Hasil untuk "Education (General)"

D. Hallahan, J. Kauffman

G. DeBoer

Robert Whyte, Manni Cheung, Katharine Childs et al.

Data literacy skills are fundamental in computer science education. However, understanding how data-driven systems work represents a paradigm shift from traditional rule-based programming. We conducted a systematic literature review of 84 studies to understand K-12 learners' engagement with data across disciplines and contexts. We propose the data paradigms framework that categorises learning activities along two dimensions: (i) logic (knowledge-based or data-driven systems), and (ii) explainability (transparent or opaque models). We further apply the notion of learning trajectories to visualize the pathways learners follow across these distinct paradigms. We detail four distinct trajectories as a provocation for researchers and educators to reflect on how the notion of data literacy varies depending on the learning context. We suggest these trajectories could be useful to those concerned with the design of data literacy learning environments within and beyond CS education.

Antoun Yaacoub, Sansiri Tarnpradab, Phattara Khumprom et al.

Artificial intelligence (AI) is rapidly transforming education, presenting unprecedented opportunities for personalized learning and streamlined content creation. However, realizing the full potential of AI in educational settings necessitates careful consideration of the quality, cognitive depth, and ethical implications of AI-generated materials. This paper synthesizes insights from four related studies to propose a comprehensive framework for enhancing AI-driven educational tools. We integrate cognitive assessment frameworks (Bloom's Taxonomy and SOLO Taxonomy), linguistic analysis of AI-generated feedback, and ethical design principles to guide the development of effective and responsible AI tools. We outline a structured three-phase approach encompassing cognitive alignment, linguistic feedback integration, and ethical safeguards. The practical application of this framework is demonstrated through its integration into OneClickQuiz, an AI-powered Moodle plugin for quiz generation. This work contributes a comprehensive and actionable guide for educators, researchers, and developers aiming to harness AI's potential while upholding pedagogical and ethical standards in educational content generation.

Wang Jiaqi, Lan Yi, Chen Xiang

This study investigates AI integration in architectural education through a teaching experiment in Zhejiang University's 2024-25 grade three undergraduate design studio. Adopting a dual-module framework (20-hour AI training + embedded ethics discussions), the course introduced deep learning models, LLMs, AIGC, LoRA, and ComfyUI while maintaining the original curriculum structure, supported by dedicated technical instructors. Findings demonstrate the effectiveness of phased guidance, balanced technical-ethical approaches, and institutional support. The model improved students' digital skills and strategic cognition while addressing AI ethics, providing a replicable approach combining technical and critical learning in design education.

Jarvis Haupt, Qin Lu, Yanning Shen et al.

Powerful artificial intelligence (AI) tools that have emerged in recent years -- including large language models, automated coding assistants, and advanced image and speech generation technologies -- are the result of monumental human achievements. These breakthroughs reflect mastery across multiple technical disciplines and the resolution of significant technological challenges. However, some of the most profound challenges may still lie ahead. These challenges are not purely technical but pertain to the fair and responsible use of AI in ways that genuinely improve the global human condition. This article explores one promising application aligned with that vision: the use of AI tools to facilitate and enhance education, with a specific focus on signal processing (SP). It presents two interrelated perspectives: identifying and addressing technical limitations, and applying AI tools in practice to improve educational experiences. Primers are provided on several core technical issues that arise when using AI in educational settings, including how to ensure fairness and inclusivity, handle hallucinated outputs, and achieve efficient use of resources. These and other considerations -- such as transparency, explainability, and trustworthiness -- are illustrated through the development of an immersive, structured, and reliable "smart textbook." The article serves as a resource for researchers and educators seeking to advance AI's role in engineering education.

Isabel Pedersen

The rapid integration of generative artificial intelligence (AI) into postsecondary education and many other sectors resulted in a global reckoning with this new technology. This paper contributes to the study of the multifaceted influence of generative AI, with a particular focus on OpenAI's ChatGPT within academic settings during the first six months after the release in three specific ways. First, it scrutinizes the rise of ChatGPT as a transformative event construed through a study of mainstream discourses exhibiting AI hype. Second, it discusses the perceived implications of generative AI for writing, teaching, and learning through the lens of critical discourse analysis and critical AI studies. Third, it encourages the necessity for best practices in the adoption of generative AI technologies in education.

