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Record W1812242784 · doi:10.5539/res.v7n12p21

Students’ Perspectives on Significant and Ideal Learning Experiences —A Challenge for the Professional Development of University Teachers

2015· article· en· W1812242784 on OpenAlexvenueno aff
Jana Kalin, Barbara Šteh

Bibliographic record

VenueReview of European Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
Fundersnot available
KeywordsIdeal (ethics)Quality (philosophy)PsychologyProfessional developmentMathematics educationReflection (computer programming)PedagogyEmpirical researchComputer sciencePolitical scienceEpistemology

Abstract

fetched live from OpenAlex

The paper presents a study of students’ significant and ideal learning experiences as triggers of university teachers’ professional development. Students’ feedback and teachers’ own reflection on their teaching act as important triggers for quality shifts in their teaching and professional development. The results of empirical research, during which we used a questionnaire with predominantly open-ended questions, will be presented. We were interested in the degree of students’ satisfaction with the quality of education and what conceptions about teacher’s and student’s role they had formed during their studies. Of the many research questions, this paper only deals with analysis of learning experiences which had a particular impact on students, and their notion of an ideal study environment. In this manner we attempted to reflect on the quality of studying and, based on significant learning situations, gain insight into the influence a teacher’s teaching may have on their students’ professional and personal development. Thus the question arises of how much university teachers are prepared for in-depth reflection on their own practices, to what degree they are ready to take into account feedback they receive from students and whether they are prepared to abandon their customary teaching practices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.170
GPT teacher head0.468
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2015
Admission routes1
Has abstractyes

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