Analysis of Students’ Personalized Learning and Engagement within a Cyberlearning System

Abstract “Advance Personalized Learning” is one of the 14 grand challenges of engineering as identified by the National Academy of Engineering. One possible approach for this advancement is to deploy systems that allow an investigator to understand the differences in the learning process of individuals. In this context, cyberlearning systems, like remote and virtual labs, that use networked computing and communication technology to reach a large number of learners offer the affordance to uniquely identify learners and track their learning process in real-time. Motivated by this idea, this study aims to investigate personalized learning and engagement within a cyberlearning system, called the Online Watershed Learning System (OWLS) that combines features of both remote and virtual labs. This cyberlearning system utilizes learning resources generated by a real-time high-frequency environmental monitoring system, called the Learning Enhanced Watershed Assessment System (LEWAS). To understand individualized learning and engagement, the OWLS is advanced with a user-tracking system. Previously, the OWLS used a Google-Analytics based user-tracking system. This new user-tracking system can identify individual users and their actions across devices. A pilot study was carried out by designing an OWLS-based learning task and implementing it within a senior level Environmental Science classroom for exploring personalized learning and engagement within the OWLS. Informed by the engagement theory and the literatures on learning analytics, the study follows a pre-experimental research design where students completed the OWLS-based learning task followed by a post-survey within the in-class time. Results indicate that students’ learning scores are significantly related to the time students were spending outside the OWLS for completing the OWLS-based task. Various engagement patterns/ strategies taken by individual students to complete the task were also revealed. The study shows that a custom user-tracking system, like the one developed in this study has the potential to overcome several limitations of the google-analytics based user-tracking system by providing fine-grained individualized student data that can help in understanding students’ engagement behaviors within a cyberlearning system. Finally, the study has implications of how a cyberlearning tool, like the OWLS, can be utilized in a hybrid classroom setting for helping students gain environmental monitoring knowledge, and skills in real-time data analysis, leveraging the idea of technology-enhanced laboratory instructions within a classroom environment.