Identifying Troublesome Jargon in Biology: Discrepancies between Student Performance and Perceived Understanding
Bibliographic record
Abstract
The excessive "jargon" load in biology may be a hurdle for developing conceptual understanding as well as achieving core competencies such as scientific literacy and communication. Little work has been done to characterize student understanding of biology--specific jargon. To address this issue, we aimed to determine the types of biology jargon terms that students struggle with most, the alignment between students' perceived understanding and performance defining the terms, and common errors in student-provided definitions. Students in two biology classes were asked to report their understanding of, and provide definitions for, course-specific vocabulary terms: 1276 student responses to 72 terms were analyzed. Generally, students showed an overestimation of their own understanding. The least accurate self-assessment occurred for terms to which students had substantial prior exposure and terms with discordant meanings in biology versus everyday language. Students were more accurate when assessing their understanding of terms describing abstract molecular structures, and these were often perceived as more difficult than other types of terms. This research provides insights about which types of technical vocabulary may create a barrier to developing deeper conceptual understanding, and highlights a need to consider student understanding of different types of jargon in supporting learning and scientific literacy.
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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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".