Evaluation Of Student Performance Research Articles

Assessment Literacy, Current Assessment Practices and Future Training: Reflections of Teachers in Higher Education

The aim of this study is to explore assessment literacy, to analyse the current assessment practices of teachers in Higher Education (HE) and to understand the need for alternative assessment practices in light of teachers’ reflections. The research employed incidental sampling to select (n=58) HE teachers from a single multidisciplinary university in Maharashtra State, India. A comprehensive literature review on assessment literacy provided the theoretical foundation, and a specialized assessment tool was developed to assess the teachers' assessment literacy level, perspectives, and practices in assessment. Data were collected through voluntary participation and analysed using quantitative and qualitative techniques. The findings revealed that the majority of HE teachers (79%) demonstrated a moderate level of assessment literacy, while 16% exhibited low levels and only 5% showed high levels. Additionally, a discrepancy was observed between the preferred assessment types of teachers and learners, with learners emphasizing active participation and real-world applications, while teachers leaned toward traditional evaluation methods. Participants were found to be literate only in specific aspects such as meaning, basic forms of assessments, and the purpose of educational assessments. Furthermore, teachers minimally acknowledge and have a lesser preference for alternative assessment methods, such as article analysis, team projects, case studies, and discussions. Overall findings indicate teachers' lack of knowledge about assessment practices that involve evaluating student performance, fostering collaboration, assessing higher-order thinking skills, and utilizing assessment as a tool for learning and improvement. This research confirms the need for training to enhance teachers' assessment literacy, promoting the adoption of alternative assessment practices in higher education to optimize student learning experiences.

Analisis Minat Belajar Siswa melalui Pendekatan Matematika Realistik pada Pembelajaran Berbasis Daring di Kelas VI Sekolah SDN Blendung

This study aims to analyze students' interest in learning through a realistic mathematics approach. This approach emphasizes the use of mathematics in real-life situations to enhance student engagement and understanding. This analysis aims to assess the level of interest and enthusiasm of students in learning mathematics through this method. The research method used is class observation, interviews, surveys and evaluation of student performance. Data was collected by observing students' involvement in learning, students' responses to assignments and challenges, in everyday life. students show high interest and enthusiasm in learning realistic mathematics. They actively participate in discussions, collaborate with classmates and ask questions. Students also show a positive attitude towards mistakes and challenges, experiment, think creatively, and see mistakes as opportunities to learn. In addition, students are able to understand mathematical concepts and relate them to real life. They see the relevance and benefits of learning mathematics in everyday life. Feedback and assessment provide additional motivation for students to continue learning and increase understanding. The results of the study concluded that a realistic mathematics approach can be effective in increasing students' interest in learning. By adopting this approach, students participate actively, see the relevance of mathematics to their lives, and develop a positive attitude towards mistakes and challenges. The implication of this research is the importance of using relevant and situational approaches in learning mathematics to increase students' learning interest.

Using Natural Language Processing and Machine Learning to Identify Internal Medicine-Pediatrics Residency Values in Applications.

Although holistic review has been used successfully in some residency programs to decrease bias, such review is time-consuming and unsustainable for many programs without initial prescreening. The unstructured qualitative data in residency applications, including notable experiences, letters of recommendation, personal statement, and medical student performance evaluations, require extensive time, resources, and metrics to evaluate; therefore, previous applicant screening relied heavily on quantitative metrics, which can be socioeconomically and racially biased. Using residency applications to the University of Utah internal medicine-pediatrics program from 2015 to 2019, the authors extracted relevant snippets of text from the narrative sections of applications. Expert reviewers annotated these snippets into specific values (academic strength; intellectual curiosity; compassion; communication; work ethic; teamwork; leadership; self-awareness; diversity, equity, and inclusion; professionalism; and adaptability) previously identified as associated with resident success. The authors prospectively applied a machine learning model (MLM) to snippets from applications from 2023, and output was compared with a manual holistic review performed without knowledge of MLM results. Overall, the MLM had a sensitivity of 0.64, specificity of 0.97, positive predictive value of 0.62, negative predictive value of 0.97, and F1 score of 0.63. The mean (SD) total number of annotations per application was significantly correlated with invited for interview status (invited: 208.6 [59.1]; not invited: 145.2 [57.2]; P < .001). In addition, 8 of the 10 individual values were significantly predictive of an applicant's invited for interview status. The authors created an MLM that can identify several values important for resident success in internal medicine-pediatrics programs with moderate sensitivity and high specificity. The authors will continue to refine the MLM by increasing the number of annotations, exploring parameter tuning and feature engineering options, and identifying which application sections have the highest correlation with invited for interview status.

