Details

Autor(en) / Beteiligte

• Liu, Shijing
• Nam, Chang S.

Titel

Quantitative modeling of user performance in multitasking environments

Ist Teil von

• Computers in human behavior, 2018-07, Vol.84, p.130-140

Ort / Verlag

Elmsford: Elsevier Ltd

Erscheinungsjahr

2018

Quelle

Elsevier ScienceDirect Journals

Beschreibungen/Notizen

• Multitasking is one of the most important skills required for human operators to perform highly-complex and safety-critical jobs. This study proposed and validated a quantitative model for the study of user performance improvement in a multitasking environment. The Multi-Attribute Task Battery-II (MATB-II) was used in the experiments as a multitasking platform. The proposed model included quantification of stimuli from each MATB-II subtask as baud rate (bits per second), selection of task difficulty and task weight, as well as the rearrangement of task weights. This research followed a two-phase experimental approach. The first phase applied the proposed model and identified a performance baseline for each individual in a multitasking environment, MATB-II. The second phase validated the proposed model using a rearranged set of multitasks for each individual. Individual differences in working memory capacity (WMC) have been estimated as predictors of varying cognitive abilities. This study also investigated the relationship between WMC, task difficulty, and multitasking performance. Significant improvement of user performance was found after the rearrangement of tasks based on the proposed approach. This research provides a framework to quantitatively evaluate multitasking systems and improve human performance in order to understand the interaction between systems and human operators. •An approach to quantify information and improve user performance in multitasking.•User performance improved after the rearrangement of task weights for multitasks.•Task difficulty is an effective method to manipulate mental demand in multitasking.•Working memory capacity is an important predictor of user performance and workload.