Learning Performance Assessment Research Articles

Teaching a New Obturation Technique in Preclinical Endodontic Training: Assessment of Student Learning Experience, Performance, and Self-Evaluation

IntroductionThis study investigated the impact of implementing hydraulic condensation (HC) as a second obturation technique in the Endodontic Preclinical course for dental students trained in warm vertical compaction (WVC). MethodsA total of 70 students performed root canal treatment of 2 extracted molars; they performed the HC technique for the first time after a demonstration. The combination of the obturation technique (WVC or HC) and the type of molar (maxillary or mandibular) was randomly assigned, resulting in 4 groups (n = 35). The students answered a questionnaire appraising their learning experience and completed a self-evaluation rubric. Masked instructors graded projects. Data were statistically analyzed (significance set at 5%). ResultsMost of the students (91%) considered learning 2 obturation techniques relevant and considered it difficult to perform the downpack in molars (P < .05). About half of the students (45.7%) responded that HC resulted in fewer errors and disagreed that WVC derives in a more homogeneous obturation; accordingly, 52.9% of the students selected the highest self-grading for HC obturations, while only 38.6% for WVC. No significant difference was found in the distribution of grades attributed by students and instructors (P > .05). Students reported significantly higher levels of confidence for HC and difficulty for WVC (P < .05). No difference was found between the techniques regarding the presence of voids. The presence of multiple errors was more frequent for WVC (P < .05). ConclusionThe findings indicate a positive learning experience and comparable performance among preclinical dental students in implementing HC as an alternative obturation technique.

Utilizing Artificial Intelligence in Teaching Arabic Language Skills (Visions and Expectations)

The study aimed to explore and understand the use of artificial intelligence in teaching the Arabic language and enhancing student performance. The study included a sample of 309 participants, including teachers, faculty members, students, and educational administrations. Questionnaires were distributed to gather their opinions and expectations regarding the use of artificial intelligence in teaching Arabic and to assess the benefits and challenges, The results showed positive insights and expectations among teachers, students, and educational administrations. Teachers believe that artificial intelligence can help them improve their performance and provide excellent educational content to students, as well as cater to their diverse needs. Students see artificial intelligence to make the learning process enjoyable and interactive, helping them improve their Arabic language skills and meet their individual needs. Educational administrations believe that artificial intelligence can enhance the quality of education, contribute to school performance improvement, and develop the level of Arabic language instruction, The study concluded that artificial intelligence can be used to improve Arabic language education through content customization, interactive learning, and accurate student performance assessment. It can also support self-learning and reduce the burden of routine tasks on teachers. However, the study also highlights the challenges that need to be considered, such as good organization and technical monitoring, as well as the importance of focusing on security and privacy.

Development of Random Forest Model for Stroke Prediction

Stroke is a significant cause of mortality and morbidity worldwide, and early detection and prevention of stroke are essential for improving patient outcomes. Machine learning algorithms have been used in recent years to predict the risk of stroke by leveraging large amounts of clinical and demographic data. The development of a stroke prediction system using Random Forest machine learning algorithm is the main objective of this thesis. The primary goal of the project is to increase the accuracy of stroke detection while addressing the shortcomings of the current system, which include real- time deployment and interpretability issues with logistic regression. The development and use of an ensemble machine learning-based stroke prediction system, performance optimization through the use of ensemble machine learning algorithms, performance assessment, and real-time model deployment through the use of Python Django are among the goals of the research. The study's potential to improve public health by lessening the severity and consequences of strokes through early diagnosis and treatment makes it significant. Data collection, preprocessing, model selection, evaluation, and real-time deployment using Python Django are all part of the research technique. Our dataset consists of 5110 rows of tuples and columns with total size of 69kg. The performance of our stroke prediction algorithm was evaluated using confusion metrics-consisting of accuracy, precision, recall and F1-score. At the end of the research, Random Forest model gave an accuracy of 98.5% compared to the existing model logistic regression which has 86% accuracy.

