Hasil untuk "Theory and practice of education"

Octavio Murekian, Surabil Sudarshan

The emergence of agentic artificial intelligence (AI) in higher education raises a critical question: does the move toward autonomous systems capable of planning, decision-making, and action constitute a fundamental shift, or an acceleration of challenges already posed by generative AI? This article argues that these developments require a reimagining of academic literacies, extending beyond traditional emphases on critical thinking and academic writing toward a more comprehensive conception of AI literacy. Drawing on research in AI ethics and digital pedagogy, it explores the implications of learning in partnership with increasingly autonomous systems. It examines how agentic AI disrupts established understandings of authorship, assessment, and intellectual labour, and proposes a framework centred on critical evaluation, prompt literacy, co-authorship, ethical awareness, and recognition of AI limitations. Learning developers are positioned as central actors in this transition, with a key role in shaping pedagogy, assessment, and institutional policy.

Paul Tschisgale, Holger Maus, Fabian Kieser et al.

Large language models (LLMs) are now widely accessible, reaching learners at all educational levels. This development has raised concerns that their use may circumvent essential learning processes and compromise the integrity of established assessment formats. In physics education, where problem solving plays a central role in instruction and assessment, it is therefore essential to understand the physics-specific problem-solving capabilities of LLMs. Such understanding is key to informing responsible and pedagogically sound approaches to integrating LLMs into instruction and assessment. This study therefore compares the problem-solving performance of a general-purpose LLM (GPT-4o, using varying prompting techniques) and a reasoning-optimized model (o1-preview) with that of participants of the German Physics Olympiad, based on a set of well-defined Olympiad problems. In addition to evaluating the correctness of the generated solutions, the study analyzes characteristic strengths and limitations of LLM-generated solutions. The findings of this study indicate that both tested LLMs (GPT-4o and o1-preview) demonstrate advanced problem-solving capabilities on Olympiad-type physics problems, on average outperforming the human participants. Prompting techniques had little effect on GPT-4o's performance, while o1-preview almost consistently outperformed both GPT-4o and the human benchmark. Based on these findings, the study discusses implications for the design of summative and formative assessment in physics education, including how to uphold assessment integrity and support students in critically engaging with LLMs.

Alex Liu, Lief Esbenshade, Min Sun et al.

As educational settings increasingly integrate artificial intelligence (AI), understanding how AI tools identify -- and adapt their responses to -- varied educational contexts becomes paramount. This study examines conversational AI's effectiveness in supporting K-12 mathematics education across various educational contexts. Through qualitative content analysis, we identify educational contexts and key instructional needs present in educator prompts and assess AI's responsiveness. Our findings indicate that educators focus their AI conversations on assessment methods, how to set the cognitive demand level of their instruction, and strategies for making meaningful real-world connections. However, educators' conversations with AI about instructional practices do vary across revealed educational contexts; they shift their emphasis to tailored, rigorous content that addresses their students' unique needs. Educators often seek actionable guidance from AI and reject responses that do not align with their inquiries. While AI can provide accurate, relevant, and useful information when educational contexts or instructional practices are specified in conversation queries, its ability to consistently adapt responses along these evaluation dimensions varies across different educational settings. Significant work remains to realize the response-differentiating potential of conversational AI tools in complex educational use cases. This research contributes insights into developing AI tools that are responsive, proactive, and anticipatory, adapting to evolving educational needs before they are explicitly stated, and provides actionable recommendations for both developers and educators to enhance AI integration in educational practices.

Harshit Agarwal, Oleksandra Uralska, Jasmin Billingsley et al.

Understanding the magnetic properties of matter plays a key role in materials physics. However, university education on fundamental magnetism is limited to a theoretical survey because of the lack of appropriate apparatus that can be applied for laboratory courses at the undergraduate level. In this work, we introduce an AC magnetometer based on the Colpitts self-oscillator with an inductor coil as a probe. We show that this type of self-oscillator can be adopted in a typical university laboratory course to learn the principles of magnetic measurement and to understand the fundamental magnetism of matter. We demonstrate the exceptional stability of the circuit with a working frequency range of ~10 kHz to 10 MHz and excellent performance to detect the diamagnetic signal from a superconductor at cryogenic temperature.

Terry Loerts, Carol Doyle-Jones

This research focuses on one teacher’s lived experience as he contributes towards a broader understanding of possibilities and constraints afforded by technologies, materials, and relationality during this recent shift in pandemic pedagogy. The pandemic drastically altered the way curriculum is enacted as there were shifts in the learning environment, the subject matter, and the way teachers and students engaged in literacy practices. To understand this shift, we explore: What are the participating teacher’s perceptions about how multiliteracies are utilized for classroom practice and pedagogy? How did this teacher’s pedagogy continue or change once the pandemic influenced curriculum enactment? This paper focuses on how one grade 6 teacher explored this pandemic pedagogical landscape in his classroom. This experience contributes to learning from the past, navigating the present, and continuing to shape the future of effective instruction in an elementary classroom.

