Task Demand evaluation with Machine Learning in eye tracking

Abstract

Information systems play an important role in today’s rapidly evolving digital economy. As task demands are amajor factor influencing how IT systems are used for decision making, it is necessary to identify the tasks that areimportant to them. Requirements automatically provide an opportunity to design advanced decision support systemsthat can respond to the needs of users at a customised level. A first step in designing such advanced decision supporttools is, for example Exploring the possibility of developing automatic task detectors. Based on the decision makingprocess, the eye eye tracking and machine learning literature, we argue that task difficulty can be reliably detectedautomatically, and unobtrusively, using only eye movements. To explore this possibility, we developed an eye trackingsystem. Task difficulty detection system and tested its effectiveness. Our results showed that our task task task detectionsystem reliably predicted increased task difficulty based on user eye movement data. These results and for research andpractice.Recent advances in specialized hardware and software provide the ability to capture and analyze physiologicalmeasurements that can reliably detect and potentially predict changes in user behaviour. This opportunity, in turn,makes it possible to imagine intelligent decision support systems that can more more effectively recognize and respondto user needs at a personalized level [6,30]. The first step towards developing such adaptive decision support systems isto explore intelligent tools that can be reliable and unobtrusive detect user needs in a reliable and unobtrusive way. Onesuch tool is a system that can automatically detect task demand. Task demand plays a significant role in how peoplechoose to process information and therefore has a significant impact on how individuals use computers to make decisions[62, 74-76]. Because computers can extend an individual’s capabilities to process information, it is often assumed thatthey can help people improve their decision-making. However, studies report that this is not always the case. Studiesshow that people often use computers in a way to reduce their effort rather than maximize their accuracy [74-76]. Suchtechnology use behaviour is not due to innate laziness or indifference; while precise and rational decisions represent theintention, by limited cognitive capacity.