Hasil untuk "Education (General)"

E. Rohmer, Surya P. N. Singh, M. Freese

B. B. Jensen, K. Schnack

D. Reay, G. Crozier, John Clayton

Peng Yang, Yunfeng Zhu, Chao Chang et al.

The rapid integration of Large Language Models (LLMs) into software engineering practice is reshaping how software testing activities are performed. LLMs are increasingly used to support software testing. Consequently, software testing education must evolve to prepare students for this new paradigm. However, while students have already begun to use LLMs in an ad hoc manner for testing tasks, there is limited empirical understanding of how such usage influences their testing behaviors, judgment, and learning outcomes. It is necessary to conduct a systematic investigation into how students learn to evaluate, control, and refine LLM-assisted testing results. This paper presents a mixed-methods, two-phase exploratory study on human-LLM collaboration in software testing education. In Phase I, we analyze classroom learning artifacts and interaction records from 15 students, together with a large-scale survey conducted in a national software testing competition (337 valid responses), to identify recurring prompt-related difficulties across testing tasks. The results reveal systematic interaction breakdowns, including missing contextual information, insufficient constraints, rigid one-shot prompting, and limited strategy-driven iteration, with automated test script generation emerging as a particularly heterogeneous and effort-intensive interaction context. Building on these findings, Phase II conducts an illustrative classroom practice that operationalizes the observed breakdowns into a lightweight, stage-aware prompt scaffold for test script generation, guiding students to explicitly articulate execution-relevant information such as environmental assumptions, interaction grounding, synchronization, and validation intent, and reporting descriptive shifts in students' testing-related articulation when interacting with LLMs.

Danielle R. Thomas, Conrad Borchers, Kirk P. Vanacore et al.

Generative Artificial Intelligence (GenAI) is now widespread in education, yet the efficacy of GenAI systems remains constrained by the quality and interpretation of the labeled data used to train and evaluate them. Studies commonly report inter-rater reliability (IRR), often summarized by a single coefficient such as Cohen's kappa (k), as a gatekeeper to ``ground truth.'' We argue that many educational assessment and practice support settings include challenges, such as high-inference constructs, skewed label distributions, and temporally segmented multimodal data, which yield potential misapplication or misinterpretation of threshold-based heuristics for IRR. The growing use of large language models as annotators and judges introduces risks such as automation bias and circular validation. We propose four practical shifts for establishing ground truth: (1) treat IRR as a diagnostic signal to localize disagreement and refine constructs rather than a mechanical acceptance threshold (e.g., k > 0.8); (2) require transparent reporting of rater expertise, codebook development, reconciliation procedures, and segmentation rules; (3) mitigate risks in LLM annotation through bias audits and verification workflows; and (4) complement agreement statistics with validity and effectiveness evidence for the intended use, including uncertainty-aware labeling (e.g., assigning different labels to the same item to capture nuance), criterion-related checks (e.g., predictive tests to check if labels forecast the intended outcome), and close-the-loop evaluations of whether systems trained on these labels improve learning beyond a reasonable control. We illustrate these shifts through case studies of multimodal tutoring data and provide actionable recommendations toward strengthening the evidence base of labeled AIED datasets.

Suman Saha, Fatemeh Rahbari, Farhan Sadique et al.

This paper explores integrating microlearning strategies into university curricula, particularly in computer science education, to counteract the decline in class attendance and engagement in US universities after COVID. As students increasingly opt for remote learning and recorded lectures, traditional educational approaches struggle to maintain engagement and effectiveness. Microlearning, which breaks complex subjects into manageable units, is proposed to address shorter attention spans and enhance educational outcomes. It uses interactive formats such as videos, quizzes, flashcards, and scenario-based exercises, which are especially beneficial for topics like algorithms and programming logic requiring deep understanding and ongoing practice. Adoption of microlearning is often limited by the effort needed to create such materials. This paper proposes leveraging AI tools, specifically ChatGPT, to reduce the workload for educators by automating the creation of supplementary materials. While AI can automate certain tasks, educators remain essential in guiding and shaping the learning process. This AI-enhanced approach ensures course content is kept current with the latest research and technology, with educators providing context and insights. By examining AI capabilities in microlearning, this study shows the potential to transform educational practices and outcomes in computer science, offering a practical model for combining advanced technology with established teaching methods.

