In Vitro Education: Examining the Virtual Culture of Online Pastoral Education
Bibliographic record
Abstract
Christian higher education institutions across Canada are experimenting with radical shifts in educational content and delivery. Cyber education is becoming an increasingly common supplement or replacement for embodied learning, especially in light of the global coronavirus pandemic. The majority of theological educators have focused their attention on finding ways to leverage technology to assist teaching; very few explore how technology itself impacts theological students, particularly those being educated for pastoral ministry. The growing number of online courses makes my guiding questions both relevant and important: what effect do shifts toward online courses have on those enrolled in programs of pastoral formation? Are students preparing for ordained ministry via online education being adequately trained? When developed well, Web-based learning can strengthen intellectual virtues. However, it inhibits two crucial aspects of pastoral ministry: character virtue formation and self-differentiation. Internet usage negatively affects social well-being, resulting in higher rates of anxiety, depression, and isolation in students; furthermore, this alters behaviour, making learners more distracted, less empathetic, and less able to concentrate and contemplate. These findings answer the question about the effects of online courses on theological students. The second question about whether pastors are being adequately trained led me to explore theological field education. The Association of Theological Schools (ATS) recognizes the value of embodied learning for pastoral formation programs and has developed a standard to meet this requirement. Unlike theological MA programs, field placements are requisites for MDiv programs so students gain practical experience in ministry. I explore field placements as contexts where embodied relationships with supervisors/mentors compensate for learning management system deficiencies and create the environment for self-differentiation and character virtues. I review two different types of field placements and determine that neither can be guaranteed to provide a context where character virtues and self-differentiation are nurtured. I conclude by suggesting the move toward online learning has only increased a turn toward academic virtues without proper consideration for student character. I also address the need for ATS to formulate clearer standards for “formation”, specifically character formation and self-differentiation in pastoral students.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".