Students’ Motivation and Engagement with Task-Based Activities Using Google Workspace
Bibliographic record
Abstract
The current demand for integrating technology into English language instruction to engage students in meaningful conversations is pressing in the digital era. Despite this, research on employing Google tools for collaborative, task-based activities in English education is scarce, particularly in meeting the needs of today's digital learners and boosting their motivation. This study aims to bridge this research gap by evaluating the impact of using task-based Google's collaborative tools on creating a cooperative learning environment and increasing students’ engagement and motivation in English classes. Over one academic year, data were collected from 65 Saudi university students with varying levels of English proficiency using approximately 45 task-based activities administered using different Google tools. According to questionnaire results, motivation was a significant predictor of students' initiative and enjoyment in using these collaborative tools. The study confirms that students appreciate these Google tools and associates their use with increased motivation to learn English. These findings suggest that educators should align their teaching with the preferences of the digital generation by incorporating tools like Google Forms with preemptive feedback, Google Slides, and Google Docs to foster meaningful English learning. This approach can narrow the divide between traditional teaching methods and the preferred learning styles of digital learners. This study was conducted prior to the surge in Artificial Intelligence (AI) tools; the study also points out that the rise of AI has expedited the creation of task-based activities, warranting further consideration in educational practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 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".