Research Statement
- Inclusive and Immersive e-Learning Design: This pipeline focuses on designing e-learning environments that are inclusive and immersive, with a particular emphasis on students with special needs. It includes studies on the use of virtual reality, social skills training, and game-based learning to promote engagement and learning for students with autism spectrum disorder. These studies also explore the effectiveness of using multimodal data fusion and learning analytics to track students' cognitive and emotional states during gameplay, as well as the impact of social and cognitive cues on learning comprehension and cognitive load in video instruction.
- Personalized OER Design: This pipeline explores the use of open educational resources (OER) to design personalized learning experiences for students. It includes studies on the development of a conceptual framework for teaching computational thinking in personalized OERs, as well as the application of sequential data analytics to design personalized digital game-based learning for computing education. These studies also examine the role of teachers in using OERs to support interactions of learners with disabilities and the treatment integrity of virtual reality-based social skills training for children with high-functioning autism.
- Learning Analytics in Seamless Learning Environments: This pipeline investigates the use of learning analytics in seamless learning environments, with a focus on exploring the relationships between technology integration, scientific inquiry, and teacher professional development. It includes studies on the use of computational approaches to ethnographic research in digital learning environments, as well as the design and deployment of a virtual social sandbox for children with autism. These studies also examine the effectiveness of automatic assessment of cognitive and emotional states in virtual reality-based flexibility training for adolescents with autism, and the use of adaptivity in educational games to promote engagement and learning.
Inclusive and Immersive e-Learning Design
Inclusive and immersive e-learning design is an essential pipeline that seeks to develop e-learning environments that provide engaging and effective learning experiences for all students. This pipeline aims to make e-learning inclusive, accessible, and effective for all students by using learning analytics, virtual reality-based training, and social skills training.
Overall, inclusive and immersive e-learning design is a pipeline that seeks to create e-learning environments that are engaging, effective, and accessible for all students. This pipeline is supported by a range of studies, including those that focus on learning analytics, virtual reality-based training, and social skills training.
- One of the key elements of this pipeline is the use of learning analytics, which is the measurement, collection, analysis, and reporting of data about learners and their contexts to optimize the learning experience. Moon et al. (2022) provide a scoping review of learning analytics in seamless learning environments, examining how learning analytics can be integrated into e-learning design to enhance student learning outcomes. This study highlights the potential of learning analytics to provide personalized learning experiences for students, tailored to their unique learning needs and preferences.
- Virtual reality-based training is another important aspect of this pipeline, with Ke et al. (2022) exploring the effectiveness of virtual reality-based social skills training for children with autism. This study examines how virtual reality technology can be used to create engaging and immersive learning environments that help children with autism to improve their social skills. The study finds that virtual reality-based training can be an effective tool for improving social skills in children with autism.
- Social interaction is also a critical element of this pipeline, with Moon and Ke (2020) investigating the relationships among middle school students’ peer interactions, task efficiency, and learning engagement in game-based learning. The study highlights how social interaction can be incorporated into e-learning design to enhance student learning outcomes. The findings suggest that peer interaction can be a valuable tool for improving task efficiency and learning engagement in game-based learning environments.
Overall, inclusive and immersive e-learning design is a pipeline that seeks to create e-learning environments that are engaging, effective, and accessible for all students. This pipeline is supported by a range of studies, including those that focus on learning analytics, virtual reality-based training, and social skills training.
Personalized OER Design
This pipeline seeks to create personalized learning experiences for students through the use of open educational resources (OERs). The design of these resources is tailored to meet individual student needs and preferences. To achieve this, the pipeline draws on computational thinking, data analytics, and digital game-based learning.
- Moon et al. (2020) propose a conceptual framework for teaching computational thinking in personalized OERs. The framework identifies the key components required for effective personalized learning, including learner modeling, content adaptation, and assessment. By incorporating these components, OERs can be designed to meet the needs and preferences of individual students.
- Liu and Moon (2023) present a framework for applying sequential data analytics to design personalized digital game-based learning for computing education. The framework uses data analytics to monitor student progress and adapt the learning experience in real-time. By providing personalized feedback and support, the framework enhances student engagement and achievement.
- Yu et al. (2022) investigate the development of a stealth assessment system using a continuous conjunctive model. This system enables personalized assessment of student learning, by identifying the specific skills and knowledge areas that require improvement. By providing personalized feedback and support, the system enhances student learning outcomes.
Learning Analytics in Seamless Learning Environments
This pipeline focuses on the use of learning analytics in seamless learning environments to provide insights into student learning and inform instructional design. The pipeline involves the use of computational approaches to ethnographic research, multimodal data fusion, and game-based learning. The pipeline is supported by articles such as Moon et al. (2022) which reviews the use of learning analytics in seamless learning environments and Seo et al. (2022) which conducts a scoping review of computational approaches to ethnographic research in digital learning environments. Moon et al. (2022) also investigate the use of multimodal data fusion to track students' distress during educational gameplay, providing insights into how student emotions can be incorporated into learning analytics. The pipeline is further supported by Moon and Ryu (2020), which investigates the effects of social and cognitive cues on learning comprehension, eye-gaze pattern, and cognitive load in video instruction, providing insights into how learning analytics can be used to optimize instructional design