Education Questions Research Articles

Leveraging Convolutional Neural Networks for Enhanced Educational Question Classification

Leveraging Convolutional Neural Networks for Enhanced Educational Question Classification

Women in Higher Education Leadership

The integration of Artificial Intelligence (AI) into the higher education landscape has caused upset to many long-held practices in the sector as educators question the future. The male-dominated leadership landscape in AI reflects broader gender disparities prevalent in STEM fields. This position paper investigates the challenges women face in academic leadership within the context of AI and proposes strategies to address these disparities. By examining reflective narratives from three women leaders in higher education, we identify themes of visibility, recognition, and inclusive leadership. Our findings highlight the importance of mentoring programs and collaborative leadership models to support women in AI leadership roles. We argue that diverse leadership not only fosters innovative solutions and equitable access but is also crucial for ethical AI development. Promoting diverse voices in AI leadership can drive meaningful change, ensuring AI technologies benefit a broader demographic and align with ethical standards. Future research should focus on strategies to enhance visibility and support for women leaders in AI, addressing both gender and cultural dynamics.

Walking and mapping methodologies: creative explorations of people’s relationships with space

This discussion note reflects on the experiences of collaborating to produce a hybrid workshop on walking and mapping methodologies (author 1 & author 2, 2023) building on research, scholarship and teaching in this area that we, the authors, have been undertaking over the past decade. The workshop explored how walking and mapping might be used in educational settings, in particular multilingual and transnational environments in higher education institutions. It brought together multidisciplinary perspectives on ‘space’, drawing on creative inquiry and applied linguistics, linguistic landscapes, and social geography. Our workshop was aimed at anyone with interests in creative approaches to research, particularly those engaged with participants’ lives and interactions within, across, and through space; and we intended to examine the challenges and opportunities of these approaches, including applications in their own research, scholarship and teaching. Here we outline the processes and thinking behind them, in the form of a two-part dialogue. We raise questions for researchers and educators about boundaries between research, teaching and scholarship, in addition to how walking and mapping methodologies might be critically engaged with in higher education.

Current and Future Directions for School Media Specialists

School media specialists have served as technological experts for their schools and have been pioneers with digital natives, helping create good digital citizens and addressing digital gaps for learners and advocate for access no matter what barriers students face. School of Library and Information Science programs have focused on creating a curriculum that will help create a school technology expert for every school library. This served schools well during the COVID-19 shutdowns, where the school had to find new ways for students to learn remotely.School media specialists have now become the target of hate in their schools. This has become increasingly complicated with states legislating what can be put in school libraries and how teachers can address historical events. School media specialists are required to make decisions on access or potentially losing their jobs. While these are important areas, they are just a small part of the job for school media specialists. The American Association of School Librarians has increased the expected role for school media specialists, having them often serve as teacher, instructional partner, information specialist, and program administrator, all in addition to serving as the expert technologist at their school. However, not all school media programs take this approach to pedagogy for school media.School media specialists create technological opportunities for schools and are a key source for teachers seeking to reach students with diverse learning styles while informing and building relationships with families on internet safety and other important topics. Teachers depend on school media specialists to provide resources and support for the classroom while school media specialists also support students’ intellectual freedom. Ultimately, the focus for school media specialists has been to try to create a school library that represents everyone’s values. While this sounds ideal, it has been a difficult road for the profession.The position of school media specialist has changed dramatically over the last four years. This panel seeks to address research contributions to scholarship on school media specialists and how Schools of Library and Information Science are changing their programs to prepare school media specialists for the workforce. There will be a moderator and a panel comprised of four faculty members who are educators, practitioners, and researchers specializing in school media. Their perspectives and expertise will be present in this discussion, along with the following discussion topics:What are your current research findings telling you?What are practitioners experiencing?What is working with educational pedagogy in school media?What is not working with educational pedagogy in school media?What new technologies have you incorporated into the curriculum?What changes are being made at Schools of Information Science programs to help ready students for the current environment?The panel will explore questions of education, research, and practitioner-based experiences to address concerns impacting school media specialists. The information will inform the future discussion and curriculum pedagogy for school media specialists. The panel discussion will be formatted around the expertise of the faculty with school media. These discussions will inform pedagogical teaching practices in school media programs and hopefully provide a framework for improvement.