The Use of AI (artificial Intelligence) in English Learning Among Engineering Students: A Case Study

Aim: This research aim is to analyze an artificial intelligence platform that can be used in imparting education as well as evaluating student performance Method: This research was conducted with a qualitative method by conducting in-depth interviews and a literature study. Results: The findings of this study shows that Artificial Intelligence technology can be used as a means of developing English learning for students. Discussion: There have been several studies that support research results, that AI can be used to improve students' English skills through applications, websites, Virtual Reality technology, and other AI-based learning and teaching systems. Limitation: The limitation of this research is that it does not examine how far the role of AI in students' English learning is. Suggestion: For further research, it is expected to test how far the role of AI is to improve students' English skills, especially Jawaharlal Nehru Technological University Anantapuramu (JNTUA)

Impacto de la inteligencia artificial en los métodos de evaluación en la educación primaria y secundaria: revisión sistemática de la literatura

The educational sector can be enriched by the incorporation of artificial intelligence (AI) in various aspects. The field of artificial intelligence and its applications in the education sector give rise to a multidisciplinary field that brings together computer science, statistics, psychology and, of course, education. Within this context, this review aimed to synthesise existing research focused on provide improvements on primary/secondary student assessment using some AI tool. Thus, nine original research studies (641 participants), published between 2010 and 2023, met the inclusion criteria defined in this systematic literature review. The main contributions of the application of AI in the assessment of students at these lower educational levels focus on predicting their performance, automating and making evaluations more objective by means of neural networks or natural language processing, the use of educational robots to analyse their learning process, and the detection of specific factors that make classes more attractive. This review shows the possibilities and already existing uses that AI can bring to education, specifically in the evaluation of student performance at the primary and secondary levels.

Impact of artificial intelligence on assessment methods in primary and secondary education: Systematic literature review

The educational sector can be enriched by the incorporation of artificial intelligence (AI) in various aspects. The field of artificial intelligence and its applications in the education sector give rise to a multidisciplinary field that brings together computer science, statistics, psychology and, of course, education. Within this context, this review aimed to synthesise existing research focused on provide improvements on primary/secondary student assessment using some AI tool. Thus, nine original research studies (641 participants), published between 2010 and 2023, met the inclusion criteria defined in this systematic literature review. The main contributions of the application of AI in the assessment of students at these lower educational levels focus on predicting their performance, automating and making evaluations more objective by means of neural networks or natural language processing, the use of educational robots to analyse their learning process, and the detection of specific factors that make classes more attractive. This review shows the possibilities and already existing uses that AI can bring to education, specifically in the evaluation of student performance at the primary and secondary levels.

DESIGN OF STUDENT SUCCESS PREDICTION APPLICATION IN ONLINE LEARNING USING FUZZY-KNN

Effective evaluation of student performance is crucial. Hence, many kinds of techniques are used such as statistics, physical examination and currently data mining techniques to evaluate student performance. Data mining techniques as known as Educational Data Mining (EDM) collect, process, report and used to find the unseen patterns in the student dataset. EDM uses machine learning techniques to dig out useful data from multiple levels of meaningful hierarchy. Various data from intelligent computer tutors, classic computer based educational systems, online classes, academic data in educational institution, and standar assesment can be process for EDM. This led universities include open and distance learning (ODL) to collect large volume of student and learning data in their learning management systems (LMS). Students in ODL are relatively familiar with LMS and many learning activities such as number of accessing materials, student participation in discussion forum recorded in LMS. The processes of using EDM to improve the quality of educational policy maker with data-based models have become a challange that institutions of higher education face today. Therefore, this study aims to design applications that predict student performance in online learning using machine learning techniques based on EDM. The machine learning technique used in this research is Fuzzy-KNN. Testing using Fuzzy-KNN produces an accuracy of 92.5%.

