TREO-Based Team Formation: A Double Optimization Framework for Educational Research

This repository contains the R-based computational framework and example dataset for forming optimized student teams using the Team Role Evaluation Orientation (TREO) model. The tool is designed for educators and researchers who wish to compare different grouping strategies—specifically Balanced (maximizing complementarity) and Overlap (maximizing redundancy) conditions. The script performs a Double Optimization process by iterating through multiple threshold values ($T$) and group sizes ($N$) to find the configuration that maximizes the statistical separation ($S$) between team conditions. This ensures that experimental groups are robustly differentiated for subsequent performance analysis. Files Included Script.docx (or .R): The complete R code for data standardization, diversity diagnostics, team formation algorithms, and automated figure generation (SVG). Example_data.xlsx: A standardized template containing two parallels (Sheets A and B) with anonymized student data and TREO scores across six dimensions: Organizer, Doer, Challenger, Innovator, Team Builder, and Connector. Instructions for Users (README Context) 1. Data Preparation Your Example_data.xlsx must follow this structure to work with the script: Columns: N°, Sex, ID_student, and the six TREO roles (Organizer, Doer, Challenger, Innovator, Team_Builder, Connector). Anonymization: Ensure ID_student is a local reference (e.g., A001, A002) to protect student privacy. Sheets: The script expects two sheets named "A" and "B" representing different class parallels or cohorts. 2. Methodology Flow The algorithm follows a rigorous five-step process: Standardization: Calculation of Z-scores based on the grand mean of the combined parallels. Diversity Diagnosis: Calculation of the Effective Role Richness (R_ef) and the Role Diversity Index (IDR) based on Shannon Entropy to assess the sample's variety. Simulation Loop: Testing of thresholds (from 0.25 to 4.0) and group sizes (from 3 to 7). Optimization: Selecting the (T, N) pair that yields the highest Separation Score (S), defined as: S = (CI_{Balanced} - CI_{Overlap}) + (OI_{Overlap} - OI_{Balanced}) where CI is the Complementarity Index and OI is the Overlap Index. Output: Automated generation of high-quality SVG plots and CSV assignment lists.本仓库包含基于R语言的计算框架与示例数据集，用于依托团队角色评估导向（Team Role Evaluation Orientation，TREO）模型构建优化后的学生分组方案。该工具面向希望对比不同分组策略的教育工作者与研究者，具体涵盖平衡型（最大化互补性）与重叠型（最大化冗余性）两类分组条件。 该脚本通过遍历多个阈值（$T$）与分组规模（$N$）执行双重优化流程，以筛选出可最大化不同分组条件间统计区分度（$S$）的配置方案，确保实验组能够被清晰区分，用于后续绩效分析。 ### 包含文件 1. Script.docx（或.R文件）：涵盖数据标准化、多样性诊断、团队组建算法与自动化SVG图形生成的完整R代码。 2. Example_data.xlsx：标准化模板文件，包含两个平行数据集（工作表A与B），内含匿名化学生数据与六个维度的TREO角色得分：组织者（Organizer）、执行者（Doer）、挑战者（Challenger）、创新者（Innovator）、团队建设者（Team Builder）与联络者（Connector）。 ### 用户使用指南（README语境） 1. 数据准备 你的`Example_data.xlsx`需遵循以下格式方可适配本脚本： - 列字段：序号（N°）、性别（Sex）、学生ID（ID_student）以及上述六个TREO角色维度得分。 - 匿名化要求：学生ID需采用本地命名规则（如A001、A002）以保护学生隐私。 - 工作表要求：脚本需使用名为"A"与"B"的两个工作表，分别代表不同班级平行组或队列。 2. 方法流程 算法遵循严谨的五步流程： 1. 标准化：基于合并平行组的总均值计算Z分数。 2. 多样性诊断：基于香农熵计算有效角色丰富度（$R_{ef}$）与角色多样性指数（IDR），以评估样本多样性水平。 3. 模拟循环：测试阈值范围（0.25至4.0）与分组规模范围（3至7）。 4. 优化：选取可获得最高区分度得分（$S$）的（$T$, $N$）组合，区分度得分定义为： $$S = (CI_{Balanced} - CI_{Overlap}) + (OI_{Overlap} - OI_{Balanced})$$ 其中$CI$为互补性指数，$OI$为重叠指数。 5. 输出：自动生成高质量SVG图形与CSV格式的分组分配列表。