Eye movement can be regarded as a pivotal real-time input medium for human-computer communication, which is especially important for people with physical disability. In order to improve the reliability, mobility, and usability of eye tracking technique in user-computer dialogue, a novel eye control system with integrating both mouse and keyboard functions is proposed in this paper. The proposed system focuses on providing a simple and convenient interactive mode by only using user’s eye. The usage flow of the proposed system is designed to perfectly follow human natural habits. Additionally, a magnifier module is proposed to allow the accurate operation. In the experiment, two interactive tasks with different difficulty (searching article and browsing multimedia web) were done to compare the proposed eye control tool with an existing system. The Technology Acceptance Model (TAM) measures are used to evaluate the perceived effectiveness of our system. It is demonstrated that the proposed system is very effective with regard to usability and interface design.

This study aims to assess which ad element-related eye movement behaviors could predict the traditional advertising effectiveness of high-order for video advertising. The data were obtained from 61 participants, each stimulated by six video ads, via an eye tracking method and questionnaires. A logistic regression was conducted to predict high and low ad effectiveness with regard to element-related eye indicators. Three core constructs of high-order advertising effectiveness commonly used in research address the memory (ad recall), affect (attitude toward ad and attitude toward brand) and desirability (purchase intention) of consumers. Three key advertising elements (product, brand and endorser) were tracked, presented by three eye movement indicators (transformed fixation time (TFT), transformed fixation number (TFN), and average gaze duration (AGD)). The findings indicated that three items are related to attitude toward ad (productrelated AGD, brand-related AGD and endorser-related TFT), attitude toward brand (brand-related TFN and AGD, endorser-related TFT), and purchase intention (productrelated AGD, brand-related TFN and endorser-related TFN). However, only two items of them are related to recall (product-related AGD and brand-related TFN). Furthermore, for all ad outcomes, consistently, eye movements on product elements and endorser elements tend to positively related to ad effectiveness, while eye movements on brand elements tent to negatively.

An eye tracking technology provides a faster and more intuitive interface to explore the learner psychology. In this paper, with the aim of assisting researcher to expand the knowledge of video lectures, we present an efficient system named VLEYE based on eye tracking. The system allows gathering the dynamic areas of interest (AOIs) and combining them with eye movement data. There are mainly three modules in the proposed system, including “eye movement recorder,”“dynamic AOI module,”and “video analyzer.”The “eye movement recorder”records the eye movement data by eye tracking device, where mouse cursor control function is applied to support the use of low-cost devices. The AOIs could be drawn manually or detected and tracked by the provided algorithm automatically in the “dynamic AOI module.”The “video analyzer”provides various visualization modes to view and compare the eye movement information for single or multiple subject, as well as statistical charts and detail fixation information. In addition to performance testing, a case study was conducted to verify the applicability of the proposed system. We demonstrate that this system works well and is an essential step forward in efficiently helping studies to better understand learner cognitive processes for dynamic stimuli within video.

Eye movement technology is highly valued for evaluating and improving digital learning content. In this paper, an educational innovation study of eye movement behaviors on digital learning content is presented. We proposed three new eye movement metrics to explain eye movement behaviors. In the proposed method, the digital content, which were slide-deck-like works, were classified into page categories according to the characteristics of each page. We interpreted the subjects’ eye movement behaviors on the digital slide decks. After data regularization and filtering, the results were analyzed to give directions for how to design an attractive digital learning content from the viewpoint of eye movement behaviors. The relationships between the subjects’ evaluation scores, page categories, and eye movement metrics are discussed. The results demonstrated that the proposed fixation time percentage (FTP) was a representative, strong, and stable eye movement metric to measure the subjects’ interest. Moreover, a reasonable portion of semantic content had a positive influence on the subjects’ interest.