Designing Evidence-Informed Microlearning for Graduate-Level Online Courses
Bibliographic record
Abstract
Microlearning is a buzzword in eLearning, referring to learning content that is delivered in short bursts or small bites. With no universally accepted definition, it means different things to different people, which further adds to its nebulous nature. This thesis study uses learning theory principles in a graduate education context to explore how students see the benefits or issues with microlearning activities integrated into an online course. Despite its popularity, much is still unknown and unclear about its educational potential especially within the context of formal higher education and how it can be integrated within university-level online courses. By means of a design-based study spanning across three iterations, I explored the role of microlearning in a graduate-level online course from a course design perspective. I achieved this by developing a model called midweek microlesson, under which graduate students enrolled in an online course in Education at a Canadian university were exposed to microlessons on a weekly basis. These microlessons were carefully designed based on theories and evidence-informed practices within the science of learning. The model intended to: a) offer students opportunities for distributed practice; and b) further augment and enrich students’ understanding of the course’s weekly theme. Qualitative and quantitative data was collected and analyzed to understand students’ interaction and experience with the microlessons. The main purpose of this exploratory study is to understand how a specific form of microlearning (i.e., microlessons) can be integrated within a graduate-level online course with the goal to enhance learners’ interaction with the content. Thematic analysis of student commentary led to the emergence of six design themes: content, purpose, timing, length & structure, format and interactivity. I discuss these in light of cognitive learning theories and what they mean for practice. Overall, this study is an attempt to lay a foundation to explore potential educational affordances of microlearning within graduate-level online courses. The study also raises awareness about the lack of theory in current discussions surrounding microlearning and presents an example of exploring and integrating microlearning in course design. Limitations of the study and areas for future exploration are discussed.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".