Qiaolin Qin, Ronnie de Souza Santos, Rodrigo Spinola

Context. The rise of generative AI (GenAI) tools like ChatGPT and GitHub Copilot has transformed how software is learned and written. In software engineering (SE) education, these tools offer new opportunities for support, but also raise concerns about over-reliance, ethical use, and impacts on learning. Objective. This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences. Method. We conducted a survey with 130 undergraduate students from two universities. The survey combined structured Likert-scale items and open-ended questions to investigate five dimensions: usage context, perceived benefits, challenges, ethical and instructional perceptions. Results. Students most often use GenAI for incremental learning and advanced implementation, reporting benefits such as brainstorming support and confidence-building. At the same time, they face challenges including unclear rationales and difficulty adapting outputs. Students highlight ethical concerns around fairness and misconduct, and call for clearer instructional guidance. Conclusion. GenAI is reshaping SE education in nuanced ways. Our findings underscore the need for scaffolding, ethical policies, and adaptive instructional strategies to ensure that GenAI supports equitable and effective learning.

Allysson Allex Araújo, Marcos Kalinowski, Maria Teresa Baldassarre

In recent years, Software Engineering (SE) scholars and practitioners have emphasized the importance of integrating soft skills into SE education. However, teaching and learning soft skills are complex, as they cannot be acquired passively through raw knowledge acquisition. On the other hand, hackathons have attracted increasing attention due to their experiential, collaborative, and intensive nature, which certain tasks could be similar to real-world software development. This paper aims to discuss the idea of hackathons as an educational strategy for shaping SE students' soft skills in practice. Initially, we overview the existing literature on soft skills and hackathons in SE education. Then, we report preliminary empirical evidence from a seven-day hybrid hackathon involving 40 students. We assess how the hackathon experience promoted innovative and creative thinking, collaboration and teamwork, and knowledge application among participants through a structured questionnaire designed to evaluate students' self-awareness. Lastly, our findings and new directions are analyzed through the lens of Self-Determination Theory, which offers a psychological lens to understand human behavior. This paper contributes to academia by advocating the potential of hackathons in SE education and proposing concrete plans for future research within SDT. For industry, our discussion has implications around developing soft skills in future SE professionals, thereby enhancing their employability and readiness in the software market.

Rodrigo Tertulino

The application of data mining and artificial intelligence in education offers unprecedented potential for personalizing learning and early identification of at-risk students. However, the practical use of these techniques faces a significant barrier in privacy legislation, such as Brazil's General Data Protection Law (LGPD), which restricts the centralization of sensitive student data. To resolve this challenge, privacy-preserving computational approaches are required. The present study evaluates the feasibility and effectiveness of Federated Learning, specifically the FedProx algorithm, to predict student performance using microdata from the Brazilian Basic Education Assessment System (SAEB). A Deep Neural Network (DNN) model was trained in a federated manner, simulating a scenario with 50 schools, and its performance was rigorously benchmarked against a centralized eXtreme Gradient Boosting (XGBoost) model. The analysis, conducted on a universe of over two million student records, revealed that the centralized model achieved an accuracy of 63.96%. Remarkably, the federated model reached a peak accuracy of 61.23%, demonstrating a marginal performance loss in exchange for a robust privacy guarantee. The results indicate that Federated Learning is a viable and effective solution for building collaborative predictive models in the Brazilian educational context, in alignment with the requirements of the LGPD.

David García-Marín, Ricardo Roncero Palomar, Marina Santín et al.

El presente estudio pretende medir las percepciones y actitudes del estudiantado de Secundaria hacia el uso de la realidad virtual (RV) en materias científicas y tecnológicas, así como analizar los posibles sesgos de género en la valoración de este recurso. Este segundo objetivo se justifica en la escasez de trabajos que aúnan el uso de la RV para la formación STEM con la variable de género. Se llevó a cabo un estudio cuasi-experimental (n = 510) basado en la aplicación en el aula y utilización por parte del estudiantado de cuatro lecciones de asignaturas STEM en RV elaboradas ad hoc para esta investigación en tres centros de Secundaria españoles de diferentes entornos poblacionales y con distintos niveles de experiencia de uso de esta tecnología. Se utilizó para ello el test Instructional Material Motivational Survey (IMMS) validado en anteriores estudios. Los datos resultantes fueron analizados mediante estadística descriptiva e inferencial. Nuestros resultados evidencian que los aspectos mejor valorados de la RV son los relativos a la estructura y diseño de las lecciones, así como su capacidad para facilitar la atención en el contenido. Se observa un notable efecto de la variable de género. Las mujeres perciben de forma significativa una mayor dificultad en la usabilidad de las lecciones y afirman que la experiencia con RV les ayuda menos a mantener la atención. Manifiestan haber aprendido menos que sus compañeros varones y se sienten menos confiadas en su aprendizaje durante el uso de estas tecnologías inmersivas.