Exploring prompt pattern for generative artificial intelligence in automatic question generation

ABSTRACT The construction of questions is an essential component in educational assessment and student learning processes. However, manually constructing questions is a complex task that requires not only professional training, substantial experience, and extensive resources from teachers but is also time-consuming. This article introduces an Automatic Question Generation (AQG) technology based on a prompt pattern to alleviate this burden and address the ongoing need for new questions in education. The essence of this method lies in constructing a prompt pattern grounded on a collective knowledge base derived from teachers, thereby enhancing the quality of the questions produced. Practical applications and expert evaluations demonstrate that integrating a prompt pattern with a collective knowledge base into Large Language Models (LLMs) results in high-quality questions with statistically significant results. These questions not only meet educational standards but also approach the quality of manually constructed questions by teachers in certain aspects. Our research further emphasizes the feasibility of AI-teacher collaboration in education.

AI in Criminal Justice Education: Assessing Educator’s Attitudes and Experiences

Since the public release of OpenAI’s ChatGPT and similar large language models in late 2022, generative artificial intelligence (AI) has become a significant societal force. Discourse about these developments through OpEds and blog posts have raised critical questions for educators about the promises and pitfalls of AI integration into higher education. Through an online survey administered to criminal justice educators (N = 68), the current study explores attitudes about and experiences with AI, and beliefs about the practical challenges AI presents. Results indicate criminal justice educators are both excited but also uncomfortable with the implications of AI for higher education. Respondents were generally concerned about the implications of AI for academic integrity and student development. Interestingly, respondents who had actually used AI tools expressed higher levels of excitement and comfort and lower levels of concern, suggesting that exposure to AI tools may play a key role in assuaging educators’ fears and concerns regarding AI tools. Additional findings and implications are discussed.

7870 Exploring The Role Of ChatGPT In Pediatric Endocrinology Education; Exam Preparation And Question Generation

Abstract Disclosure: J. Tarkoff: None. A.G. Martinez Sanchez: None. Large language models (LLMs) hold substantial promise for improving physician knowledge and expertise. Their role in medical education and the generation of diverse diagnoses can be crucial, potentially leading to positive effects on clinical outcomes. The New England Journal of Medicine pediatric case challenges have examined ChatGPT [1]. Yet, its performance in specialized areas like Pediatric Endocrinology remains unexplored. This study assesses the effectiveness of ChatGPT 4 in responding to questions from the Pediatric Endocrine Self-Assessment Program (PESAP). It also examines if the AI is suitable for creating an educational quiz for residents and fellows. Methods: ChatGPT 4 underwent testing with questions from the 2021-2022 version of PESAP, utilizing the prompt: “Can you assist from the perspective of a pediatric endocrinologist with the following patient case”. Responses were evaluated for initial correctness, and performance was analyzed across various competency categories corresponding to the 7 “umbrella sections” of the tool (Adrenal, Bone, Carbohydrate and Lipid Metabolism/Obesity, Growth, Pituitary, Reproductive System, and Thyroid). Subsequently, we personalized the model by incorporating the questions and detailed responses from the PESAP. This customization resulted in a model that was then utilized to create a 10-question proof-of-concept quiz for four board-certified pediatric endocrinologists. The quiz included a scoring system designed to measure the extent and depth of ChatGPT knowledge. Results: ChatGPT 4 accurately answered 52% of PESAP questions, demonstrating varying performance across specific categories, ranging from 30% (Adrenal) to 78% (Reproductive System). In 16 questions, ChatGPT 4 did not provide an initial answer, requiring a specific request for a response. For questions related to thyroid cancer, explicit prompts were necessary to instruct responses based on the American Thyroid Association 2015 guidelines. In the endocrinologist quiz, the average score was 80%, ranging from 60% to 100%. Discussion: Currently, using ChatGPT 4 as the final diagnostic tool, especially in pediatric endocrinology, should be approached with caution, based on our assessment. However, when incorporating distinct Pediatric Endocrinology case studies, ChatGPT 4 successfully generated valuable educational questions, reinforcing fundamental concepts in the field. We anticipate that as LLMs advance and receive direct medical training, the out-of-the-box diagnostic accuracy will improve, leading to a transformative impact on medical education through this technology.

