Scott P. McGrath, Katherine K. Kim, Karnjit Johl
et al.
The integration of artificial intelligence (AI) into healthcare is accelerating, yet medical education has not kept pace with these technological advancements. This paper synthesizes current knowledge on AI in medical education through a comprehensive analysis of the literature, identifying key competencies, curricular approaches, and implementation strategies. The aim is highlighting the critical need for structured AI education across the medical learning continuum and offer a framework for curriculum development. The findings presented suggest that effective AI education requires longitudinal integration throughout medical training, interdisciplinary collaboration, and balanced attention to both technical fundamentals and clinical applications. This paper serves as a foundation for medical educators seeking to prepare future physicians for an AI-enhanced healthcare environment.
Providing effective, personalized support is critical for helping students overcome conceptual difficulties in physics. However, established scaffolding methods, such as structured tiered support, are often too resource-intensive for widespread implementation. Therefore, this study, investigates whether an easily adaptable, custom-configured AI chatbot can offer comparable affective benefits and cognitive relief. We conducted a quasi-experimental field study with 273 ninth-grade students in Germany. Classes were randomly assigned to solve a buoyancy problem using one of three conditions: an AI chatbot, a tiered support system, or traditional textbook-style explanations. We measured intrinsic and extraneous cognitive load and affective outcomes (enjoyment, hope, hopelessness, self-efficacy, situational interest) via research-validated questionnaires. Results revealed that both interactive support systems -- the custom-configured AI chatbot and tiered hints -- were significantly more effective than the textual support in reducing students' intrinsic and extraneous cognitive load. Furthermore, the AI chatbot yielded the most comprehensive affective benefits, demonstrating significant improvements across all measured affective dimensions, when compared to the textual support. While the chatbot consistently trended more positively than the tiered hints on affective measures, these differences were not statistically significant. These findings suggest that while structured guidance is key to managing cognitive load, the interactive and social nature of AI chatbots holds unique potential for simultaneously fostering positive affective experiences, marking a promising direction for developing effective and holistic learning support tools in physics education.
Education technologies (edtech) are increasingly incorporating new features built on large language models (LLMs), with the goals of enriching the processes of teaching and learning and ultimately improving learning outcomes. However, the potential downstream impacts of LLM-based edtech remain understudied. Prior attempts to map the risks of LLMs have not been tailored to education specifically, even though it is a unique domain in many respects: from its population (students are often children, who can be especially impacted by technology) to its goals (providing the correct answer may be less important for learners than understanding how to arrive at an answer) to its implications for higher-order skills that generalize across contexts (e.g., critical thinking and collaboration). We conducted semi-structured interviews with six edtech providers representing leaders in the K-12 space, as well as a diverse group of 23 educators with varying levels of experience with LLM-based edtech. Through a thematic analysis, we explored how each group is anticipating, observing, and accounting for potential harms from LLMs in education. We find that, while edtech providers focus primarily on mitigating technical harms, i.e., those that can be measured based solely on LLM outputs themselves, educators are more concerned about harms that result from the broader impacts of LLMs, i.e., those that require observation of interactions between students, educators, school systems, and edtech to measure. Overall, we (1) develop an education-specific overview of potential harms from LLMs, (2) highlight gaps between conceptions of harm by edtech providers and those by educators, and (3) make recommendations to facilitate the centering of educators in the design and development of edtech tools.
Lisa Ruble, Christopher J. Cormier, John McGrew
et al.
Special education teacher (SET) stress and burnout is a significant problem. A total of 490 special education teachers were surveyed across the United States. The purpose of this study was to (a) assess and compare three measures of burnout/work engagement, the Maslach Burnout Inventory (MBI), the Oldenburg Burnout Inventory (OLBI), and the Utrecht Work Engagement Scale (UWES) and (b) examine change over the course of a school year that coincided with the COVID-19 pandemic. Significant measurement quality issues were observed for the MBI and OLBI, including questionable convergent validity. Burnout of SETs was found to be highly stable for the MBI and OLBI. Teachers experienced little mean change in burnout over the school year, and perceptions of the effects of COVID and demographic and school variables were generally not predictive of change in burnout for any measure. Implications are discussed.