Calum Hughes, Adrian Miranda

We give an elementary description of $2$-categories $\mathbf{Cat}\left(\mathcal{E}\right)$ of internal categories, functors and natural transformations, where $\mathcal{E}$ is a category modelling Lawvere's elementary theory of the category of sets (ETCS). This extends Bourke's characterisation of $2$-categories $\mathbf{Cat}\left(\mathcal{E}\right)$ where $\mathcal{E}$ has pullbacks to take account for the extra properties in ETCS, and Lawvere's characterisation of the (one dimensional) category of small categories to take account of the two-dimensional structure. Important two-dimensional concepts which we introduce include $2$-well-pointedness, full-subobject classifiers, and the categorified axiom of choice. Along the way, we show how generating families (resp. orthogonal factorisation systems) on $\mathcal{E}$ give rise to generating families (resp. orthogonal factorisation systems) on $\mathbf{Cat}\left(\mathcal{E}\right)_{1}$, results which we believe are of independent interest.

Anthony Peruma, Eman Abdullah AlOmar, Wajdi Aljedaani et al.

Educating students about software testing practices is integral to the curricula of many computer science-related courses and typically involves students writing unit tests. Similar to production/source code, students might inadvertently deviate from established unit testing best practices, and introduce problematic code, referred to as test smells, into their test suites. Given the extensive catalog of test smells, it becomes challenging for students to identify test smells in their code, especially for those who lack experience with testing practices. In this experience report, we aim to increase students' awareness of bad unit testing practices, and detail the outcomes of having 184 students from three higher educational institutes utilize an IDE plugin to automatically detect test smells in their code. Our findings show that while students report on the plugin's usefulness in learning about and detecting test smells, they also identify specific test smells that they consider harmless. We anticipate that our findings will support academia in refining course curricula on unit testing and enabling educators to support students with code review strategies of test code.

Dou Du, Taylor J. Baird, Kristjan Eimre et al.

Interactive notebooks are a precious tool for creating graphical user interfaces and teaching materials. Python and Jupyter are becoming increasingly popular in this context, with Jupyter widgets at the core of the interactive functionalities. However, while packages and libraries which offer a broad range of general-purpose widgets exist, there is limited development of specialized widgets for computational physics, chemistry and materials science. This deficiency implies significant time investments for the development of effective Jupyter notebooks for research and education in these domains. Here, we present custom Jupyter widgets that we have developed to target the needs of these communities. These widgets constitute high-quality interactive graphical components and can be employed, for example, to visualize and manipulate data, or to explore different visual representations of concepts, clarifying the relationships existing between them. In addition, we discuss with one example how similar functionality can be exposed in the form of JupyterLab extensions, modifying the JupyterLab interface for an enhanced user experience when working with applications within the targeted scientific domains.

Christelle Zozoungbo

This paper employs a nonlinear difference-in-differences approach to empirically examine the Maximally Maintained Inequality (MMI) hypothesis in rural Benin. The findings of this study confirm the MMI hypothesis. In particular, it is observed that when 76% of educated parents choose to educate their daughters in the absence of educational programs, in contrast to only 37% among non-educated parents, the average impact of tuition fee subsidy on enrollment probability in primary schools stands at 3.8\% for non-educated households and 0.27% for educated households. Conversely, in cases where only 27% of educated parents decide to educate their daughters without education programs, the average effect of tuition fee waivers on enrollment probability in primary schools increases to 19.64\% for non-educated households and 24\% for educated households. From the analysis of household education decisions influenced by a preference for education and budget constraints, three key conclusions emerge to explain the mechanism behind the MMI. Firstly, when the income advantage of educated households compared to non-educated households is significantly high, irrespective of the level of their preference advantage, reducing the financial cost of education induces a greater shift in education decisions among non-educated households. Secondly, in situations where educated households do not possess an income advantage relative to non-educated households, the reduction in education-related financial costs leads to a more pronounced change in education decisions among educated households. Lastly, for the low-income advantage of educated households, as the income advantage of educated households increases, non-educated households respond more to education policy than educated parents, if the preference advantage of educated households is relatively smaller.

