ClassPoint Application for Enhancing Motivation in Communication among ESL Young Learners
Bibliographic record
Abstract
Teachers and students have been exposed to an influx of educational technology in the classroom since the outbreak of COVID-19. Young students require constant stimulation, especially when learning English; otherwise, they will lose interest. The ClassPoint application (hereafter referred to as the app) is an interactive learning tool that can be tailored to the needs of individual students. This research examines how the ClassPoint app motivates students of English as a Second Language to learn English. This research employed a mixed-method design with a sample of thirty-five Year 3 primary students. They responded to a 15-item questionnaire prior to the implementation of the app, and researchers completed an observation checklist during the implementation. Ten students were randomly selected to participate in a semi-structured interview following the app's performance. The data were then analysed using descriptive statistics and thematic analysis. The findings identified several reasons why Year 3 students struggle to learn English, such as a lack of motivation towards the language and ineffective teaching strategies.The ClassPoint app also increased students' engagement and motivation to learn English. Students were more engaged in learning English when the ClassPoint app was utilised. Results indicate that interactive teaching and learning methods increase second-language acquisition motivation among students.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".