Evaluation Of Student Performance Research Articles

Adjectives in the Summary Paragraph of the MSPE: Are They Telling Us Something?

Rationale and ObjectivesMany US medical schools do not provide a summative, comparative assessment of students (class rank), instead utilizing descriptive adjectives in the summary paragraph of the Medical Student Performance Evaluation (MSPE). The objective of this study was to determine whether those adjectives correlate with student performance. Materials and MethodsApplications from all US allopathic medical schools received by a single diagnostic radiology program in the 2023-24 cycle were reviewed. The final adjectives from schools that rank students were coded as positive, negative, or neutral. For students from non-ranking schools, descriptive adjectives from the MSPE summary paragraphs were extracted and categorized based on the final adjective coding schema along with a library of commonly used positive adjectives. The frequency of adjectives was correlated with applicant academic and demographic factors using multivariable logistic regression. ResultsApplications from 97% (147/151) of US allopathic medical schools were received. 60.5% (89/147) of schools rank their students with 27.9% (41/147) using a final adjective coded to performance tier. Of the 58 non-ranking schools, 56.9% (33/58) used descriptive adjectives in the MSPE’s summary paragraph. There was no association with academic performance metrics and either generally positive adjectives or coded descriptive adjectives. There was a greater association with positive descriptive adjectives for non-white applicants (p=0.011) and generally positive adjectives for higher ranked schools (p=0.004). ConclusionUndefined descriptive adjectives in the MSPE’s summary paragraph, when benchmarked to final adjectives, do not correlate with academic performance, and may instead be used for student advocacy by medical school deans.

Artificial Intelligence: Reducing Inconsistency in the Surgical Residency Application Review Process

The incorporation of artificial intelligence (AI) into the general surgery residency recruitment process holds great promise for overcoming limitations inherent to traditional application review methods. This study assesses the consistency of AI, particularly ChatGPT, in evaluating medical student performance evaluation (MSPE) letters in comparison to experienced human reviewers. While the results suggest that ChatGPT demonstrates greater consistency in grading than human reviewers, AI still has its limitations. This underscores the necessity for careful refinement and consideration in its implementation. While AI presents opportunities to enhance residency selection procedures, further research is imperative to fully grasp its capabilities and implications.

Impact of activity-based learning on understanding linear and quadratic functions: An evaluation of student performance and satisfaction in a Thai high school

Impact of activity-based learning on understanding linear and quadratic functions: An evaluation of student performance and satisfaction in a Thai high school

The Urgent Need for Reporting Accurate and Fair Student Comparisons in the Medical Student Performance Evaluation.

The Urgent Need for Reporting Accurate and Fair Student Comparisons in the Medical Student Performance Evaluation.

Improved Decision Support System for College Sports Training Based on Id3 Algorithm

A Decision Support System (DSS) is a computer-based tool designed to assist individuals and organizations in making informed decisions. It utilizes data analysis, modeling techniques, and interactive interfaces to provide users with relevant information and insights. DSSs help streamline decision-making processes by synthesizing large amounts of data from various sources, generating forecasts, and evaluating alternative courses of action. They are particularly valuable in complex and uncertain situations where decisions have significant implications. DSSs can be used in a wide range of domains, including business, healthcare, finance, and logistics, to support strategic, tactical, and operational decision-making processes. This paper introduces an Integrated Hybrid Feature Subset Decision Support System (HFS-DSS) designed to optimize decision-making processes in student performance evaluation within educational institutions. The HFS-DSS combines feature subset selection techniques with decision support algorithms to identify key attributes and make accurate predictions regarding student performance. Through experimental evaluation, the system demonstrates its effectiveness in accurately assessing student performance based on selected features, with predictions closely aligning with actual observations. The results underscore the system's reliability and effectiveness in supporting informed decision-making processes, offering valuable insights for educators and administrators to enhance academic outcomes. The adaptability and versatility of the HFS-DSS make it well-suited for various educational contexts, providing potential applications in curriculum planning, student support services, and performance monitoring. The present paper highlights the significant contributions of the Integrated HFS-DSS towards enhancing decision-making processes and improving academic outcomes in educational institutions.

