Develop Web-based Modules to Educate High-School Students in Studying Microbial Fuel Cell Dynamics
Bibliographic record
Abstract
Abstract USA high-school students are falling behind their peers from other countries such as Finland and Korea in their mathematical performance. Solving ordinary differential equations (ODEs) is especially challenging to USA high-school and college students. It is thus necessary to re-generate the momentum of inspiring or stimulating high-school students to participate in more math-related trainings or projects. In this work, we developed the first version of a web-based training approach to train high-school students modeling skills to simulate the dynamics of microbial fuel cells (MFCs) in MATLAB Simulink. Due to its capability of digesting organic compounds from waste water to generate electricity, the MFC is regarded as one of the most sustainable approaches to treat waste water and generate bioenergy at the same time. MATLAB Simulink provides a friendly platform on which students can build ODE models by linking functional blocks from the Simulink module library. This makes solving ODE models as interesting as building Lego projects. In this work, training handouts along with recorded videos were developed for two simpler ODE models, one describing the liquid level in a tank and the other quantifying the temperature in a continuous flow stirred-tank reactor (CSTR) where an exothermal reaction takes place. As part-time student researchers, high-school students watched the videos and reproduced the results shown in the videos within 3 weeks. They then independently built the MFC ODE model in MATLAB Simulink to quantify the profile of feeding substrate concentration in MFC, the density profiles of electrogenic and methanogenic bacteria, and the electrical current signals from MFC. The MFC model consists of four differential equations, four equality equations and 25 parameters. High-school students were able to finish the project within four weeks by working on the project on weekends. While high-school students generally finished the MFC project independently, the instructor provided two Skype meetings (totally one and a half hours) to check students' programs and provide suggestive comments. A survey was given at the end of the project to evaluate the improvement students' knowledge in MFC and gather what students like and dislike the web-based training approach. The survey results showed that: 1) it is possible to attract high-school students in STEM fields by providing interesting mini-projects that are related to their daily life (e.g., bioenergy production and waste water treatment); 2) the web-based training approach was effective in conveying the training materials; 3) Skype meetings were helpful but students preferred in-person meetings; 4) the web-based training approach offered flexibility in students' schedule; 5) students' knowledge in MFC and MATLAB Simulink had been significantly improved after the training; 6) after the training, students liked the research in STEM field and planned to find a STEM major for their college study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".