Capturing the Wholeness of Learning Process in Complex Environments: A Fractal Analysis

• In Event: Integrating Life-Informed Complexity for Practical Professional and Higher Education

Abstract

1. Objective and Purpose
This session proposes an intervention index that effectively captures learners’ capacity to adapt and self-direct in complex environments. The authors challenge the overreliance on Ebbinghaus’ learning curve in understanding human learning efforts in complex environments as it oversimplifies the adaptive nature of learning with a fixated view of the learner’s inherent adaptive capacity. To address this, this study introduces an evidence-based intervention index considering individuals’ varying prior learning experiences and adaptive learning capacity. We developed an intervention index that adequately captures the wholeness of the learning experience in a complex environment simulated in a rule-based game, which we call in this study CraftDawg.
2. Perspective(s) or theoretical framework
The development of an intervention index is undergirded by complexity science and learning theory. First, informal and incidental learning theory (Author) is utilized to explain the irregularity and messiness of learning, leading to tacit knowledge (Polanyi, 1996/2009) and adaptability. Second, the wholeness of learning experience encompasses both chronological order and opportune moments of learning based on complexity theorists’ perspectives (Pendleton-Jullian & Brown, 2018a, b; Juarrero, 1990; Siemens, 2005) Third, insights from complexity scientists engaging game theory (Holland, 1998; Miller & Page, 2007; Mitchell, 2009) inspire the use of a rule-based game to observe learning processes in complex environments.
3. Modes of Inquiry & Data Sources
An experimental study was conducted, observing the online game play of 95 self-selected participants in a Minecraft-like environment. Participants’ success is measured with their health score at the end of each session. Participants, recruited in a liberal arts college, were mostly non-traditional learners. Their survival rate over five game sessions was recorded and calculated using fractal dimensions, a method commonly used to assess complexity at scale (Guo, 2017; McCauley, 1994; Skjeltorp, 2000).
4. Results
The visualization of the learning process using fractals reveals that learners’ adaptive capacity, regardless of their prior learning experience, is inadequate to support “learning your way out” without intervention, particularly on the edge of chaos. This highlights the significance of timely interventions during such critical moments.
5. Scientific significance
Incorporating complexity into the learning environment is vital yet it requires thoughtful intention and intervention. This study presents an evidence-based tool to measure and assess learning in complex environments. The intervention index assists professional educators in identifying the need for and nature of interventions, helping manage complexity in the learning environment and determining when to support learners’ self-directed learning.

Authors

• Jill Jinks, Stetson University

• Karen E. Watkins, University of Georgia