Autonomous driving paper index
Designing Proactive Interventions for Dynamic Learner States in Digital Learning Systems
One-line summary
Drawing on self-affirmation theory, we propose that learner involvement moderates this effect.
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Digital learning enables proactive intervention—providing guidance before learners explicitly request help. However, the effect of proactive support on learning performance remains unclear. On one hand, it removes the need to initiate help, potentially avoiding competence threats associated with requesting assistance. On the other hand, unsolicited help may signal inadequate ability and heighten perceived competence threats. Drawing on self-affirmation theory, we propose that learner involvement moderates this effect. Modern learning systems capture rich behavioral traces, enabling unobtrusive measurement of engagement-related behavior. Using a mixed-methods approach, we first develop a Graph Neural Network model to predict involvement from behavioral data during problem-solving, then implement this model in a digital learning system and conduct an experiment to examine how different forms of support are adapted to users’ behavioral states to influence learning performance.
Links and sources
Need this topic turned into a technical roadmap?
Full Self Driving can prepare a custom autonomous driving literature review, code map, dataset map, and B2B technology assessment.
Request B2B research
Comments