T S Ashwin, Ranganath Suraj, Khanikar Kabyashree, Khan Karishma, Rajendran Ramkumar, Mitra Ritayan
Indian Institute of Technology Bombay, IDP in Educational Technology, Mumbai, 400076, India.
Sci Data. 2025 Apr 3;12(1):563. doi: 10.1038/s41597-025-04865-5.
Spatial thinking, in general, and mental rotation, in particular, have seen sustained research attention due to such abilities playing a critical role in STEM (science, technology, engineering and mathematics) learning. The recent development of sensor-based approaches to identify, understand, and measure cognition and affect opens up new possibilities to study such topics. We collected galvanic skin response, electroencephalography, screen recording, facial expressions, manual emotion logging, task performance logs (including response times, correctness, and question difficulty), gaze, and self-reports of 38 participants as they solved mental rotation tasks under various conditions, namely, (i) with no time restriction and no feedback, (ii) with no time restriction and with feedback and (iii) with time restriction and with no feedback, respectively. The availability of such a dataset will help researchers in the spatial thinking community to study interesting questions related to strategy selection, flexibility, affective response, and group differences in mental rotation tasks. Furthermore, the learning analytics community could gain valuable insights into how providing feedback might change learning and engagement during such tasks.
一般而言，空间思维，尤其是心理旋转，因其在STEM（科学、技术、工程和数学）学习中发挥关键作用而受到持续的研究关注。基于传感器的方法最近得到发展，用于识别、理解和测量认知与情感，这为研究此类主题开辟了新的可能性。我们收集了38名参与者在各种条件下解决心理旋转任务时的皮肤电反应、脑电图、屏幕记录、面部表情、手动情绪记录、任务表现日志（包括反应时间、正确性和问题难度）、注视以及自我报告，这些条件分别是：（i）无时间限制且无反馈，（ii）无时间限制且有反馈，以及（iii）有时间限制且无反馈。这样一个数据集的可用性将有助于空间思维领域的研究人员研究与心理旋转任务中的策略选择、灵活性、情感反应和群体差异相关的有趣问题。此外，学习分析领域的研究人员可以深入了解在这类任务中提供反馈如何可能改变学习和参与度。
