Students’ Perceptions of Using Roblox in Multimodal Literacy Practices in Teaching and Learning English
Bibliographic record
Abstract
The need for digital technology involvement in education is increasingly apparent, so learning techniques are required to be prepared in various digital formats. Using digital learning materials enables teachers to implement the current concept of literacy, i.e., multimodal literacy. This study aims to find out the students' need for digital literacy and their perceptions of using the Roblox game as the learning media to improve their multimodal literacy. This descriptive study used a survey method, taking the second-year junior high school students in the 2021-2022 academic year in one of the state junior high schools in Medan, Indonesia as the respondents. The data were collected using two sets of closed-ended questionnaires. The data obtained from the first questionnaire were analyzed using a forced choice technique for yes or no answers, while the data obtained from the second questionnaire were analyzed using a 4-Point Likert Scale. The results indicate the students' need for digital literacy and their good perceptions of using Roblox as the learning media in improving their English multimodal literacy. It is concluded that teaching English at junior high school is expected to be designed in a digital format by involving games as the media, facilitating the students to improve their English multimodal literacy.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".