The assessment of undergraduate bachelor of nursing students in the collaborative clusters education model: A qualitative descriptive design

BackgroundWorldwide, undergraduate Bachelor of Nursing students are required to complete experiential learning placements in health care settings as part of the curriculum. There are a variety of facilitation models that support student learning and assessment on clinical placement. As workforce pressures increase globally, innovative approaches to clinical facilitation are required. In the Collaborative Clusters Education Model of clinical facilitation, hospital-employed clinical facilitators work collaboratively within peer groups (clusters) to collectively participate in a process of facilitating student learning and conducting assessment and moderation of student performance. The assessment process in this collaborative clinical facilitation model is not well described. AimTo describe how the assessment of undergraduate nursing students is achieved in the Collaborative Clusters Education Model. DesignA qualitative descriptive approach was employed. MethodsIn March 2021 individual and group interviews were conducted with seven clinical facilitators working in the Collaborative Clusters Education Model in one health service in southeast Queensland, Australia. Content analysis of transcribed interviews was performed. ResultsAssessment was achieved through two processes, situational scoring and moderation. In the process of situational scoring, clinical facilitators balanced the students’ perception of their role in assessment, accounted for the type of experiences available, considered multiple sources of evidence and used the Australian Nursing Standards Assessment Tool. In the process of moderation, clinical facilitators communicated with their cluster colleagues to determine a shared understanding of student history, considered data from multiple evidence sources and collaboratively evaluated the trustworthiness of student performance evaluation decisions. ConclusionsIn the Collaborative Clusters Education Model, the input of multiple assessors, working in a small team, ensured transparency in assessment processes. Furthermore, this transparency in assessment practices normalised on-going moderation, an in-built quality-check and, as such, an innovative component of assessment in the Collaborative Clusters Education Model. As nursing directors and managers seek to ameliorate the impact of nursing workforce pressures, this innovative model of collaborative assessment may serve as a valuable addition to nursing clinical assessment toolkits. Tweetable abstractThe Collaborative Clusters Education Model of Clinical Facilitation enables transparency in assessment processes and normalises moderation.

The Era of Coronavirus: Assess the Impact of Distance Learning Course on the Levels of Hand Hygiene Knowledge, Attitude and Practice of Students in Low Resources Country

Introduction: Hand Hygiene is an essential method to preclude infections in all healthcare environments. Education is essential to advance hand hygiene performance amongst nurses and nursing students. The aim is to find out the usefulness of a self-paced online hand hygiene course on the knowledge, attitude and practice of nursing students by evaluating student performance in post-tests. Methods: The study employed a quasi-experimental design in which data were collected using two questionnaires from undergraduate nursing students and they were exposed to interactive lectures and online activities related to risks, benefits, and key recommendations for hand hygiene. The following two surveys were used, Hand Hygiene Knowledge Questionnaire for Health-Care Workers; and Handwashing Assessment Inventory. Data were collected through pre-post tests. Results: Nurses reported a significant change for the better in hand hygiene comprehension, attitude, and performance of hand hygiene behaviours. The present study revealed a significant increase in hand hygiene knowledge belief, attitude, and outcomes among university students after participating in the training intervention. The majority of participants in the pretest rated their knowledge level as poor. Conclusion: Interactive training and online learning courses on hand hygiene could be used to influence the beliefs, attitudes and behaviours of students to bring about the desired change in hand hygiene practice.

Work readiness of graduating nursing students: Case study research

Work readiness of nursing graduates is still a concern. This research explored how final year students perceive themselves as work ready for RN roles and clinical practice in Australia? Using case study methodology participants were surveyed and interviewed. The five themes found were work readiness definition and perceptions; flying high; flying under the radar; unbalance of clinical evaluation; and beyond the curriculum. Clinical placements are key to practicing competency necessary for work readiness. Consistent use of a student performance evaluation tool is essential. Students are not always gaining necessary practice due to lack of opportunities putting students at risk for not being work ready. Clinical staff need further training about the importance of consistent and objective evaluation of student performance.