Evaluation of knowledge of risk factors and warning signs of stroke among paramedical students

Stroke is a medical emergency caused by a blockage or rupture in the blood vessels that supply blood to the brain. This prevents brain tissue from receiving oxygen, leading to the death of brain cells within minutes and causing neurological damage, disability, or death. Aim: The aim of the present study was to assess baseline knowledge regarding stroke risk factors, symptoms, treatment, and information resources. The “golden hour” is a time span of 60 minutes or less after stroke symptoms begin. There are two types of stroke, namely ischemic and hemorrhagic stroke. Ischemic stroke is further divided into thrombotic and embolic stroke. Hemorrhagic stroke is further divided into intracerebral hemorrhage and subarachnoid hemorrhage. Methods: The research method used in the study is a quantitative research approach. Purposive sampling techniques were used and 1050 participants were assessed The questionnaire consists of demographic details such as department, level of education, family history, and knowledge questions based on risk factors and management. Results: This study comprises 1040 patients who fulfill the inclusion criteria. The survey assessed the knowledge of risk factors and warning signs of stroke among future healthcare professionals (pharmacy, nursing, and AHS). The current findings reported that 51.8% of the paramedical students had knowledge about stroke, while the majority of them reported poor knowledge overall. The findings identified that 57.4% of the student population were knowledgeable about risk factors, 69.9% were knowledgeable about warning signs, and 51.8% were knowledgeable about management. These results were similar to many previous studies conducted around the world. Conclusion: Better knowledge about stroke, recognizing the risk factors, and being able to provide immediate management are essential for paramedical students. This is crucial for reducing stroke incidence and improving survival rates of patients by providing better medical help through early detection, rapid intervention, and better long-term health outcomes not only in a medical setup but also in their respective environments.

LLM-based automatic short answer grading in undergraduate medical education

BackgroundMultiple choice questions are heavily used in medical education assessments, but rely on recognition instead of knowledge recall. However, grading open questions is a time-intensive task for teachers. Automatic short answer grading (ASAG) has tried to fill this gap, and with the recent advent of Large Language Models (LLM), this branch has seen a new momentum.MethodsWe graded 2288 student answers from 12 undergraduate medical education courses in 3 languages using GPT-4 and Gemini 1.0 Pro.ResultsGPT-4 proposed significantly lower grades than the human evaluator, but reached low rates of false positives. The grades of Gemini 1.0 Pro were not significantly different from the teachers’. Both LLMs reached a moderate agreement with human grades, and a high precision for GPT-4 among answers considered fully correct. A consistent grading behavior could be determined for high-quality keys. A weak correlation was found wrt. the length or language of student answers. There is a risk of bias if the LLM knows the human grade a priori.ConclusionsLLM-based ASAG applied to medical education still requires human oversight, but time can be spared on the edge cases, allowing teachers to focus on the middle ones. For Bachelor-level medical education questions, the training knowledge of LLMs seems to be sufficient, fine-tuning is thus not necessary.

‘Overwhelmed by the pandemic’: the COVID-19 pandemic’s effect on students’ questions in religious education in Malaysia, Indonesia, and Türkiye

ABSTRACT This article seeks to examine the effect of the COVID-19 Pandemic on religious education (RE) in the three Muslim-majority countries of Malaysia, Indonesia, and Türkiye. It specifically explores the effects of the Pandemic on students’ interests by asking questions in RE classes and the way in which RE teachers respond to them. Malaysia, Indonesia, and Türkiye were chosen, as they all have a Muslim-majority population, and their respective curriculum encourage the constructivist learning theory, which suggests that learning should be a student-active process. This is qualitative research that employs a case study design. Semi-structured, in-depth interviews were conducted with RE teachers in the three countries to learn about the changing interests and questions of the students and how teachers respond to them. RE teachers from upper secondary schools without a religious character (i.e. common/public schools) were chosen for the interviews, as older students are more likely to ask challenging theological questions in the face of the Pandemic. The study documents the Pandemic’s effects on RE, specifically students’ interests and questions and the way in which RE teachers respond to them. Additionally, it provides insights into how RE could help students to better understand the new experience, such as the Pandemic.

