To develop and disseminate an innovative blended educational programme comprising of evidence-based neonatal healthcare practices across India and to identify and empower local champions who could sustain and scale quality improvement (QI) efforts regionally. The study was conducted in two phases and engaged healthcare professionals mentored by trained facilitators (1:8 ratio). A structured methodology was adopted comprising of: (i) assessment of learning needs, (ii) identification of the target audience and local facilitators, (iii) development of content and blended learning strategy, (iv) alignment with measurable QI objectives, (v) documentation of outcomes and (vi) implementation through the support of the National Neonatology Forum. Participant and facilitator feedback guided course evaluation. The course engaged 1073 healthcare professionals (590 doctors, 483 nurses) with 2-10years of neonatal experience mentored by 132 facilitators. Phase 1 included 250 participants from five Indian states, and 823 participants from 18 Indian states and four South-East Asia Regional Organization (SEARO) countries-Bangladesh, Bhutan, Maldives and Nepal-were enrolled in Phase 2. Overall, 57% participants completed weekly online quizzes, and the average attendance for live webinars was 50%. 43 hands-on skill workshops were held training 685 professionals. This flipped-classroom, blended model promoted exploratory and self-directed learning. Its adaptability and emphasis on faculty development and QI projects offer a replicable framework for future neonatal training initiatives.

By investigating the current state of online learning environments in medical education and students' academic emotions, this study aims to further understand the association between online learning environments and academic emotions, thereby providing insights for optimizing the online learning environment in medical education. A cross-sectional study. The study employed a convenience sampling method to select participants from a sample of 317 medical school students at a university in China. The study was conducted from September to November 2023. Data was collected using General Information Questionnaire, Online Learning Environment Assessment Scale, and College Students' General Academic Emotions Questionnaire. Pearson's correlation coefficient was used to examine the association between the online learning environment and academic emotions. And the ENTER method was chosen for multiple linear regression analysis. The results showed that content design had a significant positive association on positive activating emotions. Learning assessment had significant negative association on negative activating emotions and negative deactivating emotions. Website design had a significant positive association on negative deactivating emotions. In order to increase positive academic emotions and decrease negative emotions among medical students in online learning, improvements can be made in the areas of content design, learning assessment, and website design, which in turn will improve the effectiveness of online learning.

Assessment of student learning is commonly understood as an objective measurement of learning outcomes, targeting students’ abilities. In information literacy instruction, assessment practices in Ugandan University libraries are seldom documented, and as a result, demonstrating the value of libraries’ information literacy programs to university stakeholders and administrators becomes difficult. This paper examines the current assessment practices for information literacy instruction at selected Ugandan university libraries to improve assessment policies for information literacy. The sociocultural theory of learning was chosen to guide this study because it offers a learning environment where contextualised and authentic assessment can be done. The concepts of collaborative assessment, social interaction and zone of proximal development were important in analysing the research question. Employing a multi-site qualitative case study, data was generated from semi-structured interviews and focus group discussions. Data was analyzed using a thematic approach with the help of a Qualitative Data Analysis Software (Atlas.ti v.22). Findings indicate that the libraries conducted assessment for information literacy mainly using formative methods that included brief questionnaires, analysis of user queries, in-class questions, assignments, demonstrations and librarian role-playing (authentic assessment) which improves students’ level of potential development. Summative assessment methods were occasionally employed through the completion of evaluation forms, analysis of usage statistics and examinations. The findings reveal areas of concern—the optional and non-integrated nature of information literacy provision in universities. This paper concludes that assessment methods were less reliable due to their informal nature, but fairly valid in scenarios where real-life information tasks were given to students to apply the knowledge, understanding and skills that they had learnt. To provide evidence for demonstrating the value of libraries’ information literacy instruction to stakeholders, university libraries should integrate information literacy into the curriculum, ensuring assessment becomes an integral part of information literacy programs. Furthermore, incorporating collaborative and authentic assessment practices that replicate real-life challenges faced in the field is recommended for harnessing assessment.

