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.

使用 Google Expedition 和基于触摸屏的情感来增强虚拟学习环境和学习者情感参与的研究探讨了沉浸式学习的虚拟元素，涉及教学应用、可利用性和认知负荷等方面的衡量。研究结果可能有助于通过利用教育技术中沉浸式学习技术的特点来改进虚拟现实和增强现实体验，并通过利用磁性、光学或混合跟踪器大大提高物体追踪来提高沉浸式学习环境的有效性。

情绪驱动的优化轨迹规划在沉浸式学习中的预测

Emotion Based Prediction in the Context of Optimized Trajectory Planning  for Immersive Learning

Recent progress in the field of reinforcement learning has been accelerated
by virtual learning environments such as video games, where novel algorithms
and ideas can be quickly tested in a safe and reproducible manner. We introduce
the Google Research Football Environment, a new reinforcement learning
environment where agents are trained to play football in an advanced,
physics-based 3D simulator. The resulting environment is challenging, easy to
use and customize, and it is available under a permissive open-source license.
In addition, it provides support for multiplayer and multi-agent experiments.
We propose three full-game scenarios of varying difficulty with the Football
Benchmarks and report baseline results for three commonly used reinforcement
algorithms (IMPALA, PPO, and Ape-X DQN). We also provide a diverse set of
simpler scenarios with the Football Academy and showcase several promising
research directions.

介绍了一项新的强化学习环境 - 谷歌足球环境，提供支持多人和多代理实验的虚拟学习环境和用于测试和展示强化学习算法的三种难度场景与一组更简单的场景。