Higher Education Evaluation Research Articles

Comprehensive Analysis Using Probabilistic Linguistic Group Decision-Making and MEREC Technique With Sustainable Development Evaluation in Higher Education

University administrators should organize and coordinate various affairs, scientifically utilize resources such as budget, teaching equipment, and faculty, continuously improve teaching quality, and ensure the sustainable development of educational management. The sustainable development evaluation in higher education management is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Exponential TODIM (ExpTODIM) and PROMETHEE technique was employed to put forward the MAGDM issues. The probabilistic linguistic term sets (PLTSs) are employed as a technique for characterizing uncertain information during the sustainable development evaluation in higher education management. In this paper, the probabilistic linguistic ExpTODIM-MABAC (PL-ExpTODIM-MABAC) technique is constructed to put forward the MAGDM under PLTSs. The MEREC technique is employed to obtain the weight values under PLTSs. Finally, a numerical example for sustainable development evaluation in higher education management is put forward to validate the ExpTODIM-MABAC technique.

A Systematic Review of Digital Competence Evaluation in Higher Education

University students’ digital skills depend significantly on educators’ proficiency, necessitating regular assessments. Tools like DigComp and the TPACK model are provided in this technological context. A systematic review, following PRISMA criteria, aims to evaluate digital competencies through globally used tools. DigCompEdu is prominent, with Spain leading the research, while unvalidated instruments from Asia highlight global disparities. This review will identify key tools and expose geographical and validation gaps, stressing the need for standardized assessments. Understanding the predominance of DigCompEdu and seeing the variation that is generated in Asia highlights the poor ability to transmit self-perceived competencies to learners.

Peer editing and writing proficiency: an experimental study in South Korean higher education

ABSTRACT Peer editing enhances critical thinking, improves student writing, ultimately leading to a more productive learning environment (Kostopoulou and O’Dwyer 2021; Lundstrom and Baker 2009, “To give is better than to receive: The benefits of peer review to the reviewer’s own writing.” Journal of Second Language Writing 18:30–43; Storch 2018, “Written corrective feedback from sociocultural theoretical perspectives: A research agenda.” Language Teaching 51 (02): 262–277; Yao et al. 2021, “How university EFL writers’ beliefs in writing ability impact their perceptions of peer assessment: Perspectives from implicit theories of intelligence.” Assessment & Evaluation in Higher Education 46 (1): 151–167). However, reservations toward peer feedback in a university EFL setting persist due to the accuracy of L2 feedback (Braine, 2003; Erdel 2023, “Student feedback literacy (SFL) profiles in an L2 writing classroom and the influence of peer feedback on the SFL features.” International Journal of Education and Literacy Studies 11 (3): 112–125.). This study utilized a pretest-posttest writing assessment to analyze differences in writing outcomes in an academic research writing course at two universities in Seoul, South Korea. The researchers also examined the accuracy of the L2 peer-editing feedback. The sampling consisted of five randomized classes of university undergraduates. Two control groups received feedback via the instructor and two experimental groups received peer-editing feedback. A fifth mixed group was divided between the control and experimental groups but within the same context. The instructors used both in-class and online feedback tools for the control and experimental groups. The analysis used Sengupta’s (1998, “Peer evaluation: ‘I am not the teacher’.” ELT Journal 52 (1): 19–28) revision taxonomy to assess peer feedback accuracy. Participants completed separate pretest and posttest writing assessments. One at the start of the semester (pretest) and one at the end (posttest). The researchers used a MANOVA to compare the pretest and posttest mean average assessment scores between the control and experimental groups. The results show that peer feedback matches the effectiveness of professor feedback for pretest/posttest scores. Researchers also assessed 156 peer-editing assignments for feedback accuracy. Peer editors demonstrated statistically significant accuracy. This study recommends using peer feedback for its benefits to L2 learners and instructors.

Research impacts and trends in feedback literacy: a bibliometric analysis

Given its prominence in maximising feedback processes, feedback literacy has received large scholarly attention over the past decade. This article aims to use a bibliometric approach to provide an overview of the evolution of feedback literacy from 2012 to May 1, 2024. Based on 240 documents retrieved from Scopus, a performance analysis and science mapping were conducted to explore research trends and impacts in feedback literacy. The performance analysis identifying volume, key authors, principal journals, and leading institutions reveals the research impacts in the feedback literacy landscape. The science mapping adopted the bibliometric software, VOSviewer, to analyse keyword co-occurrences and co-citations and visualised research trends in feedback literacy. Findings reveal two key authors, David Carless and David Boud, a top principal journal, Assessment and Evaluation in Higher Education, and two leading institutions, the University of Hong Kong and Deakin University leading the feedback literacy field. It also reveals important themes in feedback literacy research: student and teacher feedback literacy, peer feedback and assessment, ecological perspectives, development and validation of feedback literacy scales, and L2 writing. This paper sheds light on the current state of feedback literacy research and provides insights and trajectories for different stakeholders in the education sector.

