Specific Gaming Features in an Interactive Powerpoint on the enhancement of Grammar Skill
Bibliographic record
Abstract
Information and Communication Technology (ICT) education becomes more prevalent in the 21st century, more emphasis been placed on developing teachers' competence in English language teaching, particularly in grammar terms. Teaching and learning in the 21st century have advanced and shifted from pen and paper to use of Information Communication and Technology (ICT) to help in the process of imparting knowledge and skills to the learners. Use of the information Communication Technology tools like computers, laptops, printers, scanners, software programs, data projectors, and interactive teaching box has made teaching and learning efficient and interesting. This study sought to determine the influence of selected gaming features in interactive PowerPoint in teaching of grammar skills among primary school students. The study adopted design of a narrative research model as it offers the respondents to give in-depth information about their experiences and allow the researcher to make independent observations. The study used observation schedule and interviews to collect data from a sample of 12 respondents teaching primary level English subject in a sub urban school in Pulau Tikus district, Penang, Malaysia. The study used thematic data analysis whereby the use of interactive PowerPoint gamming features in teaching primary school students significantly promoted good learning skills like lettering pronunciation, reading, spacing of words, coloring and joining of letters. Despite the positive outcomes of ICT utilization, teachers cited inadequate teaching resources and limited teacher training. The study recommends that teachers to be supported by adequate teaching and learning resources for efficient ICT utilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".