Bibliographic record
Abstract
Avokiddo. ABC Ride. Vers. 1.3.5. Apple App Store, https://itunes.apple.com/ca/app/avokiddo-abc-ride/id827657068?mt=8 Suggested age range: 3-6Cost: $3.99 The alphabet is a crucial building block for reading and writing so it is important to have a variety of tools for teaching it. ABC Ride can be one such tool as it turns learning about the alphabet into an exciting adventure. The game follows Avokiddo’s brother and sister duo, Beck and Bo, on a bike ride. Along the way, they come across puzzles which they must solve to unearth a particular letter. Instructions are delivered in creative alliteration such as “tie the tire to the tree”, which helps the child hear the sound the letter makes. After completing the puzzle, cheerful music plays to indicate success and the letter appears. To continue biking along, the player then must spell a word starting with that letter by dragging and dropping the letters into their correct spot. There is lots of repetition to deepen understanding as each letter is announced when picked up, and the final word is spelled out when completed. Again, the game uses sound very well to indicate success as there is plenty of encouraging cheering and music once the word is spelled. The game is extremely original in its non-traditional choice of words to be associated with each letter. It will have children and adults alike giggling with its silly puzzles such as scrubbing a dirty pig for P, blow drying an igloo for I, and jumping on jelly for J. The non-lingual interface is very intuitive which allows for even the youngest player to navigate it confidently. However, some challenges are a little hard to understand, which could be frustrating as you must complete each challenge before moving onto the next. For example, building the robot requires very precise placement of the pieces. Thankfully, after a short time, obvious hints are given such as the correct area shaking enticingly. This ensures that the player does not get stuck and give up on the game. In addition to the strategic use of sound, the graphics are stunning. The images are made of a patchwork of different textures such as cardboard and fabric, which is Avokiddo’s trademark style. One of the app’s greatest strengths is its customizability. For example, you can choose to turn off the spelling for younger players, or you can turn off the puzzle instructions for the added challenge of solving it independently. You can also choose to work on upper case or small case letters, and make the sound of each letter be announced rather than the letter when spelling. You can also decide if puzzles follow the order of the alphabet or arrive in random order. Overall, this app is a very good supplement for any young learner just starting out or well on their way to mastering the alphabet and simple spelling. Its inventive letter puzzles, well-used sound, superb graphics, and customizability make it a good choice for any school, library, or home. Rating: 3 out of 4 stars, recommendedReviewer: Kyla LeeKyla Lee is a first year student in the Library and Information Studies program at the University of Alberta, and a Library Assistant at EPL. She is very interested in helping youth develop digital literacy skills from a young age, and incorporating creative apps into programming.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.778 | 0.703 |
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".