Mapping the SDG 4 Process: Algorithmic Literacy Among Students of the University of Sarajevo
Bibliographic record
Abstract
Utilizing a mixed-method approach, this research aims to evaluate algorithmic literacy among students at the University of Sarajevo - Faculty of Political Sciences and the Faculty of Philosophy (BIH), and to assess the possibilities for improving existing practices of media and information literacy (MIL) integration. The central research question investigates how current MIL educational strategies influence students' awareness and understanding of the roles algorithms play in the digital transformation of a society striving for sustainable development. Therefore, the study encompasses a descriptive method of holistic approach elements: strategic documents, MIL book edition and MOOC modules. Subsequently, a thematic analysis of ten qualitative interviews with students further explores their experiences, attitudes, and perceptions regarding information, media, and algorithms. The research results offer insights into the potential of MIL education to support algorithmic literacy and its potential contribution to sustainable development, particularly focusing on SDG 4 - Quality Education. By aligning its findings with the objectives of a universally applicable goal, this study not only addresses the context of algorithmic literacy as an integral component of quality education but also serves as a step towards advancing the interconnectedness of open education and artificial intelligence.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".