Optimization and Application of English Word Memory Algorithm Based on Reinforcement Learning

The optimization and application of an English word memory algorithm based on reinforcement learning involve refining a system that utilizes reinforcement learning techniques to enhance vocabulary retention. By leveraging reinforcement learning, the algorithm can adapt its strategies for presenting and reviewing English words based on users' responses and performance. This adaptive approach enables personalized and effective learning experiences, wherein the algorithm adjusts the frequency and timing of word repetitions to maximize memorization efficiency. Applications of this optimized algorithm span language learning platforms, educational apps, and personalized tutoring systems, offering learners tailored support to strengthen their English vocabulary acquisition and retention skills. This paper introduces the Spider Swarm Optimized English Word Memory Algorithm (SSOEWMA) algorithm, a novel approach aimed at optimizing language processing and learning tasks. Leveraging principles of reinforcement learning and swarm intelligence, SSOEWMA demonstrates its effectiveness in enhancing various aspects of language processing, including word difficulty assessment, sentence estimation, memory retention, and student performance evaluation. Through a series of experiments and evaluations, we showcase SSOEWMA ability to optimize dataset attributes, accurately estimate sentence characteristics, and improve memory retention performance. Simulation results achieved an average word recall accuracy of 85% across multiple evaluations. Through its utilization of reinforcement learning principles and swarm intelligence, SSOEWMA significantly improved memory retention performance, reducing recall latency by an average of 20% compared to baseline measures. Additionally, the algorithm demonstrated strong learning efficiency, converging to optimal solutions with a speed increase of 30% compared to traditional methods.

Learning with AI Language Models: Guidelines for the Development and Scoring of Medical Questions for Higher Education.

In medical and biomedical education, traditional teaching methods often struggle to engage students and promote critical thinking. The use of AI language models has the potential to transform teaching and learning practices by offering an innovative, active learning approach that promotes intellectual curiosity and deeper understanding. To effectively integrate AI language models into biomedical education, it is essential for educators to understand the benefits and limitations of these tools and how they can be employed to achieve high-level learning outcomes.This article explores the use of AI language models in biomedical education, focusing on their application in both classroom teaching and learning assignments. Using the SOLO taxonomy as a framework, I discuss strategies for designing questions that challenge students to exercise critical thinking and problem-solving skills, even when assisted by AI models. Additionally, I propose a scoring rubric for evaluating student performance when collaborating with AI language models, ensuring a comprehensive assessment of their learning outcomes.AI language models offer a promising opportunity for enhancing student engagement and promoting active learning in the biomedical field. Understanding the potential use of these technologies allows educators to create learning experiences that are fit for their students' needs, encouraging intellectual curiosity and a deeper understanding of complex subjects. The application of these tools will be fundamental to provide more effective and engaging learning experiences for students in the future.

The Role of Artificial Intelligence in Medical Education: A Systematic Review.

To examine the artificial intelligence (AI) tools currently being studied in modern medical education, and critically evaluate the level of validation and the quality of evidence presented in each individual study. This review (PROSPERO ID: CRD42023410752) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A database search was conducted using PubMed, Embase, and Cochrane Library. Articles written in the English language between 2000 and March 2023 were reviewed retrospectively using the MeSH Terms "AI" and "medical education" A total of 4642 potentially relevant studies were found. After a thorough screening process, 36 studies were included in the final analysis. These studies consisted of 26 quantitative studies and 10 studies investigated the development and validation of AI tools. When examining the results of studies in which Support vector machines (SVMs) were employed, it has demonstrated high accuracy in assessing students' experiences, diagnosing acute abdominal pain, classifying skilled and novice participants, and evaluating surgical training levels. Particularly in the comparison of surgical skill levels, it has achieved an accuracy rate of over 92%. AI tools demonstrated effectiveness in improving practical skills, diagnosing diseases, and evaluating student performance. However, further research with rigorous validation is required to identify the most effective AI tools for medical education.

Development and psychometric evaluation of a 360-degree evaluation instrument to assess medical students’ performance in clinical settings at the emergency medicine department in Iran: a methodological study

In the Iranian context, no 360-degree evaluation tool has been developed to assess the performance of prehospital medical emergency students in clinical settings. This article describes the development of a 360-degree evaluation tool and presents its first psychometric evaluation. There were 2 steps in this study: step 1 involved developing the instrument (i.e., generating the items) and step 2 constituted the psychometric evaluation of the instrument. We performed exploratory and confirmatory factor analyses and also evaluated the instrument’s face, content, and convergent validity and reliability. The instrument contains 55 items across 6 domains, including leadership, management, and teamwork (19 items), consciousness and responsiveness (14 items), clinical and interpersonal communication skills (8 items), integrity (7 items), knowledge and accountability (4 items), and loyalty and transparency (3 items). The instrument was confirmed to be a valid measure, as the 6 domains had eigenvalues over Kaiser’s criterion of 1 and in combination explained 60.1% of the variance (Bartlett’s test of sphericity [1,485]=19,867.99, P<0.01). Furthermore, this study provided evidence for the instrument’s convergent validity and internal consistency (α=0.98), suggesting its suitability for assessing student performance. We found good evidence for the validity and reliability of the instrument. Our instrument can be used to make future evaluations of student performance in the clinical setting more structured, transparent, informative, and comparable.

