Evaluation Of University Teaching Research Articles

Implementing Book-end Division Approach using ClassPoint to Energize Electrical and Electronics Engineering Student Engagement

TThis study investigates the efficacy of using ClassPoint in improving student engagement during class and its impact on academic performance among electrical and electronics engineering students by using student engagement framework that established by technology-enhanced learning (TEL) environment microsystem. To achieve this objective, five instructors teaching various courses incorporated ClassPoint into their classes. Then, quantitative data on student engagement and academic performance were collected via surveys. The student’s comments are extracted from university teaching evaluation survey (EPAT) for thematic analysis. The descriptive analysis revealed a significant increase in student engagement after ClassPoint was implemented. Furthermore, students appreciated the use of ClassPoint features such as slide-drawing, multiple choice questions, and word clouds during classes. Survey results also show students have greater attentiveness, active participation, and improved interactions with their peers and instructors. Likert scale responses indicate positive correlation between the use of ClassPoint and students’ enhanced performance in class discussions, idea integration, increased interest in learning, and improved classroom dynamics. Moreover, thematic analysis shows the empowering of five element in TEL microsystem with ClassPoint increase the student engagement. This study highlights ClassPoint's effectiveness in creating an inclusive and interactive learning environment, thus, transforming teaching methods for electrical and electronics engineering students.

Exploring the application of teaching evaluation models incorporating association rules and weighted naive Bayesian algorithms

Teaching evaluation is a judgment of the value of teachers’ teaching and students’ learning, and has become an important part of teaching management and teaching processes in universities. However, the workflow of implementing teaching evaluation is relatively cumbersome, often requiring the completion of a large amount of data calculation tasks. Therefore, how to apply modern science and technology to establish a comprehensive, objective and feasible teaching evaluation system and optimize the evaluation process is an important issue that urgently needs to be solved. The study first uses the Apriori algorithm to explore the correlation between evaluation indicators and results, and then optimizes the teaching evaluation indicators. On this basis, incremental learning is used to improve the classification training ability of the weighted naive Bayesian algorithm, and it is combined with the Apriori algorithm for teaching evaluation. The results show that the fused algorithm takes only 50 seconds to process 500 transactions, and the running speed improves rapidly. As the minimum support threshold decreases, the increase in the time required by the algorithm gradually decreases, resulting in a higher running speed. In the self-built university teaching evaluation database, compared with the BP (Error Back Propagation) algorithm, the combined algorithm has a relatively small fluctuation of accuracy in classifying teaching data, stable at 80 % to 95 %. Meanwhile, in the efficiency comparison, the algorithm requires a slow increase in time as the amount of data increases, resulting in higher efficiency. The application of the algorithm after the fusion of the two in teaching evaluation has not only high accuracy, but also higher efficiency, providing reference technical support for the optimization of university teaching evaluation.

Retracted: Analysis of University Teaching Evaluation in the Era of BD

Retracted: Analysis of University Teaching Evaluation in the Era of BD

Fostering Scholarly Approaches to Peer Review of Teaching in a Research-Intensive University: Strategic Development of a Departmental SPRoT Protocol

This article draws on a 10-year institutional initiative and examines whether and how a strategic departmental Summative Peer Review of Teaching (SPRoT) Protocol was implemented at a Canadian research-intensive university. A peer review of teaching initiative (2010-12), led by a team of UBC national teaching fellows, was prompted by institutional concerns about the quality of student learning experiences and the effectiveness of teaching in a multi-disciplinary research-intensive university context. Canadian universities have long recognized the importance of attending to the evaluation of teaching practices in their particular contexts; however, the enactment of localized scholarship directed at these practices remains very much in its infancy. Traditional approaches to the evaluation of university teaching have often resulted in the over-reliance on student evaluation of teaching data and/or ad-hoc peer-review of teaching practices with numerous accounts of methodological shortcomings that tend to yield less useful and less authentic data. Using a case study research methodology, this paper examines the strategic development of a departmental SPRoT protocol at the University of British Columbia, Canada. Issues addressed in this article include contemporary approaches to the evaluation of teaching in higher education, faculty “buy-in” for the evaluation of teaching in a research intensive university, scholarly approaches to summative and formative Performance Reviews of Teaching (PRT), faculty-specific engagement in summative and formative (informal to formal) PRT training and implementation, and strategic institutional supports (funding, expertise, mentoring, technological resources).

