Student Conceptions of pH Buffers Using a Resource Framework: Layered Resource Graphs and Levels of Resource Activation
Bibliographic record
Abstract
pH buffers are used extensively in research and industry making them an important chemistry topic for students to learn. This qualitative study uses the phenomenographic method and a resource theoretical framework to provide the first insights into how students approach conceptual buffer problems. Three scaffolded buffer question sets were designed to promote in-depth conceptual responses during a think aloud interview followed by retrospective reporting. Open-coding for activated resources led to three levels of resource activation: Surface Features, Building Connections, and Interconnected. Layered resource graphs provide a visual representation of a diverse array of activated resources, how resources are connected, and which question type promoted particular activations. Some resources such as Accept or Donate H + were consistently activated in all three questions whereas other resources such as pH relative to pK a were productive only in particular contexts, thereby highlighting the contextual dependence of resource productivity. Challenges were observed in productively activating crucial resources such as Accept or Donate H + and in maintaining activations over time even within the same scaffolded question. Specific suggestions are provided on making connections between resources to promote students to a higher level of resource activation and success with buffer problems. Future research should probe the types of activities that can promote productive resource activations and connections.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".