The Effects of Team Dynamics Training on Conceptual Data Modeling Task Performance
Bibliographic record
Abstract
Database modeling is a complex conceptual topic often taught through the use of project-based teams. One of the problems with the use of project-based teams in university courses is the determination of whether this is the most effective use of instructor and student time involvement and effort level. Therefore, this study investigated the impact of providing team dynamics training prior to the commencement of short-duration project-based team conceptual data modeling projects on individual data modeling task performance (DMTP) outcomes and team cohesiveness. The literature review encompassed conceptual data design modeling, the use of a project-based team approach, team dynamics and cohesion, self-efficacy, gender, and diversity. The research population consisted of 75 university students at a North American University (Canadian) pursuing a business program requiring an information systems course in which database design components are taught. Analysis of the collected data revealed that there was a statistically significant inverse relationship found between the provision of team dynamics training and individual DMTP. However, no statistically significant relationship was found between team dynamics training and team cohesion. Therefore, this study calls into question the value of team dynamics training on learning outcomes in the case of very short duration project-based teams involved in conceptual data modeling tasks. Additional research in this area would need to clarify what about this particular experiment might have contributed to these results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".