Creating Continuing Professional Online Learning Communities
Bibliographic record
Abstract
With continuing professional education increasingly offered through distance and distributed learning technologies, adult educators need to creatively interweave and traditional face-to-face delivery strategies to craft successful continuing professional learning opportunities and communities that extend beyond typically time-limited opportunities. While some professionals choose continuing education programs that utilize computer-mediated conferencing (CMC) because of convenience, cost-effectiveness and the unique learning environment offered online, others may experience difficulty becoming active participants within online classrooms. As a result, they may lose the professional networking potential and subsequent ongoing learning opportunities and professional development that formal courses can launch (Houle, 1980). Here, we describe how one graduate program provided working professionals with an initial formal professional development opportunity that evolved into a continuing learning community. The community was brought about through careful program planning and skillful facilitation in an atmosphere of trust and respect. The University of Calgary graduate program in workplace learning was taught mostly to working professionals across Canada using a program design strategy that combined both face-to-face and delivery Background of the Program The two-year Master of Continuing Education (MCE) program begins with a three-week, summer face-to-face institute held on the University of Calgary campus. The subsequent fall and winter terms offer core and elective courses delivered by computer-mediated conferencing (CMC). The second year repeats the first-year format and students also start a final integrative project by CMC under the direction of a faculty supervisor. The oral defense at the end of the program is conducted either in person or via audio-conference, depending on the geographic location and preference of the student. The program designers chose FirstClass course delivery software for its discussion-based format, ease of use and availability to students and faculty. A key design feature of the program is the placing of students into small, 24-member cohorts that study together for both of the institutes and all core CMC courses before taking their elective CMC courses with the larger community of MCE students. The cohort model promotes learning during the course of the program and fosters continuing professional development of the participants. One Example of Trust and Respect in Online CPE One course in the program, Facilitating Learning, was designed for students seeking instruction on how to become skillful, facilitators. Among the FirstClass software facilities available for students in the course were: Rogues Gallery for student biographies; Web Hot-Spot for additional resource material; Ask the Professor and Ask the Learner for questions about course issues; and Team Presentation for the students' use in building small group presentations on topics that were eventually shared with the entire class. Many of the best practices in delivery (for example Palloff & Pratt, 1999; Wiesenberg & Hutton, 1996) were incorporated into the course. For example, the program invited learners to share their personal stories and biographies with others as a way to develop a personal identity and shared rapport with one another online; they established their own communication guidelines and codes of netiquette that demonstrated respect for others and guided their future interactions in an community. Feedback from students about their learning experiences in this course emphasized the flexibility of course access and discussions, the ease of interaction with colleagues and faculty and the abundance of opportunities to question and dialogue about all aspects of the course as it unfolded--all aspects of the program that promoted the development of an learning community. …
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".