“Learning Nomad” in Higher Education: Students’ Learning Patterns from Three Self-Designed Major Programs in Taiwan
Bibliographic record
Abstract
As higher education struggles to catch up with the constantly shifting social climate, many modern students are being left overwhelmed by the sheer volume of choices they are being offered in a phenomenon known as the “tyranny of freedom”. This issue is exacerbated when they do not have the appropriate guidance from either their parents or universities to build their own identity and find a suitable position in a functional society. Following the innovative education trend, a few top-ranking universities have started self-designed major programs, making themselves pioneers of experimental education in the traditional university system. The purpose of this study aims at discovering how Taiwanese self-designed major students organize their study maps from human and identity capital perspectives. Fifteen research participants were recruited from the three universities providing self-designed major bachelor programs and asked to participate in a semi-structured interview. The content analysis result outlines those students as “learning nomads” who break department or field boundaries to do interdisciplinary learning with clear goals by tracing their learning resources across borders. Three crucial outcomes have been found: first, identity capital mainly influences college entrance channel choices in regards to motivation and has a minor influence on how self-designed major students arrange their learning maps. Second, in regards to human capital, modularized and self-directed learning and the arrangement of theoretical and experiential knowledge do not work alone but together. Finally, learning guidance was found to be essential under the stress of tyranny of freedom.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".