Héctor Barrantes González

El objetivo del presente trabajo es realizar una exposición detallada de la resolución por radicales de las ecuaciones de grado tres y cuatro en una variable, utilizando diversos métodos. En el caso de la ecuación de tercer grado, se describen los métodos de Niccolò Fontana (Tartaglia) y Girolamo Cardano, quienes dieron la solución de dicha ecuación con coeficientes positivos. También se analiza la ecuación general de tercer grado, es decir, con coeficientes reales, y se detalla el método para hallar todas las soluciones reales y complejas. Se describe, además, el método de François Viète para un caso particular de la ecuación de grado tres. Para la ecuación de cuarto grado con coeficientes reales, se presentan los métodos de Ludovico Ferrari y René Descartes. Asimismo, se ilustran los métodos con ejemplos detallados. The aim of the present work is to make a detailed exposition of the solution by radicals ofequations of degree three and four in one variable, using different methods. In the case of the thirddegree equation, the methods of Niccolo Fontana (Tartaglia) and Girolamo Cardano are described, who were the ones who gave the solution of this equation, with positive coefficients. The generalequation of third degree equation, i.e. with real coefficients, is also given and the method for findingall the real and complex solutions is detailed. The method of François Viète, for a particular case ofthe equation of degree three, is also described. For the fourth degree equation with real coefficients,the method of Ludovico Ferrari and the method of René Descartes are described. In addition, themethods are illustrated with detailed examples.

Johannes Schneider, Bernd Schenk, Christina Niklaus

Grading exams is an important, labor-intensive, subjective, repetitive, and frequently challenging task. The feasibility of autograding textual responses has greatly increased thanks to the availability of large language models (LLMs) such as ChatGPT and the substantial influx of data brought about by digitalization. However, entrusting AI models with decision-making roles raises ethical considerations, mainly stemming from potential biases and issues related to generating false information. Thus, in this manuscript, we provide an evaluation of a large language model for the purpose of autograding, while also highlighting how LLMs can support educators in validating their grading procedures. Our evaluation is targeted towards automatic short textual answers grading (ASAG), spanning various languages and examinations from two distinct courses. Our findings suggest that while "out-of-the-box" LLMs provide a valuable tool to provide a complementary perspective, their readiness for independent automated grading remains a work in progress, necessitating human oversight.

Antonio Vicente Marafioti Garnica

A partir da perspectiva de que o campo da Educação Matemática tem se ampliado consideravelmente, de modo a incorporar novos objetos, metodologias e abordagens, este artigo questiona o papel que as Filosofias “clássicas” da Matemática têm desempenhado no desenho desse campo, principalmente no que diz respeito à constituição de uma Filosofia da Educação Matemática cujo papel deve ser promover a incorporação de práticas, procedimentos e valores que respondam mais efetivamente às demandas do tempo presente e às necessidades da prática pedagógica cotidiana.

Fátima Velásquez-Rojas, Jesús E. Fajardo, Daniela Zacharias et al.

The COVID-19 pandemic abruptly changed the classroom context, understood as the meeting space between teachers and students where a fundamental part of the construction of new knowledge takes place. This presented enormous challenges for all actors in the educational process, who had to overcome multiple difficulties and adapt to a new daily life in which the incorporation of new strategies and tools was essential. In this work we study the knowledge acquisition process in two different contexts: face-to-face (before the onset of the pandemic) and virtual (during confinement), for a particular case in higher education in Argentina. We developed an analytical model for the knowledge acquisition process, based on a series of surveys and information on academic performance. We analyzed the significance of the model by means of Artificial Neural Networks and a Multiple Linear Regression Method. We found that the virtual context produced a decrease in motivation to learn. We also found that the structure of the emerging contact network from peer interaction presents very different characteristics in both contexts. Finally, we show that in all cases, interaction with teachers is of utmost importance in the process of acquiring knowledge.

Nabeel Gillani, Rebecca Eynon, Catherine Chiabaut et al.

Recent advances in Artificial Intelligence (AI) have sparked renewed interest in its potential to improve education. However, AI is a loose umbrella term that refers to a collection of methods, capabilities, and limitations-many of which are often not explicitly articulated by researchers, education technology companies, or other AI developers. In this paper, we seek to clarify what "AI" is and the potential it holds to both advance and hamper educational opportunities that may improve the human condition. We offer a basic introduction to different methods and philosophies underpinning AI, discuss recent advances, explore applications to education, and highlight key limitations and risks. We conclude with a set of questions that educationalists may ask as they encounter AI in their research and practice. Our hope is to make often jargon-laden terms and concepts accessible, so that all are equipped to understand, interrogate, and ultimately shape the development of human centered AI in education.

Patrick Kierkegaard, Timothy Hicks, A. Joy Allen et al.