Einleitung: Gewohnheit, Erfahrung und die ‚schreibende Frau‘

Abstract This introduction summarizes some of the main hypotheses that underly the contributions of this thematic issue of ARTES which is dedicated to the role of habit/custom and experience in the writing of some Early Modern women authors. One hypothesis is that references to habit and experience may have supported Early Modern women writers in their efforts to legitimize their arguments, knowledge and writing, the reason for this being that – due to various social restrictions and expectations – women were often denied access to other fields of knowledge, especially that of so-called book knowledge. Hence, the question arises to what extent the references to habit/custom and experience are connected to the question of female education and female strategies of (self-)empowerment. I will illustrate this using the prominent example of the medieval writer Christine de Pizan. Since the specific nature of female claims can only be assessed against the background of male writing practices, I will also make some introductory remarks on habit/custom and experience with reference to Early Modern male authors.

Analysis of LLMs for educational question classification and generation

Large language models (LLMs) like ChatGPT have shown promise in generating educational content, including questions. This study evaluates the effectiveness of LLMs in classifying and generating educational-type questions. We assessed ChatGPT's performance using a dataset of 4,959 user-generated questions labeled into ten categories, employing various prompting techniques and aggregating results with a voting method to enhance robustness. Additionally, we evaluated ChatGPT's accuracy in generating type-specific questions from 100 reading sections sourced from five online textbooks, which were manually reviewed by human evaluators. We also generated questions based on learning objectives and compared their quality to those crafted by human experts, with evaluations by experts and crowdsourced participants.Our findings reveal that ChatGPT achieved a macro-average F1-score of 0.57 in zero-shot classification, improving to 0.70 when combined with a Random Forest classifier using embeddings. The most effective prompting technique was zero-shot with added definitions, while few-shot and few-shot + Chain of Thought approaches underperformed. The voting method enhanced robustness in classification. In generating type-specific questions, ChatGPT's accuracy was lower than anticipated. However, quality differences between ChatGPT-generated and human-generated questions were not statistically significant, indicating ChatGPT's potential for educational content creation. This study underscores the transformative potential of LLMs in educational practices. By effectively classifying and generating high-quality educational questions, LLMs can reduce the workload on educators and enable personalized learning experiences.

The Dogma of Running Injuries: Perceptions of Adolescent and Adult Runners.

Adults perceive certain factors to increase or decrease the risk of sustaining running-related injuries, but many of their perceptions are not supported by research. Little is known about the perceptions that adolescent runners hold. Investigating perceptions for adolescent runners is needed to assist in the development of future injury educational materials, as these resources may need to be tailored differently for adolescents and adults. To identify factors that adolescent runners perceive as risk or protective factors for running-related injuries and to compare these perceptions with those of adult runners. Cross-sectional study. Online survey. We surveyed 302 adolescent (164 females, 138 males; age = 16.0 ± 1.4 years [range, 12-19 years]) and 357 adult runners (197 women, 160 men; age = 40.7 ± 11.8 years [range, 20-77 years]). Participants completed a survey with questions about whether factors related to training habits, footwear, biomechanics, strength, stretching, or nutrition influence the risk of sustaining a running-related injury. If ≥75% of adolescents indicated that a factor increases or decreases the risk of sustaining an injury, we considered that factor to be a perceived risk or protective factor, respectively. We also performed Fisher's exact test to compare the proportion of adolescent and adult runners who responded with "increase," "decrease," "neither increase or decrease," or "I don't know" to each question. Adolescent runners perceived training habits, footwear, biomechanics, strength, stretching, and nutrition to increase or decrease the risk of sustaining a running-related injury. A larger proportion of adolescents than adults perceived that more footwear cushioning and stretching decrease injury risk, but a smaller proportion perceived that overtraining increases injury risk and strength decreases injury risk. Differences in perceptions exist between adolescent and adult runners, and future educational materials and research questions may need to be tailored for different running populations.