Bilu Gu, Yiming Lv, Jiyu Zhu et al.

BackgroundHumanistic care is a good glue for the doctor-patient relationship, and it is a general trend to improve the practice of humanistic care.MethodsA narrative research method was used to conduct semi-structured interviews with 18 master’s degree nursing students from China who were in the clinical rotation stage, and the data were content analyzed and explored from the perspective of the learners who were learning about humanistic caring practices using the social learning theory model.ResultsThere is a triple tension structure in the practice of humanistic care: At the cognitive level, there is a knowledge-activity rupture, with learners showing theoretical clarity but practical confusion. At the environmental level, it is divided into the dual role of facilitating and inhibiting environments. “rewarding” environments included positive psychological attitudes of patients, caring-friendly departmental environment, perceptually rewarding mindfulness environment, and loving family environment. In contrast, “punishing” environments included patients’ irresponsible attitudes toward themselves, poor care experiences, inflexible management mechanisms, missing incentives. At the behavioral level, there is a dialectical game between constructive and alienating practices. “forward” behaviors included personalized care in the details, respect for patient autonomy, proactive communication and empathy, systemic support and teamwork. Conversely, “backward” behaviors included mechanized procedures and emotional detachment, disregard for privacy and dignity, systemic issues that exacerbate apathy.ConclusionBased on the framework of social learning theory, this study constructs a learning trajectory model of humanistic care to explain the synergistic mechanism between cognitive dimension and environmental system and its two-way shaping of caring practice behavior. The study finds that there is a “black box” phenomenon in which the theory of humanistic care is clear but the practice of humanistic care is confusing in the cognitive dimension, and in the environmental dimension, there are systematic limitations in the traditional biomedical model. Based on the above two-dimensional analysis, this study proposes an optimization path combining cognitive explicit cultivation and environmental support system reconstruction, which points out the direction for breaking through the dilemma of humanistic care practice.

Nicy Scaria, Suma Dharani Chenna, Deepak Subramani

Developing questions that are pedagogically sound, relevant, and promote learning is a challenging and time-consuming task for educators. Modern-day large language models (LLMs) generate high-quality content across multiple domains, potentially helping educators to develop high-quality questions. Automated educational question generation (AEQG) is important in scaling online education catering to a diverse student population. Past attempts at AEQG have shown limited abilities to generate questions at higher cognitive levels. In this study, we examine the ability of five state-of-the-art LLMs of different sizes to generate diverse and high-quality questions of different cognitive levels, as defined by Bloom's taxonomy. We use advanced prompting techniques with varying complexity for AEQG. We conducted expert and LLM-based evaluations to assess the linguistic and pedagogical relevance and quality of the questions. Our findings suggest that LLms can generate relevant and high-quality educational questions of different cognitive levels when prompted with adequate information, although there is a significant variance in the performance of the five LLms considered. We also show that automated evaluation is not on par with human evaluation.

Wei Hung Pan, Ming Jie Chok, Jonathan Leong Shan Wong et al.

Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of imperfections in Artificial Intelligence Generated Content (AIGC) Detectors for academic misconduct. In this paper, we present an empirical study where the LLM is examined for its attempts to bypass detection by AIGC Detectors. This is achieved by generating code in response to a given question using different variants. We collected a dataset comprising 5,069 samples, with each sample consisting of a textual description of a coding problem and its corresponding human-written Python solution codes. These samples were obtained from various sources, including 80 from Quescol, 3,264 from Kaggle, and 1,725 from LeetCode. From the dataset, we created 13 sets of code problem variant prompts, which were used to instruct ChatGPT to generate the outputs. Subsequently, we assessed the performance of five AIGC detectors. Our results demonstrate that existing AIGC Detectors perform poorly in distinguishing between human-written code and AI-generated code.

Achraf Hsain, Hamza El Housni

This research explores the integration of chatbot technology powered by GPT-3.5 and GPT-4 Turbo into higher education to enhance internationalization and leverage digital transformation. It delves into the design, implementation, and application of Large Language Models (LLMs) for improving student engagement, information access, and support. Utilizing technologies like Python 3, GPT API, LangChain, and Chroma Vector Store, the research emphasizes creating a high-quality, timely, and relevant transcript dataset for chatbot testing. Findings indicate the chatbot's efficacy in providing comprehensive responses, its preference over traditional methods by users, and a low error rate. Highlighting the chatbot's real-time engagement, memory capabilities, and critical data access, the study demonstrates its potential to elevate accessibility, efficiency, and satisfaction. Concluding, the research suggests the chatbot significantly aids higher education internationalization, proposing further investigation into digital technology's role in educational enhancement and strategy development.

