Research method
Structural Equation Modelling (SEM)
Structural equation modelling estimates a network of directed paths among latent or observed variables, often after a factor analysis has defined those latents. Fit statistics say whether the covariance matrix is compatible with the diagram; path coefficients say how strongly one node predicts another in that sample. A significant path is not a randomised manipulation, and a mediation path that 'explains' attendance is still a decomposition of correlations in one course.
Education researchers reach for SEM when they have several questionnaire or trace variables and a theory about what mediates what — attendance via engagement, technology-integration quality via engagement, information-evaluation literacy via search literacy. It answers 'is this path diagram consistent with the covariances?' Its main limitation is design: all three papers here are observational (one longitudinal-in-a-course, two cross-sectional surveys), so they cannot prove that requiring attendance or changing a teaching practice would move the outcome.
Evidence
What the evidence shows
Drawn from 3 studies in this library. Each finding starts with a plain-language takeaway, then the denser detail. Supports means evidence for a finding; Challenges means evidence against a stated position; Qualifies marks scope with a short note on each study’s contribution. Challenged positions are labeled — they are not findings.
In a technology-enhanced assessment course, SEM showed full mediation: lecture attendance correlated with final scores until online engagement and twelve formative quizzes were in the model, after which the direct attendance path was no longer a positive predictor. That is a decomposition in one convenience-sampled education course, not a trial of banning absences.
A new Technology Integration Quality Scale (four factors, EFA/CFA in 2,281 upper-secondary students) was then placed in cluster-robust SEM: support for learning and classroom management positively predicted behavioural engagement; cognitive activation predicted digital competencies; use-frequency links were smaller; individualised teaching related negatively to engagement in the reported model. Cross-sectional perceptions cannot prove that changing those practices would raise engagement.
Among Lithuanian pre-service teachers, perceived information-evaluation literacy predicted information-search literacy and ICT self-efficacy, with an indirect path through search literacy. Self-report SEM on a cross-sectional sample cannot prove that literacy training would raise teaching ICT self-efficacy.
Open questions
Tensions and limits
Some items are genuine disagreements on the same question. Others mark different assays, populations, or outcomes — limits on how far one study travels — not a forced fight between papers.
What gets to be a mediator is a modelling choice, not a discovery of a unique true path. The TEL course paper puts LMS engagement and quizzes between attendance and grades; the TIQS paper puts quality dimensions and frequency on engagement and competencies, including an unexpected negative path; the teacher-education paper chains evaluation literacy → search literacy → ICT self-efficacy. Different diagrams on different variables are not replications of one mediation law.
- Online engagement mediates attendance and grades
- Technology integration quality and engagement
- Info literacy skills predict teachers’ ICT self-efficacy
Study Role Design N Population Outcome Online engagement mediates attendance and grades Supports CohortSEM mediation of lecture attendance → LMS engagement/formatives → final performance N=367 · 367 undergraduate education students in a TEL assessment course Undergraduate education students in a technology-enhanced assessment course Final course performance mediated by online engagement and formative quiz scores Technology integration quality and engagement Supports Cross-sectionalTIQS validation and SEM linking technology-integration quality to engagement N=2281 · 2,281 second-year Swiss upper-secondary students Swiss upper-secondary students rating classroom technology integration Behavioral engagement and digital competencies related to TIQS quality dimensions Info literacy skills predict teachers’ ICT self-efficacy Supports Cross-sectionalSEM survey linking information literacy to ICT teaching self-efficacy N=310 · 310 pre-service teachers at two Lithuanian universities Lithuanian pre-service teachers ICT self-efficacy for teaching predicted by information evaluation and search literacy
Common misconceptions
If SEM shows full mediation, attendance no longer matters and can be ignored.
In that course, attendance’s association with grades ran through engagement and formative scores. That does not prove attendance is useless; it says those process measures carried the covariance. Requiring or banning attendance was not tested.
A well-fitting CFA means the SEM paths are causal.
CFA says the four TIQS factors fit the item covariances. Cluster-robust SEM paths from those factors to engagement are still associations among student perceptions in a cross-section.
An indirect path from evaluation literacy through search literacy proves a training sequence.
It is a decomposition of self-reports in two Lithuanian universities. A trial of teaching evaluation then search skills would be a different design.
Exam-style questions
Short-answer questions that ask you to explain or compare, not recall.
What does 'full mediation' of attendance by engagement and formative performance mean in path language, and what intervention does it not justify?
Once the mediators are in the model, the remaining direct path from attendance to final score is not a positive predictor; the covariance is accounted for by the indirect routes. It does not justify concluding that attendance policies do not matter, because attendance was not randomised and the course was one TEL setting.
Why is an unexpected negative SEM path (individualised teaching → engagement) especially fragile?
It comes from a cross-sectional perception model with many theoretically positive paths. Unexpected signs can be suppressor effects, collinearity, or misfit rather than a finding that individualisation harms engagement; the authors already flag cautious interpretation.
Compare SEM in the TEL course paper with SEM in the pre-service teacher paper: what is observed versus latent, and why does that change the claim?
The course paper uses LMS traces, attendance, quiz scores and a final mark — mostly observed process and outcome measures. The teacher paper uses questionnaire latents (evaluation literacy, search literacy, ICT self-efficacy). Trace-based mediation is still not causal, but it is not the same as correlating self-beliefs with other self-beliefs.
A colleague says three SEM papers 'replicate that engagement mediates everything.' What should you check first?
Whether the variables, populations and diagrams match. Here they do not: one course’s attendance–grade traces, a four-factor technology-quality scale in secondary students, and teacher-education ICT self-efficacy. Shared software is not a shared finding.
The studies
3 studies in this library bear on Structural Equation Modelling (SEM), ordered by citations.
- Online engagement mediates attendance and grades
In a technology-enhanced education course, LMS engagement and formative assessment scores fully mediated the link between lecture attendance and final performance.
- Info literacy skills predict teachers’ ICT self-efficacy
For Lithuanian pre-service teachers, stronger perceived information evaluation and search literacy predicted higher ICT self-efficacy for teaching.
- Technology integration quality and engagement
Among Swiss upper-secondary students, validated TIQS dimensions—especially support for learning and classroom management—were positively related to behavioral engagement, while cognitive activation related to digital competencies.
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