Facilitative interactions of model- and experience-based processes: implications for type and flexibility of...

Area of Science:

• Cognitive Psychology
• Learning Sciences
• Human-Computer Interaction

Background:

• Traditional learning combines explicit instruction (model-based knowledge) and practice (experience-based knowledge).
• The interaction between these distinct knowledge types and their learning processes has been under-explored.
• Understanding this interaction is crucial for optimizing skill acquisition.

Purpose of the Study:

• To investigate the interplay between model-based and experience-based knowledge in a dynamic control task.
• To determine the benefits and drawbacks of providing explicit instruction prior to practice.
• To examine how different learning strategies affect knowledge representation and transfer.

Main Methods:

• Two experiments were conducted using a dynamic control task.
• Participants received varying combinations and orders of model-based instruction and task practice.
• Performance metrics included learning speed, retrieval time, and transfer to new task variations.

Main Results:

• Providing model-based knowledge before practice demonstrated utility in improving task learning.
• Learning via model-based instruction prior to practice incurred costs, such as slowed information retrieval.
• Knowledge acquired through this combined approach showed less effective transfer to new situations compared to experience-based knowledge alone.

Conclusions:

• The sequence of instruction and practice significantly impacts learning dynamics and outcomes.
• Explicit instruction can enhance initial learning but may introduce retrieval inefficiencies and limit transferability.
• Findings challenge the 'lookup table' model of learning, suggesting more flexible knowledge representations.