Students' actual purposes when engaging with a computerized simulation in the context of citizen science
Bibliographic record
Abstract
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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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".