Students' actual purposes when engaging with a computerized simulation in the context of citizen science
Notice bibliographique
Résumé
Abstract In today's information age, developing data science competencies has become vital to fostering responsible citizenry. However, the actual techniques learners need to become proficient in are still somewhat “in‐construction”, as the relatively new field of data science is constantly expanding to meet new data‐related demands. Data science education needs to develop innovative means to keep up with this expansion that focus less on proficiency in specific techniques, but rather introduce novices to authentic data practices, and the authentic purposes directing the authentic practices. This paper focuses on a specific practice, the use of simulations to generate and examine data, in the context of authentic scientific Citizen Science research. We provide a case study of one pair of middle school students' engagement in an extended learning sequence including simulation activities inspired by authentic data practices, adapted to also be authentic for young students. While the simulation activity was inspired by the scientists' purposes, our findings illustrate four different actual purposes the students attributed to it. We also show that as the students deepened their engagement with the simulation, they gradually appropriated its intended purpose, alongside articulating more mature views of data‐related concepts. The conclusions summarize the four different purposes the students expressed and identify aspects of design that contributed to the gradual re‐shaping process of their actual purposes. Practitioner notes What is already known about this topic Introducing students to data science and statistics has become essential nowadays. Students need to be introduced to authentic data practices, but also to the authentic purposes motivating these practices. Utilizing computerized simulations is a common authentic practice in science and statistics. The pedagogical, intended, use of computerized simulations can be inspired by the authentic purposes but should also be adapted to be authentic for the students. Students may have actual purposes that differ from the authentic and intended purposes. What this paper adds A case study of a pair of middle school students' engagement with a computerized simulation tool, as part of their participation in a Citizen Science project. The students expressed four actual purposes for the simulation. The students' initial purposes differed from the intended purposes, limiting their participation. Key aspects of the overall activity design ultimately supported the students to appropriate the intended purpose of the simulation and more deeply engage with the intended statistical notions. Implications for practice and/or policy It is important to consider that students may attribute purposes that differ from those of the teacher or the activity designer, to any learning activity they engage in. Making the intended purposes more explicit may be helpful, but potentially not enough for students to appropriate them. Researchers' prompts, students' freedom to reshape their use of the simulation tool and productive discussion norms can be beneficial aspects.
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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 | 0,006 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,007 | 0,003 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».