This study investigates grammatical reasoning in primary school learners through a sequence-based learning analytics approach, leveraging fine-grained action sequences from an interactive game targeting morphosyntactic agreement in French. Unlike traditional assessments that rely on final answers, we treat each slider movement as a hypothesis-testing action, capturing real-time cognitive strategies during sentence construction. Analyzing 597 gameplay sessions (9,783 actions) from 100 students aged 8-11 in authentic classroom settings, we introduce Hamming distance to quantify proximity to valid grammatical solutions and examine convergence patterns across exercises with varying levels of difficulty. Results reveal that determiners and verbs are key sites of difficulty, with action sequences deviating from left-to-right usual treatment. This suggests learners often fix the verb first and adjust preceding elements. Exercises with fewer solutions exhibit slower and more erratic convergence, while changes in the closest valid solution indicate dynamic hypothesis revision. Our findings demonstrate how sequence-based analytics can uncover hidden dimensions of linguistic reasoning, offering a foundation for real-time scaffolding and teacher-facing tools in linguistically diverse classrooms.

翻译：本研究通过基于序列的学习分析方法，探究小学生的语法推理过程，利用一款针对法语形态句法一致性设计的互动游戏中细粒度的操作序列。与传统评估方法依赖最终答案不同，我们将每个滑块移动视为假设检验行为，从而捕捉句子构建过程中的实时认知策略。通过分析来自100名8-11岁学生在真实课堂环境中产生的597个游戏会话（共9,783个操作），我们引入汉明距离来量化与有效语法解决方案的接近程度，并考察不同难度练习中的收敛模式。结果表明，限定词和动词是主要难点所在，操作序列偏离了通常从左到右的处理方式。这表明学习者往往先确定动词，再调整其前的成分。解决方案较少的练习表现出更缓慢且更不稳定的收敛过程，而最近有效解的变化则揭示了动态的假设修正。我们的研究结果证明了基于序列的分析方法如何揭示语言推理的隐藏维度，为语言多样化课堂中的实时支架和面向教师的工具提供了基础。