Course-Bound and Beyond-Course Interaction in Higher Education: Exploring the Latent Structure of a Perception Scale

This study explores the latent structure of students’ perceptions about (a) teacher strategies to promote interaction (FOM) and (b) perceived interaction with actors and content (ACT) in undergraduate courses. Using survey responses from 158 students, we conducted exploratory factor analysis (MINRES, varimax) after assessing factorability (KMO, Bartlett) and factor retention (parallel analysis). Analyses were conducted in Python (Google Colab) using pandas, NumPy, SciPy, factor_analyzer, and Pingouin. an interpretable two-factor solution in which FOM items load primarily on one factor, while a subset of ACT items (Act5–Act8) loads more strongly on a second factor, although several ACT items and some FOM items also show non-trivial cross-loadings. We interpreted factor loadings ≥ 0.30 and report results based on the corrected item set. Findings are exploratory and suggest that perceived promotion of interaction aligns more closely with within-course interaction targets than with extra-course ones.

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Publication Details

Journal
Computers
Published
2026-03-26
DOI
https://doi.org/10.3390/computers15040205
Primary Topic
Communication in Education and Healthcare
Type
article
Field-Weighted Citation Impact
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article

Course-Bound and Beyond-Course Interaction in Higher Education: Exploring the Latent Structure of a Perception Scale

ELOY LÓPEZ-MENESES, Andrés. F. Mena-Guacas, Eilien Tovío-Martínez, Claudia Liliana Muñoz
Computers
Communication in Education and Healthcare
article

Course-Bound and Beyond-Course Interaction in Higher Education: Exploring the Latent Structure of a Perception Scale

ELOY LÓPEZ-MENESES, Andrés. F. Mena-Guacas, Eilien Tovío-Martínez, Claudia Liliana Muñoz
article en

Abstract

This study explores the latent structure of students’ perceptions about (a) teacher strategies to promote interaction (FOM) and (b) perceived interaction with actors and content (ACT) in undergraduate courses. Using survey responses from 158 students, we conducted exploratory factor analysis (MINRES, varimax) after assessing factorability (KMO, Bartlett) and factor retention (parallel analysis). Analyses were conducted in Python (Google Colab) using pandas, NumPy, SciPy, factor_analyzer, and Pingouin. an interpretable two-factor solution in which FOM items load primarily on one factor, while a subset of ACT items (Act5–Act8) loads more strongly on a second factor, although several ACT items and some FOM items also show non-trivial cross-loadings. We interpreted factor loadings ≥ 0.30 and report results based on the corrected item set. Findings are exploratory and suggest that perceived promotion of interaction aligns more closely with within-course interaction targets than with extra-course ones.

ComputersVol. 15(4)
Universidad Cooperativa de Colombia (CO), Universidad Pablo de Olavide (ES)
Quality Education
Openalex Percentile: Top 14%
Communication in Education and Healthcare
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