Unlocking the potential of the low-code approach for a more open and innovative higher education system
Bibliographic record
Abstract
This paper presents the baseline results of the survey conducted by the LightCode1 Project Consortium, with the objective of exploring the potential of low-code application development in the field of higher education. The survey included Faculty Members, Higher Education Students and Labour Market Representatives of five European Countries (Austria,Croatia, France, Greece, and Serbia), targeting to address the growing demand for more efficient and accessible digital solutions within education institutions. The survey sought to gauge familiarity with low-code development, and potential challenges and perceived benefits of integrating low-code solutions into the educational domain. As one of the main features of the low-code approach, as well as of the LightCode project, is to provide tools and methodologies that facilitatethe development anddeployment of applications focused on supporting informed decision-making within organizations, the results presented here aligns with the main topic of the conference as the project will also contribute to enhance thestudent’s employability, which in turns also contributes to empowering societal transitions.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".