Conceptualisation
We conceptualize Self-direction Skill (SDS) as a meta skill and consider the phases of the process similar to Self-directed learning (SDL) and Self-regulated learning (SRL). Literature highlights their commonality and differences (Loyens, S. M., Magda, J., & Rikers, R. M. 2008; Saks, K., & Leijen, Ä, 2014). Both SDL and SRL have 4 key phases: Task definition – Setting goals and Planning – Enacting strategies – Monitoring and Reflecting. Previous research has explored various phases in the process of acquiring the necessary learning skills and designing pedagogical supports.
Self-direction Skills (SDS)
For the 21st century learner, self-direction skill is crucial for developing both intellectual abilities 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 the meta-skill of Self-Direction itself. Our work bridges Learning Analytics (LA) and Quantified-Self (QS) research to enable technology support for self-directed activities of learners. We propose DAPER (Data-Analyze-Plan-Execution Monitoring-Reflect), a data-driven Self-Direction skill execution and acquisition model. To support synchronize-visualize-analyze multisource data regarding learners’ learning and physical activities, we developed the GOAL (Goal Oriented Active Learner) system.
Figure. DAPER model (DATA - Analysis - Planning - Execution monitoring - Reflection) of self-direction skill execution and acquisition
To support Self-Direction Skills (SDS), we came up with a DAPER model. It conceptualizes the process of SDS execution and acquisition. Synthesizing phases from existing self-directed learning theories it explicitly focuses on the data-driven aspect in each of those phases. Based on the DAPER model, we designed and developed Goal Oriented Active Learner (GOAL) system to synchronize-visualize-analyze multisource data regarding student’s learning and physical activities (see architecture in Figure 2)
A. Data Collection
In the data collection phase, the individual collects initial behavioral data regarding the activities which they want to execute in a self-directed mode. Some of those data can be automatically logged with the help of physical sensors like the gyroscope in a wearable or mobile device which is used to compute steps taken from the user’s movements data.
B. Analysis
After data collection, in the analysis phase the individual can conduct simple analysis tasks to understand their activity trends and identify problems if any. Broadly these tasks deal with comparing the collected data with standard levels or with the group and understand trends. For example, given their recorded sleep data, learners can analyze whether their sleeping time is maintained around the same hour or varies. Given their learning data, such as past quiz scores in a subject they can check their performance trends.
C. Planning
In the planning phase the individual sets SMART (Specific, Measurable, Appropriate, Relevant and Timely) goals regarding any activities whose data was analysed. Being aware of their own trends from the analysis phase aids them to plan accordingly.
D. Execution monitoring
In this phase the plan is executed and the individual collects data to monitor progress. For example, an individual may monitor their heart rate during a specific physical exercise. In the learning scenario, a student might monitor the completion of their course content before an upcoming assessment. This phase often includes multiple cycles of re-planning and execution monitoring.
E. Reflection
In the reflection phase the individual reviews the whole process of the SDS execution. It involves evaluating the ease of each tasks and their efforts given for their chosen activities. The individual further reflects on the effectiveness of their set plan, identify if any specific strategies were used to execute the plan and their note benefits and costs based on the outcome of that activity.