Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,399 | 0,302 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».