The Construction of University Teaching Evaluation System Based on Big Data under the Concept of Blockchain

The Construction of University Teaching Evaluation System Based on Big Data under the Concept of Blockchain

External quality control in communication science:

In this article, the author gives an overview of how quality control is done inCommunication Science at Dutch and Flemish universities. He bases his overview onhis experience as a member of the Dutch/Flemish External Quality Control Commissionduring 1998/99. The article focuses briefly on the question “why quality control?”, adefinition of quality control, the process of quality control, challenges in and threatsto Communication Science, and on-site visits to departments. He finally provides theDutch checklist (reworked and translated) used by external quality control commissionsfor the evaluation of university teaching.

Analysis of University Teaching Evaluation in the Era of BD

College teaching evaluation is an important part of teaching because it not only summarizes the semester's teaching results but also guides the next semester's teaching. However, with the implementation of China's quality-oriented education reform and the arrival of the era of big data (BD), new requirements for college teaching evaluation are being put forward, which also brings many reform conditions. Teaching evaluation in the traditional sense is typically based solely on the course's student scoring data. This method is based on a single data source and thus cannot evaluate the teaching effect comprehensively, objectively or fairly. To address this issue, a teaching evaluation method in colleges and universities operating within the BD environment is proposed. This paper investigates college and university teaching evaluation in the post-BD era. The observed value of the Bartlett sphericity test statistic is 6619.943, and the corresponding probability p is nearly equal to 0. If the significance level an is 0.05, the null hypothesis should be rejected because the probability p is less than the significance level a, indicating that the correlation coefficient matrix cannot be the unit matrix. This also implies that there must be some correlation between the original variables, making factor analysis possible. BD can combine scattered and single evaluation data to create continuous and systematic data. BD can provide a large amount of data support for teaching evaluation, making it more scientific and fair; at the same time, driven by BD, teaching evaluation can return the results of teaching evaluation more quickly.

Tackling antimicrobial resistance in practice: dental students’ evaluation of university teaching supplemented by an online course

BackgroundAntimicrobial resistance (AMR) presents a global threat to public health. Engaging all healthcare professionals including undergraduates in efforts to tackle AMR is vital. Sharing and spreading good practice in teaching on AMR and antimicrobial stewardship (AMS) is a key ambition in Scotland. In 2020, the University of Glasgow Dental School supplemented teaching with mandatory completion by final year undergraduates of an online education programme on the essential role of dental teams in reducing AMR.ObjectivesTo evaluate final year dental students’ knowledge and experience of utilizing an online international educational, interactive resource to supplement university teaching: Tackling Antibiotic Resistance: What Should Dental Teams Do?MethodsCross-sectional qualitative evaluation using a self-administered questionnaire with open questions about course content, learning and personal action planning. Data were thematically analysed using NVivo12 Pro software.ResultsA total of 88 students completed a questionnaire, which indicated online training had increased their understanding of AMR and AMS from a global perspective and confirmed these topics were an integral part of their undergraduate education programme. Their action plans demonstrated enthusiasm for creating an AMS culture in clinical practice and an understanding of the need for ongoing education of themselves, their colleagues and patients.ConclusionsEducation delivery using a variety of media to support teaching and learning in Glasgow Dental School was effective in ensuring that students understand their role in tackling AMR. Students were positive about the addition of an online education programme to supplement university teaching. This approach may be beneficial for other undergraduate dentistry programmes.

Teaching Mode Based on Educational Big Data Mining and Digital Twins.

