ABSTRACT Canine-assisted interventions (CAIs), a subset of animal-assisted interventions (AAIs), are known to benefit university students’ mental health, but little is known about how these benefits may vary across different points in the semester. This quasi-experimental study assessed undergraduate well-being and mood before and after participation in CAI events held in September (n = 46) and December (n = 30). Although we predicted students would be more stressed on entering the events in December (due to upcoming final exams), we found the groups to not differ on stress (Perceived Stress Scale; p = .882) or general mood (5-point Likert scale; p = .548) before the event. However, we still found the efficacy of the events to differ based on time of semester; students experienced greater improvements in well-being (WHO; η p 2 = .140) and one of the mood measures (BMIS negativity subscale; η p 2 = .052) at the end of the semester. In addition, students’ self-determined visit duration predicted (p = .052) the PSS pleasantness difference scores; however, this effect was observed only in December. We suggest the time of semester/year as another potential variable that universities should consider when incorporating CAIs into mental health initiatives.

This study investigates the reliability, validity, and fairness of university-level calculus assessments using Rasch analysis, focusing on the progression and alignment of key concepts across midterm and final exams. A cohort of 369 students from diverse academic disciplines was assessed using standardized exams designed to align with learning outcomes. The midterm and final exams, comprising conceptual and computational items, were analyzed separately and combined to evaluate their fit to the Rasch model. Summary statistics confirmed the reliability of item and person measures, while alignment analysis highlighted conceptual continuity across assessments. The results demonstrated the assessments’ efficacy in consistently measuring student ability across different subgroups and performance levels. Wright maps and item-level statistics provided a comprehensive view of student understanding, identifying areas requiring targeted pedagogical intervention. The study revealed significant growth in student performance from midterm to final exams, with Rasch analysis enabling the establishment of a consistent scale for tracking progress over time. Findings underscored the importance of aligning assessments with learning outcomes and ensuring fairness across diverse student groups. This research highlights the potential of Rasch analysis as a tool for improving the design, implementation, and evaluation of assessments in higher education, particularly in complex subjects like calculus. By integrating these insights, educators can refine assessment practices to better support student learning and achievement.

Case Series. The study's objective is to detail the chronology of intraoperative neuromonitoring (IONM) alerts during pediatric spinal deformity surgery, the corrective maneuvers taken, and the immediate and final neurologic outcomes. IONM reduces the risk of spinal cord dysfunction during pediatric spinal deformity surgery by enabling timely intraoperative intervention. However, limited data exists regarding the chronology of alerts and the effectiveness of specific corrective actions. An institutional neuromonitoring database was reviewed (1992-2024) to identify all consecutive patients (0-18y) who underwent pediatric spine deformity surgery with at least one IONM alert. A total of 223 patients (4.2%) were identified out of 5,317 consecutive cases, of which 156 had data recovery intraoperatively, and 67 did not. Diagnoses were kyphosis (n=66), idiopathic (n=63), neuromuscular (n=43), congenital (n=24), syndromic (n=11), and other (n=16). There were 348 corrective actions for 237 alerts, most commonly correction of hypotension (n=91) and adjustment of deformity correction (n=63). Postoperatively, 34 patients with IONM alerts had neurological change from baseline. 29 patients had documented final neurological status; 21 had full recovery to baseline, 5 had partial recovery, 3 had no recovery. Among patients available for final follow-up, neurologic decline occurred in 0.15% (8/5,312) and in 3.7% (8/218) of those with intraoperative IONM alerts. IONM alerts occurred in 4.2% of cases, most frequently at correction/following correction. Corrective actions, most commonly correction of hypotension, theoretically reduced spinal cord dysfunction from 4.2% intraoperatively, to 0.5% (29/5,312) at wake-up and 0.15% (8/5,312) at final exam. This largest single-center experience with IONM in pediatric spinal deformity surgery supports the use of a multimodal IONM, systematic alert response protocol and allows for better preoperative shared-decision making.

Leveraging Generative Artificial Intelligence to Facilitate a Student-Created Review for an Undergraduate Pathophysiology Final Examination.