Challenges and Trends in Student Evaluation of Teaching: Analysis of SET Data from the University of Peloponnese

This study examines the effectiveness of Student Evaluations of Teaching at the University of Peloponnese, which has systematically collected anonymous evaluations since 2015. The analysis focused on participation rates, average scores, and the correlation between student evaluations and their academic performance. Participation rates were notably low, averaging 14.63%, with postgraduate students showing higher rates (27.33%) than undergraduates (10.77%). The average SET scores were moderately high, with postgraduates rating their courses slightly better (M = 4.137) than undergraduates (M = 3.899). A weak positive correlation was found between course grades and evaluations among undergraduates, whereas no significant correlation was observed for postgraduates. These findings highlight challenges in using SETs as reliable measures of teaching effectiveness and suggest the need for improved participation and more comprehensive evaluation methods. The results provide insights into enhancing assessment practices and contribute to the broader discourse on the validity of student evaluations in higher education.

Contradictions and Challenges: University Teachers’ Views on Performance Evaluation Models in Portugal and Spain

Teacher performance evaluation is a key component of high performing quality assurance education systems. The main focus of the present paper is to examine how international trends in teacher evaluation policies determine teachers’ working conditions in different higher education contexts. A comparative study was implemented at two public universities of Southern Europe (Portugal and Spain), with the aim of elucidating university teachers’ perceptions regarding the current teacher evaluation model. To achieve this, we conducted 28 semi-structured interviews, employing a comparative approach in their design. The findings suggest that participants encounter discrepancies within the stated objectives of teacher performance evaluation in higher education. Such evaluation is perceived as an external imposition with a very poor democratic process, focusing mostly on the quantitative perspective and with uncertain consequences.

Application of video adaptive sampling based on soft computing in higher education evaluation and E-learning interactive environment

The level of teaching quality largely reflects the overall strength of universities and has a direct impact on the quality of cultivating high-quality talents. In this context, higher education institutions are constantly striving to explore innovative methods, but this diverse educational environment also brings challenges to the evaluation methods of teacher teaching quality. This article explores the application of video adaptive sampling in AI data analysis of university education evaluation based on soft computing technology. In order to solve the data sampling problem in university education evaluation, this paper proposes a video adaptive sampling method based on soft computing. In this paper, an E-learning virtual interactive system is designed, multimedia teaching materials matching the requirements of E-learning environment are created, tools suitable for E-learning virtual interactive environment are selected and integrated, soft computing technology is adopted, intelligent algorithms and data analysis methods are combined. By extracting and analyzing the key information in the video, the video sampling strategy is dynamically adjusted to realize the adaptive video sampling. Modeling the content characteristics of videos through fuzzy logic, and using genetic algorithms to optimize the sampling frequency and accuracy of videos. The experimental results indicate that the video adaptive sampling method based on soft computing can effectively improve the accuracy and efficiency of AI data analysis in university education evaluation. Compared with traditional fixed sampling methods, this method can dynamically adjust according to the characteristics of video content, reduce redundant data sampling, and improve the accuracy of data analysis.

Performance Evaluation in Higher Education – A Comparative Approach

Abstract Higher education stands as an essential base point in the current knowledge societies, shaping the way to sustainable growth and enhanced life quality. Literature recognizes higher education institutions as powerhouses which, through their own capabilities and partnerships, can champion innovation in a transformative way. Being widely praised for the transformative output they generate through the conducted operations, literature notes that when the educational process is defined by an outstanding quality and performance, multiple benefits arise for all the stakeholders involved. However, since quality and performance are very nuanced and volatile concepts, ensuring them in higher education necessitates a good understanding of the educational ecosystem as well as its ongoing monitoring. To encourage this aim, the selection of proper key performance indicators and the consistent implementation of performance appraisal actions have to be enacted so the end beneficiaries of the educational process and other stakeholders are provided maximal quality. As performance evaluation can be deployed by considering various indicators, higher education institutions need to carefully design, apply its steps and run follow-up actions to determine what is still to be improved.