Abstract Purpose The study aims to systematically evaluate studies focused on the evaluation of Massive Open Online Course (MOOC) platforms and existing frameworks to evolve an integrated model for the comprehensive evaluation of MOOC platforms. Design/methodology/approach The study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the Web of Science (WoS) database was used to retrieve the data. A thematic analysis method was used to critically examine the existing frameworks identified in different studies to identify different criteria concerning various aspects of MOOC platforms. The identified criteria were further analysed to include different components concerning different aspects of MOOC platforms to propose a theoretically grounded integrated evaluation framework for MOOC platforms. Findings The findings highlight a diverse set of evaluation frameworks used in MOOC research, including Kirkpatrick’s Model, Quality Matters (QM) Evaluation Framework, and Web Content Accessibility Guidelines (WCAG). After critical analysis and discussion among peers, the study identified six major evaluation dimensions: course design and structure, learner engagement, instructional quality, assessment and learning outcomes, technical and administrative factors, and demographic and completion metrics. Furthermore, the results reveal that most MOOC research is concentrated in China, the UK and the US. Additionally, most studies employed quantitative and mixed-method approaches, with surveys being the most commonly used data-collection tool. Furthermore, xMOOC is a commonly studied platform, and the highest number of publications were published in 2024. Originality/value The study offers a unique integrated framework by synthesizing existing MOOC evaluation frameworks, providing a practical tool for the consistent and comprehensive assessment of MOOC platforms.

This study aims to systematically review self-regulated learning (SRL) assessment practices, key SRL strategies used by learners, and contextual factors that shape SRL in technology-enhanced English as a foreign language (EFL) higher education setting. Following the PRISMA framework, empirical studies were identified, screened, and systematically synthesized through thematic analysis. Findings from the review of 22 literature sources indicate that SRL research in the context of technology-mediated EFL relies heavily on quantitative and mixed-method designs, with standardized instruments such as the OSLQ, SOL-Q, and adapted SRL scales dominating the field. In terms of strategy use, goal setting, metacognitive regulation, and environmental structure emerged as the most prominent strategies, while cognitive-task strategies, help-seeking/social regulation, and affective-motivational regulation were less frequently emphasized. The review also identified four categories of influential factors: students, teachers, classrooms, and technology. Future research should adopt more diverse and methodologically robust approaches and explore how SRL is jointly formed through interactions between learners, instructors, peers, and digital tools, including through intervention studies that design and test pedagogical support to strengthen underdeveloped strategies.

This study aimed to: (1) examine the components and indicators of competence in project-based learning (PBL) management among science teachers; (2) investigate the current and desired states of such competence, as well as the priority needs; and (3) propose guidelines for developing competence in PBL management. The research was conducted in three phases: (1) synthesis of components and indicators, (2) survey of the current and desired states with an analysis of priority needs, and (3) formulation of development guidelines. The sample comprised 370 school administrators and science teachers, selected through multistage sampling. The research instrument was a questionnaire, and data were analyzed using percentage, mean, standard deviation, and priority needs index (PNImodified). The findings revealed that: (1) competence in PBL management comprised four components and fifteen indicators—curriculum development (3 indicators), learning design (4 indicators), PBL implementation (4 indicators), and learning assessment and evaluation (4 indicators). Overall appropriateness was rated at the highest level. (2) The overall current state was at a moderate level, whereas the desired state was high, with the greatest priority need identified in PBL implementation. (3) The proposed development guidelines emphasized systematic curriculum development, step-by-step learning design, shifting the teacher’s role to that of a facilitator, and assessments focusing on both knowledge and skills development.

This academic article aims to present the essential components of prompt engineering artistry for enabling artificial intelligence to create online learning assessment and evaluation tools that respond to the needs of digital-age learners. These tools operate consistently across all locations and timeframes, providing immediate results and feedback that foster sustainable development while ensuring comprehensive, equitable, and quality education for all learners through authentic assessment practices. The development of learner assessment tools through prompt engineering for AI implementation can be executed by instructors who establish assessment objectives, beginning with five systematic phases: First, learner analysis focuses on foundational knowledge of subject content, behavioral practices, learner characteristics, and appropriate assessment timeframes. Second, the selection of appropriate assessment tool categories. Third, prompt design involves selecting prompt engineering components and validating prompt quality to ensure alignment with learning objectives, accuracy, clarity, and appropriateness for target populations. Fourth, the selection of AI platforms for creating learning assessment tools using predetermined prompt commands. Fifth, program testing and validation to verify accuracy and appropriateness, with prompt engineering refinement and regeneration when errors are identified. Consequently, the application of AI prompt engineering artistry for creating digital-age learning assessment tools holds significant importance in driving and supporting instructional management, enabling learners to develop themselves effectively and efficiently. This approach promotes continuous learner potential fulfillment, leading to sustainable learning outcomes while facilitating educators’ ability to provide personalized, immediate feedback that enhances the overall educational experience. The systematic integration of prompt engineering with artificial intelligence represents a transformative approach to educational assessment, offering scalable solutions that maintain pedagogical rigor while addressing the diverse needs of contemporary learners in digitally mediated learning environments.

