Students' Perceptions of and Experiences With Educational Technology: A Survey
Bibliographic record
Abstract
BACKGROUND: It is generally assumed that incoming students in medical education programs will be better equipped for the "digital age" given their younger age and an educational upbringing in which technology was seemingly omnipresent. In particular, many assume that today's medical students are more likely to hold positive attitudes and increased comfortability with technology and possess greater information technology (IT) skills. OBJECTIVE: The purpose of this study was to compare responses of incoming veterinary medical students to a series of IT-related questions contained in a common questionnaire over the course of a 10-year period (2005-2015) to discern whether students' attitudes have improved and uses and comfortability with technology have increased as anticipated. METHODS: A survey measuring attitudes and preferences, computing experience, and technology ownership was administered each year for the past 10 years to incoming veterinary medical students at a large veterinary school in the United States. Students' responses to survey items were compared at 3 data points (2005, 2010, and 2015). RESULTS: Today's incoming veterinary medical students tend to indicate the same desire to improve skills using spreadsheets and web page design as incoming students from 10 years ago. It seems that despite technological advances and increased exposure to such applications and skills, there remains a challenge for students to "keep up" with the ever evolving technology. Moreover, although students continue to report they are very comfortable with using a computer (and related devices), many use their computers as typewriters or word processors, as opposed to a means for performing more advanced computing functions. CONCLUSIONS: In general, today's medical students are not expert computer users as many assume. Despite an upbringing in a digitized world, many students still lack many basic computing skills.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".