Search

Author(s): Neha Sharma [1]; Ramesh Kumar [1]; Devendra Yadav [1]; Asha Nyati [1]; Suresh Kumar Jain (corresponding author) [1]; Apoorva Panwar [1] Introduction India is a country with diverse socio-religious [...], Background: India is a country with diverse religious and cultural practices instigating skin diseases which may be missed due to lack of awareness. Contact dermatitis (CD) to lac is not uncommon yet an underreported entity. Objectives: To study clinical patterns of allergic CD due to lac bangles and to assess common contact allergens. Methodology: A retrospective study was conducted after approval by an ethical committee. Out of 850 patients attending the CD clinic from January 2021 to December 2022, the prevalence of CD lac bangles was 40 (4.7). After obtaining consent, the history of sites, duration, morphology, and DLQI were recorded from patients' medical records. Patch testing with Indian standard series (ISS) and raw lac powder mixed with control was done. Results: A total of 40 females were evaluated. The mean duration between contact and first symptom was 14.8 ± 10.43 months. Morphology was dry eczematous in 18 (45), lichenoid in 10 (25), oozy eczematous in 5 (12.5), urticarial in 5 (12.5) and vesiculobullous in 2 (5) patients. In 14 (35) females, lesions were disseminated. In 12 (30) females, CD to bindi (4), lipstick (1), Sindhoor (5), rings (1), and plastic slippers (1) were associated. The mean DLQI was 19.37 ± 3.4. Patch test results were positive with ISS allergen epoxy in 9 (22.5) patients, nickel in 6 (15), cobalt in 6 (15), paraphenylenediamine in 5 (12.5), and lac in only 2 (5) patients. Conclusion: Data observed in the present study suggests that CD to lac is a distinct clinical entity. Low positivity with lac in patch test proposes the need for standardized testing material. Keywords: Allergic contact dermatitis, cultural dermatology¸, lac, patch test, retrospective study

University educational management quality evaluation is a systematic assessment of various elements involved in the educational management process of higher education institutions. Its goal is to analyze aspects such as management systems, resource allocation, faculty quality, student services, and curriculum design, measuring the impact of management practices on teaching outcomes to ensure educational objectives are met. This evaluation system helps improve the operational efficiency and teaching quality of universities, enhancing their reputation and aligning talent cultivation with societal needs. The quality evaluation of university educational management is a task well-suited for multi-attribute group decision-making (MAGDM). Recently, the Exponential TODIM (ExpTODIM) and VIKOR methods have been introduced to address MAGDM challenges. For handling uncertain data during this evaluation, the 2-tuple linguistic neutrosophic sets (2TLNSs) have been introduced as an effective tool. In this research, the 2-tuple linguistic neutrosophic number ExpTODIM-VIKOR (2TLNN-ExpTODIM-VIKOR) method is introduced to tackle MAGDM challenges using 2TLNSs. Furthermore, a numerical study focusing on the quality evaluation of university educational management is conducted to validate the effectiveness of the 2TLNN-ExpTODIM-VIKOR approach. [ABSTRACT FROM AUTHOR]

There is virtually no evidence on the role of taxpayer education to improve tax compliance. We address this gap by providing the first evaluation of a taxpayer education program on compliance behavior, as well as taxpayer knowledge and perceptions. Using a unique dataset of administrative and survey data, we show that training new taxpayers leads to a large and significant improvement on three compliance outcomes: the probability to file declarations, to report a zero-tax amount, and tax due. These results are robust to three estimation strategies: a simple regression exploiting baseline balance across a wide set of observable variables, propensity score matching, and an IV strategy using random assignment to take part in our survey. We identify reduced compliance costs as the key mechanism at play in explaining the relationship between tax education and compliance—particularly increased knowledge and better perceptions on complexity. The effects we document persist over time, beyond the year of implementation. [ABSTRACT FROM AUTHOR]