Denne studien undersøkjer barnehagelærarars forteljingar om språkarbeid med fleirspråklege barn i barnehagen. Målet med studien var å få innsikt i barnehagelærars oppfatningar som dei byggjer praksisen med fleirspråklege barn i barnehagen på. Fire fokusgruppeintervju med totalt 20 barnehagelærarar blei gjennomførte. Den narrative analysen identifiserte fire sentrale tolkingsrepertoar for barnehagelæraranes oppfatningar av fleirspråkleg praksis: det ressursprega, det kompensatoriske, det monospråklege og det profesjonelle avmaktsrepertoaret. Tolkingsrepertoara kom til syne i alle fire intervjua, og dette tyder på eit mønster i oppfatningane. Tolkingsrepertoara var ikkje gjensidig utelukkande, til dømes kunne barnehagelærar både gi uttrykk for å oppfatte fleirspråklegheit som ressurs og samtidig kjenne avmakt i si eiga rolle. Likeins blei norskspråkleg utvikling trekt fram som målsetting for barnehagens språkarbeid samtidig som deltakarane fortalde om spontane transspråkingsaktivitetar. Dette tyder på ein variert og kompleks språkpraksis bygd på både formell og erfaringsbasert kompetanse og personlege oppfatningar. Funna gir innblikk i korleis barnehagelærarar forstår sin eigen og barnas posisjon i ein fleirspråkleg barnehagekontekst og har implikasjonar for utvikling av ein ressursbasert fleirspråkleg pedagogisk praksis.
ENGLISH ABSTRACT
Resource and Deficiency: Teachers’ Views on Complex Multilingual Practices in Early Childhood Education and Care
This study explores teachers’ experiences of multilingual practices in early childhood education and care (ECEC). The purpose was to gain insight into the underlying assumptions that shape ECEC teachers’ multilingual practices. Data were collected through four focus group interviews with 20 teachers. The analysis of the ECEC teachers’ narratives identified four emerging interpretative repertoires: resource-based, compensatory, monolingual, and feelings of professional powerlessness. All four interpretative repertoires appeared in all the interviews, which suggests a pattern of views. The interpretative repertoires were not mutually exclusive. One participant could, for instance, explicitly view multilingualism as a resource while simultaneously expressing feelings of powerlessness regarding how to practice multilingual didactics. Similarly, monolingual Norwegian development was identified as the objective of planned activities while teachers described spontaneous translingual activities. These findings indicate varied and complex linguistic practices built on formal and experience-based competencies and personal beliefs and provide insight into ECEC teachers’ understandings of their own and the children’s positions within a complex multilingual ECEC context. The results have implications for further research- based development of multilingual practices.
Alireza Nikbakht Nasrabadi, Abbas Ebadi, Masoud Fallahi-Khoshknab
et al.
BACKGROUND:
Nursing education faces many challenges that must be identified and removed to improve nursing education and promote the quality of nursing care. Identifying the challenges of nursing education helps to solve or correct the weaknesses and achieve educational goals, which ultimately help to train skilled professionals capable of providing higher quality care to patients. The present study was conducted with the aim of explaining the experiences of nursing tutors in regard to the process of challenge formation in nursing education in Iran.
MATERIALS AND METHODS:
This is a qualitative study with a grounded theory approach that was conducted in two stages. In the first stage, which took about 9 months, 18 experienced nursing tutors with managerial positions were selected by purposeful sampling from universities across the country. Data were collected through semi-structured interviews, field notes, observation, and memos. Data analysis was done by Corbin and Strauss method through MAXQDA software version 10. In the next step, using the method of Walker and Avant (2011), concepts and statements were gathered within a whole and a model was presented.
