MétaCan
Menu
Back to cohort
Record W2550494586 · doi:10.5430/ijhe.v6n1p63

Blended Learning Experience of Students Participating Pedagogical Formation Program: Advantages and Limitation of Blended Education

2016· article· en· W2550494586 on OpenAlexvenueno aff
Fatih Saltan

Bibliographic record

VenueInternational Journal of Higher Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsBlended learningAttendanceFocus groupQualitative researchPsychologyQualitative propertyMedical educationMathematics educationPedagogyComputer scienceMedicineEducational technologySociology

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the learning experience of students studying pedagogic formation in blended design with regard to attendance, self confidence, and attitudes toward both Pedagogic Formation Program (PFP) and the teaching profession. In order to achieve this aim, a qualitative case study approach was carried out. The participants of this study consisted of 154 graduated Faculty of Arts and Sciences students who were enrolled in the first blended PFP in Turkey. A qualitative case study was conducted. Data were obtained through an open-ended questionnaire (n=154) and focus group interviews (n=8). The qualitative data were analyzed by using content analysis techniques. Overall, the results indicated that blended PFP was highly promising regarding professional development, self-confidence, accessibility and eliminating some disadvantages of distance education. Specifically, inherent problems of online education continued to take place in blended design but a balanced blended approach could minimize these weaknesses. Participants indicated that face-to-face sections were more applied, authentic and effective than the online part. On the other hand, most of the participants preferred to attend the online lessons regularly. It was mainly because of availability concerns, travelling, and comfort of their home.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.061
GPT teacher head0.482
Teacher spread0.421 · 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 designObservational
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

Citations49
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Higher EducationSame topicOnline and Blended LearningFrench-language works237,207