Emotional Intelligence, Perceived Stress and Life Quality Assessment Among Dental Students.

The capacity to grasp and recognize one's own and human feelings, use cognitive awareness to control actions and behavior, and modify moods in response to difficult situations is known as emotional intelligence (EI). Perceived stress has been linked to higher levels of depression among healthcare students. This study set out to assess EI, perceived stress (PS), and life quality assessment (LQA) among dental undergraduates and to ascertain how these factors are linked to one another. The self-reported cross-sectional research was organized among preclinical and clinical years of dental students to examine their emotional intelligence and stress-coping capacity. About 146 students participated in the study, with ages ranging from 19 to 25, irrespective of gender. The students were given the Schutte Emotional Intelligence and Perceived Stress Scale tools (PSS10) by Cohen, and with World Health Organization Quality of Life (WHOQOL-BREF) tool examined the characteristics that contribute to life quality among students. The study included 146 individuals, 38 males (26.02%) and 108 females (73.97%), with an unresponsiveness rate of 2.66%. The correlation revealed statistically high significance among emotional intelligence and perceived stress (P = 0.000), perceived stress and life quality (P = 0.02), and emotional intelligence and life quality (P = 0.008). The statistical analysis of the influence of EI, PS, and LQA on academic year-wise analysis determined non-significant (P > 0.05). Due to the time constraints of their job, dentistry training may be highly stressful, yet regular evaluations of student performance and the accompanying factors would help us to understand how students behave in challenging circumstances.

Analysis of variability and trends in medical school clerkship grades

BackgroundMedical school clerkship grades are used to evaluate orthopedic surgery residency applicants, however, high interinstitutional variability in grade distribution calls into question the utility of clerkship grades when evaluating applicants from different medical schools. This study aims to evaluate the variability in grade distribution among medical schools and look for trends in grade distribution over recent years. MethodsApplications submitted to Rush University's orthopedic surgery residency program from 2015, 2019, and 2022 were collected from the Electronic Residency Application Service. Applications from the top 100 schools according to the 2023–2024 U.S. News and World Report Research Rankings were reviewed. The percentage of “honors” grades awarded by medical schools for the surgery and internal medicine clerkships were extracted from applicants' Medical Student Performance Evaluation letters. ResultsThe median percentage of honors given in 2022 was 36.0 % (range 10.0–82.0) for the surgery clerkship and 33.0 % (range 6.7–80.0) for the internal medicine clerkship. Honors were given 6.6 % more in the surgery clerkship in 2022 compared to 2015. There was a negative correlation between a higher (worse) U.S. News and World Report research ranking and the percentage of honors awarded in 2022 for the surgery and internal medicine clerkships. ConclusionThere is substantial interinstitutional variability in the rate that medical schools award an “honors” grade with evidence of grade inflation in the surgery clerkship. Residency programs using clerkship grades to compare applicants should do so cautiously provided the variability demonstrated in this study.

Implementation of an early alert system in quizzes of a high complexity subject in higher education: Improvement of student performance and teacher perception

AbstractEarly Alert Systems (EAS) play a fundamental role in education by supporting the learning process and evaluating student performance. In this study, an innovative EAS designed to specifically identify weaknesses in Cognitive Skills (CS) in first-year higher education students is presented, focusing on a challenging course at a South American university. Through big data technology, approval rates linked to various cognitive skills were evaluated using quizzes. Subsequently, teachers, after analysis and dialogue, applied adapted strategies to strengthen these skills in their classes, which were evaluated in subsequent exams. The sample under study included 1,691 students from various health majors enrolled in a complex subject. The experimental group (994 students) that participated in the EAS was compared with a control group (697 students). Evaluations validated by the academic team were used, applying three quizzes throughout the 2022-20 academic period, and the results were analysed with the Power BI computer tool, generating online reports that highlighted CS by section. The EAS, based on teacher surveys, is innovative and it also positively impacts student achievement, evidenced in overall approval rates, by teacher and section. In addition to promoting collaboration among professors, it also improves the teaching quality and suggests a positive impact on the learning of complex subjects in Higher Education Institutions (HEIs). This comprehensive approach to early monitoring of student performance shows promise for the ongoing improvement of educational quality.

