learning-science-guide

Learning Science Guide

A comprehensive skill for applying evidence-based learning science principles to educational research, instructional design, and teaching practice. Grounded in cognitive psychology and educational neuroscience.

Foundational Learning Theories

Cognitive Load Theory (Sweller, 1988)

Working memory has limited capacity. Effective instruction manages three types of cognitive load:

# Estimate cognitive load using element interactivity
def estimate_intrinsic_load(elements: list, interactions: list) -> str:
    """
    elements: list of knowledge components
    interactions: list of (element_i, element_j) tuples that must be
                  processed simultaneously
    """
    interactivity = len(interactions) / max(len(elements), 1)
    if interactivity < 0.3:
        return "low intrinsic load - suitable for independent study"
    elif interactivity < 0.7:
        return "moderate intrinsic load - scaffold with worked examples"
    else:
        return "high intrinsic load - use fading strategy and segmenting"

# Example: teaching statistical regression
elements = ['variable', 'coefficient', 'intercept', 'residual', 'R-squared']
interactions = [('coefficient', 'variable'), ('intercept', 'residual'),
                ('coefficient', 'R-squared'), ('residual', 'R-squared')]
print(estimate_intrinsic_load(elements, interactions))

Constructivism and Active Learning

Constructivist approaches emphasize that learners build knowledge through experience. Key active learning strategies with measured effect sizes (Freeman et al., 2014, PNAS):

• Think-Pair-Share: d = 0.41
• Problem-Based Learning (PBL): d = 0.68
• Peer Instruction (Mazur): d = 0.74
• Inquiry-Based Labs: d = 0.52