Number Concept Games on Tablet Computers: Interactive and Attracting the Interest of Pre-schoolers Students
Bibliographic record
Abstract
Learning aided by tablet computers is one of the methods in acquiring knowledge interactively that involves animations and games that are useful to children. Games based on the concept of mathematics (numbers 1–5) were implemented regarding the suitability of the activity for pre-schoolers learning. This study attempted to identify the effects of using tablet computers towards the understanding of early mathematical concepts for pre-schoolers. Two research questions were identified, which involved a teacher and 10 four years old students in the National Child Development Research Centre (CDRC) in Universiti Pendidikan Sultan Idris (UPSI). The first research question involved the appropriateness of the game software concept using tablet computers. Meanwhile, the second research question involved the level of understanding of the numbers concept of pre-school children using tablet computers. This study involved a qualitative approach through interviews of classroom teachers, student's observations using tablet computers and test for pre-schoolers on number concepts. This study found that it was easy for classroom teachers to teach the game software of number concept using tablet computer. Classroom teachers also expressed their satisfaction towards using the game software of number concept that was more child-friendly. Moreover, children were also found it easy to use tablet computers in carrying number concept activities. The test of understanding the number concept showed that most children were able to determine the form, count, pronounce, and write the numbers of 1–5 well. As a conclusion, this study can help pre-school teachers in using tablet computers and suitable games of number concept for teaching and learning of four years old students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".