Kaska Porayska-Pomsta

This paper examines the historical foundations, current practices, and emerging challenges for Artificial Intelligence in Education (AIED) within broader AI practices. It highlights AIED's unique and rich potential for contributing to the current AI policy and practices, especially in the context of responsible AI. It also discusses the key gaps in the AIED field, which need to be addressed by the community to elevate the field from a cottage industry to the level where it will deservedly be seen as key to advancin AI research and practical applications. The paper offers a five-point manifesto aimed to revitalise AIED' contributions to education and broader AI community, suggesting enhanced interdisciplinary collaboration, a broadened understanding of AI's impact on human functioning, and commitment to setting agendas for human-centred educational innovations.This approach positions AIED to significantly influence educational technologies to achieve genuine positive impact across diverse societal segments.

Charles Koutcheme, Nicola Dainese, Sami Sarsa et al.

The emergence of large language models (LLMs) has sparked enormous interest due to their potential application across a range of educational tasks. For example, recent work in programming education has used LLMs to generate learning resources, improve error messages, and provide feedback on code. However, one factor that limits progress within the field is that much of the research uses bespoke datasets and different evaluation metrics, making direct comparisons between results unreliable. Thus, there is a pressing need for standardization and benchmarks that facilitate the equitable comparison of competing approaches. One task where LLMs show great promise is program repair, which can be used to provide debugging support and next-step hints to students. In this article, we propose a novel educational program repair benchmark. We curate two high-quality publicly available programming datasets, present a unified evaluation procedure introducing a novel evaluation metric rouge@k for approximating the quality of repairs, and evaluate a set of five recent models to establish baseline performance.

S. Ball

Abstract Based on the ‘case’ of educational reform in India, this paper explores the emergence of both new trans-national spaces of policy and new intra-national spaces of policy and how they are related together, and how policies move across and between these spaces and the relationships that enable and facilitate such movement. The paper is an attempt to think outside and beyond the framework of the nation state to make sense of what is going on inside the nation state. In particular, it takes seriously the need to rethink the frame within and scales at which the new policy actors, discourses, connections, agendas, resources, and solutions of governance are addressed – and the need to move beyond what Beck calls ‘methodological nationalism’ . In other words, the paper argues that thinking about the spaces of policy means extending the limits of our geographical imagination. To address this argument, it combines the presentation and discussion of data with some more general discussion of policy networks and mobilities.

M. Uşak, A. Masalimova, E. I. Cherdymova et al.

Nowadays, we all are sitting in our homes and watching what is going on in the world, as if we are watching a science fiction movie, in which we have the leading role. Novel Coronavirus (COVID-19), which first appeared in Wuhan (Abdulamir, & Hafidh, 2020; Ait Addi et al., 2020 Aljofan, & Gaipov, 2020; Sorooshian, 2020) and later turned into a pandemic affecting the entire world, does not discriminate between the degree of democracy, finances, religion, gender, ethnicity and region. World is a fireplace and we all are “burning”. In the countries where the pandemic is progressing rapidly, all health professionals, regardless of their area of expertise, have been called to the field. This reminds us of the need to revive the general perspective that we have begun to forget. That is to say, no matter how specific our area of expertise is, we are obliged to keep the general perspective of our field and basic doctrines constantly fresh.

Roozbeh Aliabadi, Aditi Singh, Eryka Wilson

The integration of artificial intelligence (AI) into education has the potential to transform the way we learn and teach. In this paper, we examine the current state of AI in education and explore the potential benefits and challenges of incorporating this technology into the classroom. The approaches currently available for AI education often present students with experiences only focusing on discrete computer science concepts agnostic to a larger curriculum. However, teaching AI must not be siloed or interdisciplinary. Rather, AI instruction ought to be transdisciplinary, including connections to the broad curriculum and community in which students are learning. This paper delves into the AI program currently in development for Neom Community School and the larger Education, Research, and Innovation Sector in Neom, Saudi Arabia s new megacity under development. In this program, AI is both taught as a subject and to learn other subjects within the curriculum through the school systems International Baccalaureate (IB) approach, which deploys learning through Units of Inquiry. This approach to education connects subjects across a curriculum under one major guiding question at a time. The proposed method offers a meaningful approach to introducing AI to students throughout these Units of Inquiry, as it shifts AI from a subject that students like or not like to a subject that is taught throughout the curriculum.