Reading on the Computer Screen: Does Font Type has Effects on Web Text Readability?
Bibliographic record
Abstract
Reading on the World Wide Web has become a daily habit nowadays. This can be seen from the perspective of changes on readers’ tendency to be more interested in materials from the internet, than the printed media. Taking these developments into account, it is important for web-based instructional designers to choose the appropriate font, especially for long blocks of text, in order to enhance the level of students’ readability. Accordingly, this study aims to evaluate the effects of serif and san serif font in the category of screen fonts and print fonts, in terms of Malay text readability on websites. For this purpose, four fonts were selected, namely Georgia (serif) and Verdana (san serif) for the first respondents and Times New Roman (serif) and Arial (san serif) for the second respondents. Georgia and Verdana were designed for computer screens display. Meanwhile, Times New Roman and Arial were originally designed for print media. Readability test on a computer screen was conducted on 48 undergraduates. Overall, the results showed that there was no significant difference between the redability of serif and san serif font of both screen display category and print display category. Accordingly, the research findings and the literature overview, suggest that Verdana and followed by Georgia as the better choice in displaying long text on websites. Likewise, as anticipated, Times New Roman and Arial fonts provide good readability for print media, which reinfoces their status as the printing font category. However, with the current computer screen capability, it can still be an alternative option for instructional web developers.
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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".