RESULTS:
Twelve male university tutors and six female tutors participated in this study. The mean age of the participants was 45.38 ± 2.1 years, and their mean work experience was 18.66 ± 3.8 years. The findings showed that the challenges of nursing education could be categorized into three categories (defective educational cycle, efforts to restore the educational structure, and learning is a member of educational family), and 12 subcategories. After analyzing the data, trying to provide a need-based education was determined to be the central theme of this study. The components of model developed in this study were presented in the three axes of improving the tutor’s teaching skills, managing the situation and educational facilities, and facilitating student’s participation in the education process and decision-making.
CONCLUSION:
Identifying barriers and facilitators of need-based education in health policy making can enable nurses to build a better future for their nursing position. To provide need-based education, it is necessary to create a suitable ground and platform for proper education. Such platform and ground are a necessity of educational, therapeutic, and community environments. To achieve this goal, it is recommended to improve the professional capabilities of tutors and support educational organizations.
Special aspects of education, Public aspects of medicine
The present study aimed to identify and classify factors affecting women's free time with an emphasis on physical activity using an interpretive structural modeling approach. This study is applied research in terms of orientation and mixed in terms of research philosophy. The statistical population of this study included free-time experts, sports management experts, and experts in holding family and women's sports events. Theoretical sampling was used to identify the number of people and find the research direction. A theoretical saturation was achieved after conducting 15 in-depth interviews. In the qualitative section of the study, an in-depth interview tool was used to collect data. In the quantitative section, a questionnaire was used. To analyze the data in the qualitative section, three overlapping processes of open, axial, and selective coding were used. To analyze the data in the quantitative section, the interpretive structural modeling method was used in the ISM software. The results of the qualitative section revealed that the factors affecting women's free time with an emphasis on physical activity include 115 open codes, 33 concepts, and 14 primary categories. The results of the structural-interpretive model in the quantitative section also revealed the understanding of the need for women's sports, effective equipment and facilities, the socialization status of women's free time, and the income and financial status of women at the first individual level. The factors of the second level family’s social status, creating organizations for women's free time, and increasing women's awareness of free time were in the second level. Increasing and promoting physical activities in the form of free time for women and increasing people's socialization were placed in the third level. Financial support in the private and public sectors, education and paying attention to related research findings, and improvement of individual mental state were in the fourth level, and improving living conditions were in the fifth level.
1. Introduction
Physical activity and sports are one of the best most effective ways to spend free time among women and girls. It can lead to their physical and mental health. Also, the inclusion of sports in women's and girls' free time requires increasing the level of awareness of people in society, accurate and correct management and planning, and appropriate facilities and infrastructure. Thus, it is essential to conduct detailed and comprehensive studies in this area to identify the challenges and limitations that women and girls face in doing physical activities and sports in their free time and to provide appropriate solutions to solve them. A comprehensive model should be provided to officials to improve the quality of free time of women and girls of the families with an approach based on physical and sports activities. Given what was stated, it is crucial to know how women and girls spend their free time as it ensures their mental and physical health and is an effective means for creating their physical, intellectual, and ethical growth. It will prevent many social deviations. Thus, the present study aims to identify and classify the factors affecting women's free time with an emphasis on physical activities using an interpretive structural modeling approach.
