Learner stimulus intent: a framework for eye tracking data collection and feature extraction in computer programming education

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.