The programming curriculum within ISIS
Bibliographic record
Abstract
From 2014 to 2017, the Islamic State in Irak and Syria (ISIS), a terrorist political organization of Salafist jihadist ideology, had put in place an operational and relatively stable educational system. Among its Complementary Programs, ISIS included a curriculum for programming using the Scratch software. In this article, we discuss this curriculum by analyzing the content of the official ISIS programming textbook, with the objectives of characterizing: 1) the curriculum's pedagogical intentions and definition of programming; 2) the programming curriculum; and 3) the religious and military indoctrination value. We found that, first, ISIS's programming curriculum intentions are more about religious and military injunctions to build the caliphate than they are about developing 21st-century skills such as computational thinking. Second, although the progression of learning in the sequence of activities designed by ISIS seems logical and, overall, well-ordered, the ISIS programming curriculum does not encourage the development of 21st-century skills such as problem solving, discovery learning, or creativity-nor for that matter, the transfer of programming knowledge to different contexts. Finally, the textbook is particularly rich in elements of military and religious indoctrination and effectively participates in the indoctrination of students by helping to inculcate values consistent with ISIS's jihadist ideology. This contribution seeks to better understand ISIS's approach to education, which could provide support for initiatives aimed at rebuilding impacted education systems and groups.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".