The Dimensionality of Student Ratings of Instruction: What We Know and What We Do Not*

Abrami, Philip C.; d’Apollonia, Sylvia; Rosenfield, Steven

doi:10.1007/1-4020-5742-3_10

Cited by 102 publications

(88 citation statements)

References 40 publications

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“…Meyers (2009) identified the caring dimension as an integral part of teaching and learning in college. Abrami et al (1997) found the "personal role" of professors to be important to students, specifically addressing professors' concern for them and fostering interaction. Often instructors do not interact with students, which can be perceived as noncaring.…”

Section: Suggestions For Business and Engineering Educatorsmentioning

confidence: 99%

Motivation and Analytics: Comparing Business and Engineering Students

Scala

Tomasi

Goncher

et al. 2018

INFORMS Transactions on Education

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Abstract. Data analytics is a foundational topic for engineering as well as business students given its importance in subsequent coursework and curriculum. Many common, interdisciplinary analytical topics exist between the engineering and business fields; undergraduate students may approach learning those topics in various ways depending on program or major. This research examines differences in performance on analytics between engineering and business students that may be explained by differences in motivation and attitude. We use a survey and a lecture on trendlines with a common homework assignment to compare the two groups of students. Instructors of an engineering and a business course that incorporate analytics gave the same lecture on the use of spreadsheets to analyze trendline data, assigned the same individual homework assignment, and administered an end-of-module survey. The survey was built from the established MUSIC ® Model of Academic Motivation. Analysis of the student data will address differences in motivation and how the program or major impacts student perception of analytical problem solving and contributes to performance on related assignments. We discuss quantitative and qualitative differences between engineering and business majors, concluding with a discussion of future work and some strategies for educators to use when teaching analytics.

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Section: Suggestions For Business and Engineering Educatorsmentioning

confidence: 99%

Motivation and Analytics: Comparing Business and Engineering Students

Scala

Tomasi

Goncher

et al. 2018

INFORMS Transactions on Education

View full text Add to dashboard Cite

show abstract

“…Abrami et al (1997) showed the definition of effective learning in three perspectives. The first perspective was called the product definition, emphasizing on the students' positive changes.…”

Section: Effective Learningmentioning

confidence: 99%

“…Effective learning in the laboratory happened if the three stages were well connected. If we referred to the effective learning perspective by Abrami et al (1997), this research employed the process definition.…”

Section: Effective Learningmentioning

confidence: 99%

Effective Laboratory Work in Biochemistry Subject: Students’ and Lecturers’ Perspective in Indonesia

Anwar

Senam²,

Laksono³

2017

IJHE

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Biochemistry subject had problem in learning and teaching, especially in laboratory work. We explored laboratory learning implementation in Biochemistry subject. Participants of this research were 195 students who took biochemistry subject and 4 lecturers of biochemistry in three universities in Indonesia. We obtained data using questionnaires and free response data. Questionnaires students' analysis showed there were two statements that very positive perception, five statements that obtained positive perception and ten statements with negative perception. Biochemistry lecturers questionnaire analysis showed seven statements that had been implemented and nine statements had not been implemented. Free response data indicated that effective learning in the laboratory was affected by several aspects which were pre-lab stage that could increase the students' motivation, lab-work stage with complete tools and materials as well as better ability of the assistants, and post-lab stage that could give feedback to the experiments' report and chances for the students to present their investigation results.

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“…Il existe donc une certaine variabilité sur le construit qui est mesuré. La plupart de ces instruments mesurent des dimensions primaires (de premier ordre) qui se rapprochent, dans certains cas, des trois rôles de l'enseignant retenus par Feldman (1976) et repris dans les travaux d'abrami (abrami et al, 2007 ;abrami, rosenfield, & Dedic, 2007 ;d'apollonia & abrami, 1997 (Fontaine, 2009 ;thorpe, 2002). À l'uQar, ils balisent la déci-sion prise par l'institution au sujet de la ressource professorale, mais ne sont pas considérés dans le sommatif du test.…”

Section: Dimensionnalitéunclassified

Évaluation de la qualité de l’enseignement par les étudiantes et étudiants

Harvey

Hébert

2014

mee

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Les évaluations de l’enseignement par les étudiants et étudiantes sont utilisées à des fins académiques et formatives, mais sont également évoquées d’une manière sommative lors de décisions liées à la carrière professorale. Dans le cadre de la présente recherche, deux objectifs sont poursuivis. Le premier objectif est de déterminer les qualités psychométriques de l’instrument utilisé à l’Université du Québec à Rimouski. Avec l’avènement des technologies de l’information et de la communication, les conditions dans lesquelles se déroulent ces évaluations ont été modifiées. Le second objectif est de vérifier les impacts de la modalité de passation en ligne vs papier sur les qualités psychométriques de l’instrument. Le corpus de données est constitué des évaluations réalisées durant les années 2007- 2008 (20245 évaluations) et 2010-2011 (16432 évaluations). Les résultats attestent qu’un modèle hiérarchique décrit mieux la structuration du questionnaire que des modèles alternatifs, que la modalité de passation a une influence sur le taux de réponses et, dans certains cas, sur le taux de satisfaction des enseignements, mais qu’elle n’influence ni la fiabilité ni la dimensionnalité des évaluations. Les implications de ces résultats sont discutées.

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The Dimensionality of Student Ratings of Instruction: What We Know and What We Do Not*

Cited by 102 publications

References 40 publications

Motivation and Analytics: Comparing Business and Engineering Students

Motivation and Analytics: Comparing Business and Engineering Students

Effective Laboratory Work in Biochemistry Subject: Students’ and Lecturers’ Perspective in Indonesia

Évaluation de la qualité de l’enseignement par les étudiantes et étudiants

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