Evidence of Psychological Engagement when Raising a Virtual Child
Bibliographic record
Abstract
Students are very familiar with digital media and computers. The aim of this study was to take advantage of this skill-set and examine evidence of psychological engagement in a personalized web-based learning experience, given the more general interest in student engagement of students in Higher Education. In this study, 117 students each raised their own virtual child as a term-long project within a child development course. The website MyVirtualChild© was used, in which students make parenting decisions, receive feedback, and write assignments designed to integrate course material with their simulated parenting experience. Teaching evaluation data showed students felt the program helped with learning and critical thinking skills. In addition, an open-ended question on the final exam was coded for emotional and behavioral content, and showed that most students felt they formed a relationship with their child and had positive feelings such as happiness with and pride towards their child. Some also made comments that showed self-reflection about either their own parenting skills or childhood experiences, and related the program to course content. This suggests that students found the program emotionally engaging and personally meaningful, which are aspects of psychological engagement in learning.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".