Bibliometric analysis of assessment and evaluation in higher education: 2012–2023

Launched in 1975, Assessment & Evaluation in Higher Education (AEHE) is a prominent international journal that focuses on assessment and evaluation within higher education. This bibliometric analysis of AEHE aims to gain insights into its evolution since its expansion in 2012 and identify its critical contributions to higher education. Bibliographic data associated with a corpus of 1,047 documents published in AEHE from 2012 through 2023 were initially exported from Scopus. Using the specific software, VOSviewer, performance analysis and science mapping were conducted to obtain the following findings: (i) The domination of Australia and the UK as contributors to this journal suggests that culture background significantly affects publication patterns. (ii) Prof. David Boud, along with his cooperative network, emerges as the most prolific author with a remarkably scholarly impact on AEHE. (iii) Prominent themes include concerns about feedback and the practice of assessment. (iv) References responding to the critique mounted for the traditional understanding of feedback serve as essential background materials for readers seeking a better understanding of published works. (v) From 2017 to 2018, there was a completion of conceptualisation regarding feedback as learner-centred processes; starting from 2021 onwards, research frontiers have shifted towards “evaluative judgement”, “feedback literacy”, and “cheating”.

Students’ perceptions on different sources of self-feedback

ABSTRACT Feedback is crucial for improving student learning. In this regard, overcoming the transmissive conception of feedback in favour of its dialogic function introduces new reflections concerning the internal generative feedback process. In this regard, Nicol [(2020). The power of internal feedback: Exploiting natural comparator processes. Assessment & Evaluation in Higher Education, 46(5), 756–778. https://doi.org/10.1080/02602938.2020.1823314; (2021). Guiding learning by activating students’ inner feedback. Times Higher Education] outlines the concept of self-feedback, i.e., the continuous comparative process that students produce from various external information and sources triggering internal feedback. In this context, the present research aims to explore students’ perceptions concerning the activation of internal/self-feedback, investigating which sources are most effective from their point of view, and researching the relationships between different individual characteristics. The results show that students’ individual differences and contextual cultural factors might play a crucial role in creating ideal environments to enable the generation of self-feedback. Finally, students consider the teacher to be most effective in activating self-feedback, and many have never experienced self-feedback or automated feedback situations.

The Path to Continuous Development in Higher Education: Interplay between Quality Service, Student Satisfaction, and Performance Evaluation

This study explores the mutual interaction among quality services, student satisfaction, and performance evaluation in higher education and their impact on sustainable development. Through literature review and case analysis, it discusses how these elements collectively drive progress in higher education and enhance educational quality. The research findings indicate that effective quality services, satisfactory student experiences, and scientific performance evaluation are key factors in achieving sustained development in higher education.

ExpTODIM-driven framework for 2-tuple linguistic neutrosophic MAGDM with applications to teaching quality evaluation in higher education

Since the new century, the main theme of my country’s higher education is to improve the quality of teaching. To this end, the education administrative department and the vast number of colleges and universities have done a lot of work. Looking back, this teaching quality construction involving thousands of colleges and universities across the country has attracted much attention from all walks of life. The government as the organizer promotes the active participation of colleges and universities in the form of teaching evaluation under the leadership of administrative authority and the “quality engineering” project with resources and reputation, which has played a huge role in improving the teaching quality of colleges and universities. The teaching quality evaluation in higher education is a classical multi-attribute group decision-making (MAGDM) issue. Recently, the Exponential TODIM (ExpTODIM) method has been used to cope with MAGDM issues. The 2-tuple linguistic neutrosophic sets (2TLNSs) are used as a tool for characterizing uncertain information during the teaching quality evaluation in higher education. In this paper, the 2-tuple linguistic neutrosophic number ExpTODIM (2TLNN-ExpTODIM) is built to solve the MAGDM under 2TLNSs. In the end, a numerical case study for teaching quality evaluation in higher education is given to validate the proposed method. The main contribution of this paper is constructed: (1) the Exponential TODIM (ExpTODIM) method is extended to the PLTSs; (2) the 2-tuple linguistic neutrosophic number ExpTODIM (2TLNN-ExpTODIM) is built to solve the MAGDM under 2TLNSs; (3) Finally, a numerical case study for teaching quality evaluation in higher education is given to validate the proposed method.

Prospects and epistemic challenges for using Chat Generative Pre-trained Transformers in higher education evaluation

Prospects and epistemic challenges for using Chat Generative Pre-trained Transformers in higher education evaluation

An interactive data-driven multiple-attribute decision-making technique via interval-valued intuitionistic fuzzy sets for teaching quality evaluation in higher education

