Improvised learning for pre-primary students using augmented reality
Bibliographic record
Abstract
In this age of digital advancements, where technologies are changing in a fraction of time. From the abacus which made tutoring math easy millennia back, to word processor which changed the way research paper is being written and presented. After every era, with the advancements in the technology has not only given shaped the education but also transformed it. There was a time when the education world of black on white changed to projected presentations. However, now in this paper, we believe to go beyond the two-dimensional space and create a whole new educational world for children. Augmented Reality (AR) has successfully made classroom learning more interactive and engaging for students as well as for teachers to deliver their lectures. AR is the combination of the real-world with computer- generated world. It is one of the most emerging fields in computer science. The conventional approach for learning can be stressful and to a certain extent less effective for some students. So, we propose a system in which students use smart devices like tablets, mobile, etc. that act as an alternative to boring supportive textbooks. Also, we plan to develop an application consisting of two modules like interactive learning and fun examination, a hybrid of the traditional approach and innovative practical illustrations of complicated concepts leading education in another dimension.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| 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".