Attitudes of Greek Foreign Language Teachers Regarding the Role of Authentic Assessment in Foreign Language Teaching

The evaluation of student performance, which is closely tied to all school functions and is a fundamental responsibility of teachers in their role as educators, has been a challenging and contentious issue in educational policy and practice in recent years. It plays a crucial role in the educational process, as it is essential for fostering creativity and human development within the school environment. Between traditional assessment based mainly on written and oral measurable tests and authentic assessment that highlights the role of the pupil through the self-evaluation or other techniques such as the materials folder, teachers are called upon to undertake a difficult task. In this article the results of a qualitative research in relation to the role of authentic assessment in foreign language teaching that took place in the Attica Region in Greece from March 10 to April 10, 2022 were presented. A total of 20 foreign language teachers (English, French, German) participated in semi-structured interviews and expressed their views on the relevant questions. The research problem posed in this study was to investigate the views and attitudes of foreign language teachers (English, French, German) regarding the framework of authentic assessment in foreign language teaching. The survey asked teachers to share their opinions on the role of authentic assessment in foreign language teaching, their preferred assessment criteria, the use of alternative forms of assessment, and their satisfaction with their training in authentic assessment. In general, it emerged that permanent secondary school teachers with many years of service prefer traditional forms of assessment and choose techniques that result in measurable criteria. In contrast, younger contractual teachers are more receptive to alternative forms of evaluation. However, no significant difference was observed on the issue of training. This survey aimed to gather language teachers' perspectives on authentic assessment in order to enhance textbooks and training programs.

Education planning analysis using augmented reality, virtual reality, and blockchain

The development of information technology has changed the educational landscape by providing new opportunities to enhance the learning experience. This article aims to analyze educational planning using advanced technologies such as Augmented Reality (AR), Virtual Reality (VR), and Blockchain. Through this approach, we explore the potential of integrating these technologies in optimizing educational planning, including curriculum management, student performance evaluation, and increased student participation in the learning process. The research method used is a literature study to review literature related to AR, VR, and Blockchain technology in an educational context. The results of the analysis present ideas on how the application of this technology can improve efficiency, effectiveness, and transparency in educational planning. The conclusion of this study highlights the importance of integrating advanced technologies in educational planning to support the achievement of better learning objectives.

Development Curriculum Management Office Integrated Independent Campus Program to Optimize 21st Century Learning for Management Students

Objective: This research aims to develop an impact study curriculum integrated with the Merdeka Campus Program in the context of learning management offices in Indonesia. The Study also seeks to analyze the benefits and challenges that arise during the implementation process of field implementation. Method: This Study uses the literature review method to analyze the Independent Campus Program concept and office management concept. In addition, the analysis carried out by the industry requires going through surveys and interviews with related companies. Student performance evaluation data is also collected to measure the effectiveness of the integrated curriculum. Result: Research results show that Curriculum integration benefits from enhancement Skills practical students, empowerment, experience valuable industry, and improvement readiness work. However, challenges in planning, resources power, evaluation, and alignment with need industries are also identified. Conclusion: The development of a curriculum integrated with the Independent Campus Program has a positive impact in increasing the high relevance of education and preparing students to face the world of office management work. The challenges can be overcome with careful planning and a solid commitment to meet better education goals.

ACADEMICIANS' VIEWS ON FOREIGN LANGUAGE TEACHING: ONLINE DISTANCE-LEARNING VERSUS FACE-TO-FACE INSTRUCTION

The objectives of this study are to examine the challenges faced by academics teaching foreign languages (Arabic and English) via distance education, in comparison with face-to-face instruction; and to suggest strategies and solutions for managing the challenges and difficulties encountered in an online-teaching platform. The recent Covid-19 pandemic provided a real life lab experiment from which to glean information and draw lessons from, given that online distance learning was primarily utilized during the pandemic to facilitate uninterrupted education. Accordingly, 77 academics providing foreign language education remotely during the pandemic were interviewed. These interviews were conducted in the fall semester of the 2022-2023 academic year. The survey questions were developed by the authors, based on established methods. Open-ended questions were developed as an online data collection tool, and data were obtained with a semi-structured interview form (aptly named "The problems faced by instructors teaching foreign languages in Arabic and English during the Covid-19 pandemic"). This study employed a qualitative research method and utilized a phenomenological design that was structured based on this research method. This technique is based on the idea that teaching should be functional, and that the degree of efficacy with which specific learning functions are achieved should be observable and subject to evaluation. The findings of this study suggest that face-to-face education is more effective than distance education, with regard to learning functionality. However, the study also finds that combining online distance learning with face-to-face instruction (i.e. hybrid teaching) appears to elevate learning functionality to an even greater extent. With regard to distance learning alone, the most common problem experienced and thus the greatest perceived challenge to educators was a lack of student interest and motivation. Additionally, assessment and evaluation of student performance in online environments were found to be not sufficiently accurate. The consensus opinion of the survey respondents is that hybrid teaching approaches offer greater effectiveness with regard to both the stimulation of student-interest and more accurate assessment of student performance.