2. Literature Review
Several studies have approved the benefits of exercise and physical activities on physical and mental health and preventing chronic diseases caused by a sedentary life (Lopez Gabbard and Rodriguez, 2013). Studies have revealed that people who express high levels of extraversion are more likely to participate in physical activity. However, people who express high levels of neuroticism tend to be less physically active (Keklainen, 2022 & Gottold, 2018). The way of spending free time plays a major role in people's mobility and physical activity, so the change in lifestyle due to the change in technology and modern life has reduced physical activities and people have been exposed to complications caused by it. Results of the studies indicate that a reduction in physical and sports activities is greater in females than in males (Valdelina et al., 2010). In this regard, Fakhri et al. (1401) examined the structural model of the effect of sports-based free time on the social self-efficacy of students with the mediating role of spiritual intelligence. The results revealed that sports-based free time positively affects social self-efficacy with the mediating role of spiritual intelligence. The results revealed that by increasing free time along with sports and physical activity and improving the spiritual intelligence of students, their social self-efficacy also increases and vice versa. Pourzabih Sarhami et al. (2023) reported that gender discrimination should be minimized and women's sports and recreation per capita should be increased with correct policy-making. Special attention has been paid to issues such as improving women's movement literacy in society, modeling and improving public attitudes toward women's physical and sports activities, diversifying recreational and sports activities for women, providing tax and insurance discounts for sports activists, and other cases, which were mentioned in the study. Giles and Uncesio (2020) conducted a study on single women's free time during the coronavirus outbreak. In this critical study, the researchers analyzed the impacts of these practices on their free time in two single, professional, childless women living alone. The researchers argued that in the current pandemic, women who do not have caregiving responsibilities may face more resentment or humiliation due to being engaged in regular free time activities. Researchers indicated that rejection from social and caring networks can cause feelings of selfishness, shame, and fear when participating in free time. Gimmez et al. (2021) investigated the sports and free time activities of Muslim women and stated that sporting events can foster nationalism. An analysis of current political and public discourses on Muslim women's sports and free time showed that this group is facing many limitations. Political regulations and attitudes interfere not only with the recreational activities of Muslim women but also with their clothing and movements. It is recommended that theoretical perspectives such as the social justice perspective in sports and free time activities of Muslim women be presented and supported so they can have equal access to sports and physical activities in their free time.
3. Methodology
The present study is applied research in terms of orientation, comparative in terms of approach, descriptive in terms of purpose, and mixed (qualitative-quantitative) research in terms of research philosophy. Using the grounded theory method with Glazer's approach, the factors affecting women's free time were identified and classified with an emphasis on physical activities, and their mutual effects were investigated using interpretive structural modeling. The statistical population of this study in both sections included free-time experts, sports management experts, and experts in holding family and women's sports events. Theoretical sampling was used to identify the number of people. Theoretical saturation was achieved after conducting 12 in-depth and unstructured interviews, but the interviews continued up to 15 people for more confidence. In the qualitative section of the study, the in-depth interview tool was used to collect data. In the quantitative section of the study, a questionnaire was used as a research tool. The indicators of this questionnaire are adapted from the data of the qualitative section of the study. Accordingly, the extracted codes were placed in the form of questions and designed as a questionnaire. It was also prepared in the form of a questionnaire whose content validity was confirmed by 10 sports management professors who were outside the sample of the qualitative group. Accordingly, based on the grounded theory, 115 open codes, 33 concepts, and 14 primary categories were identified and categorized. Then, to examine the relationship between the sub-categories of the questionnaire, a structured self-interaction matrix was developed. In this step, the respondents were asked to specify the type of pairwise connection of the factors based on the introduced codes (V, A, X, O).
4. Results
Finally, using the final access matrix, the themes of factors affecting women's free time were classified with an emphasis on physical activities. Based on Figure 1, understanding of the need for women's sports(D), effective equipment and facilities (E), the socialization status of women's free time (K), and the income and financial status of women (M) were placed in the first level, individual factors (B) were placed in the second level. The family’s social status (A), creating organizations for women's free time (F), and increasing women's awareness of free time (J) were placed in the third level. Increasing and promoting physical activities in the form of free time for women (C) and increasing people's socialization (G), financial support in the private and public sectors (I), education and paying attention to related research findings (L), and improvement of individual mental state were placed in the fourth level, and the improvement of living conditions (H) was placed in the fifth level.