Diagnostic tests are typically administered in a multiple-choice (MC) format due to their advantages of objectivity and time efficiency. The MC-deterministic input, noisy "and" gate (DINA) family of models, a representative class of cognitive diagnostic models for MC items, efficiently and parsimoniously estimates the mastery profiles of examinees. However, the existing models often overestimate the latent traits of examinees when they respond with partial knowledge, which is often observed in educational assessment. Therefore, the novel models of the MC-DINA family that can appropriately handle such responses were developed in this study. Unlike the existing models, the proposed models placed no restrictions on the Q-vector, which represents attribute specifications. Simulation and empirical studies verified that the proposed approach could resolve the overestimation problem. [ABSTRACT FROM AUTHOR]

In the training of teacher students, simulated teaching is a key method for enhancing teaching skills. However, traditional evaluations of simulated teaching typically rely on direct teacher involvement and guidance, increasing teachers' workload and limiting the opportunities for teacher students to practice independently. This paper introduces a Retrieval-Augmented Generation (RAG) framework constructed using various open-source tools (such as FastChat for model inference and Whisper for speech-to-text) combined with a local large language model (LLM) for audio analysis of simulated teaching. We then selected three leading 7B-parameter open-source Chinese LLMs from the ModelScope community to analyze their generalizability and adaptability in simulated teaching voice evaluation tasks. The results show that the internlm2 model more effectively analyzes teacher students' teaching audio, providing key educational feedback. Finally, we conducted a system analysis of the simulated teaching of 10 participants in a teaching ability competition and invited three experts to score manually, verifying the system's application potential. This research demonstrates a potential approach to improving educational evaluation methods using advanced language technology. [ABSTRACT FROM AUTHOR]

This study investigates the impact of displaying grades versus not displaying grades on student performance and emotions in secondary education, while keeping feedback comments constant. Using a longitudinal design, we examined intra-individual changes in performance and emotional responses among 99 students across four classroom groups as they experienced phases of grade display and non-display. Contrary to the notion of grades solely as detrimental, our findings reveal a complex relationship. Initially, grade display decreased performance and evoked negative emotions, but these effects dissipated over time, suggesting student adaptation. Our study highlights the significant impact of feedback comments, suggesting their importance independent of grades. These results call for a sophisticated understanding of grading practices, emphasizing longitudinal research to capture the evolving effects of grades and feedback. Practical implications for educators include maintaining stable grading practices and providing preparatory guidance to mitigate initial negative impacts, contributing to optimizing educational assessment strategies. [ABSTRACT FROM AUTHOR]

The recent advancements in large language models (LLMs) have brought significant changes to the field of education, particularly in the generation and evaluation of feedback. LLMs are transforming education by streamlining tasks like content creation, feedback generation, and assessment, reducing teachers' workload and improving online education efficiency. This study aimed to verify the consistency and reliability of LLMs as evaluators by conducting automated evaluations using various LLMs based on five educational evaluation criteria. The analysis revealed that while LLMs were capable of performing consistent evaluations under certain conditions, a lack of consistency was observed both among evaluators and across models for other criteria. Notably, low agreement among human evaluators correlated with reduced reliability in LLM evaluations. Furthermore, variations in evaluation results were influenced by factors such as prompt strategies and model architecture, highlighting the complexity of achieving reliable assessments using LLMs. These findings suggest that while LLMs have the potential to transform educational systems, careful selection and combination of models are essential to improve their consistency and align their performance with human evaluators in educational settings. [ABSTRACT FROM AUTHOR]

