[论文解读] Emotion Based Prediction in the Context of Optimized Trajectory Planning for Immersive Learning

Akey Sungheetha, Rajesh Sharma R+1 more|arXiv (Cornell University)|Dec 18, 2023

Educational Games and Gamification被引用 11

一句话总结

论文研究将 Google Expedition 与触摸屏情感结合以增强沉浸式学习，评估教学影响、认知负荷和对象跟踪改进。它报告了更高的后测预测分数，并讨论了跟踪技术。

ABSTRACT

In the virtual elements of immersive learning, the use of Google Expedition and touch-screen-based emotion are examined. The objective is to investigate possible ways to combine these technologies to enhance virtual learning environments and learners emotional engagement. Pedagogical application, affordances, and cognitive load are the corresponding measures that are involved. Students will gain insight into the reason behind their significantly higher post-assessment Prediction Systems scores compared to preassessment scores through this work that leverages technology. This suggests that it is effective to include emotional elements in immersive learning scenarios. The results of this study may help develop new strategies by leveraging the features of immersive learning technology in educational technologies to improve virtual reality and augmented reality experiences. Furthermore, the effectiveness of immersive learning environments can be raised by utilizing magnetic, optical, or hybrid trackers that considerably improve object tracking.

研究动机与目标

• 研究情感感知元素如何融入沉浸式学习环境。
• 评估将情感与沉浸式技术结合的教学应用与可用性（可供性）。
• 评估情感信息化沉浸式学习场景中的认知负荷影响。
• 分析情感要素是否提升学习者的后测结果。

提出的方法

• 在沉浸式学习情境中使用 Google Expedition 及触摸屏情感测量。
• 将教学应用、可用性/可供性和认知负荷作为关键衡量指标进行评估。
• 在沉浸式学习设置中提出轨迹优化与基于情感的预测。
• 探索磁性、光学或混合追踪器以增强环境中的对象跟踪。

实验结果

研究问题

• RQ1 在沉浸式学习环境中结合情感要素是否能改善学习结果？
• RQ2 基于情感的沉浸式学习的教学可用性与认知负荷含义是什么？
• RQ3 追踪技术（磁性/光学/混合）如何影响情感预测在沉浸式学习中的有效性？

主要发现

• 与前测相比，学习者的后测预测分数显著提升。
• 表明在沉浸式学习情境中加入情感元素是有效的。
• 追踪技术（磁性、光学或混合）能够显著改善沉浸式环境中的对象跟踪。
• 研究强调了利用沉浸式学习特征以提升 VR/AR 教育体验的潜在策略。