Large language models (LLMs) have recently gained attention in automated writing evaluation (AWE) due to their flexibility, ease of use, and free accessibility. However, most existing studies have relied on standardized rubrics and detailed scoring guidelines to guide model outputs. Recent evidence suggests that LLMs can adapt their scoring behavior through example-based calibration. Building on this insight, the present study examines whether ChatGPT-4o can mirror individual instructors’ evaluative tendencies. Data consisted of 100 previously graded final exam writing samples from Saudi students of English as a second language (ESL), provided by five instructors at a Saudi university’s Bachelor of Arts program. GPT (generative pre-trained transformer) was calibrated using instructor-graded writing samples to enhance its alignment with human grading criteria. Subsequent analysis involved 82 samples, excluding those used in calibration. Results revealed a strong positive and statistically significant correlation ( r = 0.816, p < .001) between GPT scores and teacher-assigned scores. Descriptive analyses further indicated differential scoring tendencies: GPT was more generous toward lower-quality writings, assigning higher mean scores than human raters, whereas teachers tended to award higher scores than GPT for high-quality writings. These findings suggest that GPT, particularly when effectively calibrated, can mirror teacher grading practices, though with notable differences at performance extremes. Consequently, this study highlights GPT’s potential as a complementary assessment tool in ESL writing instruction.

This study investigates the effect of combining peer tutoring with a project-based learning (PjBL) model on the academic performance of students enrolled in an Educational Statistics course within an elementary teacher education program. A quasi-experimental design was employed with two intact classes (n = 42) from a state university in Indonesia. The experimental group received peer-assisted PjBL instruction, while the control group experienced conventional PjBL without peer tutors. Both groups followed the same syllabus, instructor, and assessment structure. Data were collected through standardized Midterm (UTS) and Final (UAS) exams validated by subject-matter experts. The findings revealed that students in the experimental group achieved significantly higher academic performance and demonstrated greater improvement from midterm to final exams compared to the control group, with a moderate effect size (Cohen’s d = 0.59). The integration of peer tutoring within project-based activities effectively reduced anxiety, encouraged collaboration, and deepened conceptual understanding of statistical principles. These results confirm that peer-supported, project-based instruction offers a practical and contextually relevant approach to improving statistical learning outcomes among pre-service teachers. The study underscores the importance of active, student-centered pedagogies in preparing future educators to navigate data-rich educational environments. Future research is recommended to examine the long-term retention, scalability, and adaptation of this model across different subjects and institutional settings.

The rapid development of Generative Artificial Intelligence (GAI) technologies is driving profound changes in education. This study investigates the construction of human–Artificial Intelligence (AI) collaborative teaching models supported by GAI, with the goals of improving teaching efficiency, enabling personalized learning, and optimizing the allocation of educational resources. The proposed framework integrated intelligent content generation, real-time tutoring, adaptive learning pathways, and a teacher–student–AI collaboration mechanism. The generative model employed was LLaMA-2-13B, which was domain-adapted through supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). The experiment was conducted in a university course titled Data Structures and Algorithms, involving 60 students in the experimental group and 60 students in the control group. Multi-dimensional data were collected and analyzed, including academic performance, student engagement, interaction depth, technology acceptance, and long-term retention. The quality of AI-generated content was evaluated using Bilingual Evaluation Understudy (BLEU, 0.74) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE, 0.79), with a Cohen’s κ value of 0.86 indicating high inter-rater consistency. The results showed that the GAI-driven human–AI collaborative model significantly improved final exam scores (85.6 vs. 78.3, p < 0.001), average assignment grades (91.2 vs. 84.7, p < 0.001), and learning satisfaction (p < 0.05), while reducing cognitive load and enhancing personalized and interactive learning. This study provides both a theoretical framework and practical guidance for innovating educational models in the era of intelligent technology, offering valuable insights for advancing the digital transformation of education.

Advancing Systems-Based Patient Workup Skills Through Redesign of a Critical Care Pharmacy Elective.

