Title
Adaptive Support for Acquisition of Self-Direction Skills using Learning and Health Data
Abstract
For the 21st century learner, developing self-direction skill is crucial for both academic activities and maintaining one's healthy lifestyle. While there are technology supports for specific self-regulated learning tasks and health monitoring, research is limited on how to support development of meta-skill of self-direction process itself. In our work, we focus on designing seamless technology infrastructure to foster self-directedness of learners. We consider learning and physical activities data as a context and DAPER (data collection-analyze-plan-execution monitoring-reflect), as a data-driven self-direction skill execution and acquisition model. We bridge Learning Analytics and Quantified-Self approaches to develop the GOAL (Goal Oriented Active Learner) system to support synchronize-visualize-analyze multisource data regarding learners' learning and physical activities. This paper proposes a measurement rubric as a basis of adaptive scaffolding for skill development during the process.
Year
DOI
Venue
2019
10.1109/ICALT.2019.00025
2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)
Keywords
Field
DocType
GOAL system,Learning Analytics,Self-direction skills,Evidence-based Practice
Rubric,Active learning,Learning analytics,Goal orientation,Computer science,Knowledge management,Self-Direction Skills,Evidence-based practice
Conference
Volume
ISSN
ISBN
2161-377X
2161-3761
978-1-7281-3486-4
Citations 
PageRank 
References 
1
0.41
2
Authors
6
Name
Order
Citations
PageRank
Rwitajit Majumdar131.97
Yuan Yuan Yang210.41
Huiyong Li310.75
Gökhan Akçapinar410.75
Brendan Flanagan532.32
Hiroaki Ogata679696.69