5. Conclusion
Based on the results of this study and the population of women in Iran, and knowing their special characteristics and conditions in the Islamic country of Iran, it is necessary to have proper knowledge of the current status before any planning. Also, appropriate local and government measures should be taken to meet their needs and demands by identifying their real problems and needs. n The high-level planners of the country, who are mostly men, should have a better understanding and a more complete insight into the physical activity status of women and their demands. Hence, since public sports and increasing women's participation in them play a vital role in their health and society’s health, and it is useful for the individual and social economy in reducing treatment costs, it is recommended that health planners increase the participation of women in sports and physical activity in their free time. Also, considering all aspects, it is necessary to develop appropriate and comprehensive programs and applications that compensate for the existing deficiencies, provide the conditions for more participation of women in this area, and provide exercise for all people.
The general availability of general-purpose Large Language Models continues to impact on higher education, yet they may not always be useful for specialized tasks. When using these models, oftentimes the need for particular domain knowledge becomes quickly apparent, and the desire for customized bots arises. Customization holds the promise of leading to more accurate and contextually relevant responses, enhancing the educational experience. This report relates insights and experiences from one particular technical university in Switzerland, ETH Zurich, to describe what "customizing" Large Language Models means in practical terms for higher education institutions.
This survey is an updated and improved version of the previous one published in 2013 in this journal with the title data mining in education. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.
Higher education scholars are interested in an artificial intelligence (AI) technology called ChatGPT, which was developed by OpenAI. Whether ChatGPT can improve learning is still a topic of debate among experts. This concise overview of the literature examines the application of ChatGPT in higher education to comprehend and produce high-level instruction. By examining the essential literature, this study seeks to provide a thorough assessment of the advantages and disadvantages of utilizing ChatGPT in higher education settings. But it's crucial to consider both the positive and negative elements. For this rapid review, the researcher searched Google Scholar, Scopus, and others between January 2023 and July 2023 for prior research from various publications. These studies were examined. The study found that employing ChatGPT in higher education is beneficial for a number of reasons. It can provide individualized instruction, and prompt feedback, facilitate access to learning, and promote student interaction. These benefits could improve the learning environment and make it more fun for academics and students. The cons of ChatGPT are equally present. These problems include the inability to comprehend emotions, the lack of social interaction chances, technological limitations, and the dangers of depending too much on ChatGPT for higher education. Higher education should combine ChatGPT with other teaching techniques to provide students and lecturers with a comprehensive education. However, it is crucial to consider the positives, negatives, and moral issues before adopting ChatGPT in the classroom.
Understanding teachers' perspectives on AI in Education (AIEd) is crucial for its effective integration into the educational framework. This paper aims to explore how teachers currently use AI and how it can enhance the educational process. We conducted a cross-national study spanning Greece, Hungary, Latvia, Ireland, and Armenia, surveying 1754 educators through an online questionnaire, addressing three research questions. Our first research question examines educators' understanding of AIEd, their skepticism, and its integration within schools. Most educators report a solid understanding of AI and acknowledge its potential risks. AIEd is primarily used for educator support and engaging students. However, concerns exist about AI's impact on fostering critical thinking and exposing students to biased data. The second research question investigates student engagement with AI tools from educators' perspectives. Teachers indicate that students use AI mainly to manage their academic workload, while outside school, AI tools are primarily used for entertainment. The third research question addresses future implications of AI in education. Educators are optimistic about AI's potential to enhance educational processes, particularly through personalized learning experiences. Nonetheless, they express significant concerns about AI's impact on cultivating critical thinking and ethical issues related to potential misuse. There is a strong emphasis on the need for professional development through training seminars, workshops, and online courses to integrate AI effectively into teaching practices. Overall, the findings highlight a cautious optimism among educators regarding AI in education, alongside a clear demand for targeted professional development to address concerns and enhance skills in using AI tools.
