Browsing by Subject "Student evaluation of teaching"

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Open Access
Dimensions of student evaluation of teaching forms at university level
(2022-09) Karaca, Rabia Ceren
When instructional effectiveness is evaluated, it is essential to gather data from different sources to obtain valid judgments. Among several ways to assess instructional effectiveness, Student Evaluation of Teaching (SET), also known as Student Ratings of Instruction (SRI), is a process applied towards the end of the term in many academic settings to collect data generally through a form on the students’ views about an instructor’s in-class teaching performance. However, certain factors might affect students’ responses to the items in the form during the SET process. That is, the dimensions of the form might be different from the initially developed one and different dimensions with different items might arise. The aim of the current study is to analyze the dimensions of an SET form in the preparatory school of a non-profit private university to see how different groups perceive the items in the SET form. The participants are all English language learners from different English language levels. The data were collected in the Fall term, in the 12th week of teaching, in the first contact hour. The data were analyzed through a software program running principal component analysis (PCA). The findings revealed that the structure of the form can be considered unidimensional and there is one dominant latent factor, named as instructional effectiveness by the researcher. In other words, the responses of the different groups are mostly related to instructional effectiveness and are not affected by other potential factors.
Open Access
Do university students really need to be taught by the best instructors to learn?
(Cogent OA, 2017) Kalender, I.
The present study sought to contribute to the discussion on linearity relationship between teaching and learning at university level. Although the basic assumption that students who are taught by effective instructors learn better is acknowledged, defining the effective instructor seems not so simple. This study attempted to (i) cluster instructors with respect to instructional practices rated by students, and (ii) identify different instructional profiles that may be associated with high learning, rather than just focusing on relationship between instructional practices and learning. Using student ratings from 625 courses in a university setting, subgroups were defined in terms of instructional practices via a segmentation approach. Then, distinct profiles showing high instructional effectiveness were extracted by investigating learning level differences as measured by the end-of-semester grades and self-reported learning levels. Results indicated that the students need not to be taught by the best instructors to reach high learning levels. Effective learning can also take place under lack of some aspects of instructional practices if other aspects receive higher ratings to compensate for the missing aspects.
Open Access
Measurement invariance of student evaluation of teaching across groups defined by course-related variables
(International Online Journal of Educational Sciences, 2015) Kalender, İ.
In the present study, comparability of scores from student evaluation of teaching forms was investigated. This is an important issue because scores given by students are used in decision making in higher education institutions. Three course-related variables (grade level, course type, and course credit) were used to define student subgroups. Then, multi-group confirmatory factor analysis was used to assess invariance of factorial structure, factor loadings and factor means across groups. It was found that although a common factorial structure held across groups, fully invariant factor loadings were observed only across instructors who teach different course types. For other groups, only partial invariance of factor loadings was obtained. Analyses also revealed that none of the subgroups had invariant factor means, indicating a possible bias. Results indicate that comparison of instructors based on student ratings may not be valid as it is mostly assumed.