Background: Effective pharmacotherapy requires strong collaboration between physicians and pharmacists, highlighting the need for interprofessional education (IPE) in university curricula. This study evaluated the impact of an IPE program on medical and pharmacy students, focusing on their perceived development of interprofessional collaborative competencies, perceived learning outcomes, and clinical collaboration perceptions. Methods: A mixed-method approach was employed to evaluate an IPE program that consisted of three mandatory activities with increased complexity and autonomy, that were integrated into the medical and pharmacy students' curricula. From September 2022 to June 2023, using a retrospective pre-post approach, students rated their competence levels after an educational activity using the Interprofessional Collaborative Competency Attainment Scale (ICCAS). The participants also answered open-ended survey questions about their learning outcomes. Medical students participated in both uniprofessional education (UPE) and IPE, while pharmacy students participated exclusively in IPE. Effect sizes for competency development were estimated, and subgroup analyses were performed to examine the impact of multiple IPE activities. Semi-structured interviews provided additional qualitative insights, which were analyzed using thematic analysis. Results: Of the 309 surveys completed (response rate 64%, with 274 medical and 35 pharmacy students), all 21 ICCAS items showed statistically significant improvement in both UPE (n = 127) and IPE (n = 182) (p < 0.05). While effect sizes for UPE were small across all items, IPE had medium effect sizes for six items and large effect sizes for two items. Overall, students rated themselves as more capable of interprofessional collaboration after IPE, with 55% (n = 124) reporting feeling 'somewhat better' and 6% (n = 14) feeling 'much better,' compared to 16% (n = 12) and 0%, respectively, after UPE. Competency development seems to improve slightly with an increased number of IPE activities. Pharmacy students reported somewhat higher post-activity scores than medical students did. Qualitative data from open-ended survey responses and interviews with six medical students and four pharmacy students highlighted a deeper understanding of professional roles and a greater appreciation for collaborative work through this program. Conclusions: This IPE program focused on pharmacotherapy improved self-perceived interprofessional collaborative competencies among medical and pharmacy students. Through multiple interprofessional activities, students can develop a deeper understanding of professional roles and improve collaborative skills. [ABSTRACT FROM AUTHOR]

The general aim of the study is to evaluate the use of educational technologies in shaping the future according to the opinions of school administrators. In line with the purpose of the study, the study was conducted using qualitative research methods. In addition, according to the participant opinions, they stated that educational technologies are very important in terms of making education more accessible, interesting and effective in terms of their use in education. They state that technology can further support students' learning processes if it is integrated correctly into education. School administrators have expressed that there is a great need for diversification of technological tools used in educational processes. [ABSTRACT FROM AUTHOR]

This critical ethnographic case study explores the impact of Performance-Based Assessment Tasks (PBATs) on high school dynamics and instructional practices, particularly in schools serving immigrant communities. PBATs, considered alternatives to standardized testing, have shown promise in enhancing student engagement, critical thinking, and college readiness. Drawing on interviews and fieldwork at four International High Schools, the study investigates how PBAT adoption influences day-to-day operations, teacher-student interactions, and school culture. Results reveal that PBAT implementation requires early investment in skill development and mentorship, transforming educators into facilitators and students into active learners. Moreover, the oral presentation aspect fosters multilingual proficiency. Despite challenges, PBATs offer a pathway to equitable assessment practices and educational transformation. Framed within a justice-oriented framework, this research highlights the potential of PBATs to create inclusive and empowering learning environments for immigrant and marginalized students. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the e-waste aspect of sustainability in education, with a specific interest in engineering education. Specifically, it focuses on recycling through reclaiming electronic components from e-waste and reusing them in repairs or in the design and construction of new devices. Design/methodology/approach: A systematic literature review is performed according to the PRISMA methodology. In total, 27 articles are analysed as to publication parameters, characteristics and evaluation of educational interventions on e-waste and evaluation results across major domains of learning (cognitive, affective and 21st century skills). Findings: The reviewed subject is under-research; publications are rare and mostly in conference proceedings. The majority of interventions take place at university level, in face-to-face mode, using a practical approach in hands-on labs. Educational methods draw from modern, learner-centred pedagogies such as collaborative learning and constructionism. Topics focus on innovative design and construction, while interventions tend to become embedded in engineering curricula/courses. Evaluation of learners' gains across domains of learning is rare and follows informal procedures that shake the reliability of results. Domains other than the cognitive are scarcely and subjectively evaluated. Originality/value: Contrary to other aspects of sustainability, the aspect of e-waste has not been reviewed. The applied, hands-on approach and the analytic, synthetic, collaboration and creativity skills it requires are all much valued in education. The current review, therefore, comes to inform, inspire and guide educators and researchers in planning and implementing activities on this subject. [ABSTRACT FROM AUTHOR]

