Identifying Influencers in High School Student ICT Career Choice.
Bibliographic record
Abstract
This paper examines the role of influencers in Canadian high school student decisions to pursue Information and Communications Technology (ICT) careers and education. With growing rates of retirements of ICT workers expected over the next 10-15 years, industry representatives are concerned that the shortfall in replacement workers will have a significant detrimental impact on business. Various authors and panels have cited the need to attract more high school students to enroll in ICT post secondary programs. However, what is not clear is how or why students make decisions to pursue ICT in university and as a career. This paper examines the various influencers that affect students ’ decisions to choose an ICT education and career. As part of an ongoing program this paper presents the results of three surveys-- with responses from 111 Canadian guidance counsellors, 141 ICT university students and 1335 first year business and IT management students. The survey findings suggest that parents are the strongest influencers and guidance counsellors are the weakest influencers. To achieve any significant improvement in the numbers of students choosing ICT careers, it is recommended that ICT industry representatives must speak directly with students and their parents. The survey results do suggest that students are attracted by the relatively high income potential of ICT careers and the entry to a business environment that ICT skills provide. Further, guidance counsellors see math and science subjects as much more important for success in an ICT career than do students.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".