Learning multitasking in applied contexts

Abstract

This thesis examined the learning of complex multitasking activities. Multitasking (MT) involves performing more than one task at a time and is commonplace in daily life. Previous research has shown the benefits of dual-task training in comparison to single-task (ST) training, a phenomenon which has been termed Dual-Task Practice Advantage (DTPA). DTPA has been consistently demonstrated in MT studies comprising simple experimental designs; however, there is a lack of research into the ways in which people learn complex multitasking activities in real-life contexts, such as driving. Therefore, the aim of this thesis was to examine how people learn to multitask in everyday life and to identify potentially effective MT learning regimes. In the first study, an online survey was conducted to explore 72 participants’ experiences of multitasking, their implicit and explicit multitasking learning strategies, and their opinions and attitudes towards MT. Participants’ data showed that, for everyday activities, particularly learning to drive, the most used and most preferred method was a mixture of MT and ST training conditions, the so-called mixed (Mix) learning regime. In the second study, participants completed an online MT learning experiment in which they could choose their preferred learning regime (ST, MT, or Mix). The online MT study involved an auditory task with four differently pitched tones and a visual task with four different numbers. Participants were divided in two groups, the Fixed group trained in a pure MT regime, and the Choice group who had a free choice of training conditions. The results from both groups demonstrated significant learning effects, which did not differ significantly from each other. A further analysis of the Choice group showed a significant overall trend in choices (better performance leads to choosing more challenging tasks). The DTPA phenomenon was observed in the analysis of the MT learning effects, specifically in the positive correlation between the number of MT blocks chosen by participants and the MT learning effects obtained after the training phase. The third study was conducted in a Driving Simulator Laboratory to compare three regimes (ST, MT, or Mix) and to identify the most effective MT learning regime. The study included four tasks – a driving task, a math task, a memory task and a monitoring task – performed separately as ST or in parallel as MT. Results showed that the most effective way to train a complex MT activity was the MT regime. The ST regime was the least effective. Moreover, the results supported the proposition that the DTPA phenomenon is applicable to learning challenging applied MT activities. Overall, the results showed that while the most used and preferred MT learning approach was the Mix regime, training in MT is the most effective approach to learning MT activities.