AbstractThe increasing use of low-stakes international assessments highlights the importance of test-taking efforts. Previous studies have used self-reported and response time-based measures to examine this effort. Although differences between these measures have been suggested, their association with performances and potential gender gaps remains underexplored. This study addresses this gap by using PISA 2022 data from 20,560 Canadian students (50% female) with polynomial regression and response surface analysis. Findings revealed that the congruence and discrepancy between self-reported effort (SRE) and response behaviour effort (RBE) significantly affect test achievements. Students showing high levels in both efforts achieved the highest scores, with congruence outperforming discrepancy. When discrepancies exist, students with higher RBE outperformed those with higher SRE. Gender differences emerged, with RBE having a significantly stronger impact on males’ math and SRE having a slightly greater effect on females’ reading. These findings offer new insights into effort and gender gaps in educational assessments. [ABSTRACT FROM AUTHOR]

Environmental pollution, waste of resources, economic recession, political games and other problems force human beings to take the path of sustainable development. Promoting the sustainable development of education has become the mainstream recognition of all countries in the world. Based on the background of sustainable development, this paper studies the evaluation of graduate education. Firstly, the thesis is based on the evaluation of the effect of integrating red gene into graduate education. Secondly, the evaluation index system of the graduate education effect is constructed by using the expert group decision-making method, and the analytic hierarchy process is applied to determine the index weight. Finally, the paper uses the comprehensive index method to get the result of the educational effect evaluation. Through the empirical analysis, the following conclusions are drawn: (1) The evaluation index system of the graduate education effect can be carried out in five aspects: A academic discipline construction, B daily management, C party and league construction, D academic integrity, E curriculum ideology and politics. (2) The index weight of graduate student effect evaluation has typical heterogeneity. The lowest weight value is 18.16%; The highest weight is 24.25%. (3) The average score of graduate education effect evaluation of the university in the survey sample is 1.00996, which is in the interval [1.0,1.1], corresponding to the grade II, indicating a very good educational effect. [ABSTRACT FROM AUTHOR]

Higher Education must assume the commitment to provide quality education, as proposed by the UN in the 2030 Agenda. In this sense, dialogic learning stands out as a methodological strategy that promotes participation, communication and autonomous learning. This work aims to describe the design and validation process of an instrument that analyzes the university student's assessment of educational experiences based on dialogic learning. Taking the principles of dialogic learning as a reference for the design of the instrument, its content validity has been ensured by a panel of experts (n=9) through the Delphi method and the application of a pilot test to students of the Degree in Primary Education at the University of Jaén (n=132). For construct validity, the univariate normality of each of the items has been verified, as well as the application of the exploratory factor analysis model based on principal components and Varimax rotation and Pearson correlation analysis. Reliability analysis uses Cronbach's alpha coefficient and the two-half method. Results show an instrument with high reliability rates and positive internal consistency; it is made up of four factors, which are: contributions to learning, dialogue and communication skills, diversity and social inclusion, and dialogic reflection. It is a psychometrically robust tool, being an appropriate and necessary instrument to evaluate the impact of dialogic learning on university students, responding to some of the recommendations proposed by the United Nations to advance towards the achievement of the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Copyright of Dados - Revista de Ciências Sociais is the property of DADOS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Spanish Journal of Comparative Education / Revista Española de Educación Comparada is the property of Editorial UNED and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Arab Studies in Education & Psychology is the property of Association of Arab Educators and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: As the intersection of artificial intelligence (AI) and education deepens, predictive analytics using machine learning (ML) and deep learning (DL) models offer novel approaches to assessing student performance in online environments. However, challenges remain in accurately predicting high achievers and identifying students at risk due to limitations in traditional assessment models. This study explores the capabilities of these models in predicting academic achievement and highlights their potential role in reshaping educational assessment paradigms. Objectives: To evaluate the efficacy of various AI models—including Random Forest, XGBoost, and recurrent neural networks (RNNs)—in identifying at-risk students and differentiating levels of academic achievement, with an emphasis on inclusive and adaptive educational assessments. A key focus is on leveraging these models to create more inclusive and adaptive educational assessments. Methods: We analyzed a dataset comprising interaction data from the Open University Learning Analytics Dataset (OULAD), which includes clickstream data on student interactions with course materials from over 32,000 students. The models were trained and evaluated using performance metrics such as accuracy, precision, recall, and F1-scores, specifically targeting predictions of student withdrawals and distinctions. Results: The models effectively identified students at risk of withdrawing, with the Random Forest model achieving an accuracy of 78.68% and deep learning models approximately 77%. However, accurately predicting high achievers posed challenges, suggesting a complex relationship between interaction data and academic success. This limitation underscores the need for more nuanced modeling approaches to improve predictions for top-performing students. Conclusion: This research demonstrates the promise of AI-driven models in enhancing educational assessments while also highlighting current limitations in capturing academic excellence. Our findings indicate a need for ongoing development of AI tools that are ethically designed and capable of supporting dynamic, inclusive assessment strategies. Future research should focus on incorporating additional factors, such as student motivation and study behaviors, to enhance predictive accuracy, particularly for high achievers. Such advancements can contribute to a more equitable and effective educational landscape. [ABSTRACT FROM AUTHOR]

Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty, evaluating educational institutions can be difficult. The concept of a possibility Pythagorean fuzzy hypersoft set (pPyFHSS) is more flexible in this regard than other theoretical fuzzy set-like models, even though some attempts have been made in the literature to address such uncertainties. This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union, intersection, complement, OR- and AND-operations. Some results related to these operations are also modified for pPyFHSS. Additionally, the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results. Lastly, an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making (MADM) problems. A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system. The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS, as it generalizes the classical possibility fuzzy set-like theoretical models. Similarly, while considering significant evaluating factors, the flexibility of pPyFHSS is observed through structural comparison. [ABSTRACT FROM AUTHOR]

The integration of 21st-century educational e-assessment methods by secondary teachers holds immense potential for transforming teaching and learning practices. Prior to the pandemic and new curriculum, traditional assessment methods faced challenges, such as the need to maintain social and physical distancing and conduct in-person tests and examinations. Consequently, the popularity and utilization of electronic assessments surged. As the world transitions post-COVID-19, there is ongoing debate regarding the merits of returning to traditional assessment approaches versus integrating online assessment methods. Thus, this descriptive study aims to describe the opportunities and challenges associated with the utilization of e-assessment in the context of a public secondary high school. With a focus on practicality, authenticity, consistency, and transparency, the study examines the perspectives of 30 secondary teachers actively engaged in utilizing digital assessment tools. Through a meticulously designed questionnaire based on the Alternative Digital Assessment Theory and prACT framework, the study gathers insights into the perceived opportunities and challenges faced by teachers in integrating e-assessment into their pedagogical practices. The findings reveal a generally positive outlook among teachers towards the opportunities presented by e-assessment, particularly in terms of transparency and consistency. Teachers strongly agree that digital assessment methods enhance transparency by promoting open communication and collaboration between students and educators, while also ensuring consistency in feedback provision and grading practices. However, challenges related to practicability emerge as significant concerns, with limited technological infrastructure and training posing obstacles to the seamless implementation of e-assessment. Additionally, the study identifies age-related variations in teachers' perspectives on the challenges posed by e-assessment. [ABSTRACT FROM AUTHOR]

The classroom social–emotional climate is critical to students' wellbeing and learning ability. In practice and research, an assessment instrument with strong validity is invaluable, providing crucial information about the classroom's social–emotional and learning climate. This allows for a more accurate appreciation of the quality of the learning environment. A valid instrument is helpful and essential in improving the classroom climate in practice and demonstrating intervention outcomes in research. The primary aim was to evaluate the concurrent validity of the Educational Climate Assessment Tool (EduCAT)—a newly developed classroom observation instrument based on a solid theoretical framework and previous empirical research. The concurrent validity testing involved examining pairwise and multivariate canonical correlations between scores from the EduCAT scales (stimulation, improvement, structure, relationship, and influence) and scores from the Classroom Assessment Scoring System (CLASS®) administered concurrently in 30 kindergarten to third grade (K-3) classrooms in four schools in Sweden. The pairwise correlation results show that the scores of the two observation instruments have strong and moderate positive correlations, as hypothesized. The canonical correlation identifies which scales in EduCAT correlate more strongly to scales in CLASS®. The results indicate that the observations measured with EduCAT tap similar aspects of classroom climate quality to those measured with CLASS®, thereby confirming the new protocol's concurrent validity and instilling confidence in its use. [ABSTRACT FROM AUTHOR]