Construction of Entrepreneurial Environment for College Graduates Based on Big Data Technology

The 21st century is the era of big data. The wide application of big data in various fields of society has triggered changes in all walks of life, and it has also brought new opportunities and challenges to the cultivation of talents in colleges and universities. The entrepreneurial environment has a strong influence on entrepreneurial activities, and the choice of entrepreneurial environment plays an important role in college students’ entrepreneurship. College students’ entrepreneurship is carried out in a certain environment, and the quality of the entrepreneurial environment will directly affect the effectiveness of college students’ entrepreneurial activities. Under the background of the development of the Internet economy, college students have more ways to start their own businesses. With the support of big data, the types, methods, and performance evaluation of college students’ entrepreneurship can be evaluated based on the effective application of big data. Big data is an important means of constructing an environment for innovation and entrepreneurship. In order to solve the problems of “information island” and low data utilization in the existing innovation and entrepreneurship information platform in colleges and universities, we use big data technology to break the data island and integrate multichannel, multilevel, and multitype data, so as to build a perfect university. The entrepreneurial environment of graduates is of great significance.

Web Application for Student Performance Evaluation and Remuneration System

Accreditation plays an important role in ranking colleges, requiring correct date. Students Evaluation System helps the engineering faculties to maintain student grades database in an efficient manner and helps in evaluating it to develop a criterion for accreditation. Faculty remuneration system, on the other hand, is a structured approach to determine the compensation and benefits that faculty members receive for their teaching, research, and service activities by generating excel sheet with accurate remuneration received by faculties for conduction of Examination orals and practical. Both the student performance evaluation system and faculty remuneration system are essential components of any educational institution. Effective implementation of these systems requires careful planning, regular monitoring, and continuous improvement based on feedback and data analysis.

Primary Care Residency Perspectives on Medical Student Performance Evaluations.

With the transition of the United States Medical Licensing Examination (USMLE) Step 1 exam to pass-fail, residency directors are exploring alternative objective approaches when selecting candidates for interviews. The Medical Student Performance Evaluations (MSPE) portion of the application may be an area where objectivity could be provided. This study explored program directors' (PDs) perspectives on the utility of the MSPE as a discriminating factor for residency candidate selection. We invited PDs of primary care residencies listed in the American Medical Association FRIEDA database to participate in a mixed-methods study assessing opinions on the MSPE, and the importance of student skills and application components when considering a candidate for interview. We obtained summary statistics for Likert-scale responses. We used inductive thematic analysis to generate themes from open-ended comments. Two hundred forty-nine PDs completed the survey (response rate=15.9%). Patient communication (83.6%) and teamwork (81.9%) were rated as very/extremely important skills, and being a graduate of a US medical school in the past 3 years (73.1%), no failures on board exams (58.2%), and MSPEs (54.8%) were rated as very/extremely important application components. Six hundred seventy-eight open-ended comments yielded themes related to desire for more transparency and standardization, importance of student attributes and activities, and other important components of applications. PDs place a high value on the MSPE but find it limited by concerns over validity, objectivity, and lack of standardization. The quality of MSPEs may be improved by using a common language of skill attainment such as the Association of American Medical Colleges' Entrustable Professional Activities and using the document to discuss students' other attributes and contributions.

Validity and Educational Impact of Clinical Entrustable Professional Activities Linked to the Nutrition Care Process for Work-Based Assessment of Entry-Level Dietetics Students: Evaluation of a3-Year Implementation Study in Australia.

