Old tradition, new technologies: comprehension and retention using augmented reality
Bibliographic record
Abstract
Traditional media, especially printed media, such as newspapers, books, and posters, is static. The increased usage of smart devices has trigged the use of mobile augmented reality (AR) in commercial print media. This technology allows consumers to overlay supplemental multimodal content on the real-world environment through the use of an AR app on a smartphone or tablet. The addition of mobile AR provides an extra dimension through which to absorb or dismiss an advertisement’s content. The main objective of this study is to determine if the use of mobile augmented reality in print affects the customer’s ability to understand and remember the message of an advertisement. Assessing augmented reality’s affect on printed media will help determine the value of investing in this technology and its future potential in the advertising landscape. This experiment was designed to test whether participants in two groups, one that used Layar, an augmented reality application, and one that did not, were still able to understand and remember the messages and content of a Nissan car advertisement featured in the Toronto Star. Through interviews and questionnaires subjects were tested on their recall ability and message comprehension given the passage of time. The results indicate that AR poses no significant advantage or disadvantage when compared to traditional print media. It appears that regardless of the mode of communication the advertisement’s messages are successfully conveyed. The results have shown that over time the viewer is likely to remember what interests them independently of whether augmented reality is used or not.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".