Invention Education (IvE), a form of problem-based learning, presents new challenges for educational assessments in public schooling because traditional assessments were designed to evaluate learning in singular disciplines. This study explores the challenges and possibilities for assessing new knowledge and capabilities acquired through students' engagement with multiple disciplines through IvE. Guided by constructivist and sociocultural theories, as well as an understanding of IvE principles and practices derived from the literature on IvE, we examine the phases of work within a national IvE program for high school students and educators. We then examine ways existing assessments align with the work at each stage of the IvE process. Findings from this case study underscore the need for a flexible assessment system with multiple measures (e.g., disciplinary knowledge and practices, skill inventories, etc.). The system must account for variations in learning contexts, individual and collective achievements, and varying lengths of time students engage in IvE. [ABSTRACT FROM AUTHOR]

The current study describes the process of development and validation of a rubric for assessing educational robotics skills among middle school students. A multiphase method has been followed, including a literature study, expert consultation, content validation, pilot testing, reliability analysis, and construct validation. The resulting dual-category rubric designing and programming skills were further elaborated by specific criteria and performance levels. Content validation identified seven key criteria: stability and durability, motors, innovation, code organization, dependability and reliability, movement and rotation, and innovative use of sensors. The results of inter-rater reliability analysis for all criteria indicated good agreement, with Krippendorff's alpha ranging from 0.941 to 1.000. Additionally, construct validation using exploratory factor analysis confirmed a two-factor structure that aligns with the intended domains of design and programming skills, explaining 67.4% of the total variance. The rubric was consistent with learning/teaching theories such as Bloom's taxonomy, constructivist learning theory, and self-determination theory. This rubric fulfills the existing need for a specific assessment tool in educational robotics and supports educational practices through very detailed feedback to students on their performance. This adds much to educational assessment and education in robotics and enhances teaching and learning outcomes for STEAM education in various settings. [ABSTRACT FROM AUTHOR]

This perspective paper describes the strategic development of evaluation and organizational learning programs focused on building core knowledge and skills at a university with high research activity. New program development allows academic leaders to introduce relevant and cutting-edge knowledge to students. Nevertheless, the program development process must be more explicit, as it faces many logistical and technical challenges. We argue that academic program development needs to be an integrated and collaborative process paying intentional attention to program needs, context, mission, student learning outcomes, and program of study. The article describes our experiences implementing these processes, explains the interrelationships between the processes, and shares our perspective on academic program development. Our story began with establishing a faculty team and conducting literature reviews to identify gaps in existing evaluation education programs. The interest surveys indicated that 46% of the respondents are interested in pursuing an advanced degree. In addition, 88% of employers and supervisors indicated their preference for a master's degree in evaluation. Forty-two percent of the respondents also pointed to a strong interest in an online program. The program team used this survey data to determine the next steps for program development. Finally, the paper contends that a collaborative stance is central to academic program development. The paper describes our approach to building solid collaborations with students, prospective employers, leadership at the department, college, and university levels, and vocational organizations. Through our program development experience, we advocate for adopting a systematic, collaborative, and context-sensitive approach to program development. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: The present study aimed to design and test an education model based on a blended approach among nurses at medical universities. Methods and Materials: In this qualitative study, the participants included experts and specialists in the field of medical education, nursing department managers, and nursing faculty members (academic staff) with specific years of experience in medical education at Lorestan University of Medical Sciences, totaling 15 individuals. The study was fundamental in nature. The research tools included semistructured interviews and a review of the literature and previous research. The findings were analyzed using document analysis, library studies, thematic analysis, and the Delphi method. Findings: The results indicated that the education model based on the blended approach emerged in the form of five components: pedagogy of education, educational objectives, educational content, teaching methods, and educational evaluation. The quantitative data analysis results showed that the blended approach education for students included 41 indicators within five main components (pedagogy of education, educational objectives, educational content, teaching methods, educational evaluation), of which three components (pedagogy of education, educational objectives, teaching methods) were derived from literature review, and two components (educational content, educational evaluation) were derived from data analysis of interviews. Conclusion: The results suggest that the blended approach in education, encompassing pedagogy, educational objectives, content, methods, and evaluation, effectively addresses the diverse needs of nursing students. By integrating traditional and virtual methods, this model enhances flexibility, engagement, and the overall quality of education, supporting better academic performance and satisfaction among nursing students at Lorestan University of Medical Sciences. [ABSTRACT FROM AUTHOR]