Improving the quality of higher education teaching is a systematic project. The improvement and formulation of relevant laws, regulations, and measures at the macro level are the minimum and specific requirements for the overall private universities, and are the basic guarantee for controlling the healthy and orderly development of universities. At the micro level, school management needs to focus on two aspects: leadership level construction and teacher level construction. Only by scientifically controlling the above issues and comprehensively considering them can the persistent problem of low teaching quality be fundamentally and gradually solved. In short, the construction of the quality assurance and evaluation system for higher education teaching in China is still in the initial stage of development. Therefore, universities should start from the guarantee and evaluation system to promote the construction of teaching process monitoring and evaluation systems, and improve the level of education and teaching quality on the basis of highlighting higher education teaching reform and research. The teaching quality evaluation of higher education in the era of artificial intelligence is a MADM. In this study, in light with interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted geometric (IVIFHIHWG) technique and induced OWG (I-OWG) technique, the induced IVIFHIHWG (I-IVIFHIHWG) technique is administrated. Then, the I-IVIFHIHWG technique is exploited to manage the MADM under IVIFSs. Finally, the numerical example for teaching quality evaluation of higher education in the era of artificial intelligence is exploited to verify the I-IVIFHIHWG technique. Thus, the main research contributions are administrated: (1) the I-IVIFHIHWG technique is administrated in line with the IVIFHIHWG and I-OWG technique; (2) the I-IVIFHIHWG technique is exploited to manage the MADM under IVIFSs; (3) the numerical example for teaching quality evaluation of higher education in the era of artificial intelligence and some comparative studies were exploited to verify the I-IVIFHIHWG technique.

Assessment and Evaluation in Higher Education – The Case of Greek University Students

Assessment, in the context of the educational process, is a necessary condition, since through it improvement of the educational work is achieved, with the aim of improving the education provided and, ultimately, students’ progress. In this paper, postgraduate students’ opinions regarding their assessment during their undergraduate studies are explored. From their answers, it can be concluded that assessment is a complex and multifactorial process for them since the elements that influence its application are many and varied. Postgraduate students consider the application of assessment beneficial because it contributes to the development and cultivation of their personal skills, but they emphasize that specific conditions, techniques, and methods should be applied for its results to be objective and impartial.

Performance evaluation in teaching: Dissecting student evaluations in higher education

Numerous studies have highlighted the significant role of Student Evaluations of Teaching (SETs) as a key metric for assessing teaching quality in Higher Education (HE). Building upon these insights, our study introduces an innovative four-tiered model, derived from diverse research, to examine the reliability of SETs. This model addresses biases associated with SETs, delving into both statistical anomalies and cognitive biases, with particular emphasis on often-overlooked hidden context and timing factors. We reveal that these biases can distort SET scores, leading to potentially inaccurate representations of both individual and comparative academic performances. The implications of our research are significant for those influencing HE policy-making and performance evaluation. We echo previous calls for a more expansive approach to teaching effectiveness, essential for genuine insight into teaching quality. By adopting this perspective, HE can design better-informed strategies, ensuring policies and practices reflect the diverse nature of teaching excellence.

Effective signal reconstruction from multiple ranked lists via convex optimization

The ranking of objects is widely used to rate their relative quality or relevance across multiple assessments. Beyond classical rank aggregation, it is of interest to estimate the usually unobservable latent signals that inform a consensus ranking. Under the only assumption of independent assessments, which can be incomplete, we introduce indirect inference via convex optimization in combination with computationally efficient Poisson Bootstrap. Two different objective functions are suggested, one linear and the other quadratic. The mathematical formulation of the signal estimation problem is based on pairwise comparisons of all objects with respect to their rank positions. Sets of constraints represent the order relations. The transitivity property of rank scales allows us to reduce substantially the number of constraints associated with the full set of object comparisons. The key idea is to globally reduce the errors induced by the rankers until optimal latent signals can be obtained. Its main advantage is low computational costs, even when handling n<<p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n < < p$$\\end{document} data problems. Exploratory tools can be developed based on the bootstrap signal estimates and standard errors. Simulation evidence, a comparison with the state-of-the-art rank centrality method, and two applications, one in higher education evaluation and the other in molecular cancer research, are presented.

A novel integrated picture fuzzy MACONT method and its application in teaching quality evaluation in higher education

A novel integrated picture fuzzy MACONT method and its application in teaching quality evaluation in higher education

Choice Based Credit System (CBCS): A new reforms of curriculum and evaluation in higher education

Choice Based Credit System (CBCS): A new reforms of curriculum and evaluation in higher education

The Application and Potential of Artificial Intelligence Participating in College Teaching Evaluation

The dominance could be applied to many more areas as new artificial intelligence (AI) advances. There are great defects in the traditional evaluation methods of higher education. The evaluation of people is dominant, resulting in excessively subjective results and wasting human resources. These issues adversely affect many stakeholders. Therefore, the combination of the objectivity of artificial intelligence and the traditional method of teaching evaluation has an exploratory significance for the development of teaching and emphasizes the technical ability that the new artificial intelligence can realize. The purpose of this study is to explore the potential of a new method for artificial intelligence in higher education evaluation. Based on the analysis of existing experimental case reports and related studies by the method of literature synthesis, it is certain that the new artificial intelligence will help improve the quality of teaching evaluation in higher education. This has a positive impact on all fields, including schools, teachers, and students. The teaching objectives of the school can be better and more efficiently accomplished under the evaluation system. This study confirms the feasibility of incorporating artificial intelligence into university evaluation.