WIP: Student and Faculty Experience with Blended Learning in a First-Year Chemistry for Engineers Course
Bibliographic record
Abstract
Abstract WIP: Student and Faculty Experience with Blended Learning in a First-Year Chemistry for Engineers Course Chemistry for Engineers, is an introductory chemistry course taken by most engineering students at our university during their first term of study. Online content was developed to facilitate the implementation of a blended learning format, rather than traditional lecture format, for this course. The motivation for a blended approach was i) to create time for more valuable instructor–student interactions, allowing the instructor to reinforce challenging concepts, lead problem-solving workshops and lead experiential learning activities, and, ii) to allow students to explore content at their own pace, thereby accommodating the diversity of students’ high-school chemistry preparation. During Fall 2016, a blended learning format was piloted with three of the twelve sections of the course for half of the course content. Data from surveys administered throughout the term were combined with course grade data in an effort to compare and contrast student experience, satisfaction and performance between a blended learning and traditional lecture model of instruction. While the results from the Fall 2016 study are inconclusive due to challenges with survey administration and implementing the blended learning model, lessons were learned with respect to the readiness of the students for self-directed learning and the integration of the online and in-class components. During Fall 2017, five sections of the course are using a blended learning model for the entire course, implementing the lessons learned from the Fall 2016 study. In January, data from surveys administered throughout the term will be combined with course grade data, again in an effort to compare and contrast student experience, satisfaction and performance between a blended learning and traditional lecture model of instruction. This presentation will i) describe instructor experience in developing and deploying online content to support the blended learning, and, ii) compare and contrast student experience, satisfaction and performance between the two learning models.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".