Culture adaptive Internet of Things roaming course for non native English speaking students
Bibliographic record
Abstract
Internet of Thing (IOT) is relatively new field that is on a rise. Teaching students IOT is a challenging task because of increasing dynamics of the field itself. We present a new course that is flexible, dynamic, and mobile. It is a university level course at University of Zagreb, Faculty of Organization and Informatics. Because of the ERASMUS programme it is possible to open the course to the foreign students. Further more, thanks to bilateral agreements the same course is physically carried out in Laval, France. Course is adaptive in both terms of duration and location. Regardless of each, it has to be carefully crafted to fit both criteria. Course is roaming, meaning that all of the preparations and equipment can and should be mobile and easy to setup on any location in the world. IOT involves certain embedded software development, meaning that teaching should be carried on an appropriate equipment: hardware development and prototyping boards. Experience in selecting such hardware for the course is presented. Furthermore, IOT includes various connectivity and technologies. Internet connectivity setup for the roaming equipment should be reliable and easy to apply on site. Relying on existing network is not recommended due to many reasons, one of which might be the use of captive portals, IOT micro-controllers do not bode well with captive portals. Teaching communication aspect of IOT can be quite challenging if connection is unreliable. Course is project based and designing project assignments or project guidelines, should consider local environment. Projects guided by the concept of service learning can have significant impact on a local community. Anyhow, goal of the project in this course should be students feeling accomplished. Having dynamic course it would be advisable to remain on certain technology, however evolution of the course has discarded such technologies that became obsolete. Instead of single LMS several different tools are used, this might be an overhead but those tools are already known to the students. Student perception of the course and increasing number of enrolment shows that it has positive impact and perspective future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.013 |
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".