Entrustable professional activities (EPAs) describe key workplace activities and are increasingly being used for student work-based assessment. An EPA-based work-based assessment tool offers potential to increase understanding of dietetics student skill development and opportunity for standardised work-based student assessment. To determine construct validity and educational impact of an EPA work-based assessment tool for dietetics placement students in clinical settings. Using a time series design, supervisor and student self-assessment data collected from an EPA-based assessment tool from three cohorts of dietetics students and supervisors from 2017-2019 was analyzed. Dietetics students (n= 145) from an accredited dietetic training program in Australia and affiliated metropolitan and rural hospitals. Construct validity was determined through analysis of supervisor evaluation of student performance against EPAs over time. Educational impact was determined through comparing student performance across EPAs to predicted milestones to identify areas of least entrustment. Student performance over time and differences between student self-assessment and supervisor assessment for each EPA were investigated using Wilcoxon signed-rank tests and linear mixed-model analysis. Descriptive statistics summarised student performance against each EPA. Performance significantly increased over time in 35 out of 37 EPAs. Significant differences between supervisor and student self-assessment were evident in 9 out of 37 EPAs. Dietetics student performance varied across EPAs with 88.2% of students achieving entrustment for nutrition management EPAs compared with 100% for professionalism EPAs. The tool's construct validity was established. EPAs identified areas of Nutrition Care Process skills development requiring additional support. Data collected by the tool can inform teaching.

An Elite Privilege: Top-Ranked Medical Schools Provide Fewer Comparative Performance Data on Their Students.

The objective of this study was to determine differences in the reporting of performance data on medical student performance evaluations (MSPEs) by medical school ranking. MSPEs from all US allopathic and osteopathic medical schools received by a single diagnostic radiology residency program during the 2021-2022 application cycle were retrospectively reviewed. Preclinical class and core clerkship grades were categorized as pass/fail or multitiered. Comparative summative assessments provided in the MSPEs were recorded. Medical schools were grouped by their US News & World Report rankings, and the proportion of reported performance metrics for each group was compared. Information from 95% of US allopathic medical schools (148 of 155) and 73% of osteopathic medical schools (27 of 37) was collected, on the basis of 1,046 applications received. For preclinical classes, multitiered grading was reported by no schools ranked in the top 10, 17% of schools ranked 11th to 50th, 52% of schools ranked 51st to 100th, and 59% of unranked schools (P < .001). For core clinical clerkships, multitiered grades were reported by 70% of the top 10 ranked schools, 90% of schools ranked 11th to 50th, 94% of those ranked 51st to 100th, and 94% of unranked schools (P= .0463). Comparative summative assessments were reported by none of the top 10 ranked schools, 56% of schools ranked 11th to 50th, 80% of those ranked 51th to 100th, and 81% of unranked schools (P < .001). Higher ranked medical schools are less likely to provide comparative assessment data on their MSPEs, which may disadvantage students from lower ranked medical schools.

Embracing our responsibility to ensure trainee competency.

Embracing our responsibility to ensure trainee competency.

Program assessment in undergraduate acoustics at Kettering University

Kettering University has been teaching acoustics as part of undergraduate engineering for decades, and the senior-level acoustics laboratory course has assumed a critical role in the continuous improvement process for our ABET-accredited programs in Applied Physics and Engineering Physics. This project-based course addresses learning objectives focused on transferable skills in test and measurement, literature and theoretical foundations, and computational modeling. These are set in the context of two modules, on sound source directivity and modal testing. The assessment structure is built around rubrics, used for both formative and summative evaluation of student performance on authentic tasks including recording activity in lab notebooks and scientific writing. A 32-question multiple choice instrument is deployed as a pre-test and post-test to assess gains in content knowledge from the course. Finally, we discuss specific examples of assessment for effective teamwork (ABET EAC Outcome 5) and developing and conducting experiments and analyzing and interpreting data (ABET EAC Outcome 6).

Identifying Meaningful Patterns of Internal Medicine Clerkship Grading Distributions: Application of Data Science Techniques Across 135 U.S. Medical Schools.