Huixin Gao, Tanya Evans, Anna Fergusson

This scoping review examines the use of student explanation strategies in postsecondary mathematics and statistics education. We analyzed 46 peer-reviewed articles published between 2014 and 2024, categorizing student explanations into three main types: self-explanation, peer explanation and explanation to fictitious others. The review synthesizes the theoretical underpinnings of these strategies, drawing on the retrieval practice hypothesis, generative learning hypothesis, and social presence hypothesis. Our findings indicate that while self-explanation and explaining to fictitious others foster individual cognitive processes enhancing generative thinking, peer explanation have the potential to combine these benefits with collaborative learning. However, explanation to fictitious others have the potential to mitigate some of the negative impacts that may occur in peer explanation, such as more knowledgeable students dominating peer discussions. The efficacy of the methods varies based on implementation, duration, and context. This scoping review contributes to the growing body of literature on generative learning strategies in postsecondary education and provides insights for optimizing the integration of student explanation techniques in mathematics and statistics.

Briley L. Lewis

Writing is a critical skill for modern science, enabling collaboration, scientific discourse, public outreach, and more. Accordingly, it is important to consider how physicists and astronomers are trained to write. This study aims to understand the landscape of science writing education, specifically in physics and astronomy, in higher education in the United States. An online survey probing various aspects of their writing training in both undergraduate and graduate school was administered to 515 participants who have obtained training in physics and/or astronomy, or related fields, at the level equal to or beyond upper-division undergraduate study. Humanities and writing requirement courses appear to have a key role in general writing education, while laboratory courses and feedback from mentors are the dominant modes of science writing education in undergraduate and graduate school respectively. There is substantial variation in the quality of writing education in physics and astronomy, often dependent on the student's institution and/or mentor. Some participants also report that their success in disciplinary writing was a result of a solid foundation from K-12 education and/or self-direction towards resources; such reliance on past experiences and student background may contribute to inequality in the field. Many participants also stated a clear desire for more structured writing training to be available in the field. We provide suggestions for how to implement such training to meet the needs of the community identified in the survey.

Graciela Cordero-Arroyo , María Guadalupe Tinajero Villavicencio, Roxana Patricia León González

En el ciclo escolar 2022-2023 el Consejo Técnico Escolar fue el dispositivo de formación seleccionado por la Secretaría de Educación Pública para la actualización docente en el nuevo Plan de Estudio para la educación preescolar, primaria y secundaria de 2022. El objetivo de este estudio fue documentar las condiciones que posibilitaron la actualización de los docentes en el nuevo Plan, así como aquellas que lo obstaculizaron. Se observaron reuniones de Consejo Técnico Escolar en once escuelas de educación básica en Baja California, a lo largo del ciclo escolar 2022-2023. Se hizo un análisis cualitativo de contenido de 193 registros de observación. Los resultados demuestran que el Consejo Técnico Escolar es un espacio flexible y de intercambio, pero también presenta limitaciones significativas como dispositivo de formación para el diseño curricular, contenido en el que el profesorado no ha sido originalmente formado.   The School Technical Council as a formative device: the experience of the 2022-2023 school year Abstract  In the 2022-2023 school year, the School Technical Council was the training device selected by the Ministry of Public Education for teacher updating in the new Study Plan for preschool, primary and secondary education, 2022. The objective of this study was to document the conditions that made it possible for teachers to update this new Plan, as well as those that hindered it. School Technical Council meetings were observed in eleven basic education schools in Baja California throughout the 2022-2023 school year. A qualitative content analysis was carried out on 193 observation records. The results demonstrate that the School Technical Council is a flexible and exchange space, but it also presents significant limitations as a training device for curricular design, content in which teachers have not originally been trained.