Many medical training models include discrete rotational blocks, which can be a barrier to the longitudinal assessment and coaching of learners. Learner handover is the sharing of information on a given learner's progress between faculty supervisors and could aid in longitudinal feedback. Perspectives on learner handover by faculty supervisors and educational leaders vary. Learners' perspectives on learner handover between faculty supervisors are lacking. This study aimed to obtain the perspectives of internal medicine postgraduate learners on learner handover practices and desired characteristics of formalized learner handover. Postgraduate learners in a single internal medicine program at a single center in Canada were invited to participate. This qualitative study used dyadic/triadic interviews conducted in a virtual format to collect data. A clinician-educator outside of the training program facilitated the interviews. Two team members conducted iterative, inductive analysis to develop codes and themes using thematic analysis. The final themes were generated after a series of reflexive discussions with our purposefully assembled research team of faculty and learners with varied experience and perspectives within medical education. Seven of the 86 (8%) postgraduate learners chose to participate. Despite participating in learner handover, the learners did not identify the process as a known phenomenon. The identified benefits include longitudinal coaching, self-regulated learner development, and advanced knowledge of the strengths and weaknesses of the clinical teaching unit learners on rotation. The risks of biasing supervisors and learner anxiety were noted. To improve learner handover practices, learners suggest that learner handover should be learner-centric, have a minimal impact on the current assessment burden, standardized, and transparent. A model to implement learner handover that integrates these findings with the existing literature and promotes self-regulated learning is described. Postgraduate learners have similar perspectives on learner handover as other groups in medical education. A model for learner handover that leverages existing learner handover activity into a process for developing a self-regulated learner is created from these findings and the literature. This can inform postgraduate training programs on how to formalize learner handover practices to benefit the learner's development and provide targets for faculty development.

The simulation-based self-directed learning model via metaverse has been developed with an intention to enhance analytical thinking and health counseling. Thereby, the design of the said learning model is based mainly on the principles of simulation-based learning that comprises 3 steps, i.e., pre-brief, scenario phase, and debriefing phase, integrated with the principles of self-directed learning, which consists of 4 steps, i.e., assess readiness to learn, set learning goals, engage in the learning process, and evaluate learning. The combination of the aforementioned two learning processes with the aid of metaverse technology facilitates the fabrication of the new immersive learner-centered experiences that encourage learners to have more active participation. It is believed that the simulation-based self-directed learning model via metaverse developed in this study shall not only result in the improvement of analytical thinking skills as evidenced by the reflection of learning assessment, but also develop health counseling skills acquired from the contexts of virtual practices. According to the results of this research, it is found that the suitability of the simulation-based self-directed learning model via metaverse is at very high level; and this can be summarized that the learning model of this kind can be applied as a guideline for further developing other simulation-based self-directed learning models in order to enhance analytical thinking skills and health counseling in the contexts of digital education.

The single case study under consideration explores how tablet-enabled translation assisted in facilitating inclusive education in an English classroom of Year 7 at an English high school. Using Sociocultural Theory that focuses on learning as a process that is socially mediated (Vygotsky, 1978), the paper discusses how an Arabic-speaking new student could gain access to the content of the curriculum, become an engaged and active participant by using a tablet-based translation tool. Real-time translation of learning materials and assessments prompted the student to work alongside peers equitably. The data sources comprised worksheets, handwritten notes, translated outputs, classroom observations and teacher reflections which were analysed thematically to determine the patterns of access, engagement, and social inclusion. Ethical measures covered anonymisation and confidentiality. The findings of this one case study indicate how widespread digital tools may reduce the language barrier, level the playing field, and teach the inclusive pedagogy in a classroom environment.

This study presents a comprehensive bibliometric analysis of the DSSAT crop modeling field from 1990 to 2024, identifying its evolutionary trajectory and emerging frontiers. A comprehensive bibliometric analysis and network visualization were conducted using VOSviewer, CiteSpace, and Bibliometrix. Analyzing 6165 Scopus-indexed publications, we found the research focus has shifted from foundational yield simulation and calibration toward addressing complex climate-water-food challenges. Three distinct developmental phases were identified: an initial establishment phase, a methodological refinement phase, and a current technology integration phase dominated by machine learning and remote sensing applications. The results reveal that machine learning, model-data fusion, and sustainability assessment represent the most active research frontiers. This analysis provides a systematic map of the field’s intellectual structure and offers evidence-based predictions for its future development, highlighting the transition of DSSAT from a specialized crop model to an interdisciplinary decision-support platform for sustainable agriculture.

