Learning everyday multitasking activities-An online survey about people's experiences and opinions

Area of Science:

• Cognitive Psychology
• Learning Sciences
• Human-Computer Interaction

Background:

• Multitasking (MT) is common, yet optimal learning strategies for complex, real-world tasks remain unclear.
• Existing research often contrasts parallel MT, single-task (ST), and mixed (Mix) learning, favoring MT for simple tasks (dual-task advantage).
• The preferred and actual learning regimes for everyday complex MT activities are not well-understood.

Purpose of the Study:

• To investigate real-life learning regimes and preferences for multitasking activities.
• To determine if laboratory findings on MT learning efficiency generalize to complex, everyday tasks.
• To explore the relationship between individual polychronicity and MT learning preferences.

Main Methods:

• An online survey administered to 72 participants.
• Participants described real-life MT learning experiences (e.g., learning to drive).
• The Multitasking Preference Inventory (MPI) assessed individual polychronicity.

Main Results:

• Mixed (Mix) learning regimes were the most used and preferred for everyday complex activities like driving.
• Parallel MT learning regimes were the least preferred.
• A positive correlation was found between polychronicity and preference for MT learning regimes.

Conclusions:

• Everyday complex multitasking activities are predominantly learned using a combination of single-task and multitasking training (Mix regimes).
• Learners prefer to master individual components before engaging in full multitasking.
• Individual differences in polychronicity predict preferences for specific MT learning strategies.