Hierarchical Aggregate Assessment (HAA): An Assessment Process of Teams with Several Levels of Hierarchy in Education
Bibliographic record
Abstract
Usually, the assessment of teams with several levels of hierarchy is done in the Management field with Management Information Systems (MIS). The problematics studied in the present paper is to consider the assessment of teams with several levels of hierarchy in the field of Education. Regarding this issue, very few authors and scientists have done work on these teams that have an inverted treelike structure similar as large organizations. Both teams studied in the Management and Education fields are evaluated by the processing of the information contained in each node of the three or by the work, production and performance of each individual with three traversal algorithms. In that particular case, links and similarities are established between Management and Education fields because both assess treelike structure organizations with several levels of hierarchy. The concept of Hierarchical Aggregate Assessment (HAA) is based on the assessment of teams with several levels of hierarchy in education. This assessment process is done on a treelike organization similar as the ones in management information systems. The process is done in three phases: the first phase consists in the team formation and the attribution of hierarchical levels to team members that is the aggregation process; the second phase is the presentation of a test or an assessment task done in team to the student; and the third phase is the team dislocation and the return to the initial phase until the course is done. This iterative process consists of the course curriculum management. While the process iterates, assessment data are collected through the process as summative and formative assessment data that can be used to determine the course success or to guide student for improvement. The aim of this paper is to define the HAA process in education that is similar to Management Information Systems (MIS). There is a lot of research and literature produced on Management Information Systems and also on teamwork assessment. In education, most of the research concerning teamwork assessment has been done on teams with a unique level of hierarchy. The main measurement tools to assess team in education according to previous research are team leaders and team member’s assessment grids. To explore this field of research, an E-Learning Internet application named “Cluster” has been developed with a research and development (R & D) methodology and tested with high school students and Army Cadets. Resistance to change has been a major obstacle to the implementation of the “Cluster” application in organizations. Knowledge acquisition rate was similar as traditional classroom teaching but failure rates were 20% in traditional teaching and 80% in the case of distance learning with “Cluster” application. However, despite resistance to change, the “Cluster” application proved the HAA theory that teams with several levels of hierarchy could be assessed in an educational context.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".