TPACK Profile of Biology Teachers During Their Learning Process After Participating in Numerical Taxonomy and its Training Program

This study aimed to analyze the TPACK (Technological Pedagogical Content Knowledge) profiles of biology teachers in their learning process after participating in numerical taxonomy and its training program. TPACK is a construction of knowledge that must be possessed by teachers in the 21st century. Numerical taxonomy and its training aim to develop biology teachers’ TPACK in the classification of living things. This training equips biology teachers with the knowledge needed to integrate TPACK components into the learning process, including contents related to TPACK and to the classification of living things, especially numerical taxonomy, learning technology, learning strategies, and methods to integrate TPACK into the learning process. This research is a descriptive study in which the data of biology teachers’ TPACK during the learning process were obtained from PaP-eRs and the results of the learning performance assessment. The results showed that biology teachers’ TPACK during the learning process after numerical taxonomy training had good results, and there has been an interactive phase between the TPACK components in the learning process. This is proven by teachers’ ability to develop good PaP-eRs and teachers’ performance based on the learning assessment results. It was further found that the use of technology in the learning process has been optimally applied by teachers. Meanwhile, the delivery of motivation and misconception were less developed by the teachers during the implementation of the TPACK learning process. Keywords: TPACK, numerical taxonomy, training program, biology teachers

Potential interactive effect of positive expectancy violation and sleep on memory consolidation in dogs

In dogs, as in humans, both emotional and learning pretreatment affect subsequent behaviour and sleep. Although learning often occurs in an emotional-social context, the emotion-learning interplay in such context remain mainly unknown. Aims were to assess the effects of Controlling versus Permissive (emotional factors) training (learning factors) styles on dogs’ behaviour, learning performance, and sleep. Family dogs (N = 24) participated in two command learning sessions employing the two training styles with each session followed by assessment of learning performance, a 2-h-long non-invasive sleep EEG measurement, and a retest of learning performance. Pre- to post-sleep improvement in learning performance was evident in dogs that received the Permissive training during the second learning session, indicating that dogs that experienced a more rewarding situation than expected (positive expectancy violation) during the second training session showed improved learning success after their afternoon sleep. These results possibly indicate an interactive effect of expectancy violation and sleep on enhancing learning.

Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study

Artificial intelligence (AI) has repeatedly been shown to encode historical inequities in healthcare. We aimed to develop a framework to quantitatively assess the performance equity of health AI technologies and to illustrate its utility via a case study. Here, we propose a methodology to assess whether health AI technologies prioritise performance for patient populations experiencing worse outcomes, that is complementary to existing fairness metrics. We developed the Health Equity Assessment of machine Learning performance (HEAL) framework designed to quantitatively assess the performance equity of health AI technologies via a four-step interdisciplinary process to understand and quantify domain-specific criteria, and the resulting HEAL metric. As an illustrative case study (analysis conducted between October 2022 and January 2023), we applied the HEAL framework to a dermatology AI model. A set of 5420 teledermatology cases (store-and-forward cases from patients of 20 years or older, submitted from primary care providers in the USA and skin cancer clinics in Australia), enriched for diversity in age, sex and race/ethnicity, was used to retrospectively evaluate the AI model's HEAL metric, defined as the likelihood that the AI model performs better for subpopulations with worse average health outcomes as compared to others. The likelihood that AI performance was anticorrelated to pre-existing health outcomes was estimated using bootstrap methods as the probability that the negated Spearman's rank correlation coefficient (i.e., "R") was greater than zero. Positive values of R suggest that subpopulations with poorer health outcomes have better AI model performance. Thus, the HEAL metric, defined as p (R>0), measures how likely the AI technology is to prioritise performance for subpopulations with worse average health outcomes as compared to others (presented as a percentage below). Health outcomes were quantified as disability-adjusted life years (DALYs) when grouping by sex and age, and years of life lost (YLLs) when grouping by race/ethnicity. AI performance was measured as top-3 agreement with the reference diagnosis from a panel of 3 dermatologists per case. Across all dermatologic conditions, the HEAL metric was 80.5% for prioritizing AI performance of racial/ethnic subpopulations based on YLLs, and 92.1% and 0.0% respectively for prioritizing AI performance of sex and age subpopulations based on DALYs. Certain dermatologic conditions were significantly associated with greater AI model performance compared to a reference category of less common conditions. For skin cancer conditions, the HEAL metric was 73.8% for prioritizing AI performance of age subpopulations based on DALYs. Analysis using the proposed HEAL framework showed that the dermatology AI model prioritised performance for race/ethnicity, sex (all conditions) and age (cancer conditions) subpopulations with respect to pre-existing health disparities. More work is needed to investigate ways of promoting equitable AI performance across age for non-cancer conditions and to better understand how AI models can contribute towards improving equity in health outcomes. Google LLC.

Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies

Recently, transfer learning has gained popularity in the machine learning community. Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge learned from one or more related domains to improve prediction accuracy in a target domain with limited data. However, for time series forecasting (TSF) applications, transfer learning is relatively new. This paper addresses the need for empirical studies as identified in recent reviews advocating the need for practical guidelines for Transfer Learning approaches and method designs for time series forecasting. The main contribution of this paper is the suggestion of a comprehensive framework for Transfer Learning Sensitivity Analysis (SA) for time series forecasting. We achieve this by identifying various parameters seen from various angles of transfer learning applied to time series, aiming to uncover factors and insights that influence the performance of transfer learning in time series forecasting. Undoubtedly, symmetry appears to be a core aspect in the consideration of these factors and insights. A further contribution is the introduction of four TL performance metrics encompassed in our framework. These TL performance metrics provide insight into the extent of the transferability between the source and the target domains. Analyzing whether the benefits of transferred knowledge are equally or unequally accessible and applicable across different domains or tasks speaks to the requirement of symmetry or asymmetry in transfer learning. Moreover, these TL performance metrics inform on the possibility of the occurrence of negative transfers and also provide insight into the possible vulnerability of the network to catastrophic forgetting. Finally, we discuss a sensitivity analysis of an Ensemble TL technique use case (with Multilayer Perceptron models) as a proof of concept to validate the suggested framework. While the results from the experiments offer empirical insights into various parameters that impact the transfer learning gain, they also raise the question of network dimensioning requirements when designing, specifically, a neural network for transfer learning.

Assessment of Educational Digital Game-Based Learning and Academic Performance of Grade Six Pupils

Digital games have surpassed the movie box office and are making their way to revolutionize the contemporary teaching methods of today’s classrooms. Educators, parents, and policymakers are debating its effect on the students, and more researchers are offshooting to prove its effectiveness and suitability for the recent digital natives. This study, therefore, found empirical evidence underpinning the efficacy of Educational Digital Game Learning on the learners’ academic performance, specifically in English concepts, using a quasi-experimental control group and descriptive design. Two classes of grade VI students were involved and labeled as the control and experimental group. During the study, the researcher used a validated test instrument. The researcher also filmed how EDGBL was conducted in the classroom and recorded interviews with experienced students. Frequency counts, percentages, weighted mean, and t-tests for independent samples were used to investigate the aims and establish the results. The findings revealed that students who underwent Educational Digital Game-Based Learning had improved their academic performance in learning English concepts. It was also found that those students who were exposed to EDGBL developed more positive habits in learning English. In contrast, those exposed to the traditional method tended to misbehave and have a monotonous learning environment. Moreover, conducting EDGBL as a method requires teachers to have practical facilitating skills to maintain solid work coordination throughout the implementation of EDGBL.

A study on the measurement and standardized assessment model of student learning outcomes in vocational institutions

Abstract As society requires a deeper understanding and demand for the actual abilities of students in higher education institutions, traditional assessment tests no longer meet the current needs. This paper first divides assessment techniques into two main categories from an application perspective: assessment of student learning performance and in-depth cognitive diagnosis. Students are automatically provided with appropriate learning content based on their ability level and learning style, providing them with accurate and timely feedback. Secondly, a new fuzzy inference model is proposed to determine students’ student outcomes by addressing the obvious shortcomings of the fuzzy sets usually used for student outcome assessment. Finally, the validity and usefulness of its assessment model are verified by the student learning performance on a real data set. The results show that the fuzzy inference assessment model designed in this paper can obtain an assessment accuracy of 85.8% for the learner’s learning outcomes, which has a good assessment effect. And the fuzzy inference assessment model also retains the greatest advantage of linear fitting regression, which reflects the correlation between the parameters of students’ learning behaviors and the final learning outcomes. The assessment method based on the fuzzy inference model predicts learners’ learning risks and provides learning interventions in advance for smart learning, and also provides new ideas for deepening education reform.

