Enhancing Students’ Vocabulary Learning Through Interactive Digital Media: Learners’ Perceptions and Outcomes
Bibliographic record
Abstract
The goal of this study is to find out how digital media affects students' vocabulary learning. Most studies have focused on improving students’ vocabulary acquisition through traditional teaching methods, but the current study focuses on enhancing students’ vocabulary learning using computer assisted learning method. The study highly benefits both language teachers and students in developing their vocabulary knowledge in interactive ways. A mixed research approach (quantitative and qualitative) was used in this study. The participants in this study were selected using a sample random sampling technique. The total number of the respondents is 230 undergraduate Engineering students from two private universities in Chennai, India. In this study, 153 male and 77 female students participated as a sample. A questionnaire was used as data gathering tool. The questionnaire consists 12 items. Excel software was employed to analyze the collected data. The findings of the study reveal that students have positive attitudes toward using technology in the classroom to develop their vocabulary knowledge. The findings also show that digital media helps students expand their vocabulary more effectively than traditional methods. Besides, the findings reveal that students feel more motivated to learn new vocabulary using digital media when compared to traditional methods, and digital media offers a more interactive and engaging way to learn vocabulary. In addition, the study suggests that English teachers should use digital media in the classroom to enhance learners' vocabulary learning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".