Teacher practice
Faculty training that shifts teaching innovation attitudes
Open access · cc by · source: Europe PMC
A university faculty development program improved professors’ attitudes toward teaching innovation and LMS use, plus knowledge of innovative teaching elements.
Study at a glance
- Design
- Other — Pre–post faculty development program evaluating attitudes toward teaching innovation
- N
- N=695 · 695 university professors participated
- Population
- University faculty (full-time and part-time) in a faculty training program
- Outcome
- Attitudes toward transformative/innovative teaching after the development experience
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
After the program, attitudes toward innovation and the LMS improved, and professors reported greater knowledge of how to implement and judge the suitability of innovative teaching elements. Patterns were broadly similar across full-time and part-time faculty, with some faculty-level differences in post-program attitudes.
Methodology
At Universidad Francisco de Vitoria, researchers evaluated a faculty training program with a single-group quasi-experiment and pre-post measures. Hundreds of full-time and part-time professors completed instruments on attitudes toward innovation and LMS, knowledge of teaching-practice elements, and reported classroom application across program editions.
Limitations
Without a control group, maturation, self-selection, or concurrent campus changes could explain gains. The study measures faculty attitudes and self-reported knowledge/application, not direct student learning outcomes.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
Teacher and instructional practices around digital tools, training, and classroom enactment shape how technology-supported learning unfolds for students.
Evidence for the claim as stated.
Empirical studies of teacher practice measure strategies, perceptions, and classroom negotiation rather than assuming adoption equals instructional change.
Evidence for the claim as stated.
A single-group faculty programme at Universidad Francisco de Vitoria enrolled 695 participants (mean age 45.46, SD 9.74). Attitudes toward innovation and the LMS improved, and professors reported greater knowledge of how to implement innovative teaching elements, with broadly similar patterns for full-time and part-time faculty. Without a control group, maturation and concurrent campus change remain competing explanations; student learning was not measured.
Evidence for the claim as stated.
Pre/post windows are not interchangeable designs. Peer feedback and faculty training are single-group rises. The ITS paper randomises after exclusions and adjusts for prior scores. Chatbots contrast two intact classes. Mentoring randomises classrooms and finds a null average with a subgroup gain. COVID UDL is a retrospective perception contrast, not a scored artefact. Calling all of them 'pre-test/post-test evidence that teaching improved' hides those differences.
Evidence for the claim as stated.
Universidad Francisco de Vitoria evaluated faculty training as a single-group quasi-experiment with 695 participants. Attitudes toward innovation and the LMS improved, and professors reported greater knowledge of innovative teaching elements, with broadly similar full-time and part-time patterns. No control group means maturation, self-selection and campus change remain live; student learning was not the outcome.
Evidence for the claim as stated.
Unit of assignment and what 'worked' do not match. DES assigns schools (746 vs 667 students) and reports engagement. MIM assigns historical cohorts (26 vs 28 quiz completers) and reports interaction on optional tasks. The ITS paper randomises individuals after exclusions and reports an adjusted post-test. Faculty training has no comparison group. Scaffolding compares conditions among ~9 volunteers. These are not six estimates of one quasi-experimental engagement effect.
Evidence for the claim as stated.
Outcomes disagree in kind. ITS moves a fraction post-test. DES and MIM target engagement/interaction. Faculty training moves self-reported attitudes. AI-analytics moves SNA metrics and offers a decision aid. Importing 'the quasi-experiment worked' across those endpoints overclaims.
Evidence for the claim as stated.
A faculty-training programme (695 participants) used multi-item instruments on attitudes toward innovation and the LMS, knowledge of teaching-practice elements, and reported application, then showed pre/post attitude and knowledge gains in a single-group quasi-experiment. Those instruments may be factor-scored internally, but the design that supports 'attitudes improved' is pre/post without a control — not a CFA proving that innovation training caused student learning.
Evidence for the claim as stated.
Measurement models and change scores are different jobs. TIQS uses EFA/CFA then SEM paths, including an unexpected negative individualised-teaching path. The online-quality paper uses EFA to rank importance, not to predict engagement. The teacher-education paper uses SEM path coefficients among self-beliefs. The faculty paper reports pre/post attitude change for 695 trainees without a control. One software family (factors, SEM, scales) is not one empirical result.
Evidence for the claim as stated.
Open questions
Tensions this paper is part of
From concept pages' “where studies disagree.” Disagreement means the same question; scope means different assays, populations, or outcomes.
Pre/post windows are not interchangeable designs. Peer feedback and faculty training are single-group rises. The ITS paper randomises after exclusions and adjusts for prior scores. Chatbots contrast two intact classes. Mentoring randomises classrooms and finds a null average with a subgroup gain. COVID UDL is a retrospective perception contrast, not a scored artefact. Calling all of them 'pre-test/post-test evidence that teaching improved' hides those differences.
- Supports · Online peer feedback that improves argumentative essays
- Supports · Can a dialogue ITS fix fraction multiplication?
- Supports · Do educational chatbots raise project-course grades?
- Supports · Can showing shared interests improve student-teacher mentoring?
- Supports · Did remote COVID teaching feel less engaging to students?
Unit of assignment and what 'worked' do not match. DES assigns schools (746 vs 667 students) and reports engagement. MIM assigns historical cohorts (26 vs 28 quiz completers) and reports interaction on optional tasks. The ITS paper randomises individuals after exclusions and reports an adjusted post-test. Faculty training has no comparison group. Scaffolding compares conditions among ~9 volunteers. These are not six estimates of one quasi-experimental engagement effect.
Outcomes disagree in kind. ITS moves a fraction post-test. DES and MIM target engagement/interaction. Faculty training moves self-reported attitudes. AI-analytics moves SNA metrics and offers a decision aid. Importing 'the quasi-experiment worked' across those endpoints overclaims.
Measurement models and change scores are different jobs. TIQS uses EFA/CFA then SEM paths, including an unexpected negative individualised-teaching path. The online-quality paper uses EFA to rank importance, not to predict engagement. The teacher-education paper uses SEM path coefficients among self-beliefs. The faculty paper reports pre/post attitude change for 695 trainees without a control. One software family (factors, SEM, scales) is not one empirical result.
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