The Application of Scenario Simulation Teaching Combined with Case Teaching in Medical Education: Taking Nursing Practice Teaching as An Example

Objective: This study aims to investigate the application effect of scenario simulation teaching combined with case teaching method in nursing practice teaching, and compare it with traditional nursing practice teaching. Methods: One hundred nursing students from a university in 2019 were selected as the research subjects, and were randomly divided into an experimental group and a control group, with 50 students in each group. The experimental group received nursing practice teaching using scenario simulation teaching combined with case teaching method, while the control group received traditional nursing practice teaching. The effectiveness of the two teaching methods was evaluated through examination scores, clinical competency assessment, non-technical skill level comparison, and student satisfaction survey. Results: The students in the experimental group performed better than those in the control group in terms of examination scores, clinical competency assessment, non-technical skill level, and student satisfaction. Specifically, the excellent rate and passing rate of the examination scores and clinical competency assessment of the experimental group were significantly higher than those of the control group, with statistically significant differences (P&lt;0.05); after the teaching, the non-technical skill level scores of the experimental group were higher than those of the control group (P&lt;0.05); the satisfaction ratings of the experimental group on teaching content, teaching methods, teaching environment, teacher’s teaching style, and teaching resources were significantly higher than those of the control group (P&lt;0.05). Conclusion: Scenario simulation teaching combined with case teaching method has good application effect in nursing practice teaching, which can effectively improve students’ learning performance and clinical competency assessment scores, enhance students’ understanding and application ability of teaching content, and increase students’ learning interest and participation.

Retraction Note: Cognitive learning performance assessment and analysis with CSCL applied on the NetGuru platform and CSPL applied on the TAoD platform for the network experiment class

Retraction Note: Cognitive learning performance assessment and analysis with CSCL applied on the NetGuru platform and CSPL applied on the TAoD platform for the network experiment class

Remote Teaching during COVID-19 Emergency: Teaching and Assessment Strategies and the Role of Previous Training

The Emergency Remote Teaching experience during the COVID-19 pandemic resulted in schools reviewing teaching methods and strategies. The Italian Society for Educational Research (SIRD) conducted a survey in which more than 16,000 Italian teachers took part. The aim of this study is to be a first step in analysing the data from the Tuscany and Umbria regions by comparing them with national data in relation to the teaching and assessment methods and strategies used, and with the previous teacher training in distance learning. Secondly, the paper aims to deepen the link between the use of certain teaching and assessment methods, the previous training, and the quality of the distance learning experience perceived by teachers. For this purpose, some items and scales from the questionnaire used in the SIRD survey and some categories from the qualitative analysis of the responses to the open-ended questions were selected. The study, first and foremost, revealed some peculiarities of the two regions, including Tuscany’s lower evaluation of the effectiveness of the experience. On the national level, a positive relationship was found between teachers using interactive teaching strategies and “alternative” assessment activities and the higher perception of both effectiveness for student learning and adequate performance assessment. Finally, the role of previous training is underlined as useful only if it is not generally technology-centred, but rather related to teaching and assessment methods in virtual learning environments.

Assessment of core teaching competency of health professional educators in Ethiopia: an institution-based cross-sectional study

ObjectivesUnderstanding the competency of educators is key to informing faculty development, recruitment and performance monitoring. This study aimed to assess the core teaching competency of nursing, midwifery and biomedical educators,...

