Bridging the Gap between Classrooms and Research Laboratories: One Teacher's RET Experience Working in a Mycology Lab
Bibliographic record
Abstract
In the ever-expanding realm of science, educators struggle to share new discoveries and techniques with their students. Keeping abreast of recent advances can be daunting, even for the most motivated teacher. Fortunately, the National Science Foundation's (NSF) Research Experiences for Teachers (RET) program helps educators keep up with the fast-moving research community. The RET program enables K-12 science teachers to perform research projects in NSF-supported laboratories and brings the excitement of cutting-edge science into the classroom. In this article, I describe my RET-supported experiences working in a laboratory that studies the ecology and evolutionary biology of fungi, and I provide advice on how teachers may find RET opportunities in their own communities. RET awards are made as supplements to ongoing NSF research grants, which are conducted under the direction of Principal Investigators (PI), who are typically faculty members at universities. The PI of an active award may request an RET supplement, which can pay for the K-12 teacher's stipend, research supplies, and other expenses, such as travel to a scientific meeting or costs associated with curriculum development. My RET project was conducted under the direction of David Hibbett, a faculty member at Clark University in Worcester, Massachusetts, and Manfred Binder, a research fellow in Hibbett's laboratory. Hibbett and Binder are Co-PIs on a current NSF grant to study the evolutionary relationships of the Boletales, a group of mushrooms. With support from an RET supplement to Hibbett and Binder's NSF grant, I performed research at Clark University for eight weeks during the summer of 2006 and attended the annual meeting of the Mycological Society of America in Quebec City, Canada. What led to my RET project The path to my 2006 RET project actually began back in 2003, when I participated in a mycology workshop led by Hibbett at Clark University. The workshop was funded as part of another NSF grant, Assembling the Fungal Tree of Life, on which Hibbett was the PI. Thinking I would learn about traditional taxonomy--the branch of science that seeks to sort species into ordered groups based on morphology and anatomy--I was surprised to find that Hibbett's lab resembled a high-tech biotechnology facility. Researchers in his lab are using the latest tools in molecular biology and bioinformatics to probe the cryptic world of fungal classification and evolution. Ecology, molecular biology, and evolution--topics that can seem unconnected in the minds of most high school students--are being interwoven into a dynamic new science. After taking the mycology workshop, I became active in the Massachusetts Association of Biology Teachers (MABT), a regional branch of the National Association of Biology Teachers (NABT). Through this network I reestablished contact with Hibbett and learned that he and Binder were looking for a teacher to collaborate with, through their Boletales grant. The collaboration they proposed involved development of a learning module for high school students in fungal molecular ecology (in which DNA sequences are used to identify fungi in the environment). I eagerly agreed, hoping I could learn more about mycology and ways to involve my students in a research-based curriculum. Hibbett came to my school in the fall of 2005 and together we led classes in basic fungal biology, molecular ecology, and evolution. The experience was positive. Students learned about an ecologically important group of organisms and the connections between molecular biology and organismal biology, but I was not completely satisfied. One problem was that students did not actually generate the DNA sequences that they analyzed. Instead, these were provided by Hibbett's laboratory as text files. In addition, I was uncomfortable with my understanding of the details of the research. The solution, I realized, was for me to gain firsthand research experience, which would allow me to design more engaging classes for my high school students. …
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.040 | 0.023 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".