This qualitative case study investigated the impact of a university preparatory school’s final English proficiency exam on students’ productive skills (speaking and writing) and on their emotions and opinions about learning English. 40 students and 7 instructors from a foundation university in Türkiye participated in the study. Data were collected through students’ written and spoken tasks, open-ended surveys, and instructors’ assessment notes, and analyzed using content analysis. Findings indicate that the overall exam had more varied impacts on students than each of the specific tests for speaking and writing. The impact of the exam on students’ emotions and opinions ranged from positive to negative. Among the productive skills, speaking elicited more negative emotional reactions than writing. While both speaking and writing skills were positively impacted by the exam, the effect was stronger on speaking skills, especially for students with higher proficiency levels. In contrast, no clear relationship was found between proficiency levels and the impact on writing skill. In that, higher proficiency did not correlate with greater improvement. These results suggest that the broader the exam content, the more complex its influence on learners is. The findings have implications for educators and test developers, emphasizing the importance of designing low-stakes exams that encourage positive outcomes and reduce anxiety, especially in the assessment of productive skills.

An adequate assessment of what students have understood during the term is an essential precondition for self-regulated learning (SRL) as it allows for adapting learning strategies to enhance academic performance. This study compared self-assessed and objectively measured understanding of the learning material as predictors of academic performance and their interplay with general and academic self-efficacy. In a bachelor’s Psychological Assessment course, 271 psychology students regularly completed online formative assessments (OFA) throughout the term. Before each OFA, they self-assessed how well they had understood the content of the previous lecture. Performance in the OFA was used as an objective measure of understanding. Furthermore, self-efficacy was assessed at the beginning of the term. Both self-assessed and objectively measured understanding were significant predictors of the grade in the final exam as a measure of academic performance. When considered concurrently in a latent regression analysis, only objectively measured understanding predicted academic performance. Academic self-efficacy predicted academic performance above both measures of understanding, maybe because it covers how students deal with the specific demands of the exam. According to the present results, students engaging in SRL should rely on objective indicators of their understanding instead of self-assessments to select appropriate learning strategies.

Abstract Background With the popularity of college social media platforms, the emotional expression of college students on campus social media has become an important external representation of their psychological state. The incidence of anxiety among college students is relatively high, and traditional screening methods that rely on self-assessment scales or interviews have shortcomings in real-time and continuous monitoring. Despite the continuous development of artificial intelligence (AI) in text emotion recognition and mental health monitoring, relevant research still lacks empirical analysis of campus situations combined with standardized psychological scales. Based on this, the research integrates AI text analysis and standardized anxiety scales to construct a detection framework that can support real-time recognition and dynamic warning of anxiety emotions among college students, aiming to provide technical support for psychological health monitoring and public health intervention in universities. Methods The study collected anonymous text data from a campus social platform of a certain university within 12 months, and synchronously organized participants to fill out the Generalized Anxiety Disorder Scale (GAD-7) to quantify anxiety levels. After cleaning, segmenting, and feature filtering the text data, a standardized corpus is constructed, and sentiment annotation is performed on some texts based on GAD-7 scores to form a training dataset. Subsequently, a pre trained language model was used for text feature representation, and an anxiety emotion discrimination model was constructed by integrating emotion dictionary features. Finally, through a time sliding window mechanism and near real-time data processing strategy, the model output was quickly responsive and dynamically updated to support real-time continuous detection of anxiety emotions among college students. Results The research findings indicate a high degree of consistency between the model and the GAD-7 scale evaluation results. In the test set, the model achieved a recognition accuracy of 0.88 for individuals with high anxiety, a recall rate of 0.85, and an F1 score of 0.86. The text anxiety score predicted by the model is significantly positively correlated with the total score of GAD-7 (r = 0.72). Stratified analysis found that students with moderate or above GAD-7 scores had a significantly higher proportion of high anxiety texts compared to the low group, and the difference was statistically significant. The time series results show that the proportion of high anxiety texts in the final exam stage has increased by about 55% compared to the middle of the semester, and the model output trend is consistent with. Discussion Research indicates that combining AI-based text analysis with standardized scales enables real-time detection and early warning of anxiety among college students without relying on active reporting, providing a low-burden, high-coverage auxiliary tool for mental health management in universities. This approach helps identify high-risk periods and groups, facilitating a shift from passive response to proactive intervention in psychological services. Future efforts could expand multi-school sample validation and intervention effect tracking to enhance the model's applicability and service value within actual public health systems.