Physical Education Amidst Pandemic: Contactless Interactions on Students Remote Learning Tasks and Performance in Physical Education

This descriptive study determines the Physical Education amidst Pandemic: Contactless Interactions on Student?s Remote Learning Tasks and Performance in Physical Education. Three hundred fifty-three randomly selected (353) students were the subject of this study from grade 7 at Sta. Catalina Integrated National High School, District of Majayjay. It aimed to answer the questions such as the level of Contactless Interactions in terms instruction, feedback and support, learning resources, performance assessment and performance monitoring. Also, it sought answers the level of students Remote Learning Tasks in terms of aesthetic sensitivity, knowledge and practice of sports, motor and sports skills and Performance Tasks, and lastly, the level of performance in Physical Education of grade 7 students. On the basis of the foregoing findings, the following conclusion was drawn that there is a significant relationship between the contactless interactions and the students? remote learning tasks. The researcher then come up to the conclusion that the null hypothesis of which states that the level of students? contactless interaction in physical education classes has no significant relationship on their remote learning tasks? is rejected. This calls for the acceptance of the alternative which incites that there is a significant relationship. On the other hand, study reveals that there is no relationship between the contactless interactions to the students? performance in physical education. It can infer that the null hypothesis stating that ?The level of students? contactless interaction in physical education classes has no significant relationship on their performance? is true. Hence, there is no significant relationship between the two. Based on the drawn conclusions resulted to the following recommendations: It is highly suggested that there must be modifications and improvement for the instruction use so that students may be able to clearly understand their task despite of contactless interactions. Differentiated instructions can also be used to enhance students? comprehension in answering their tasks. It is recommended that the performance monitoring be focused on the students? need and enable them to learn at their own pace. Furthermore, teachers may also emphasize the value of learning physical education and promotes its importance for the learners. Enhancement program and/or extended physical activities may help them to fully understand the help of physical education for their daily lives. Grade 7 students may not that incline in their physical strength and activities, so that it is highly recommended to provide engaging activities wherein their can exhibit their sports skills despite of contactless interaction. Lastly, they may find the meaning physical education out of the context if they can experience more hands-on activities.

Deep Learning-Powered Prediction of Human-Virus Protein-Protein Interactions.

Identifying human-virus protein-protein interactions (PPIs) is an essential step for understanding viral infection mechanisms and antiviral response of the human host. Recent advances in high-throughput experimental techniques enable the significant accumulation of human-virus PPI data, which have further fueled the development of machine learning-based human-virus PPI prediction methods. Emerging as a very promising method to predict human-virus PPIs, deep learning shows the powerful ability to integrate large-scale datasets, learn complex sequence-structure relationships of proteins and convert the learned patterns into final prediction models with high accuracy. Focusing on the recent progresses of deep learning-powered human-virus PPI predictions, we review technical details of these newly developed methods, including dataset preparation, deep learning architectures, feature engineering, and performance assessment. Moreover, we discuss the current challenges and potential solutions and provide future perspectives of human-virus PPI prediction in the coming post-AlphaFold2 era.

Online exam proctoring technologies: Educational innovation or deterioration?

AbstractDuring the coronavirus disease‐2019 (Covid‐19) pandemic, many universities have adopted online exam proctoring technologies to monitor and control an increasing number of student cheating incidents. Although it looks like a natural and effective solution for a fair assessment of student online learning performance, the authors argue that proctoring technologies are rooted in problematic assumptions about educational fairness and authoritarian pedagogical approaches. The authors have conducted a qualitative case study in a large‐sized, top‐tier university in South Korea to investigate the negative impacts of adopting proctoring technologies on student subjectivities, pedagogical relationships and educational outcomes, which have not been fully discussed in previous studies. By utilising Foucault's theorisation of disciplinary governmentality, the authors effectively demonstrate that the binary subjectification of students as cheaters and the cheated has degraded the value of student engagement in university education whilst creating more competitive and distrusting relationships amongst students and between students and teachers. Nevertheless, without challenging the unethical consequences of online proctoring technologies or fundamentally unfair social and educational systems, students willingly accept and adopt them as docile bodies, which has led to educational deterioration rather than innovation. Practitioner notesWhat is already known about this topic There is an increasing number of online exam proctoring technologies available with advanced technical features. During the coronavirus disease‐2019 (Covid‐19) pandemic, many universities have adopted online exam proctoring technologies with exam‐related policies and regulations to stop student cheating behaviours. Previous studies have discussed both advantages and disadvantages of adopting online exam proctoring technologies, including specific ethical concerns. What this paper adds Online exam proctoring technologies are deeply rooted in problematic educational approaches such as teacher‐centred knowledge transmission. The adoption of online exam proctoring technologies has produced negative impacts on student subjectivities, pedagogical relationships and educational outcomes. Focusing on student cheating as an individual and interpersonal problem neglects the more fundamental issue of social and educational inequality. Implications for practice and/or policy The negative consequences and damages created by the adoption of online exams and online exam proctoring technologies need to be carefully reflected. The notion of academic fairness needs to be approached with a broader perspective, considering the different social and academic circumstances each student is in. It is essential to critically engage students with the broader conversation about educational fairness to develop them as critical thinkers and future leaders in their chosen fields.