Géssika Mendes Vieira, Vania Maria de Oliveira Vieira

Este artigo faz parte de um estudo maior, vinculado a uma pesquisa de mestrado desenvolvida em 2020, financiada pela CAPES e inserida na RIDEP - Rede Internacional de Pesquisas sobre o Desenvolvimento Profissional de Professores[1]. No estudo maior, o objetivo geral foi compreender as Representações Sociais dos alunos de um curso de Publicidade e Propaganda de uma IES mineira sobre as implicações dos instrumentos de avalição da aprendizagem utilizados pelos seus professores. Para o alcance desse objetivo foi realizado um estudo a partir da obra de Mizukami (1986) sobre as diferentes abordagens de ensino-aprendizagem e como elas se manifestam na prática avaliativa. É disso que trata o presente artigo, cujo objetivo é analisar como as diferentes abordagens de ensino-aprendizagem descritas por Mizukami (1986) se manifestam na prática avaliativa, destacando suas características, processos e representantes, e avaliar a relevância dessas abordagens na atualidade. A partir de um estudo bibliográfico e exploratório, discutiu-se as abordagens Tradicional, Comportamentalista, Humanista, Cognitivista e Sócio-cultural e suas características, bem como os seus principais representantes. Foi possível verificar que cada abordagem percebe o processo avaliativo de uma forma diferente -  a tradicional tem a perspectiva de medir as informações que o estudante reproduz, a comportamentalista busca constatar o que foi aprendido, a humanista não trabalha com padrões e considera o indivíduo como o centro do seu processo de aprendizagem, a cognitivista considera a assimilação e as noções do conhecimento e, por fim, a sócio-cultural trabalha com um processo contínuo de avaliação mútua da prática educativa entre estudante e professor.      

Gal Puris, Angela Chetrit, Eldad Katorza

As medical imaging continues to expand, concerns about the potential risks of ionizing radiation to the developing fetus have led to a preference for non-radiation-based alternatives such as ultrasonography and fetal MRI. This review examines the current evidence on the safety of MRI during pregnancy, with a focus on 3 T MRI and contrast agents, aiming to provide a comprehensive synthesis that informs clinical decision-making, ensures fetal safety and supports the safe use of all available modalities that could impact management. We conducted a comprehensive review of studies from 2000 to 2024 on MRI safety during pregnancy, focusing on 3 T MRI and gadolinium use. The review included peer-reviewed articles and large database studies, summarizing key findings and identifying areas for further research. Fetal MRI, used alongside ultrasound, enhances diagnostic accuracy for fetal anomalies, particularly in the brain, thorax, gastrointestinal and genitourinary systems, with no conclusive evidence of adverse effects on fetal development. While theoretical risks such as tissue heating and acoustic damage exist, studies show no significant harm at 1.5 T or 3 T, though caution is still advised in the first trimester. Regarding gadolinium-based contrast agents, the evidence is conflicting: while some studies suggest risks such as stillbirth and rheumatological conditions, animal studies show minimal fetal retention and no significant toxicity, and later clinical research has not substantiated these risks. The existing literature on fetal MRI is encouraging, suggesting minimal risks; however, further investigation through larger, prospective and long-term follow-up studies is essential to comprehensively determine its safety and late effects.

Yossi Ben-Zion, Roi Einhorn Zarzecki, Joshua Glazer et al.

Seemingly we are not so far from Star Trek's food replicator. Generative artificial intelligence is rapidly becoming an integral part of both science and education, offering not only automation of processes but also the dynamic creation of complex, personalized content for educational purposes. With such advancement, educators are now crafting exams, building tutors, creating writing partners for students, and developing an array of other powerful tools for supporting our educational practices and student learning. We share a new class of opportunities for supporting learners and educators through the development of AI-generated simulations of physical phenomena and models. While we are not at the stage of "Computer: make me a mathematical simulation depicting the quantum wave functions of electrons in the hydrogen atom", we are not far off.

Wojciech Cichosz, Jarosław Lisica, Roman Buchta et al.

