Bibliographic record
Abstract
The nature of computer use has changed remarkably in the past fifty years. However, most undergraduate computer science courses are still often taught through an old paradigm that is not adequate to address modern concerns. This 90 minute seminar will address some issues relevant to preparing computer scientists for the 21st century. These include issues central to human-computer interaction (HCI) such as cognitive and perceptual aspects of computer users, ergonomics, and human factors. Although there has been literature on this topic for at least the past 15 years, it is still not widely recognized nor understood by the majority of computer science educators. Computer science graduates are often expected to have an understanding of many issues surrounding the interaction between humans and computers when they are in the workplace. However, most computer science graduates are ill equipped to deal with such issues, and could benefit if they were given more consideration in the university curriculum. In recent years, interest in HCI has grown enormously in both industry and academia. The Association for Computing Machinery (ACM) recently reported that its special interest group in HCI is the fastest growing of all its interest groups, and has recommended the development of new HCI programs in universities to combat a shortage of professionals with the skills and training to advance the design of more usable technologies.\nTalking about this issue can hopefully arouse awareness among computer science educators about its importance. Additionally it is hoped that seminar participants will be able to understand some of the main issues surrounding HCI teaching and education and how to begin to address them. The seminar will examine a number of contemporary issues regarding computer science education and what experts are saying about it.
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.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.055 | 0.038 |
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".