Abstract Background The purpose of this study is to develop a theory-driven understanding of the barriers and facilitators underpinning physicians’ attitudes and capabilities to implementing SARS-CoV-2 point-of-care (POC) testing into primary care practices. Methods We used a secondary qualitative analysis approach to re-analyse data from a qualitative, interview study of 22 primary care physicians from 21 primary care practices across three regions in England. We followed the three-step method based on the Behaviour Change Wheel to identify the barriers to implementing SARS-CoV-2 POC testing and identified strategies to address these challenges. Results Several factors underpinned primary care physicians’ attitudes and capabilities to implement SARS-CoV-2 POC testing into practice. First, limited knowledge of the SARS-CoV-2 POC testing landscape and a demanding workload affected physicians’ willingness to use the tests. Second, there was scepticism about the insufficient evidence pertaining to the clinical efficacy and utility of POC tests, which affected physicians’ confidence in the accuracy of tests. Third, physicians would adopt POC tests if they were prescribed and recommended by authorities. Fourth, physicians required professional education and training to increase their confidence in using POC tests but also suggested that healthcare assistants should administer the tests. Fifth, physicians expressed concerns about their limited workload capacity and that extra resources are needed to accommodate any anticipated changes. Sixth, information sharing across practices shaped perceptions of POC tests and the quality of information influenced physician perceptions. Seventh, financial incentives could motivate physicians and were also needed to cover the associated costs of testing. Eighth, physicians were worried that society will view primary care as an alternative to community testing centres, which would change perceptions around their professional identity. Ninth, physicians’ perception of assurance/risk influenced their willingness to use POC testing if it could help identify infectious individuals, but they were also concerned about the risk of occupational exposure and potentially losing staff members who would need to self-isolate. Conclusions Improving primary care physicians’ knowledgebase of SARS-CoV-2 POC tests, introducing policies to embed testing into practice, and providing resources to meet the anticipated demands of testing are critical to implementing testing into practice.

Bayramali Ranjgar, Malek Mirhashemi, Hassan Pasha Sharifi

The aim of present meta-analysis was to examine the effects of personal, family and schooling factors on academic performance and droppout among Iranian secondary students. The published and archived researches in journals, databases, and printed ones relating to academic achievement and dropout have collected. According to the inclusion criteria, 65 articles included in the analysis. After coding the articles, using Orwin’s method, 98% inter-rater agreement among raters have been gained. The random-effect model of DerSimonien and Laird was used with software. It was revealed that in predicting academic achievement, the average effect sizes for all factors and combination of factors 0.270(95CI%:0.218-0.321, Z=9.801, P<0.001) and for dropout 0.521(95CI%:0.404-0.621, Z=7.580, P<0.001). According to the findings, it was revealed that in academic achievement, personal factors and in dropout, schooling factors play main roles, so it is recommended to pay attention to the personal characteristic of students and constructive interactions among parents with youth and school staff.

Maria Augusta Nelson, Lesandro Ponciano

This work presents an approach for using GitHub classroom as a shared, structured, and persistent repository to support project-based courses at the Software Engineering Undergraduate program at PUC Minas, in Brazil. We discuss the needs of the different stakeholders that guided the development of the approach. Results on the perceptions of professors and students show that the approach brings benefits. Besides the lessons learned, we present insights on improving the education of the next generation of software engineers by employing metrics to monitor skill development, verifying student work portfolios, and employing tooling support in project-based courses.

Tom Farrelly, Eamon Costello, Enda Donlon

In this paper we chart the history of selected metaphors that have been used to describe Virtual Learning Environments (VLEs) over the last 15 years. Martin Weller famously claimed in 2007 that “the VLE is dead”. This provocation positioned the VLE as an object of history, forcing us to consider its past, present and future. This notion of historical mapping is important as many educational technologies that failed to deliver on their promises can be easily forgotten. Hence, we sought to develop a short history of VLE metaphors. Using a defined search and selection strategy we selected 30 metaphors spanning a 15-year period from 2004 to 2019 derived from a variety of sources ranging from social media to scholarly publications. We first arranged the metaphors according to a chronological timeline. Next we sought to unpack their significance by thematically analysing them using the notion of metaphorical concepts. Through this thematic analysis, six organizing metaphorical concepts were generated: Straitjacket, Behemoth, Digital Carpark, Safe Space, Smorgasbord and Pathfinder. We then used these metaphorical concepts as a lens, to map and explore historical developments and debates over the past two decades of educational technology. As the world scrambles to go online during the COVID-19 pandemic there has never been a more important time to remember and reflect upon digital learning history. Through this work we contribute to the history of educational technology by remembering its metaphors and what they have taught us. After we had navigated these historical seas, we noticed that the VLE was still here, as a limpet resolutely awaiting the next wave.

Punya Mishra

Ethan L. Arnold, Ming-Shau Liu, Rohit Prabhu et al.

Helium atom scattering studies have the potential for making numerous breakthroughs in the study of processes on surfaces. As this field remains active, there will frequently be new young researchers entering the field. The transition from student to researcher is often met with difficulty, consequently wasting limited time available for a PhD or masters level research. Addressing this issue, we present an educational package for emerging research students in the field of helium atom scattering. We hope that this package serves as sufficient material to significantly accelerate the progress made by new postgraduate students.