In the face of the challenges of the modern world, young people often struggle with feelings of helplessness and lack of hope. UNICEF reports that nearly 20% of European teenagers suffer from mental health disorders. In response to these issues, the concept of the pedagogy of hope, particularly developed within Christian educational thought, is gaining significance. The pedagogy of hope emphasizes the integral development of the individual – physical, emotional, social, and spiritual – and promotes trust, critical thinking, and psychological resilience. This article examines the foundations of the pedagogy of hope and its potential as a response to the educational needs and challenges faced by young people.

Lic. Ernesto Sixto Carcassés Sánchez, Dra. C. Ada Iris Infante Ricardo, Lic. Celys Bárbara Milán Guevara

Durante el proceso pedagógico es preciso desarrollar cualidades laborales, tales como: ser laborioso, responsable, solidario, independiente, sensible, creativo y perseverante, que propicien la formación integral de los estudiantes para lograr su desempeño a la altura que demanda la sociedad de hoy. El presente trabajo tiene el objetivo de exponer un proyecto formativo realizado en el Instituto Preuniversitario Enrique José Varona, de Holguín: el periódico estudiantil El Varona dice, que estimula la actividad de investigación social, la crónica, el artículo periodístico y la orientación hacia carreras universitarias como Periodismo, Historia, Ciencias Exactas, Sociales, Humanísticas y Pedagógicas, como continuidad de la prensa de los años 40 y 50 del siglo pasado en el instituto. Fueron de utilidad los métodos de análisis–síntesis, la inducción-deducción, el análisis documental, los talleres de opinión crítica y construcción colectiva y las entrevistas individuales y grupales.  El resultado fundamental se concreta en el cambio de actitudes de los estudiantes respecto a la actividad laboral, el desarrollo de cualidades y la generalización e impacto del periódico en otros centros educacionales y en la juventud estudiantil holguinera como ejercicio de democracia y formación de valores.  

Luis Darmendrail, Alice Gasparini, Andreas Müller

The present text provides a short, non-technical account of some historical and educational background and, based on this, of the rationale of the use of mobile devices in physics education.

A. Hwong, Y. Li, R. Morin et al.

Introduction Older adults with schizophrenia often have multiple chronic conditions, or multimorbidity, yet most prior research has focused on single medical conditions. Objectives To characterize multimorbidity patterns and utilization among older adults with schizophrenia to understand how multimorbidity affects this population and their clinical service needs. Methods This retrospective cohort study included veterans aged 50 years and older with schizophrenia and followed their comorbid diagnoses and utilization (outpatient, inpatient, and emergency) from 2012 to 2019. Comorbid diagnoses included myocardial infarction, congestive heart failure, stroke, chronic obstructive pulmonary disease (COPD), cancer, dementia, traumatic brain injury, hepatitis C, osteoarthritis, renal disease, chronic pain, sleep disorder, depression, dysthymia, posttraumatic stress disorder (PTSD), general anxiety disorder, alcohol use disorder, other substance use disorder, and tobacco use disorder. Latent class analysis was used to identify latent profiles of psychiatric and medical comorbidity. Chi-square and F-tests were used to assess differences in demographics, comorbidities, and utilization across the latent classes. Results The cohort included 82,495 adults with schizophrenia. Three distinct multimorbidity classes were identified: Minimal Comorbidity (67.0% of the cohort), High Comorbidity (17.6%) and Substance Use Disorders and Related Conditions (SUDRC) (15.4%). The Minimal Comorbidity class had <10% prevalence of all comorbid diagnoses. The High Comorbidity class had >20% prevalence of congestive heart failure, COPD, dementia, renal disease, sleep disorder, and depression. The SUDRC class had >70% prevalence of alcohol and drug use disorders and >20% prevalence of COPD, hepatitis C, depression, and PTSD. Although the High Comorbidity class had the highest rates of chronic medical conditions, the SUDRC class had the highest rates of emergency and inpatient medical care and emergency, inpatient, and outpatient mental health care utilization. Comparing across classes, all p-values were <.001 for utilization. Conclusions Older adults with schizophrenia are a heterogeneous group with distinct multimorbidity classes and different patterns of utilization. Those with high prevalence of substance use disorders had the highest rates of emergency and inpatient medical and overall mental health care utilization. Tailoring integrated care services to target specific clinical needs could improve outcomes for this population. Disclosure of Interest None Declared

Mohammad Asadi, Vinitra Swamy, Jibril Frej et al.

