ASSESSING THE PURPOSE AND IMPORTANCE UNIVERSITY STUDENTS ATTRIBUTE TO CURRENT ICT APPLICATIONS
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
In this study we surveyed students in a mid-sized university in Ontario, Canada to explore various aspects associated with their use of computer-based applications. For the purpose of analysis, the computer applications under study were categorized according to the Human-Computer-Human Interaction (HCHI) model of Desjardins (2005) in which interactions between users and digital technology are categorized into four classes of interaction, namely, Technical Interactions (interactions with the digital devices themselves), Social Interactions (interactions with other users through digital devices), Informational Interactions (interactions with information through digital devices), and Computational Interactions (interactions with data processing software through digital devices). The survey attempted to assess the following four aspects of computer application use (in the context of the HCHI model): importance, purpose, frequency, and confidence. In this paper we report on preliminary findings regarding the purpose and importance students attributed to the applications under study. Frequency and confidence studies were reported elsewhere— Partosoedarso, DiGiuseppe, vanOostveen, & Desjardins (2013). Preliminary findings indicate that, in general, students in this study tended to engage in technical, social, and informational interactions primarily for personal purposes and computational interactions for school purposes. In terms of importance, students ascribed the greatest importance to social interactions, followed by technical, informational, and computational interactions, in that order.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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