Abstract: In early grade students’ deficit of persistent learning is mostly covered by Foundational Literacy and Numeracy (FLN) program and have become a central focus of education policy. Foundational skills include the ability to read with meaningful comprehension and different basic and complex arithmetic operations by the end of upper primary. With the help of foundational skills meaningful participation in primary education and economic development of learners and students can be possible. This study shows the effectiveness of FLN programs on learning outcome with the help of different statistical analysis and economic theories prepare integrated framework. Effectiveness of any program cannot be automatically translate into learning achievement for that different statistical evidence from large scale learning assessment used to indicates about that school. Different surveys like Annual Status of Education Report (ASER) and National Achievement Surveys (NAS) gives the comparative results between expectation and actual student competencies at different grade level and also shows the substantial gaps by comparison of student’s result. FLN program find a solution of a problem of mismatch through continuous assessment, improvement of teacher capacity, changes in curriculum and structured pedagogy. By using different methods and models like descriptive statistics, inferential methods and econometric models shows the significant improvement in outcome and result of early literacy and numeracy and shows program beneficiaries and its effect on education. Meaningful learning gains can be known when effect sizes show the range between 0.2 to 0.6 standard deviation across multiple studies.

ObjectiveTo evaluate the immediate learning outcomes of an AI- and sensor-integrated cardiopulmonary resuscitation (CPR) retraining system (Intelligent Practical Training System, IPTS) with conventional face-to-face retraining in healthcare professionals.MethodsWe conducted a randomized trial of 136 healthcare professionals, allocated 1:1 to a 30-minute IPTS or face-to-face groups. IPTS integrates AI and sensors to enable instructor-free learning and examiner-free assessment. Learning outcomes were measured objectively using the manikin, without providing any feedback or assessment results to participants. Primary outcomes were chest compression depth (CC) accuracy and CC rate. Secondary outcomes included tidal volumes of the two rescue breaths and CPR self-efficacy.ResultsNo evidence supports the superiority of IPTS in CC depth accuracy (P_adj = .878) or CC rate (P_adj = .729). However, the IPTS group achieved significantly superior rescue breath quality (first: rank-biserial r = −.585, 95% CI [−0.737, −0.416], P_adj < .006, large; second: rank-biserial r = −.512, 95% CI [−0.687, −0.323], P_adj < .006, large), with median values falling within the American Heart Association recommended range (first: 548.99 mL, second: 542.68 mL), whereas the face-to-face group's values were excessively high (first: 806.97 mL, second: 800.23 mL). Both groups showed significant and similar improvements in CPR self-efficacy, indicating comparable psychological benefits.ConclusionUnder controlled conditions, IPTS did not demonstrate superiority in immediate outcomes for CC quality and CPR self-efficacy compared to sensor-free face-to-face instruction, but achieved superior immediate outcomes for rescue breaths. These results demonstrate the feasibility of a novel integration of AI and sensors in medical education.

Assessment of learning involves determining whether the content and objectives of education have been mastered by administering quality tests. However, the quality of the test items used in evaluating students’ achievement has been a major area of concern in teaching and research. This paper assessed the quality of Nigerian Government and Economy (NGE) Examination items of Northwest University, Kano by conducting item analysis. The developed one hundred (100) questions was administered to N=256 undergraduate students. The data obtained was analysed by conducting item analysis to determine the item difficulty, item discrimination indices, as well as the distractors analysis. Results indicate that, based on difficulty indices 55(55%) items were “defective” or “faulty”, 66(66%) of the items have poor discriminating power. Similarly, the result of the distracter analysis showed 59(28%) of the distracters been flawed, having failed to meet the set minimum standards. Based on the findings it can be concluded that the test used has not been validated during the item development processes. It is recommended that, the Nigerian Government and Economy (NGE) test items used in measuring students’ achievement should be adequately improve through formal processes of standardisation and validation by conducting item analysis to ensure its reliability and minimise measurement errors.

Individual student success is influenced by the assessment system adopted by the teacher since continuous assessment, assessment for learning, provides feedback to the students for further improvement in regular classes. The study explored the opportunities offered by the CAS (Continuous Assessment System) and the challenges faced by teachers in implementing the continuous assessment system of grades one to three in Nepal, pursuing a qualitative method through a social constructivist lens. Four teachers practising the continuous assessment system in their schools were purposively selected as the research participants. The data were collected using semi-structured interviews and were analysed thematically. The findings show that a continuous assessment system offers an opportunity for students' learning improvement for weak students. Diverse abilities of students are assessed through CAS since it uses different tools of evaluation, not just a paper and pencil test and is extended beyond conventional methods. In addition, it develops the students' critical thinking skills. However, CAS has different implementation challenges, such as time-consuming, a lack of training, and difficulty in managing remedial teaching for weak students.

