Improving Student Interaction with Chemical Engineering Learning Tools: Screencasts and Simulations
Bibliographic record
Abstract
Abstract Improving Student Interaction with Chemical Engineering Learning Tools: Screencasts and SimulationsChemical & biological engineering faculty have developed over 950 screencasts covering topicsin chemical engineering courses. Screencasts are short videos (typically less than 10 minutes)with narration and are made by digital capture of a tablet PC screen. Screencasts can introduce atopic, solve an example problem, explain a concept, explain a diagram and process, demonstratesoftware use, review for an exam, or present a mini-lecture. They can be used in combinationwith textbooks, online reading quizzes, homework assignments, and office hours. Thispersonalized method of learning empowers students by giving them control over the rate ofinformation delivery and when they receive information. As of October 2013, these videos hadbeen watched/downloaded over 2.7 million times and have an overwhelmingly positive responsefrom students in our classes and as seen through YouTube comments.Although many screencasts demonstrate problem solving skills and suggest students attempt tosolve the problems on their own before watching the step-by-step solutions, they areunidirectional in their information delivery. Without student comments, we are unable todetermine student misconceptions and issues with the materials. This became the motivation tocreate interactive screencasts. Interactive screencasts start by posing a conceptual question thatis followed by embedded video links to video responses based on the answers chosen (see figureon next page). If a student clicks on a wrong answer, they are led to a video explaining why theiranswer is wrong and then asked to choose another answer. This continues until the correctanswer is chosen and the video solution is revealed. These screencasts provide a significantadvantage over textbook examples since they require the student to answer the question withoutbeing able to look at the solution. Students have been tremendously excited about these videosand have used them to “test themselves” after classes and prior to exams. Analytics also enableus to track how answers are being chosen, thus aiding our efforts to identify confusing concepts.Another effort to improve student interaction involves hands on computer simulations. We areusing Mathematica based simulations (see figure on next page) to enhance student learning andbetter connect conceptual mastery with physical modeling of systems. These simulations allowsusers to manually control variables and almost instantly visualize the effects on the systembehavior. This provides a useful resource for promoting student interaction during assignmentsand supporting in class questioning where students are asked to predict system outcomes. Thereis a growing library of chemical engineering simulations at the Wolfram Demonstrations Project.We have also begun developing our own simulations and screencasts that explain their use.We will present on these resources and how to use them to improve student interaction andactive learning methods within chemical engineering classes. We would like to participate in aregular oral session to showcase some of these materials. If not available, we would not beopposed to presenting a poster.At the end of a short presentation of the question, the video pops up answers that have embeddedlinks. The boxes around the answers indicate “hotspots” that are clickable. Each link opens up aseparate video.Wolfram Demonstrations Project Simulation: Multiple steady-states in continuous culture withsubstrate inhibition. Sliders and output buttons control how resulting analysis is presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".