Effectiveness of Guests in Large Enrolment Online Courses as an Instructional Strategy
Bibliographic record
Abstract
This paper introduces preliminary findings of an evaluation of the design-effectiveness of the guest instructional strategy focusing on guest speakers' effectiveness for learning (Costello, 2012; 2014a, b) and enhancing social presence (Short, Williams & Christie, 1976) in a networked learning course. HKR 1000, Fitness and Wellness, is an introductory online course widely subscribed by students at Memorial University, seeing about 1000 yearly registrations. In each of the three (fall, winter, spring) thirteen-week semesters, sections of 80 students are virtually combined into one course shell within the learning management system (LMS), Brightspace, formerly Desire2Learn (D2L). In the spring and fall 2015 semesters the design-effectiveness of the guest instructional strategy was evaluated based on students' perspectives (N=688). Effectiveness was determined by whether guests impacted student learning, related to course content, provided real life relevance, and fostered social presence. While large enrolments in online courses are not typical or favoured, it is possible to effectively design instructional strategies that use social learning (Rohr & Costello, 2015) to enhance learning and social presence. Content for HKR 1000 focuses on key topics for living healthy, active lifestyles, including basic principles of physical fitness, cardiorespiratory endurance, muscular strength and endurance, flexibility, body composition, nutrition, stress and weight management. To further highlight the link between real world experiences and course content, guest video presentations and discussions were included in the online course environment. Guests bring a unique perspective to a learning community as an integral, authentic resource impacting student success both in their learning and their professions (Costello, 2014b, Finkelstein, 2006). The use of guests as an instructional strategy (Costello, 2014a, Lowenthal, 2009) also served as a means to increase social presence in the course. The guests each appeared for a week during the semester, their topics being tightly linked to specific course content. Each had a unique, relevant area of expertise to share and provided a short 2-4 minute video clip and engaged in discussion with the students in the LMS discussion forum for the remainder of their respective week. An anonymous survey was administered in the learning management system to collect student feedback on these matters. Questions explored whether the students viewed the videos, posted questions, and read the discussion forums. In addition students were asked to reflect on the approachableness and warmth of each guest. Results indicate a positive guest impact on student learning, provision of real life relevance and enhancement of social presence.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".