Beyond Drag and Drop: Balancing Experience and Innovation in Online Technical Communication Course Development
Bibliographic record
Abstract
Abstract When adapting a technical writing course to an online learning management system, it’s tempting to rely on existing pedagogies first and then integrate technology into that tried-and-true structure. Yet, a strictly pedagogy-first attitude assumes that technologies are neutral and that any practices can be simply mapped onto any technologies to serve any student. But merely dragging and dropping face-to-face content into an online course misses opportunities for the multiple means of representation and customized learning experiences that technology can afford (Camplese & McDonald, 2010; Kumar & Wideman, 2014; Moxley, 2008; Schreiner, Rothenberger & Sholtz, 2013). Our proposed paper, “Beyond Drag and Drop: Balancing Experience and Innovation in Online Technical Communication Course Development,” written by two technical communication instructors and an instructional designer, draws on best practices in Universal Design for Learning (UDL) to evaluate newly-designed hybrid and online technical communication courses. Technical communication courses generally include students who are from multiple disciplines and who may be resistant to taking a required course offered by faculty outside their major. A UDL framework that enables students to engage with course content in multiple ways can both lessen student resistance and increase students’ confidence in their professional skills. By comparing face-to-face assignments and student outcomes with online assignments and outcomes, we demonstrate how the incorporation of UDL principles encouraged us to make our courses more engaging, accessible, and flexible for diverse groups of students. We also highlight the recursive nature of these changes by explaining the ways our online course development has influenced the design of our face-to-face classrooms and assignments. C. W. Camplese, and S. McDonald, “Disrupting the Classroom,” Edge, Volume 5, Issue 4, 2010, pages 1-19. Available: https://scholarsphere.psu.edu/downloads/70795771g T. Daher, S. Bernstein, and B. Meyer, “Using Blended Learning to Address Instructional Challenges in a Freshman Engineering Course,” American Society for Engineering Education Conference and Exposition, June 26-29, 2016 Available: https://www.asee.org/public/conferences/64/papers/15344/view P. Fidaldo and J. Thormann, “Reaching Students in Online Courses Using Alternative Formats,” International Review of Research in Open and Distributed Learning, Volume 18, Number 2, April – 2017 K. L. Kumar and M. Wideman, “Accessible by design: Applying UDL principles in a first year undergraduate course,” Canadian Journal of Higher Education Volume 44, No. 1, 2014, pages 125 – 147 J. M. Moxley. “Datagogies, writing instruction, and the age of peer production,” Computers and Composition, Volume 25, Number 2, pages 182-202. F. G. Smith, “Analyzing a college course that adheres to the Universal Design for Learning (UDL) framework,” Journal of the Scholarship of Teaching and Learning, Vol. 12, No. 3, September 2012, pp. 31 – 61. D. Rose, W. Harbour, C. S. Johnston, S. Daley, and L. Abarbanell, “Universal Design for Learning in Postsecondary Education: Reflections on Principles and their Application,” National Center of Universal Design for Learning, Available: http://www.udlcenter.org/sites/udlcenter.org/files/UDLinPostsecondary.pdf Schreiner, M. B., Rothenberger, C. D., & Sholtz, A. J. (2013). Using brain research to drive college teaching: Innovations in universal course design, Journal on Excellence in College Teaching, 24(3), 29-50. S. Wu, “Accessibility, Usability, and Universal Design in Online Engineering Education,” American Society for Engineering Education Conference and Exposition, June 14-15, 2015, Seattle, WA. Available: https://www.asee.org/public/conferences/56/papers/12194/view
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".