Data mining technology has gradually become an important data analysis and knowledge discovery technology widely used in many modern industries. Data mining is a technique to find its regularity from a large amount of data by analyzing each data. It mainly includes three steps: data preparation, regularity search, and regularity representation. Data preparation is to select the required data from relevant data sources and integrate them into a data set for data mining; regular search is to find out the regularity contained in the data set by a certain method; regular expression is to be as user-readable as possible. The way of understanding (such as visualization) will represent the found patterns. This research mainly discusses the improvement of teaching mode based on digital twin-based education big data mining. Through the research on the basic principles of data mining and digital twin technology, the student evaluation tool module based on digital twin and the relevant data analysis tool module of students based on digital twin education big data mining are developed. Data mining is carried out from the data of student performance, personal basic information, and evaluation information to find the correlation between various factors, find the hidden laws, and provide support for teaching decision-making. This paper also solves the problem of frequent communication with remote databases according to the characteristics of the database data required by students and improves the efficiency and scalability of education big data mining technology based on digital twins. The goal of the virtual interactive system of the digital twin-based CNC platform is to have both three-dimensional real-time monitoring and remote control functions based on a three-dimensional virtual CNC panel. This research integrates the three-dimensional real-time monitoring and remote control of the virtual interactive system, analyzes the system operation process, develops the system interface, and improves the system sub-functions; it builds an experimental environment, conducts example tests on various functions of the digital twin platform virtual interactive system, and performs virtual interactions system performance indicators are analyzed. 60% of students believe that their innovation ability has been improved after the implementation of the digital twin teaching model; 50% of students believe that their self-evaluation ability has been improved. Applying digital twin's educational big data mining to student information management, university teaching evaluation, student performance analysis, and examination system, it has played a very good guiding role in improving the level of school teaching management.

Evaluation of university teaching as sound performance appraisal

As research on student evaluations of teaching (SET) has dominated our understanding of teaching evaluation, debate over SET implementation has turned attention away from basic principles for appraising employee performance as established in human resources literature. Considering SET research and its practical significance in isolation from relevant human resources literature not only risks unlawful remedies for issues such as bias in SET but also risks replacing one form of bias with another. Meanwhile, the full potential of human resources tools to support consistent evaluation of teaching remains unrealized. To address this gap, this article clarifies how teaching evaluation can be conducted as sound performance appraisal by deploying SET and peer review of teaching within a larger framework of established human resources techniques. A review of recent literature articulates prominent themes in research on SET and peer review of teaching and outlines key principles for performance appraisal and performance management. Those principles are used to analyze representative faculty evaluation policies and procedures and clarify the weaknesses of both traditional and recently revised approaches to teaching evaluation. The final section elaborates performance appraisal techniques relevant to teaching evaluation. These include planful use of results and/or behavior approaches to performance appraisal, robust rating instruments for behavioral performance appraisal, targeted collection of information from multiple stakeholders, and job analysis. Efforts to de-emphasize quantitative SET data to address issues such as bias can be strengthened through the incorporation of performance appraisal tools that clearly articulate performance criteria and standards and that gather both qualitative and quantitative data on employee performance.

Intelligent Decision System of Higher Educational Resource Data under Artificial Intelligence Technology

It aims to apply the neural network algorithm to the mining of educational resource data and provide new ideas for the intelligent development of teaching evaluation. The potential correlations between the teaching evalua-tion results and the teacher’s age, gender, professional title, and academic qualification are analyzed with the Apriori algorithm, which is improved with the decision tree based on the research of the existing university teaching evaluation system. The back propagation (BP) neural network model is improved based on the differential evolution algorithm (DEA). The DEA-BP model is applied to the prediction of teaching evaluation results for analysis. The results show that the execution time of the improved association rule algorithm (ARA) is significantly better than that of other models. In addition, the teacher’s age (40 - 50 years old or 50 - 60 years old), gender (female), professional title (senior or deputy senior), and academic qualifications (undergraduate or master) have certain correlation with the teaching evaluation results (excellent). When the DEA-BP algorithm is adopted to predict the teaching evaluation results, the average absolute error (1.05%) and the relative accuracy rate (95.44%) between its prediction value and the true value are optimal. Therefore, the ARA algorithm and DEA-BP algorithm based on the decision tree can intelligently extract the potential laws and knowledge in the teaching evaluation data, and provide support for teaching evaluation decisions. Thus, it exerts the role of promotion in the mining of educational resource data in universities and the intelligent development of decision-making systems

