Bibliographic record
Abstract
Green, Berbank and Jonathan Skuse (Popleaf). Teach Your Monster to Read. Usborne Foundation, Vers. 3.2, Apple App Store, https://itunes.apple.com/ca/app/teach-your-monster-to-read/id828392046?mt Suggested Age Range: PreK+ Cost: $6.99 Teach Your Monster is a series of games designed for the first two years a child is learning to read. It was funded by the Usborne Foundation, a charitable fund set up by Peter Usborne, head of Usborne UK Publishing and his children to support initiatives to develop early literacy initiatives. To this end, Usborne has made the desktop version freely available from their website. (https://www.teachyourmonstertoread.com/). It was created by a diverse group of producers, designers, and developers with the lead game developers and designers listed as Berbank Green and Jonathan Skuse from the Popleaf Software development company. The development team also included educational consultants from UK post secondary institutions specializing in early literacy and digital games. The series is broken into the following games: Game 1: First Steps For children just starting to learn letters and sounds, Game 2: Fun With Words, and Game 3: Champion Reader. For ease of use by parents and teachers, there is a detailed break down of the letter-sound combinations, words and sentences covered in each game, including a PDF overview. (https://www.teachyourmonstertoread.com/about-the-game/what-does-each-game-cover). For the purposes of this review, my 5-year-old son and I looked at the app version of the first game. Like many children’s apps and games, it begins with the opportunity to customize your avatar, in this case a monster. It also builds upon this theme with an interactive rewards system offering a choice of accessories or virtual treats for their monster every time they have learned a new grapheme and when a “world” has been completed. The monster has crashed its spaceship on an alien planet and the king has offered to fix it if he retrieves all of his lost letters. The story set up is concise and does not interfere with getting started but it is also engaging and is woven throughout the levels in an effective way that changes slightly with each level (in terms of the graphics and the activities that are available), yet the narrative and gameplay retains elements of previous worlds so as to provide consistency and ease of navigation. There is a wide variety of “mini games” to choose from as players are learning each grapheme; new ones are added in each world but previous ones are still available. The game adjusts to the learner; graphemes which were not identified correctly are repeated more often. It is brilliantly scaffolded: following the grapheme minigames, players practice blending sounds to make words, identifying challenging “non decodable words” and breaking down or “segmenting” words into sounds. The graphics are colourful, bold and visually appealing to children. Other enhancements such as sound effects and narration are extremely effective and add to the learning and gaming experience. Our one point of criticism is that the grapheme sound in the “run” mini game is slightly less audible than some of the other sound effects (i.e. background music, “jumping” sound, etc.). However, the player is given several opportunities to hear it. Teach Your Monster to Read has a Teacher Area and is designed to be used in the elementary school classroom. It has options for account creation with options for teacher and/or parent monitoring of child progress. The fact that the online/desktop version is freely available is great news for non-profit literacy centres and libraries whose patrons include families who might not have access to the app. A truly impressive multimedia experience in all respects, which reflects the experience and creativity of the development team: https://www.teachyourmonstertoread.com/about-us. Although the Apple App stores lists the recommended age as 4+, I would suggest visiting the website as it states that is designed to cover “two years of the reading journey” and, as mentioned above, provides a very thorough breakdown of what is covered in each game with the first one beginning at Pre-K “learning sounds”. Therefore, parents and teachers can determine the appropriate age for each game based on individual ability and prior exposure to early literacy activities. Highly Recommended: 4 out of 4 starsReviewer: Kim Frail Kim is a Public Services Librarian at the H.T. Coutts Education Library at the University of Alberta. Children’s literature is a big part of her world at work and at home. She also enjoys gardening, renovating and keeping up with her kids.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.399 | 0.302 |
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".