Productive Disciplinary Engagement in a Remote Laboratory Activity With an Eighth-Grade Science Class
Bibliographic record
Abstract
Remote laboratories offer students an opportunity to use analytical laboratory instruments through the Internet, in real time, from their classroom. Although remote laboratory activities offer great potential for engaging students’ interest and student learning, little work has been done on using them with middle school students. This study focused on middle school students’ engagement during a remote laboratory activity. With the help of facilitators, 18 eighth-grade students worked in six groups of three to remotely operate a Shimadzu TOC/TN Analyzer in a university chemistry laboratory, in real time, to measure the total nitrogen content in their river water samples. We video- and audio-recorded the students’ discourse with each other and with facilitators during the activity. Following transcription, discourse was coded for types of engagement as defined by the Productive Disciplinary Engagement framework, which posits three types of engagement: general engagement—students make active contributions; disciplinary engagement—students’ contributions are connected to the discipline of science; and productive disciplinary engagement—students make intellectual progress. All six groups demonstrated general engagement and disciplinary engagement. Students talked about both the technology, such as the video camera that allowed them to view inside the university laboratory, as well as the science, such as how the machine was actually doing the measuring. Two groups showed evidence of moving towards productive disciplinary engagement when they discussed the meaning of their results. Our results suggest that remote laboratory activities can engage middle school students and can be a site for their learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".