Students evaluation of University teaching: Case of the clinical prosthodontics training

Introduction: Student assessment of teaching aims to identify the strengths and weaknesses of teaching. This study aimed to analyze the evaluation of the clinical prosthodontics teaching by students of the Institute of Odontology and Stomatology of the Cheikh Anta Diop University of Dakar. Material and Methods:This was a cross-sectional descriptive study of 103 Master 1 and Master 2 students in dental surgery. The variables were related to items concerning framing, clinical teaching and assessment of clinical prosthodontics learning during the last 2 years of dental studies. Using the Likerts scale, the student had to make a quantitative assessment by assigning a score ranging from 1 to 5, depending on the degree of accreditation. The SPSS® software version 17.0 was used for statistical analysis. The comparison of averages used the Student t test at 5% risk of error. Results:The majority of the sample (79.6%) felt that the number of teachers was insufficient. Teachers were not available according to 51.4% of students. Training objectives were clearly defined for 81.5% of students. For 44.7% of them, the number of course hours was sufficient for clinical learning. Concerning the certification evaluation of learning, 66% of the students disapproved of the single final formula. Conclusion:The practice of clinical teaching evaluation by students is an essential pillar of the overall approach to improve the prosthodontics teaching. Keywords:Prosthodontics, Clinical teaching, Assessment, Students.

A tale of two quests: The (almost) non-overlapping research literatures on students' evaluations of secondary-school and university teachers

Many 1000s of studies have been conducted on the validity and diagnostic usefulness of students' evaluations of university teaching (SET), but there is a surprising lack of research on ratings by secondary students. Integrating these two disparate research areas, we evaluate the appropriateness of university SET instruments to secondary settings. Secondary students evaluated an effective and less effective teacher using items adapted from two university instruments, supplemented by items for secondary settings, and rated the appropriateness and importance of each item (N = 761 sets of ratings of more than 400 teachers, Years 7–11, 10 schools). All items were seen as appropriate and important. Factor analyses of responses to both instruments supported their a priori factor structure, and multitrait-multimethod analyses supported their convergent and discriminant validity. We discuss directions for further research at the secondary level based on the extensive body of research on the reliability, validity, and usefulness of SETs at the university level.

Methodology for quantitative evaluation of university teaching. Application to the subject of Project Management

A quantitative methodology for the assessment of a university subject has been developed, taking into account the opinion of the three agents involved in reality: students, professors and companies. The methodology is based on the concatenation of 4 matrices similar to the known House of the Quality of the Quality functions deployment (QFD) method. This methodology allows to know the degree of integration of the skills taught in the workplace. Continuous monitoring of each subject will allow to obtain a feedback about the implemented improvements based on previous results.This methodology has been applied to the Project Management university subject, which is taught in all undergraduate degrees of the School of Engineering and Industrial Design of the Polytechnic University of Madrid (UPM). Companies related to the content of the subject have collaborated through surveys. Project Management students and teachers are also participants in this study. First guidelines for improving and optimizing the relationship between university and companies have been obtained for Project Management subject.

Bias hypotheses under scrutiny: investigating the validity of student assessment of university teaching by means of external observer ratings

To advance the discussion on the validity of student evaluations of university teaching, student ratings of two teaching dimensions – student involvement and rapport – were compared with corresponding observer ratings. Seven potential bias variables were tested with regard to their impact on the students’ teaching assessment: three teacher characteristics (first impression, enthusiasm, humour) and four student characteristics (prior interest, expected grades, study experience, class attendance). Bias was defined as an impediment of the students’ assessment of teaching on course level. By means of bivariate correlations with course averages and two-level latent moderated structural equations, data of 1,716 students in 80 courses were analysed. Results showed that all three teacher characteristics were genuinely connected to rapport, and even explained variance of the student-rated variable when controlling for observer-rated rapport. The assessment of student involvement was not modified by the teacher characteristics except for teacher enthusiasm, which affected the student evaluation when controlling for observed involvement and, moreover, moderated the relation between the observed and the student-rated variable. For the examined student characteristics, no biasing effects were found – neither on rapport nor on student involvement.

