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Record W4400044794 · doi:10.1108/tlo-02-2023-0022

Exploring new frontiers of experiential learning landscape: a hybrid review

2024· review· en· W4400044794 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Learning Organization · 2024
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningGeographyPsychologyMathematics education

Abstract

fetched live from OpenAlex

Purpose Experiential learning is crucial in education, as it offers hands-on, practical experiences that enable individuals to develop their skills and knowledge more engagingly and interactively. In recent years, experiential learning has become a significant aspect of education. To provide academic scholars with a thorough roadmap for further investigation, this study aims to provide useful insights into the bibliometric and content analysis of experiential learning, including keywords, well-known authors, publications, nations and topics. Design/methodology/approach This research does a rigorous bibliometric analysis to give a thorough and visually instructional assessment of the evolution and advancement of the literature on experiential learning. Its fast development between 1976 and 2022 is meticulously tracked in the research. By using VOSviewer and Biblioshiny tools, the present study presents a concise overview of 507 records retrieved from the Scopus database using the keyword “Experiential Learning”, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol. Deeper text mining was done using Python libraries “Pandas” and “Natural Language Toolkit” and regular expressions. Findings The findings reveal a surge in the number of publications on experiential learning and provide insights, particularly using the theory, context, characteristics, methodology analysis, supporting researchers and practitioners to understand learning better and provide perspectives for future research. Descriptive bibliometric analysis showed that most contributions are from the USA, the UK and Canada. In-depth content analysis revealed five clusters: developments in learning, management education, engineering curricula, organisational learning and knowledge management and entrepreneurship education. The keyword co-occurrence analysis enabled linkages between relevant fields of study and significant research domains. The most commonly used theories were: experiential learning theory, social learning theory, relational coordination theory, empowerment theory, feedback learning theory, effectuation theory and human capital theory. Originality/value This study uses information from the Scopus database to do a bibliometric analysis of experiential learning from 1976 to 2022. This study serves as a valuable resource for researchers in the field, helping them to position their work more explicitly within the existing literature and highlighting potential areas for future research. It does this by thoroughly analysing the literature on experiential learning using bibliometrics.

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.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.931
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.137
GPT teacher head0.302
Teacher spread0.165 · 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