Innovative Approaches in Student Performance Evaluation through Neuro-Fuzzy Intelligent Systems

Innovative Approaches in Student Performance Evaluation through Neuro-Fuzzy Intelligent Systems

Rapid detection of non-normal teeth on dental X-ray images using improved Mask R-CNN with attention mechanism.

Dental health has been getting increased attention. Timely detection of non-normal teeth (caries, residual root, retainer, teeth filling, etc.) is of great importance for people's health, well-being, and quality of life. This work proposes a rapid detection of non-normal teeth based on improved Mask R-CNN, aiming to achieve comprehensive screening of non-normal teeth on dental X-ray images. An improved Mask R-CNN based on attention mechanism was used to develop a non-normal teeth detection method trained on a high-quality annotated dataset, which can segment the whole mask of each non-normal tooth on the dental X-ray image immediately. The average precision (AP) of the proposed non-normal teeth detection was 0.795 with an intersection-over-union of 0.5 and max detections (maxDets) of 32, which was higher than that of the typical Mask R-CNN method (AP = 0.750). In addition, validation experiments showed that the evaluation metrics (AP, recall, precision-recall (P-R) curve) of the proposed method were superior to those of the Mask R-CNN method. Furthermore, the experimental results indicated that proposed method exhibited a high sensitivity (95.65%) in detecting secondary caries. The proposed method took about 0.12s to segment non-normal teeth on one dental X-ray image using the laptop (8G memory, NVIDIA RTX 3060 graphics processing unit), which was much faster than conventional manual methods. The proposed method enhances the accuracy and efficiency of abnormal tooth diagnosis for practitioners, while also facilitating early detection and treatment of dental caries to substantially lower patient costs. Additionally, it can enable rapid and objective evaluation of student performance in dental examinations.

Virtual mashine Labtainers for emulation network vulnerabilities

The number of cybercrimes is constantly increasing and currently poses a significant threat to our society and economy. There is also a growing number of threats to our personal data, confidentiality, children, and the elderly due to financial fraud, which is why cybersecurity specialists are in high demand across all industries. In such circumstances, the question of the availability of cybersecurity specialists and their training methods. The number of disciplines related to cybersecurity is rapidly increasing in higher education institutions. The training of future cybersecurity specialists is a top priority for our national security, financial stability, and economic development. The article discusses the utilization of the Labtainers virtual environment for organizing lab sessions in cybersecurity disciplines. It explores ARP protocol vulnerabilities, principles, and mechanisms behind ARP-Spoofing attacks. The analysis delves into existing tools for executing such attacks. The article provides comparative equipment characteristics for setting up lab environments using physical hardware and virtual machines. It highlights the advantages of containerization for deploying test network environments based on Docker containers. For investigating the Labtainers environment in organizing lab sessions, a lab exercise on simulating ARP-Spoofing attacks and analyzing network traffic using the Wireshark network sniffer was chosen. Labtainers offer over 50 cybersecurity lab exercises and tools for creating custom ones. To perform these exercises on personal equipment, importing a virtual machine for VirtualBox hypervisor or installing Labtainers on a Linux OS is necessary. Subsequently, configurations for specific lab work can be set up and exercises executed according to instructions. Labtainers is designed for deploying configured Docker containers, allowing the creation of complex network topologies even on modest computers. It automates assessment, enables student performance evaluation, and facilitates lab work execution in an isolated environment. Students complete lab exercises entirely on their own computers, regardless of the Linux distribution and packages installed. Labtainers operates on a virtual machine with Docker or on any Linux distribution with Docker installed. Additionally, Labtainers’ lab exercises are available as cloud-based virtual machines, such as on Azure.

A quasi-experimental evaluation of student academic performances in single sex secondary classrooms

Teacher beliefs and environmental conditions influence student performance outcomes. In this non-equivalent comparison quasi-experimental design study, students enrolled in ten secondary Principles of Agriculture courses assigned to a control or treatment group based upon their sexual identity. Teachers were asked periodically about their expectations of student performances. Over the course of the academic semester, students within the treatment group maintained higher grade point averages and better attendance. Boys from the treatment group maintained a higher percent of non-discipline referrals than boys from the control group. In addition, correlations are made between the teachers’ expectations, and student performance and career aspirations. The researchers recommend that secondary schools consider single-sex classrooms for entry-level agricultural courses and for additional research to be considered to further strengthen the preliminary findings.