Analysis and Design of University Teaching Evaluation System Based on JSP Platform

Analysis and Design of University Teaching Evaluation System Based on JSP Platform

Good Teaching and Satisfied University Graduates

This paper examines the extent to which students' evaluations of university teaching and classroom dynamics contribute to overall satisfaction with their university experience. Data were collected from 1453 graduates of the University of Alberta who completed questionnaires follow- ing the 1993 spring Convocation. A multi-item index measuring students' evaluations of university teaching and classroom experiences was employed as the central predictor in a multiple regression analysis of overall satisfaction with the university experience. Positive perceptions of teaching had a strong impact on satisfaction, controlling on gender, age, faculty of enrollment, GPA, prior postsecondary experience, assessments of skill development, satisfaction with university learning-related resources, and several other control variables. The findings highlight the continued importance of efforts to encourage good teaching in universities.

An integrated strategy for the analysis of student evaluation of teaching: from descriptive measures to explanatory models

Over the last decade, the assessment of university teaching quality has assumed a prominent role in the university system with the main purpose of improving the quality of courses offered to students. As a result of this process, a host of studies on the evaluation of university teaching was devoted to the Italian system, covering different topics and considering case studies and methodological issues. Based upon this debate, the contribution aims to present an integrated strategy of analysis which combines both descriptive and model-based methods for the treatment of student evaluation of teaching data. More specifically, the joint use of item response theory and multilevel models allows, on the one hand, to compare courses’ ranking based on different indicators and, on the other hand, to define a model-based approach for building up indicators of overall students’ satisfaction, while adjusting for their characteristics and differences in the compositional variables across courses. The usefulness and the relative merits of the proposed procedure are discussed within a real data set.

Goodness of fit measures for logistic regression model: an application for students’ evaluations of university teaching

This study investigates the assessment of university teachers. The evaluation of university teaching plays an important role in the university system in the light of the fact, that a part of the financial transfer from educational ministry is based on the quality of teaching. From Statistical point of view the use of regression models has been widely applied in this area. The logistic regression model is particularly used as discrete choice model using dichotomous dependent variable. For many regression analyses the lack of a goodness-of-fit measure is more important than coefficient interpretability. The goal of this paper is to present an overview of a few easily employed methods for assessing the model fitness of Logistic Regression Model by Pseudo- $$R^{2}$$ .Moreover the assessment is carried out through a simulation study to analyse the pattern (behaviour) of each measure, with precise focus on change of multiple correlation among the variables. In this paper a survey on student satisfaction (SS) of university teaching system was conducted. The instrument used in this paper is a questionnaire proposed by a different research group in 2010. The collected data was elaborated by a full reflective Structural Equation Model using PLS path model estimation. The initial results showed that the influence of the Organization and Infrastructures on the Student Satisfaction were statistically insignificant. Therefore a more complex model was supposed, the final results showed that the influence of Organization and Infrastructures on the SS was indirect, that is the Organization and the Infrastructures exert an influence upon the SS through the Didactics.

Il sistema italiano di valutazione della didattica universitaria: analisi critica della normativa vigente.

The Italian National Agency for the university and research evaluation (Agenzia Nazionale di Valutazione di Universita e Ricerca - ANVUR) funded in November 24, 2006 with the Law 286 (art. 2, par. 138-140). On expiry of the first Board of Directors, we believe it is time to take stock and try to evaluate the work of those who have been called upon to rationalize the quality evaluation of the Italian University System. In this work we will focus only on the aspects of the evaluation of university teaching envisaged in the ANVUR - AVA system. The AVA system was aimed, among its other objectives, to strengthen selfassessment of the quality and effectiveness of the university educational activities. In this paper, we will compare, in particular, AVA current regulations with what is actually achieved and achievable by the universities, both from a logical and technological point of view. Finally, regardless of the different techniques of assessments adopted by the various universities, it is hoped that the Agency may in the future provide a single system of analysis of the results of the evaluation process, flexible and open to innovative proposals coming the statisticians that deal with the assessment of the quality of teaching. We feel that the Statistical Information System for Evaluation of Educational SISVALDIDAT1 could be the system having the above-mentioned characteristics.