Perancangan Sistem Informasi Kinerja Dosen Sebagai Acuan Kenaikan Level

Evaluation of lecturer performance is currently still done manually by three parts, namely assessment of learning performance by study programs, assessment of research and community service by LPPM, and overall assessment by the rector. Because it doesn't use a database, data from one section cannot be accessed by other sections, so each section must repeat the same transaction, besides that lecturers must make copies of documents to be submitted to each section that requires it, this is considered inefficient. For this reason, it is necessary to make a lecturer performance information system so that the lecturer's performance appraisal activity becomes more effective. Another benefit of a computerized system is that it can minimize subjectivity in the assessment process, the assessment becomes transparent and lecturers can monitor the results of their performance whenever needed. The method used in developing this system is the User Experience Design Process method, which includes product devintion, research, analysis, design, implementation. Data was collected by observation, interviews, and literature study. The system modeling uses the Unified Modeling Language (UML). the tools used are use case diagrams, activity diagrams, sequence diagrams and class diagrams. The implementation uses the PHP and HTML programming languages ??and MySQL as the database. The results of this study can integrate data on the performance assessment process of lecturers from different departments, the information generated from each process and its supporting documents can be accessed by all privately related departments, and the subjectivity of the assessment team can be minimized.

Mathematical representation ability on geometry material viewed from interest for learning in jigsaw cooperative learning performance assessment

Mathematical representation ability on geometry material viewed from interest for learning in jigsaw cooperative learning performance assessment

The Analysis of the Ability of Preservice Teachers in Compiling an Analysis Rubric for Learning Performance Assessments

This study aimed to explore techniques to make analysis rubrics on performance assessments based on the teaching experience of preservice teachers. The method of this study was descriptive qualitative. The random sampling technique was used to select the final semester of Biology Education Study Program students who had taken the Learning Evaluation course, Curriculum Development, Community Service Program, and Field Experience Practices. The data was collected using a closed-ended questionnaire with a Likert scale, interviews to obtain data related to students’ ability to compile an analysis rubric, and documentation to determine the suitability of the analysis of performance assessment rubrics in the sample lesson plan. The results showed that the ability of some preservice teachers to compile an analysis rubric on performance assessment was still inadequate (35. 49%). The obstacles experienced by the preservice teachers were in determining rubric criteria, compiling rubric descriptions, integrating biology-specific material with scientific work capabilities, and scoring the analysis rubric. This showed that the preservice teachers needed to have sufficient training in compiling an assessment rubric that is specific for biology material to optimize the assessment of learning performance.

Performance Assessment of Nepalese Banks Based on Learning and Growth Perspective

The purpose of this paper is to assess performance of Nepalese banks under the learning and growth perspective of the BSC model and to study comparative status of learning and growth in three types of banks - the state owned, private and joint venture banks. This paper follows descriptive research design approach. The questionnaire survey has been applied as primary tool for collecting required information and 466 responses were collected from bank managers. To assess the responses to the BSC model's learning and growth perspective, a five-point Likert-type scale of strongly disagree (1) to strongly agree (5) has been used. The findings of study have indicated that joint venture banks stood at top, state owned banks in second and private banks in the bottom to adopt learning and growth perspective performance. The descriptive analysis has shown satisfactory status of the banks. Among the fourteen items used for assessment of learning and growth performance, improving employee capabilities, teamwork, and better culture and leadership were more emphasized areas by the banks to enhance learning and growth performance.&#x0D;