Measuring collaborative problem solving (CPS) synergy remains challenging in learning analytics, as classical manual coding cannot capture emergent system-level dynamics. This study introduces a computational framework that integrates automated discourse analysis with the Synergy Degree Model (SDM) to quantify CPS synergy from group communication. Data were collected from 52 learners in 12 groups during a 5-week connectivist MOOC (cMOOC) activity. Nine classification models were applied to automatically identify ten CPS behaviors across four interaction levels: operation, wayfinding, sense-making, and creation. While BERT achieved the highest accuracy, GPT models demonstrated superior precision suitable for human-AI collaborative coding. Within the SDM framework, each interaction level was treated as a subsystem to compute group-level order parameters and derive synergy degrees. Permutation tests showed automated measures preserve construct validity, despite systematic biases at the subsystem level. Statistical analyses revealed significant task-type differences: survey study groups exhibited higher creation-order than mode study groups, suggesting "controlled disorder" may benefit complex problem solving. Importantly, synergy degree distinguished collaborative quality, ranging from excellent to failing groups. Findings establish synergy degree as a sensitive indicator of collaboration and demonstrate the feasibility of scaling fine-grained CPS analytics through AI-in-the-loop approaches.

翻译：在协作问题解决（CPS）协同性的测量方面，学习分析领域仍面临挑战，因为传统的手动编码方法无法捕捉涌现的系统级动态。本研究引入了一个计算框架，将自动化话语分析与协同度模型（SDM）相结合，以从群体沟通中量化CPS协同性。数据收集自一项为期5周的联通主义慕课（cMOOC）活动中12个小组的52名学习者。研究应用了九种分类模型，以自动识别跨越四个交互层级（操作、路径探寻、意义建构和创造）的十种CPS行为。虽然BERT模型取得了最高的准确率，但GPT模型展现出更优的精确度，适用于人机协同编码。在SDM框架内，每个交互层级被视为一个子系统，用以计算群体层面的序参量并推导协同度。置换检验表明，尽管在子系统层面存在系统性偏差，自动化测量仍保持了构念效度。统计分析揭示了显著的任务类型差异：调查研究小组展现出比模式研究小组更高的创造序参量，这表明“受控的无序”可能有益于复杂问题解决。重要的是，协同度能够区分协作质量，覆盖从优秀到失败的不同小组。研究结果确立了协同度作为协作敏感指标的地位，并证明了通过人在回路的AI方法扩展细粒度CPS分析的可行性。