Residency program directors use clerkship grades for high-stakes selection decisions despite substantial variability in grading systems and distributions. The authors apply clustering techniques from data science to identify groups of schools for which grading distributions were statistically similar in the internal medicine clerkship. Grading systems (e.g., honors/pass/fail) and distributions (i.e., percent of students in each grade tier) were tabulated for the internal medicine clerkship at U.S. MD-granting medical schools by manually reviewing Medical Student Performance Evaluations (MSPEs) in the 2019 and 2020 residency application cycles. Grading distributions were analyzed using k-means cluster analysis, with the optimal number of clusters selected using model fit indices. Among the 145 medical schools with available MSPE data, 64 distinct grading systems were reported. Among the 135 schools reporting a grading distribution, the median percent of students receiving the highest and lowest tier grade was 32% (range: 2%-66%) and 2% (range: 0%-91%), respectively. Four clusters was the most optimal solution (η 2 = 0.8): cluster 1 (45% [highest grade tier]-45% [middle tier]-10% [lowest tier], n = 64 [47%] schools), cluster 2 (25%-30%-45%, n = 40 [30%] schools), cluster 3 (20%-75%-5%, n = 25 [19%] schools), and cluster 4 (15%-25%-25%-25%-10%, n = 6 [4%] schools). The findings suggest internal medicine clerkship grading systems may be more comparable across institutions than previously thought. The authors will prospectively review reported clerkship grading approaches across additional specialties and are conducting a mixed-methods analysis, incorporating a sequential explanatory model, to interview stakeholder groups on the use of the patterns identified.

Fixing a Broken Clerkship Assessment Process: Reflections on Objectivity and Equity Following the USMLE Step 1 Change to Pass/Fail.

Clerkship grading is a core feature of evaluation for medical students' skills as physicians and is considered by most residency program directors to be an indicator of future performance and success. With the transition of the U.S. Medical Licensing Examination Step 1 score to pass/fail, there will likely be even greater reliance on clerkship grades, which raises several important issues that need to be urgently addressed. This article details the current landscape of clerkship grading and the systemic discrepancies in assessment and allocation of honors. The authors examine not only objectivity and fairness in clerkship grading but also the reliability of clerkship grading in predicting residency performance and the potential benefits and drawbacks to adoption of a pass/fail clinical clerkship grading system. In the promotion of a more fair and equitable residency selection process, there must be standardization of grading systems with consideration of explicit grading criteria, grading committees, and/or structured education of evaluators and assessors regarding implicit bias. In addition, greater adherence and enforcement of transparency in grade distributions in the Medical Student Performance Evaluation is needed. These changes have the potential to level the playing field, foster equitable comparisons, and ultimately add more fairness to the residency selection process.

The Consequences of Step 2 Clinical Skills Examination Discontinuation for Medical Schools and Sustainability Plans for Clinical Skills Assessment.

Comprehensive clinical skills examinations using standardized patients are widely used to assess multiple physician competencies. However, these exams are resource intensive. With the discontinuation of the Step 2 Clinical Skills (CS) exam in 2021, how medical schools will change their approaches to comprehensive clinical skills exams is unknown. This study explores school responses to this change and future directions of comprehensive clinical skills exams using the program sustainability framework. This cross-sectional, descriptive study surveyed medical school curriculum deans at 150 Liaison Committee on Medical Education-accredited U.S. medical schools from September to October 2021. The 30-question survey included questions about medical school and participant role, current comprehensive clinical skills exams, sustainability dimensions, and challenges and future directions. Descriptive statistics were used to characterize responses, and content analysis was used to identify themes in the open-ended responses. Educators at 75 of 150 institutions (50%) responded. Sixty-three respondents (84%) reported conducting a comprehensive clinical skills exam. The comprehensive clinical skills exam assessed readiness for graduation (51 [81%]), provided feedback for students (49 [78%]), evaluated curricula (38 [60%]), provided information for medical student performance evaluation or communication with residency (10 [16%]), and assessed other factors (6 [10%]), including preparation for Step 2 CS in the past and readiness for advancement to fourth year of medical school (multiple responses were allowed). Factors facilitating sustainability included sufficient funding to continue the exam (55 [87%]) and the belief that clinical skills assessment in medical school is now more important after discontinuation of the Step 2 CS exam (55 [87%]). Challenges to sustainability included organizational capacity and limited interinstitutional collaboration. Educators remain committed to the purpose of comprehensive clinical skills exams. Adapting to changed licensing requirements while sustaining clinical skills exams enables innovation and improvement in assessment of clinical competence.