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THROUGH A MORE DISCERNING LENS: UNDERSTANDING COLLEGE STUDENT EXPECTATIONS AND EXPERIENCES OVER THE COURSE OF A SEMESTER.

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Title: THROUGH A MORE DISCERNING LENS: UNDERSTANDING COLLEGE STUDENT EXPECTATIONS AND EXPERIENCES OVER THE COURSE OF A SEMESTER.
Authors: BRANCA, SYLVIA H., SLUSSER, EMILY
Source: College Student Journal. Jun2022, Vol. 56 Issue 2, p180-196. 17p.
Subjects: Student evaluation of teachers, Universities & colleges, College students, Semester system in education, Teaching
Abstract: The end-of-semester routine for collecting student evaluations of teaching is nearly ubiquitous among colleges and universities in the United States. Ideally, student feedback is used to improve instruction, advance curriculum, and facilitate student success. While well intentioned, student evaluations of teaching often fall short of measuring more nuanced elements of college students' classroom experiences, and do not provide a mechanism for comparing early impressions with lived experiences. This study examines college students' expectations and experiences in a large lecture-based college course using a comprehensive survey administered at the beginning and end of the semester (N=223). Results indicate that students begin the semester relatively optimistic about their expected engagement and participation in the class but have some concerns about their ability to develop meaningful relationships with their instructor and classmates. Students' lived classroom experiences generally mirror their expectations, with notable implications regarding their motivation to engage in the course and their academic success. Findings provide rich insight into student perspectives and can help instructors think more deeply about ways to support students' academic success over the duration of a course. [ABSTRACT FROM AUTHOR]
Copyright of College Student Journal is the property of Project Innovation Austin LLC and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: The end-of-semester routine for collecting student evaluations of teaching is nearly ubiquitous among colleges and universities in the United States. Ideally, student feedback is used to improve instruction, advance curriculum, and facilitate student success. While well intentioned, student evaluations of teaching often fall short of measuring more nuanced elements of college students' classroom experiences, and do not provide a mechanism for comparing early impressions with lived experiences. This study examines college students' expectations and experiences in a large lecture-based college course using a comprehensive survey administered at the beginning and end of the semester (N=223). Results indicate that students begin the semester relatively optimistic about their expected engagement and participation in the class but have some concerns about their ability to develop meaningful relationships with their instructor and classmates. Students' lived classroom experiences generally mirror their expectations, with notable implications regarding their motivation to engage in the course and their academic success. Findings provide rich insight into student perspectives and can help instructors think more deeply about ways to support students' academic success over the duration of a course. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of College Student Journal is the property of Project Innovation Austin LLC and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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