In light of the widespread adoption of technology-enhanced learning and assessment platforms, there is a growing demand for innovative, high-quality, and diverse assessment questions. Automatic Question Generation (AQG) has emerged as a valuable solution, enabling educators and assessment developers to efficiently produce a large volume of test items, questions, or assessments within a short timeframe. AQG leverages computer algorithms to automatically generate questions, streamlining the question-generation process. Despite the efficiency gains, significant gaps in the question-generation pipeline hinder the seamless integration of AQG systems into the assessment process. Notably, the absence of a standardized evaluation framework poses a substantial challenge in assessing the quality and usability of automatically generated questions. This study addresses this gap by conducting a comprehensive survey of existing question evaluation methods, a crucial step in refining the question generation pipeline. Subsequently, we present a taxonomy for these evaluation methods, shedding light on their respective advantages and limitations within the AQG context. The study concludes by offering recommendations for future research to enhance the effectiveness of AQG systems in educational assessments. [ABSTRACT FROM AUTHOR]

Ask NAEP, a chatbot built with the Retrieval-Augmented Generation (RAG) technique, aims to provide accurate and comprehensive responses to queries about publicly available information of the National Assessment of Educational Progress (NAEP). This study presents an evaluation of this chatbot's performance in generating highquality responses. We conducted a series of experiments to explore the impact of incorporating a retrieval component into GPT-3.5 and GPT-4o large language models and evaluated the combined retrieval and generative processes. This work presents a multidimensional evaluation framework using an ordinal scale to assess three dimensions of chatbot performance: correctness, completeness, and communication. Human evaluators assessed the quality of responses across various NAEP subjects. The findings revealed that GPT-4o consistently outperformed GPT-3.5, with statistically significant improvements across all dimensions. Incorporating retrieval into the pipeline further enhanced performance. The RAG approach resulted in high-quality responses. Ask NAEP reduced the occurrence of hallucinations by increasing the correctness measure from 85.5% of questions to 92.7%, a 50% reduction in non-passing responses. The study demonstrates that leveraging large language models (LLMs) like GPT-4o, along with a robust RAG technique, significantly improves the quality of responses generated by the Ask NAEP chatbot. These enhancements can help users to better navigate the extensive NAEP documentation more effectively by providing accurate responses to their queries. [ABSTRACT FROM AUTHOR]

The integration of generative AI in K12 education and assessment development holds the potential to revolutionize instructional practices, assessment development, and content alignment. This article presents analytical insights and findings from early adoption studies utilizing AI-powered tools developed by Finetune--Generate and Catalog. Generate enhances the efficiency of assessment item development through customized natural language generation, producing high-quality, psychometrically valid items. Catalog intelligently tags and aligns educational content to various standards and frameworks, improving precision and reducing subjectivity. Through three comprehensive case studies, we explore the practical applications, benefits, and lessons learned from employing these AI systems in real-world educational settings. The purpose of this series of studies was to investigate the ways generative AI is currently being used in practical applications in test development to improve processes and products. The studies demonstrate significant reductions in time and costs, enhanced accuracy, and consistency in content alignment, and improved quality of educational and assessment materials. The findings underscore the substantial benefits and critical importance of customized AI systems, rigorous training for both AI models and users, and adopting appropriate evaluation metrics. With the use of off-the-shelf generative AI models expanding rapidly, it is vital that the effectiveness of AI systems that are highly customized through collaborations with measurement experts be presented, in order to maximize benefits and uphold the fundamental principles and best practices of test development. [ABSTRACT FROM AUTHOR]