A Personal Retrospective in Exploring Educational Technologies to Aid Increasing Student Engagement: Tales from Pre-COVID-19, During the Pandemic, & Beyond
Bibliographic record
Abstract
Educational technologies (EdTech) have long been used to augment education and learning (EL) experiences in the classroom with varying degrees of success. When thoughtfully deployed, managed, and supported the utilization of EdTech can have perceived positive impacts for students and instructors. These positive experiences can include an increase in student attendance and participation, an improvement in student understanding of course content, an improvement in the ability to manage and deliver EL experiences, and an ability to provide greater fairness, freedom, and autonomy for all. From before, during, and now beyond/living with the COVID-19 pandemic, several EdTech systems, including TopHat, Quizlet, Mentimeter, Socrative, H5P, and Adobe Captivate were explored within software systems engineering courses at the University of Regina (UR). The underlying goal of this exploration was to iteratively create, deliver, and reflect upon which technologies could lead to more positive EL experiences and outcomes and, ultimately, which technologies could help increase student engagement. This paper provides a personal retrospection on the use of these EdTech systems and the perceived pros and cons of their use over the last several years. Furthermore, as now in 2023 with academic institutions, and specifically the UR’s engineering faculty, leaning towards reverting fully to pre-pandemic operations, a discussion and reflection on the perceived benefits of a hybrid in-person/technology-facilitated approach to course design and delivery are provided. Opportunities for continued exploration are also discussed.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".