Introduction: This manuscript evaluates the outcomes of a pioneering Integrative Medicine Course implemented at Eduardo Mondlane University, aiming to strengthen medical and health students’ knowledge and skills while fostering a holistic approach to patient care. Delivered in two editions—an intensive in‑person program and an extended online format—the course sought to improve understanding of integrative medicine.Methods: A mixed‑methods approach was adopted, combining data from participatory observation, pre‑ and post‑course surveys, focus group discussions, and final course evaluations. Self‑assessments of knowledge and skills were collected before and after the course. Final exam results were analyzed to assess knowledge acquisition. The in‑person edition was conducted over 1 week (September 16–20, 2024), and the online edition spanned 8 weeks (January–February 2025). Both formats included weekly lectures, practical sessions, and interactive discussions. The Wilcoxon signed‑rank test was used to evaluate changes in knowledge and skills.Results and discussion: A total of 164 students enrolled, with 134 completing the course (completion rate of 82%). Most participants were female (81%), with an average age of 23 years (SD ± 3.7); 61% were medical students, and three were postgraduate students. Satisfaction was high, with 66% awarding the highest rating. The most highly rated aspects included instructors (78%), course organization (77%), and resources provided (75%). Phytotherapy emerged as the most relevant topic, followed by traditional medicine, mental well‑being, and nutrition. The participatory teaching approach was preferred, accounting for 63% of mentions. Post‑course evaluations showed significant improvements in knowledge, interest, attitudes, and competencies (p < 0.05). All students passed the final exam, with an average score of 18/20.Conclusions: The course successfully enhanced students’ understanding and application of integrative medicine. Both delivery formats proved effective in engaging learners and fostering critical skills. This initiative establishes a foundation for advancing integrative medicine education and research in Mozambique.

An Idea to Explore-Construction and Application of Virtual Simulation Experiments in Molecular Biology Practical Teaching for International Medical Undergraduates.

Background: Assessment in nursing education combines continuous assessment scores (CAS) and examination scores (EXS) to evaluate student competence. While continuous assessment (CA) is intended to promote consistent learning and predict summative outcomes, limited evidence exists in Zambia on its impact on the final examination scores (FES). Objective: The general objective of the study was to examine the impact of continuous assessment scores on the final examination scores for student nurses examined between 2022 and 2024 at Eden University, Lusaka, Zambia, using a retrospective analysis of the July 2022 intake. Methods and Materials: A study employed a retrospective quantitative correlational study design in which a total of 15 courses were analysed by assessing the performance of students in each course and semester for the period of three years. This study used a semi-structured questionnaire in which a total population sampling technique was employed for the courses that were undertaken by the July 2022 intake for a period of three years. Data Analysis: Data was analysed using Statistical Package for Social Sciences (SPSS) version 26. The Pearson's correlation coefficient formula was used to calculate the correlation coefficient between dependent and independent variables. Conclusion: The analysis of continuous assessment scores (CAS) and final examination scores (FES) for student nurses at Eden University revealed a strong positive correlation between CAS and FES across most courses, indicating that students performing well in CAS tend to excel in FES. Overall, the study has shown that CAS is a good predictor of FES performance, emphasizing the importance of continuous assessments in identifying students who need support. Therefore, the study has revealed that CAS has a significant impact on FES for student nurses at Eden University in the School of Nursing and Midwifery Sciences (SoN &amp; Mid Sc). It is against this study that there is a need to provide additio

Two-Stage Final Exams: An Assessment Strategy for Enhanced Collaborative Learning and Reduced Student Stress

