Evidence map›Paper›PMID 36345401›Full record

ArticleLearning environments research2023

Perspective of educational environment on students' perception of teaching and learning.

Falguni Singh, Monika Saini, Ashish Kumar, Seeram Ramakrishna, Mousumi Debnath

Abstract read
In one paragraph

Article in Learning environments research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Falguni SinghDepartment of Psychology, Manipal University Jaipur, 303007 Dehmi Kalan, Rajasthan India.
Monika SainiDepartment of Mathematics & Statistics, Manipal University Jaipur, 303007 Dehmi Kalan, Rajasthan India.
Ashish KumarDepartment of Mathematics & Statistics, Manipal University Jaipur, 303007 Dehmi Kalan, Rajasthan India.
Seeram RamakrishnaDepartment of Mechanical Engineering, National University of Singapore, 119260 Singapore, Singapore.
Mousumi DebnathDepartment of Biosciences, Manipal University Jaipur, 303007 Dehmi Kalan, Rajasthan India.ORCID 0000-0002-7094-466X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cross-sectional research was designed using the Dundee Ready Education Environment Measure (DREEM) to examine the impact of the educational environment on students' perceptions of learning and teaching in a university offering higher education. Using a Likert scale, 300 participants from various streams were assessed. There were no disparities in the students' judgments in the five DREEM categories of discipline, accommodation, gender, level of study, and disciplines. In general, students judged that the student-teaching environment was satisfactory. To investigate the validity of the DREEM, we conducted Cronbach alpha analysis and exploratory factor analysis (EFA) employing principal component analysis with varimax rotation. Only 44 of the 50 factors were deemed to be significant, with a cumulative explained variance of 46.539%. The total construct dependability of the EFA nine-factor solutions was 0.954. Finally, only five acceptable factors explained 46.539% variance using the criteria of eigenvalue > 1 and Cronbach's alpha > 0.6. Factor analysis was appropriate as evidenced by the Kaiser-Meyer-Olkin Measure of Sampling Adequacy of 0.942. With a Cronbach alpha of 0.955, the revised first domain of "student perception of learning" was the most trustworthy DREEM item, accounting for 20.19% of the variance. The highest factor loading was for the item "Teaching helps me become a more active learner". A redesigned DREEM might be valuable for assessing the educational environment at institutions providing higher education.

Indexed as

DREEMEducational environmentFactor analysisReliabilityStudents’ perceptionTeaching–learning

Identifiers

PMID36345401
PMCPMC9629770

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.