The teachers’ skills to manage a class are an effort to attain students' competence in learning participation. This research aims at analyzing the teachers’ skills to manage a classroom and describing their implementation in the learning process. It employs qualitative research method by using interview, observation, and questionnaire techniques to collect the data. The analyzed data analysis were collected, reduced, and concluded. The data validity was done by conducting source triangulation and technique triangulation. Source triangulation is used to trial the credibility of the data by checking it against data from various sources. In addition, technique triangulation is used to test the data's credibility by using different techniques on the same source. The findings showed that the classroom teachers could fairly manage their classes in accordance with classroom management principles. The teachers displayed a warm and enthusiastic attitude. They provided challenges and reinforcement, instilled discipline, concerned about the classroom environment, designed a positive learning environment, and were able to be a good communicator. Therefore, the teachers’ skills to manage the class was classified in a good category (69.1%).
Christiana Zhang, Judy Greengold, Sean Tackett
et al.
Abstract Background Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States. While HPV is a vaccine-preventable illness, vaccine utilization rates in the United States remain low, particularly among adults. Methods The objective of this study was to assess the impact of an online, asynchronous educational module on HPV vaccination for adult primary care providers. We designed and implemented the module for family medicine, internal medicine, medicine/pediatrics, and obstetrics/gynecology providers in a community practice network affiliated with a large academic health system. We evaluated the effect of the module on provider knowledge, attitudes, and self-reported behaviors with pre-, post-, and delayed post-tests, using Likert-scales for measurement. We summarized data with descriptive statistics and compared changes in individuals using paired t-tests. Results One hundred forty-four out of 223 providers completed the module (response rate of 65%). At baseline, internists had the lowest knowledge scores compared to other specialties (pre-test mean of 3.6, out of 5, SD 1.2). Internists were also the least likely to counsel patients on HPV vaccination (mean 1.6, SD 0.9). There was a statistically significant improvement in knowledge from pre-test to post-test (from mean of 3.8 to 4.6, out of 5, p < .001) across all specialties. There was also statistically significant improvement in mean confidence for all providers from pre-test to post-test to identify patients aged 19–26 (3.3 to 3.7, p < .001) and patients aged 27–45 (2.7 to 3.5, p < .001) who needed vaccination. There was a statistically significant improvement in likelihood to counsel eligible patients on the risks of HPV infection (mean 2.3 to 2.8, p-value 0.002). The delayed post-test demonstrated retention of improved knowledge, confidence, and self-reported behavior. Conclusions This study demonstrated that an asynchronous online module was effective at improving confidence, knowledge, and self-reported behavior of adult primary care providers in recommending HPV immunization. Given the important role that healthcare providers play in vaccine uptake, this study suggests that an online educational intervention can be a powerful tool to encourage increased utilization and delivery of the HPV vaccine. Further efforts are needed to educate internists and providers who take care of the adult population on HPV vaccination.
Valdemar Švábenský, Jan Vykopal, Pavel Čeleda
et al.
Hands-on cybersecurity training allows students and professionals to practice various tools and improve their technical skills. The training occurs in an interactive learning environment that enables completing sophisticated tasks in full-fledged operating systems, networks, and applications. During the training, the learning environment allows collecting data about trainees' interactions with the environment, such as their usage of command-line tools. These data contain patterns indicative of trainees' learning processes, and revealing them allows to assess the trainees and provide feedback to help them learn. However, automated analysis of these data is challenging. The training tasks feature complex problem-solving, and many different solution approaches are possible. Moreover, the trainees generate vast amounts of interaction data. This paper explores a dataset from 18 cybersecurity training sessions using data mining and machine learning techniques. We employed pattern mining and clustering to analyze 8834 commands collected from 113 trainees, revealing their typical behavior, mistakes, solution strategies, and difficult training stages. Pattern mining proved suitable in capturing timing information and tool usage frequency. Clustering underlined that many trainees often face the same issues, which can be addressed by targeted scaffolding. Our results show that data mining methods are suitable for analyzing cybersecurity training data. Educational researchers and practitioners can apply these methods in their contexts to assess trainees, support them, and improve the training design. Artifacts associated with this research are publicly available.
