Is the computer science curriculum ready to teach students towards hardwarizing?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Computer technology changes rapidly, especially in the last decades. IEEE and ACM have developed curriculum recommendations for computer technology in the last 50 years and always add extensions or modify the content to keep the pace with the ongoing changes. Nowadays, five curricula are defined within the Computing Curricula: Computer Science, Computer Engineering, Information Systems, Information Technology and Software Engineering. Although some of them are updated in a period of 4-5 years, there are examples lasting for quite a long time, such as Computing from 2005 and Computer Engineering curricula from 2004. Still, the latest emerging technologies - Cloud computing, Internet of Things, Internet of Everything, Big Data, Machine to Machine and Human to Machine communications and interaction, software-defined everything, smart cities, high performance scaled computing, etc., raise the challenges if these curricula are ready to cover modern trends. Even more, the real question is whether they should be changed, upgraded or give rise to a new curriculum? This paper analyzes the new emerging trends and technologies and how they are covered in the current curricula that are present at our faculty (Computer Science and Computer Engineering). We present how a track of courses and their syllabuses are adapted towards these new emerging technologies, without changing the whole curriculum. There are multiple results of these changes. Students now can choose a track and learn the courses with increased interest; they can see the "whole picture" after finishing all courses of the track; they prepare more complex projects and they are happier with the changes. Finally, several diploma theses emerged that follow the current trends in the computer technology, which prepare the students to be already good engineers on the labor market. We strongly believe that with our new approach, the motivation for learning the hardware-based courses will be returned to the students, which will facilitate the trend of decreasing interest and number of engineering students.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it