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• W2034346211 abstract "In current software every click of the users is logged, therefore a wealth of click-through information exists. Besides, recent technologies have made eye-tracking affordable and an alternative to other human-computer interaction means (e.g. mouse, touchscreens). A big challenge is to make sense of all this data and convert it into useful information. This paper introduces a possible solution placed in the context of decision-making processes. We show how the decision maker's activity can be traced using two means: mouse tracing (i.e. clicks) and eye-tracking (i.e. eye fixations). Then, we discuss a mining approach, based on the log, which extracts a Decision Data Model (DDM). We use the DDM to determine, post-hoc, which decision strategy was employed. The paper concludes with a validation based on a controlled experiment.Keywords: Decision-Making Process Model, Data-Flow Model, Decision Process Mining1 IntroductionThis paper aims to show how decision-making process models can be automatically extracted from human-computer interaction logs. Since our research is placed in IT, most of the interaction with a software is performed my mouse and by looking at a computer screen. Therefore, the traces leftby the user interacting with the software are mouse clicks (an additional data like values typed from the keyboard) as well as screen objects (e.g. menus, textboxes) at which the user stared. Aggregating those two data sources as well as making sense of this new data is the main focus of this paper.Modern software stores more and more data about everything connected to it. For example, Google sores information about searches of each user, about pages visited, about links opened, etc. Also, any web-shop stores data on products viewed as well as any click performed during each visit (e.g. Amazon sends personalized e-mails with price updates if a user just looked at the details of certain products, let alone added anything in the shopping cart and then discarded it). ERP systems also log the activities of users, and even go as far as storing information on changes made to the tables storing data. Therefore, we think that there is enough support to claim that click-through data may be, or is, stored by any software.Eye-tracking is a technique used to output the point of a stimulus where a subject looks at. For our research, the stimulus is the interface of software displayed on a computer screen. Eye-tracking hardware follows the physical movements of the eye (most common technique is to film the pupils with video cameras). Eye-tracking software converts the physical movements into (computer screen) coordinates and matches them to object in the interface. Therefore, a log of objects (e.g. buttons, menu items, textboxes, etc.) that capture user's attention is available. Eye tracking becomes cheaper and more and more implementations are available. The most common example is the use of eye-tracking in current smartphones. For example, Samsung Galaxy S4 uses the front camera and dedicated software to determine if the user is looking at the bottom of the page so it will automatically scroll documents or if the user is looking away from the screen so it will pause running videos. Also, eye-tracking systems are integrated into laptops on the market (Lenovo laptop presented at CeBit 2011) or soon to be put on the market (Tobii ultrabook presented at CeBit 2013). Those implementations seek to replace 'classical' interaction methods like mouse, touchpad or touchscreens with eye-gaze interaction (e.g. user clicks a button just by looking at it).Having established there is a wealth of logged data on the behavior of users interacting with software, we wish to argue that it can be mined for building models. This is the drive behind process mining research area. Activity logs are mined in order to extract the control-flow perspective of business processes. We got inspiration to apply the same approach to the decision making research. …" @default.
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• W2034346211 date "2014-03-30" @default.
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• W2034346211 title "Integrating Click-Through and Eye-Tracking Logs for Decision-Making Process Mining" @default.
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• W2034346211 doi "https://doi.org/10.12948/issn14531305/18.1.2014.05" @default.
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