Research shows that the global society as organized today, with our current technological and economic system, is impossible to sustain. We are living in the Anthropocene, an era in which human activities in highly industrialized countries are responsible for overshooting several planetary boundaries, with poorer communities contributing least to the problems but being impacted the most. At the same time, technical and economic gains fail to provide society at large with equal opportunities and improved quality of life. This paper describes approaches taken in computing education to address the issue of sustainability. It presents results of a systematic review of literature on sustainability in computing education. From a set of 572 publications extracted from six large digital libraries plus snowballing, we distilled and analyzed the 90 relevant primary studies. Using an inductive and deductive thematic analysis, we study 1) conceptions of sustainability, computing, and education, 2) implementations of sustainability in computing education, and 3) research on sustainability in computing education. We present a framework capturing learning objectives and outcomes as well as pedagogical methods for sustainability in computing education. These results can be mapped to existing standards and curricula in future work. We find that only a few of the articles engage with the challenges as calling for drastic systemic change, along with radically new understandings of computing and education. We suggest that future research should connect to the substantial body of critical theory such as feminist theory of science and technology. Existing research on sustainability in computing education may be considered as rather immature as the majority of articles are experience reports with limited empirical research.
Merten Nikolay Dahlkemper, Simon Zacharias Lahme, Pascal Klein
This study aimed at evaluating how students perceive the linguistic quality and scientific accuracy of ChatGPT responses to physics comprehension questions. A total of 102 first- and second-year physics students were confronted with three questions of progressing difficulty from introductory mechanics (rolling motion, waves, and fluid dynamics). Each question was presented with four different responses. All responses were attributed to ChatGPT, but in reality one sample solution was created by the researchers. All ChatGPT responses obtained in this study were wrong, imprecise, incomplete, or misleading. We found little differences in the perceived linguistic quality between ChatGPT responses and the sample solution. However, the students rated the overall scientific accuracy of the responses significantly differently, with the sample solution being rated best for the questions of low and medium difficulty. The discrepancy between the sample solution and the ChatGPT responses increased with the level of self-assessed knowledge of the question content. For the question of highest difficulty (fluid dynamics) that was unknown to most students, a ChatGPT response was rated just as good as the sample solution. Thus, this study provides data on the students' perception of ChatGPT responses and the factors influencing their perception. The results highlight the need for careful evaluation of ChatGPT responses both by instructors and students, particularly regarding scientific accuracy. Therefore, future research could explore the potential of similar "spot the bot"-activities in physics education to foster students' critical thinking skills.
The current paper presents an introduction to a special issue focusing on mathematical flexibility, which is an important aspect of mathematical thinking and a cherished, but capricious, outcome of mathematics education. Mathematical flexibility involves the flexible, creative, meaningful, and innovative use of mathematical concepts, relations, representations, and strategies. In this introduction we discuss the most relevant theoretical, methodological, and educational considerations related to mathematical flexibility, which form the background of the empirical studies presented in the special issue. Collectively, these studies provide a broader understanding of the mathematical flexibility, its subcomponents, influences, and malleability.
This study aimed to determine the management of improving the quality of resources at the Tahfidz Al-Qur'an Al Haromain Banjarmasin Institute. This research is descriptive qualitative research, using interviews, observations, and documentation as data collection techniques. The data analysis is carried out in stages: data reduction, data display and concluding. The study results indicate that the Tahfidz Al-Qur'an Al Haromain Educational Institution has managed to improve the management of the quality of resources in its institution. Planning efforts for all resources are carried out; the supervisory process is also carried out on the management of human resources, leaders, teachers, students, and parents, then the management of financial resources for those who apply mandatory infaq and for those who have donors have the control of donations from outside donors, management facilities and infrastructure and others that are by the existing theory.
Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the rise of MOOCs. Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand. We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research, as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.