发表机构
Humboldt University of Berlin; Berlin University of Applied Science（柏林洪堡大学; 柏林应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在机器人介导的问答游戏中声音检测算法准确性对学生动机的影响，通过对比CNN和互相关算法，发现互相关算法检测更可靠，能显著提升学生动机，为算法精度-动机关系模型提供实证支持。
AI 中文摘要
在竞争性学习活动中，机器人不准确的决策可能会降低学生对公平性和能力的认知，最终影响他们的动机。本文研究了声音检测算法的准确性是否会在机器人介导的问答游戏中影响学生的动机。一个Pepper人形机器人主持了一场基于交互式蜂鸣器的问答游戏，在涉及40名大学生的对照实验中，使用卷积神经网络（CNN）和互相关算法这两种声音检测方法进行评估。参与者被平均分配到CNN组（n = 20）和互相关组（n = 20）。两组在相同条件下完成相同的问答，仅在用于第一响应者识别的声音检测算法上有所不同。使用内在动机量表（IMI）评估学生动机，通过实时检测准确性评估算法性能。结果表明，互相关方法在课堂条件下实现了更可靠的声音检测，并且在所有IMI子量表上的得分显著更高，表明学生的兴趣、感知能力、努力、感知选择更高，感知压力更低（反向编码后）。这些发现为提出的算法精度-动机关系（APMR）模型提供了实证支持，表明算法准确性不仅是一种工程性能指标，而且是影响机器人辅助教育环境中学习者动机的重要因素。
英文摘要
In competitive learning activities, inaccurate robot decisions may reduce students' perceptions of fairness and competence, ultimately affecting their motivation. This paper investigates whether the accuracy of sound detection algorithms influences student motivation during a robot-mediated quiz game. A Pepper humanoid robot hosted an interactive buzzer-based quiz in which two sound detection approaches, a Convolutional Neural Network (CNN) and a Cross-Correlation algorithm, were evaluated using a controlled between-subjects experiment involving 40 university students. Participants were equally assigned to a CNN group (n = 20) and a Cross-Correlation group (n = 20). Both groups completed the same quiz under identical conditions, differing only in the sound detection algorithm used for first-responder identification. Student motivation was assessed using the Intrinsic Motivation Inventory (IMI), while algorithm performance was evaluated through real-time detection accuracy. The results indicate that the Cross-Correlation approach achieved more reliable sound detection under classroom conditions and produced significantly higher scores across all IMI subscales, demonstrating greater student interest, perceived competence, effort, perceived choice, and lower perceived pressure (after reverse coding). These findings provide empirical support for the proposed Algorithmic Precision-Motivation Relationship (APMR) model, demonstrating that algorithmic accuracy is not merely an engineering performance metric but an important factor influencing learner motivation in robot-assisted educational environments.
