Flipped Instructional Design Factors in an Introductory and an Advanced Data Science Course
Bibliographic record
Abstract
In this full research paper, we evaluate the flipped instructional designs of two undergraduate data science courses at a Midwestern university: an introductory course on database fundamentals and an advanced database design course.This study is built upon our prior work in which we identified a set of eight instructional design factors for effective flipped classrooms in the literature and assessed their efficacy with senior students.Our analysis relies on students' course evaluations, self-reported survey data, focus group responses, course performance data, and instructor observation data to answer the following research questions:1. How do the eight instructional design factors for effective flipped classrooms serve novice versus advanced data science students?2. How should instruction in flipped classrooms be varied for novice versus advanced data science students?Our analysis indicates that novice data science students have different instructional needs and challenges compared to their senior peers, particularly in relation to activities that require peer collaboration and were unmoderated by the instructor.We share the results of our quantitative analysis of self-reported survey data in which students ranked the aforementioned instructional design factors based on their effectiveness for their learning and qualitative analysis which takes student comments (from a free-response survey and focus group data) and instructor observation data to contextualize these rankings and inform our instructional design recommendations.These recommendations address students differing academic and interactional needs within the classroom and are to be implemented within the introductory course in its next iteration:(a) group norming and standardization around expectations for communication/collaboration, (b) transparent disclosure of the learning objectives for each activity, (c) offering guidelines to support students in providing actionable peer feedback, and (d) introducing low-stakes peer evaluations.We conclude with a discussion on the general affordances of the flipped classroom model for both introductory and advanced data science instruction compared to traditional lecture-based approaches.
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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 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".