Using Ontologies in e-Learning in Software Engineering Course

Ontologies enhance educational processes by structuring knowledge and supporting personalized learning. This study investigates their use in e-learning for generating educational content and test questions within software engineering. We discuss two ontologies: one for educational content creation and the other for test question generation. By integrating these ontologies, we improve the adaptability and effectiveness of both educational resources and assessments, offering a more comprehensive learning experience.

Elevating leadership learning: Critical considerations for training, design, and implementation.

This article focuses on the research and scholarship of leadership training. We begin by defining and giving context to the concept of leadership training. Then, we provide a high-level literature review of the history and components of leadership training (e.g., content, pedagogy, and assessment). Next, we connect leadership training with the leadership learning framework (LLF) to address common critiques of leadership training. The article concludes with projections of leadership training in the future and ethical questions for leadership educators to consider when designing leadership training programs.

Basic mathematical skills and fraction understanding predict percentage understanding: Evidence from an intelligent tutoring system

Research on fostering learning about percentages within intelligent tutoring systems (ITSs) is limited. Additionally, there is a lack of data‐driven approaches for improving the design of ITS to facilitate learning about percentages. To address these gaps, we first investigated whether students' understanding of basic mathematical skills (eg, arithmetic, measurement units and geometry) and fractions within an ITS predicts their understanding of percentages. We then applied a psychological network analysis to evaluate interdependencies within the data on 44 subtopics of basic mathematical concepts, fractions and percentages. We leveraged a large‐scale dataset consisting of 2798 students using the ITS bettermarks and working on approximately 4.1 million mathematical problems. We found that advanced arithmetic, measurement units, geometry and fraction understanding significantly predicted percentage understanding. Closer inspection indicated that percentage understanding was best predicted by problems sharing similar features, such as fraction word problems and fraction/natural number multiplication/division problems. Our findings suggest that practitioners and software developers may consider revising specific subtopics which share features with percentage problems for students struggling with percentages. More broadly, our study demonstrates how evaluating interdependencies between subtopics covered within an ITS as a data‐driven approach can provide practical insights for improving the design of ITSs. Practitioner notesWhat is already known about this topic Longitudinal studies showed that basic mathematical skills predict fraction understanding. There is only limited evidence on whether similar predictions can be observed for percentage understanding—in general and within intelligent tutoring systems. Process data from such intelligent tutoring systems can be leveraged to pursue both educational research questions and optimizing digital learning software. Problems involving percentages typically are word problems requiring multiplications and/or divisions. What this paper adds Similar to the case of fractions, students' performance on advanced arithmetic, measurement units and geometry significantly predicted performance with percentages. Students' performance with fractions also predicted performance with percentages significantly. A psychological network analysis was applied to evaluate specific interdependencies between a range of subtopics (eg, Multiplying and dividing fractions, Adding and subtracting fractions and Calculating with percentages). Fraction word problems and fraction problems involving multiplication/division turned out to be the best predictors of understanding percentages. Implications for practice and/or policy When facing difficulties with percentages, revision of previous mathematical concepts sharing similar features (eg, fraction word problems, fraction/natural number multiplication/division problems) may be advised. Software developers may consider implementing such data‐driven revision recommendations for students facing difficulties within intelligent tutor systems. Psychological network analysis can be utilized as a learning analytics method for easy‐to‐access visualizations illustrating relationships between a large range of different subtopics.