Research indicates that students learn effectively when they actively engage in planning, monitoring, and reflecting on their learning—a process known as self-regulated learning (SRL). This mixed-methods study examined the impact of a semester-long SRL intervention in an undergraduate physics course. In the first year, the instructor taught the course under standard conditions. The following year, after brief training sessions, the same instructor added digital prompts targeting various aspects of SRL to their course assignments. Results showed that students in this intervention group performed better on the final exam after controlling for preexisting self-efficacy, metacognitive awareness, and GPA. However, SRL outcomes did not increase in the intervention condition. Analysis of the prompts revealed that the instructor disproportionally focused on content- and management-related prompts and underutilized motivation, metacognition, and cognitive strategy prompts. Interview with the faculty indicated that the intervention was straightforward to implement, contributed to humanizing the course, and that additional training would have been desirable to help design prompts. Provided appropriate training and support structures are available, these findings show that integrating SRL strategies through digital prompts can be a practical and effective approach for supporting instructors in fostering students’ SRL.

First-year nursing students gain foundational knowledge as they develop their understanding of the profession. Providing nursing students with educational experiences that foster the application of theoretical knowledge may support their learning and preparation for high-stakes examinations. A video-based simulation using the ShadowBox technique was used to guide first-year undergraduate nursing students' application and review of theoretical principles focused on caring behaviors. The simulation-based experience (SBE) was developed to support students' review of theoretical concepts in preparation for the final examination. A total of 37 students participated in an unfolding case presented as four videorecorded vignettes. Students engaged in decision-making as they explored caring behaviors to address a patient's concerns. Learners participated in debriefing sessions after observing an expert nurse's response to the dilemmas presented. Nursing students highlighted that the SBE using the ShadowBox technique was an effective and engaging approach to apply and review foundational concepts.

Pseudo-Meigs syndrome owing to a retained fibroid following total laparoscopic hysterectomy for multiple leiomyomas: Acase report and review of the literature.

This study aimed to evaluate clinical and pathological factors associated with the occurrence of empty node packets during sentinel lymph node mapping in patients with endometrial cancer. We performed a retrospective cohort study including patients with histologically confirmed endometrial carcinoma who underwent sentinel lymph node mapping between November 2012 and December 2023. An empty node packet was defined as the intra-operative removal of a presumed sentinel node with no lymphoid tissue identified on final pathological examination. Logistic regression models were used to identify independent predictors. Of 489 patients who had sentinel lymph node mapped, 23 (4.7%) had an empty node packet. In the univariate analysis, body mass index and myometrial invasion were significantly associated (p < .05). In the multi-variable analysis, only body mass index remained independently associated (odds ratio 1.075, 95% confidence interval 1.01 to 1.14, p = .022) with an empty node packet. Tumor histology, grade, type of tracer, and surgical approach were not associated. No nodal recurrences occurred in patients with an empty node packet. Empty node packets are uncommon but clinically relevant during sentinel lymph node mapping for endometrial cancer. Higher body mass index was the only independent predictor, underscoring the influence of patient-related factors on mapping accuracy.

This study aims to find out the Difference in Mid-Semester Exam Scores (UTS) and Final Semester Exam Scores (UAS) Mathematics Learning for Elementary -1 Students in Semester III of the Elementary School Teacher Education Study Program (PGSD), Teacher Training and Education College of Terang Bangsa (STKIP) for the 2025/2026 Academic Year. The research used was an independent sample t-test to see if there was a difference or comparison between the Mid-Semester Exam (UTS) and the Final Semester Exam (UAS) of the elementary -1 mathematics learning course. A sample of 11 third semester students consisting of 3 males and 8 females data was taken from the Mid-Semester Exam Score (UTS) and Final Semester Exam Score (UAS) of the mathematics learning course for the elementary school -1 Academic Year 2025/2026. The results of the analysis show that the Mid-Semester Exam Score (UTS) is in the poor category, the Final Semester Exam (UAS) score is in the fairly good category. The results of the study showed that there was a significant difference between the Mid-Semester Exam Score (UTS) and the Final Semester Exam (UAS) Grade for Elementary School Mathematics Learning -1 Academic Year 2025/2026. The recommendation in this study is that there is a significant difference between the mid-semester exam score (UTS) and the Final Semester Exam (UAS) score, so it is highly expected that the relevant parties will improve it even more.