The present study aimed to design and validate an educational model for Allameh Tabataba'i University under the conditions of the COVID-19 and post-COVID-19 periods. The research employed a mixed-methods design. Using thematic analysis and grounded theory approaches, the study proposed two distinct educational models. The research population included full-time and adjunct faculty members, PhD students, and top-ranking students from the faculties of Allameh Tabataba'i University. The sample was selected through purposive sampling, and following the principle of theoretical saturation, a total of 13 interviews were conducted. The proposed models were validated using the fuzzy Delphi technique, and confirmation of the models was obtained from 10 subject-matter experts. The first model, based on the ADDIE instructional design framework, included five components: educational infrastructure and support, instructional and content design, evaluation and learning assessment, educational roles and interactions, and emerging technologies with artificial intelligence. The second model presented a sub-theory within the framework of grounded theory. In this model, the design of an effective educational model was identified as the central phenomenon influenced by causal conditions such as change-inducing factors, contextual conditions such as technological infrastructure, and intervening conditions such as university policy. Ultimately, strategies such as the development of infrastructure and flexible instructional design were proposed, leading to learner-centeredness and crisis preparedness as the resulting outcomes.

Comparative assessment of machine learning and band ratios for robust water quality assessment in inland waters

Abstract The accurate forecasting of Indoor Temperature is a cornerstone of effective energy&amp;#xD; management and occupant comfort in low-energy buildings. Conventional estimation&amp;#xD; methods often rely on extensive datasets or complex physical modeling. Therefore, this&amp;#xD; study addresses the need for accurate yet computationally efficient forecasting models by&amp;#xD; investigating forecast performance using a minimal set of input variables: only Outdoor&amp;#xD; Temperature and electric heater consumption. To this end, a comparative analysis was&amp;#xD; conducted between two fundamentally different approaches: first, a mathematical model&amp;#xD; was constructed with its coefficients determined using classical regression methods&amp;#xD; (Ordinary Least Squares, Recursive Least Squares, and Gradient Descent); subsequently,&amp;#xD; three advanced recurrent neural network models (Simple Recurrent Neural Network, Long&amp;#xD; Short-Term Memory, and Gated Recurrent Unit) were employed for their ability to&amp;#xD; capture time-series dependencies. Model performance was rigorously evaluated using&amp;#xD; f&amp;#xD; ive-fold cross-validation. Regarding the results, the findings demonstrate a clear&amp;#xD; performance superiority of the advanced models. While the mathematical model achieved&amp;#xD; moderate accuracy (RMSE = 0.2186, R2 = 0.9924), the Gated Recurrent Unit model&amp;#xD; delivered the most precise forecasts, achieving an average RMSE of 0.0835, MAPE of&amp;#xD; 0.2146, and an R2 of 0.9928. These findings highlight the superior effectiveness and&amp;#xD; robustness of recurrent neural network models, particularly the GRU, for high-accuracy&amp;#xD; Indoor Temperature forecast in low-energy buildings, even when constrained to minimal&amp;#xD; input data, thus validating a pathway for implementing highly efficient and streamlined&amp;#xD; predictive control systems.

A systematic review of resilience strategies with emphasis on the role of machine learning and quantitative assessment under adverse operating conditions

An emphasis on science practices in university science education provides a logical route to student development of scientific literacy and critical thinking. Unlike scientific literacy and critical thinking which have been the subjects of much debate and controversy, the science practices are consensus-based and well-defined. As the disaggregated components of critical thinking, science practices are well-positioned to support and systematically assess student development of critical thinking. In this paper, we report on our examination of general chemistry laboratory curricula for opportunities to engage students in science practices using the Three-Dimensional Learning Assessment Protocol, 3D-LAP. Our findings suggest that the laboratory curricula provide opportunities for students to engage in some practices, while others are consistently underrepresented. Additionally, there is a disproportionate emphasis on mathematical thinking which not only has equity implications, especially for marginalized students, but could also have the unintended consequence of students developing a distorted view of the intellectual work of chemistry. The findings of our study indicate a need for greater balance with respect to the practices in the general chemistry laboratory curricula, and have the potential to inform the design, development, or adaptation of lab curricula to better support scientific literacy and critical thinking. We couple accountable disciplinary knowledge and constructive alignment to discuss these and other findings and implications of our work for teaching, learning, and research.