Analyzing Self-Explanations in Mathematics: Gestures and Written Notes Do Matter

Area of Science:

• Cognitive Psychology
• Educational Technology
• Learning Sciences

Background:

• Self-explanations are crucial for making sense of new information.
• Previous research on self-explanations often overlooks spontaneous written and non-verbal data.
• Bodily actions and written notes are recognized as important learning components.

Purpose of the Study:

• To investigate how incorporating written and non-verbal data influences the interpretation of self-explanations.
• To determine if a more holistic analysis of self-explanations yields greater accuracy.

Main Methods:

• Video recordings of 33 undergraduate students learning complex numbers were analyzed.
• Data categorization progressed from verbal only, to verbal and written, and finally to verbal, written, and non-verbal data.
• Students' self-explanations were assessed for adequacy based on different data sets.

Main Results:

• Analysis incorporating written notes and non-verbal gestures/actions proved more accurate than verbal data alone.
• The inclusion of multimodal data significantly enhanced the categorization of self-explanations.
• The impact was particularly pronounced when classifying self-explanations as adequate or inadequate.

Conclusions:

• Multimodal analysis, including written and non-verbal cues, provides a richer understanding of student self-explanations.
• Educational research should consider integrating diverse data streams for more robust learning assessments.
• This approach offers a more nuanced evaluation of learning processes and outcomes.