Learning analytics can guide human tutors to efficiently address motivational barriers to learning that AI systems struggle to support. Students become more engaged when they receive human attention. However, what occurs during short interventions, and when are they most effective? We align student-tutor dialogue transcripts with MATHia tutoring system log data to study brief human-tutor interactions on Zoom drawn from 2,075 hours of 191 middle school students' classroom math practice. Mixed-effect models reveal that engagement, measured as successful solution steps per minute, is higher during a human-tutor visit and remains elevated afterward. Visit length exhibits diminishing returns: engagement rises during and shortly after visits, irrespective of visit length. Timing also matters: later visits yield larger immediate lifts than earlier ones, though an early visit remains important to counteract engagement decline. We create analytics that identify which tutor-student dialogues raise engagement the most. Qualitative analysis reveals that interactions with concrete, stepwise scaffolding with explicit work organization elevate engagement most strongly. We discuss implications for resource-constrained tutoring, prioritizing several brief, well-timed check-ins by a human tutor while ensuring at least one early contact. Our analytics can guide the prioritization of students for support and surface effective tutor moves in real-time.

翻译：学习分析可以引导人类导师有效解决AI系统难以支持的学习动机障碍。当学生获得人类关注时，他们会变得更加投入。然而，在短期干预期间会发生什么？何时干预最为有效？我们将学生与导师的对话记录与MATHia辅导系统日志数据对齐，研究了从191名中学生课堂数学练习的2,075小时中提取的Zoom平台简短人类导师互动。混合效应模型显示，以每分钟成功解题步骤衡量的参与度在人类导师访问期间更高，并在访问结束后保持提升。访问时长呈现收益递减规律：无论访问时长如何，参与度在访问期间及紧随其后均会上升。时机同样重要：后期访问比早期访问产生更大的即时提升，但早期访问对于抵消参与度下降仍然至关重要。我们创建了能够识别哪些导师-学生对话最能提升参与度的分析工具。定性分析表明，采用具体、分步式支架并明确工作组织的互动对参与度的提升作用最为显著。我们讨论了资源受限辅导的启示：优先安排人类导师进行多次简短且时机恰当的检查，同时确保至少有一次早期接触。我们的分析工具可以指导对学生支持优先级的排序，并实时呈现有效的导师教学策略。