Time series is the most prevalent form of input data for educational prediction tasks. The vast majority of research using time series data focuses on hand-crafted features, designed by experts for predictive performance and interpretability. However, extracting these features is labor-intensive for humans and computers. In this paper, we propose an approach that utilizes irregular multivariate time series modeling with graph neural networks to achieve comparable or better accuracy with raw time series clickstreams in comparison to hand-crafted features. Furthermore, we extend concept activation vectors for interpretability in raw time series models. We analyze these advances in the education domain, addressing the task of early student performance prediction for downstream targeted interventions and instructional support. Our experimental analysis on 23 MOOCs with millions of combined interactions over six behavioral dimensions show that models designed with our approach can (i) beat state-of-the-art educational time series baselines with no feature extraction and (ii) provide interpretable insights for personalized interventions. Source code: https://github.com/epfl-ml4ed/ripple/.

Alexandre Borovik

In the Soviet Union a reform movement in mathematics education was triggered by Andrey Kolmogorov in the 1970s, and followed by a counter-reform. This movement was rooted in the very different socioeconomic conditions of that time and place, and followed a strategy with very significant contrasts to similar programs in the USA, England, or France. This provides an interesting case study which may illuminate the way such movements arise and succeed or fail, and, at the social level, certain fundamental commonalities of constraints as well as significant differences according to local conditions. We shall show that the principal reasons of the failure of the Kolmogorov reform were political: (1) The reform ignored the reality of the socio-economic conditions of the country; (2) The human factor was ignored, and very little attention was given to professional development and retraining of, and methodological help to, the whole army of teachers; (3) An attempt to transfer mathematical content and methods from the highly successful advanced extension stream for mathematically strong and highly engaged children to mainstream education was an especially grievous error.

George Boateng, Samuel John, Andrew Glago et al.

Africa has a high student-to-teacher ratio which limits students' access to teachers. Consequently, students struggle to get answers to their questions. In this work, we extended Kwame, our previous AI teaching assistant, adapted it for science education, and deployed it as a web app. Kwame for Science answers questions of students based on the Integrated Science subject of the West African Senior Secondary Certificate Examination (WASSCE). Kwame for Science is a Sentence-BERT-based question-answering web app that displays 3 paragraphs as answers along with a confidence score in response to science questions. Additionally, it displays the top 5 related past exam questions and their answers in addition to the 3 paragraphs. Our preliminary evaluation of the Kwame for Science with a 2.5-week real-world deployment showed a top 3 accuracy of 87.5% (n=56) with 190 users across 11 countries. Kwame for Science will enable the delivery of scalable, cost-effective, and quality remote education to millions of people across Africa.

Heiko Dietrich, Tanya Evans

Traditional lectures are commonly understood to be a teacher-centered mode of instruction where the main aim is a provision of explanations by an educator to the students. Recent literature in higher education overwhelmingly depicts this mode of instruction as inferior compared to the desired student-centered models based on active learning techniques. First, using a four-quadrant model of educational environments, we address common confusion related to a conflation of two prevalent dichotomies by focusing on two key dimensions: (1) the extent to which students are prompted to engage actively and (2) the extent to which expert explanations are provided. Second, using a case study, we describe an evolution of tertiary mathematics education, showing how traditional instruction can still play a valuable role, provided it is suitably embedded in a student-centered course design. We support our argument by analyzing the teaching practice and learning environment in a third-year abstract algebra course through the lens of Stanislav Dehaene's theoretical framework for effective teaching and learning. The framework, comprising "four pillars of learning", is based on a state-of-the-art conception of how learning can be facilitated according to cognitive science, educational psychology, and neuroscience findings. In the case study, we illustrate how, over time, the unit design and the teaching approach have evolved into a learning environment that aligns with the four pillars of learning. We conclude that traditional lectures can and do evolve to optimize learning environments and that the erection of the dichotomy "traditional instruction versus active learning" is no longer relevant.