Multi-Index and Hierarchical Comprehensive Evaluation System for Training Quality of Science and Engineering Postgraduates

Autor/inn/en	Duan, Peitong; Niu, Huijun; Xiang, Jiawen; Han, Caiqin
Titel	Multi-Index and Hierarchical Comprehensive Evaluation System for Training Quality of Science and Engineering Postgraduates
Quelle	In: Journal of Baltic Science Education, 21 (2022) 3, S.408-427 (20 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Han, Caiqin)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1648-3898
Schlagwörter	Engineering Education; Science Education; Evaluation Methods; Student Evaluation; Student Characteristics; Physics; Educational Quality; Scores; Academic Ability; Graduate Students; Foreign Countries; Validity; Reliability; Masters Programs; Decision Making; Student Attitudes; Research Training; Mentors; College Faculty; Integrity; Publications; Awards; Conference Papers; Competition; China + Suchen Sie Ihr Suchwort? Ingenieurausbildung; Naturwissenschaftliche Bildung; Schulnote; Studentische Bewertung; Physik; Quality of education; Bildungsqualität; Graduate Study; Student; Students; Aufbaustudium; Graduiertenstudium; Hauptstudium; Studentin; Ausland; Gültigkeit; Reliabilität; Magister course; Magisterstudiengang; Decision-making; Entscheidungsfindung; Schülerverhalten; Fakultät; Integrität; Award; Auszeichnung; Konferenzmaterial; Wettkampf
Abstract	It is essential to establish a multi-dimensional postgraduate quality evaluation system for student assessment and training. This study aimed to explore the construction of the multiindex and hierarchical comprehensive evaluation system for postgraduate training in science and engineering based on the Context, Input, Process, Product (CIPP) model using Analytic Hierarchy Process. It involved 756 postgraduates in physics and engineering who were randomly selected via the Internet. Data were collected from the questionnaire about postgraduates' basic information. After collection, Factor Analysis was used to verify the rationality of the design of second-level and third-level indicators, and adjust the corresponding weights. On this basis, Cluster Analysis was used to classify the training quality of the postgraduates based on their scores on academic ability, basic quality, and social ability indicators. The results revealed that the index system includes 4 first-level indicators,12 second-level indicators and 36 third-level indicators, and different weights being assigned to the indicators according to their influence on the training quality of postgraduates in science and engineering. This study also provides some reference for the quality of science and engineering postgraduate training in Chinese universities by proposing relevant measures, which could be interesting also for international audience. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01