The Interconnectivity of Heutagogy and Education 4.0 in Higher Online Education
Bibliographic record
Abstract
Industry 4.0 advancements in technology are creating a dynamic and fast changing world that affects how we live and work. Educators need to rethink existing teaching approaches to better prepare learners for future careers that Industry 4.0 will create. The World Economic Forum defined a new education model, called Education 4.0, which contains eight major changes to redefine learning in the new economy. Heutagogy, or self-determined learning, is an approach that promotes critical thinking, social-emotional skills, and life-long learning. These skills are necessary for Education 4.0. The purpose of this paper is to recommend the principles of heutagogy as an effective teaching and learning approach to meet the needs of Education 4.0. The approach of the study examines existing literature on Education 4.0 and heutagogy. A conceptual model that interconnects heutagogy to the four learning principles of Education 4.0 will be offered as a key finding to answer the research question: How does heutagogy in higher online education meet the needs of Education 4.0? The paper provides a base for further research and discussion into how heutagogy and other approaches can support the needs of Education 4.0 to prepare learners for a changing world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".