Bibliographic record
Abstract
Purpose The purpose of this work is to illustrate the processes involved in managing teams in order to assist designers and developers to build software that support teamwork. A deeper investigation into the role of team analytics is discussed in this article. Design/methodology/approach Many researchers over the past several decades studied the success factors of a team. Despite many efforts, there is still no consensus on how a team should ideally be formed. Consequently, how one decides to form teams in a class depends on the domain, classroom context and pedagogical objectives. Therefore, software used to support an instructor in forming teams must be flexible enough to accommodate a variety of use cases and support the users throughout the lifecycle of teamwork. In this work, the author proposes a framework for designing general-purpose team management software. The author reviews existing team formation software and focuses specifically on opportunities for advancing research in team analytics. Findings In this context, the author identifies four areas of research opportunities for team analytics. Originality/value Lastly, the author proposes a series of research questions (RQs) and discusses the pedagogical, design, technical and social challenges involved.
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 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 | 0.125 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".