Free Digital Learning for Inclusion of Migrants and Refugees in Europe: A Qualitative Analysis of Three Types of Learning Purposes
Bibliographic record
Abstract
The increasing number of migrants and refugees arriving in Europe places new demands on European education systems. In this context, the role that free digital learning (FDL) could play in fostering inclusion has attracted renewed interest. While the existing literature highlights some general design principles for developing FDL for migrants and refugees, there is little information on the use of FDL at specific education levels, or for specific learning purposes. This paper presents the results of a qualitative study that was carried out as part of the Moocs4Inclusion project of the Joint Research Centre (JRC) between July and December 2016. The study, which has a European focus, disaggregates the analysis of FDL initiatives by what were identified as its three most common purposes: a) language learning, b) civic integration and employment, and c) higher education. For each of these topics, the study sheds light on the approaches used by a wide sample of initiatives, users’ levels of awareness of what is available and take up, and migrants’ and refugees’ perceptions of the current offer. In order to collect the information needed to cover different approaches and perspectives, semi-structured interviews with 24 representatives of 10 FDL initiatives and four focus groups with 39 migrants and refugees were carried out. The results show that there are indeed overlaps between the purposes of FDL initiatives and their design principles. Specific recommendations on how to better design FDL initiatives for migrants and refugees, taking into account their specific purposes, have also been identified.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".