MétaCan
Menu
Back to cohort

TEACHING RESEARCH METHODOLOGY (TRM) AND SUPERVISING RESEARCH WORK (SRW): CONCEPTIONS, DIFFICULTIES, AND PRACTICES OF LEBANESE UNIVERSITY INSTRUCTORS

2021· article· en· W3204689088 on OpenAlexfundno aff
Eman Shaaban, Assaad Yammine, Iman Khalil

Bibliographic record

VenueInternational Journal of Research -GRANTHAALAYAH · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
FundersAlexandria UniversityUniversité LibanaiseConservatoire National des Arts et MétiersAgence Universitaire de la Francophonie
KeywordsDiversification (marketing strategy)Variety (cybernetics)Medical educationWork (physics)Face (sociological concept)Best practiceSubject (documents)PsychologyPedagogyMedicineSociologyEngineeringComputer scienceManagementLibrary scienceSocial science

Abstract

fetched live from OpenAlex

Research methodology is an essential subject in higher education, however, it is challenging to instructors because of the complexity of the course material. This study aims to highlight the conceptions, difficulties and practices of the Lebanese University research methodology instructors and research work supervisors. For this purpose, research experts constructed and validated a questionnaire. 81 instructors and / or supervisors voluntarily participated. Results showed diversification in conceptions of participants with respect to some common essential concepts within the framework of research methodology, this reflects the complexity of the content and can hinder teaching research methodology (TRM) as well as supervising research work (SRW). Moreover, the participants indicated that they face many difficulties during SRW. The majority of the instructors elaborate their own resources for teaching methodology, they implement student-centered teaching methods and variety of assessment methods allowing students to explore and practice methodology concepts. In addition, they update their knowledge and practices on their own by attending conferences, performing research, discussing with colleagues, and by reflecting on their practices. Thus, our findings imply the need to precise the competencies required for TRM and SRW, and to encourage instructors and supervisors to reflect on their practices and share their experiences.

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)0.082
metaresearch head score (Gemma)0.065
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0820.065
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.003
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 head0.579
GPT teacher head0.621
Teacher spread0.041 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Research -GRANTHAALAYAHSame topicEvaluation of Teaching PracticesFrench-language works237,207