how the resulting Participatory Design (PD) reflects and harnesses the multifaceted nature of student interest and how it can be leveraged to design learning experiences that connect with learners’ lived digital experiences. Conclusions : Participatory design is an effective student-centered approach for tailoring computational learning experiences aligned to students’ voices, values, and interests. The use of various participatory design activities revealed different facets of students’ interests that informed the creation of an interest-driven curriculum that could not have been created without the input of the students themselves.

Study program recommendations are pivotal for the accreditation of study programs. In Germany, one of the most important recommendations used in the accreditation of computing curricula is published by the Gesellschaft der Informatik e.V. (GI), the largest German professional society of computer science. This work investigates the difference between reality at institutions and the GI recommendations. Systematically gathered syllabi of Northern Germany computing curricula have been coded both deductively and inductively according to Mayring’s qualitative content analysis (QCA) method. The descriptions of 197 mandatory courses belonging to 13 program descriptions were analyzed. In addition to the 17 subject areas already described in the GI recommendations, four new subject areas have been identified that can be considered widespread. On the other hand, four subject areas from the GI recommendations could not be found as part of the mandatory curriculum. The study identified a notable divergence between current study programs and the current GI recommendations. However, as only mandatory syllabi were investigated, this study contains some blind spots with regard to electives and study specializations as well as with regard to a regional selection bias. Secondary findings concern the handling of learning outcomes in German syllabus descriptions and the GI recommendations themselves.

Phone Shaped Computers and Phubbing in Education: Classroom Phubbing and General Phubbing Among Prospective Teachers

In the past five decades, a significant amount of research has focused on strategies for vocabulary learning, given its crucial role in language acquisition. This study utilized CiteSpace to systematically review 340 studies on strategies for vocabulary learning from 1971 (when they were first indexed) to 2024, in the Social Sciences Citation Index and the Arts & Humanities Citation Index collection. The review focused on three aspects: development stages, key scholarly foundations, and main research streams. The findings reveal that the field of strategies for vocabulary learning research has undergone four distinct phases of development: the embryonic stage (1971-1990), the growth stage (1991-2005), the expansion stage (2006-2015), and the rapid development stage (2016-2024). Notably, there has been a substantial increase in annual publications, particularly since 2006, which can be attributed to the prolific publication of scholarly monographs and the impact of modern technology. The primary focus of strategies for vocabulary learning research lies in motivational belief, mobile technology, and digital game-based learning, with meta-analysis serving as an important research methodology. Furthermore, the findings highlight the interdisciplinary nature of vocabulary learning strategies, with contributions from computer science, psychology, and education.

Computational thinking (CT) has been recognized globally as a fundamental 21st-century competency essential for elementary students. However, research analyzing teacher readiness and implementation experiences in the Indonesian context remains limited. This research aims to analyze how teachers with different expertise levels understand, implement, and perceive computational thinking instruction at grades 3-4 in Indonesian elementary schools. This study adopted a qualitative phenomenological content analysis approach with 12 semi-structured interview questions administered to three elementary school teachers with varying expertise levels (expert, intermediate, novice). Teachers were selected using purposive sampling from elementary schools in Bandung. The interview data were analyzed systematically using ATLAS.ti 24 software, generating 36 individual codes across five major themes: conceptual understanding, teaching strategies, implementation challenges, perception of effectiveness, and professional development needs. The findings reveal significant expertise stratification among teachers, with distinct patterns of CT understanding and implementation approaches. All three teachers demonstrated genuine commitment to student learning and acknowledged that professional development training alone is insufficient without institutional support. Expert teachers showed comprehensive understanding, intermediate teachers demonstrated practical adaptation, and novice teachers showed foundational approaches with positive agency. This research provides evidence-based insights into teacher readiness essential for formulating systemic recommendations. Successful CT implementation requires differentiated professional development, structured mentoring systems, institutional support mechanisms, and long-term policy commitments.

The article outlines the content, objectives, tasks, and organizational features of the educational computer internship within the framework of the educational and professional program "Computer Mathematics" at Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University. Particular attention is paid to the development of practical skills in working with the \LaTeX{} typesetting system among higher education applicants, which is essential for the high-quality preparation of term papers, theses, research articles, and presentations. The internship involves an individual project consisting of theoretical and practical components. In the theoretical part, students study the interfaces of \LaTeX{} editors, the syntax of basic commands, and the features of formatting texts, tables, formulas, graphics, and creating presentations. The practical part includes preparing the text of a term paper and a corresponding presentation for its defense. The educational computer internship is organically integrated into the educational process and synchronized with the completion of a term paper in functional analysis, enabling an effective combination of students’ theoretical knowledge and practical skills. The competencies acquired as a result of the internship contribute to the enhancement of the academic culture of future professionals and their integration into the scientific community.

Abstract— The National Education Policy (NEP) 2020 establishes a transformative framework for Indian education, emphasizing flexibility, interdisciplinarity, and holistic learning. However, students often face difficulty linking multidisciplinary subject selections to meaningful career outcomes. This paper presents a computational model that maps multidisciplinary subject combinations to potential career trajectories, aligning with NEP 2020’s learner-centric vision. The proposed framework integrates artificial intelligence (AI) reasoning and natural language processing (NLP) techniques, including TF- IDF and cosine similarity, to analyze student inputs—subjects, IQ, EQ, and aptitude—and recommend career domains consistent with NEP guidelines. The model is implemented using Fast API, MongoDB, and React, enabling real-time processing, subject mapping, and result visualization. Experimental evaluation demonstrates that computational reasoning can effectively operationalize NEP’s multidisciplinary philosophy, improving policy-aligned career guidance for secondary-level learners. Keywords— NEP 2020, multidisciplinary education, educational computing, AI reasoning, NLP similarity, decision- support systems, career guidance.

The needs of the IT industry, changes in the field of digital technologies, require updating the educational programs of the school computer science course and the corresponding dynamic adaptation of the content of the future teachers training. Analysis of recent research and publications has shown that one of the priority content lines of the school computer science course is the line “Security and Responsibility”. The task of the course “Information Protection”, included in the educational program “Information Science and Robotics” of the specialty “A4 Secondary Education A4.09 Informatics” at the V. Vynnychenko Central Ukrainian State University, defines competencies for future computer science teachers that are consistent with the expected learning outcomes of secondary education students and the current needs of IT companies. The article substantiates the content of the program of the educational discipline “Information Protection” for future teachers. The correspondence of the professional competencies of future computer science teachers and the learning outcomes of secondary school students to the content of the proposed course, which includes, in particular, such topics as “General concepts of information protection”, “Basics of cybersecurity”, “Cyberattacks”, “Methods of protecting data, devices and networks” and others, is presented. It is also noted that certain issues of data and information systems security are also considered in the course “Databases and information systems”. In the article it is concluded that the proposed content of the academic discipline “Information Protection” for the training of future computer science teachers takes into account modern regulatory requirements for school computer education, as well as the expectations of IT companies regarding the digital competencies of school graduates. Further research is planned to be directed towards the creation of a holistic methodological system for teaching the discipline “Information Protection” for students majoring in Secondary Education (Informatics).