Learning effect of online versus onsite education in health and medical scholarship – protocol for a cluster randomized trial

BackgroundThe disruption of health and medical education by the COVID-19 pandemic made educators question the effect of online setting on students’ learning, motivation, self-efficacy and preference. In light of the health care staff shortage online scalable education seemed relevant. Reviews on the effect of online medical education called for high quality RCTs, which are increasingly relevant with rapid technological development and widespread adaption of online learning in universities. The objective of this trial is to compare standardized and feasible outcomes of an online and an onsite setting of a research course regarding the efficacy for PhD students within health and medical sciences: Primarily on learning of research methodology and secondly on preference, motivation, self-efficacy on short term and academic achievements on long term. Based on the authors experience with conducting courses during the pandemic, the hypothesis is that student preferred onsite setting is different to online setting.MethodsCluster randomized trial with two parallel groups. Two PhD research training courses at the University of Copenhagen are randomized to online (Zoom) or onsite (The Parker Institute, Denmark) setting. Enrolled students are invited to participate in the study. Primary outcome is short term learning. Secondary outcomes are short term preference, motivation, self-efficacy, and long-term academic achievements. Standardized, reproducible and feasible outcomes will be measured by tailor made multiple choice questionnaires, evaluation survey, frequently used Intrinsic Motivation Inventory, Single Item Self-Efficacy Question, and Google Scholar publication data. Sample size is calculated to 20 clusters and courses are randomized by a computer random number generator. Statistical analyses will be performed blinded by an external statistical expert.DiscussionPrimary outcome and secondary significant outcomes will be compared and contrasted with relevant literature. Limitations include geographical setting; bias include lack of blinding and strengths are robust assessment methods in a well-established conceptual framework. Generalizability to PhD education in other disciplines is high. Results of this study will both have implications for students and educators involved in research training courses in health and medical education and for the patients who ultimately benefits from this training.Trial registrationRetrospectively registered at ClinicalTrials.gov: NCT05736627. SPIRIT guidelines are followed.

Analysis of higher order thinking skills and lower thinking skill items in summative evaluation of Akidah-Akhlak learning

This research explores the implementation of summative evaluation in Islamic Religious Education (Akidah Akhlak) subjects at SMP Muhammadiyah 4 Yogyakarta, with a focus on implementing Higher Order Thinking Skills (HOTS). Summative evaluation is considered very important in measuring the achievement of the Islamic Religious Education program. High-level critical and creative thinking skills (HOTS) are considered important in solving everyday problems, especially in the era of globalization. This research uses a qualitative descriptive method with document content analysis, especially the final semester assessment questions for Aqidah Akhlak class VII made by the Regional Leadership of Muhammadiyah (PWM) DIY. The findings show that of the 12 final semester assessment questions, 6 questions (15%) meet the HOTS criteria, while 28 questions (70%) fall into the low-level thinking ability (LOTS) category. In addition, 6 items (15%) did not have a correct answer among the available options. These findings underscore the need to increase the integration of HOTS in designing summative evaluation questions for Islamic Religious Education (PAI). This research aims to describe and collect information about the implementation of summative evaluations and assess the results at SMP Muhammadiyah 4 Yogyakarta. Effective summative evaluation is expected to make a significant contribution to improving the quality of Islamic education at the secondary level.

Should Religious Schools Be Publicly Funded? Issues of Religion, Discrimination, and Equity

ABSTRACT This issue offers a critical opportunity to reflect on an enduring question in education: Should religious schools be state-funded? To facilitate this reflection, this issue offers six studies from Canada, Spain, and the United States. Each delves into the unique relationships between state-funded schooling and religion in their respective contexts. In particular, these studies examine how the relationships have shifted due to numerous factors, including changing legal rulings, political ideology, demographic shifts, global migration, and education privatization. The authors carefully integrate (and interrogate) the histories and places where they conducted their analyses. Taken together, these studies offer invaluable and timely insights into the intended and unintended consequences of state funding that expands school choice, marketization, and privatization, particularly with respect to religion. This issue thus aims to inform the ongoing debate about the (potential) impact that publicly funding religious schools has on equity, segregation, and discrimination. Ultimately, we hope this issue highlights the importance of a nonsectarian approach to public education so as to create an inclusive education space wherein all human identities are welcomed and affirmed.

Continuing Education Credits and Questions

Continuing Education Credits and Questions