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Record W4200434211 · doi:10.33423/jhetp.v21i13.4789

The Role of Centers of Teaching and Learning in Supporting Higher Education Students Learning

2021· article· en· W4200434211 on OpenAlexaboutno aff
George Asimakopoulos, Thanassis Karalis, Katerina Kedraka

Bibliographic record

VenueJournal of Higher Education Theory and Practice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPaceProductivityMedical educationTeaching and learning centerHigher educationPsychologyActive learning (machine learning)Face (sociological concept)PopulationMathematics educationTeaching methodPedagogySociologyPolitical scienceMedicineComputer science

Abstract

fetched live from OpenAlex

This paper studies the Centers for Teaching and Learning (CTL) of the 100 top Universities in the world and investigates their role and services. The vast majority of these Centers is located in educational institutions of the US, the UK, Australia and Canada. CTL services cover many areas and target several portions of the university population. They try to meet contemporary requirements and aim to enhance teaching, learning and research processes. They usually do not restrict their fields of activities to teaching staff (faculty members, post-graduate students), extending them to students and research staff as well. Literature review indicates that the main areas in which students face difficulties and need help are: the pace of study, academic and study skills, some psychological factors, active participation in learning and self-directed learning. As a result, to help learning and enhance academic success, CTLs try to offer activities that boost academic skills (studying strategies and productivity enhancement), acceptable academic behavior, digital skills and general support and guidance.

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)0.010
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.401
Teacher spread0.390 · 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 designNot applicable
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

Citations9
Published2021
Admission routes1
Has abstractyes

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