Chandrika K R, Amudha J
Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India.
Sci Rep. 2025 Apr 7;15(1):11860. doi: 10.1038/s41598-025-88172-4.
Eye tracking technology offers valuable insights into the cognitive processes of learners in computer programming education. This research presents a novel framework called the Learner Stimulus Intent that offers useful insights into learners' cognitive processes in computer programming education and has significant implications for assessment in computer science education. The comprehensive data collection, extraction of eye gaze and semantic features, and effective visualization techniques can be utilized to evaluate students' understanding and engagement, offering a more nuanced and detailed picture of their learning progress than traditional assessment methods. Furthermore, the four distinct datasets generated by the framework each offers unique perspectives on learner behavior and their cognitive traits. These datasets are outcomes of the framework's application, embodying its potential to revolutionize the way we understand and assess learning in computer science education. By utilizing this framework, educators and researchers can gain deeper insights into the cognitive processes of learners like cognitive workload, processing order of information, confusion in mind, attention etc, ultimately enhancing instructional strategies and improving learner outcomes.
眼动追踪技术为计算机编程教育中学习者的认知过程提供了有价值的见解。本研究提出了一个名为“学习者刺激意图”的新颖框架，该框架为计算机编程教育中学习者的认知过程提供了有用的见解，并对计算机科学教育中的评估具有重要意义。全面的数据收集、眼动注视和语义特征的提取以及有效的可视化技术可用于评估学生的理解和参与度，与传统评估方法相比，能提供关于他们学习进度更细致入微和详细的情况。此外，该框架生成的四个不同数据集各自提供了关于学习者行为及其认知特征的独特视角。这些数据集是该框架应用的成果，体现了其有可能彻底改变我们理解和评估计算机科学教育中学习的方式。通过使用这个框架，教育工作者和研究人员可以更深入地了解学习者的认知过程，如认知工作量、信息处理顺序、思维混乱、注意力等，最终增强教学策略并改善学习者的学习成果。
