An Open Learning Design, Data Analytics and Visualization Framework for E-Learning (Phase 2)
Grant Data
Project Title
An Open Learning Design, Data Analytics and Visualization Framework for E-Learning (Phase 2)
Principal Investigator
Professor Law, Nancy Wai Ying
(Co-Investigator (Co-I) (for projects led by other university))
Co-Investigator(s)
Professor Pong Ting Chuen
(Co-Investigator)
Professor Qu Huamin
(Project coordinator)
Professor Kwok Yu Kwong
(Co-Investigator)
Dr O'Reilly Una-May
(Co-Investigator)
Dr Hemberg Erik
(Co-Investigator)
Dr Bridges Susan Margaret
(Co-Investigator)
Professor Luo Qiong
(Co-Investigator)
Duration
27
Start Date
2018-07-01
Completion Date
2020-09-30
Amount
1417400
Conference Title
An Open Learning Design, Data Analytics and Visualization Framework for E-Learning (Phase 2)
Keywords
Data Analytics, E-Learning, Open Learning Design, Visualization Framework
Discipline
Others - Education
HKU Project Code
ITS/388/17FP
Grant Type
Innovation and Technology Support Programme (Tier 2)
Funding Year
2018
Status
Completed
Objectives
Phase 2 (Y3-Y4) of this project aims to facilitate K-12 and MOOC instruction and research by (1) developing open-source analytical and visualization methods for K-12 resource-based e-learning in mathematics, (2) articulating design patterns in integrated learning and analytics for computational thinking (CT) in MOOCs and K-12, and (3) evaluating (1) and (2) on a pilot scale. Analytical and visualization methods are targeted because e-learning in K-12 education is often centered around resources such as item banks and videos, but these platforms lack students' interaction data in test items, limiting the possibility of further data analytics. CT and mathematics are both important areas in STEM education. CT is a key 21st century competence encompassing knowledge, skills and their application to solve problems. The integrated patterns will provide data-driven feedback to inform e-learning design. Phase 1 (Y1-Y2) integrated three e-learning technology components for MOOCs: 1) learning design tools for course design and integration with specific learning analytics and visualization; 2) analytical methods, including learner behavior and predictive analytics; and 3) visualization interfaces to support stakeholder interpretation of learning analytics. Phase 2 activities will enhance all three components and put them to the test.