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In online learning, there is a need to create more effective interaction between e-learning content and learners. In particular, increasing the learners’ motivation by stimulating their interests is very important. However, for any e-learning system to be effective, the knowledge transfer must occur in a usable, accessible, and functional manner.

Eye tracking can be used to improve the functionalities of an e-learning system. It can dynamically capture users’ behaviors in such a way that determines what they are doing, how much attention they are giving to each topic, where they are stuck, at and in what order they are reading content.

What is Eye Tracking?
Eye tracking is nothing more than using a device to track the movement of the eyes to understand where the subject is looking and for how long [1]. Eye movements are recorded by means of fixation, saccade, and fixation durations.

Fixation is the time taken for processing a particular image by fovea. Saccade is the time taken by fovea to focus its attention from one image to another. In short we can say that saccades are the time interval between two fixations [3].

There are a few different types of eye-tracking devices. Head-mounted devices are by far the most suitable for studying reading behavior. On the other hand, devices that are integrated into TFT monitors or standalone eye-tracking units are more typically used for market research and usability.

What Can e-Learning Professionals Gain from Tracking Eye Movement? The data collected from eye-tracking devices indicates the person’s interest level and focus of attention. From eye position tracking and indirect measures, such as fixation numbers and duration, gaze position, and blink rate, it is possible to draw information about the user’s level of attention, stress, relaxation, problem solving, successfulness in learning, tiredness, and more [1]. Even emotions can be tracked, and based on the data, the eye-tracking system can provide more personalized learning.

Applications that use eye tracking can be categorized as either diagnostic or interactive [3]. Diagnostic applications show where the learner’s attention has been caught, thus providing evidence of the learner’s focus of attention over time. In the interactive type, the eye movements are used to replace an input system, such as mouse, allowing the user to interact with a computer using only the eyes [4].

So far, few projects have explicitly considered the use of eye tracking for e-learning. Among these projects, AdELE (adaptive e-learning with eye tracking) [2] is probably the first that proposed the use of real-time eye tracking to capture learner behavior.

Another interesting project is the empathic software agent interface developed to facilitate empathy-relevant reasoning and behavior, in which eye movements are used to determine the learner’s interest and to provide feedback to character agents [1].

The Cons
As with any technology, eye tracking has its weaknesses. One intrinsic limitation is accuracy. It is well-know that the eye constantly performs microsaccade movements, which result in defected data when gathering eye position information [1]. Another limitation arises from the fact that the retina has a small area of high resolution (fovea) to capture a visual object, and to see the object clearly, it has to be centered in the fovea [1]. This makes it difficult to recognize the exact position where the subject is gazing, thus affecting the accuracy of the captured data.

Still, eye-tracking technology can provide many benefits to e-learning, such as facilitating adaptive and personalized learning, as shown in the Adaptive eLearning system [3], which can make some assumptions about the learner’s emotions and react accordingly. When using eye tracking in e-learning, the learner pays more attention to the learning system and also tends to have a higher level of motivation [4].

About the Authors
Hend S. Al-Khalifa is an advisory board member for this publication and is part of the Information Technology Department, College of Computer and Information Sciences (CCIS), at King Saud University in Saudi Arabia. Remya P. George is a research assistant, also at CCIS, King Saud University. Before joining the Information Technology Department, she worked as a software engineer at several software firms in India. Her current research work is focused on eye-tracking for web design and e-learning.

References
1. Calvi, C. Porta, M., and Sacchi, D. 2008. “e5Learning, an E-Learning Environment Based on Eye Tracking.” In Proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies. 376-380.

2. Gütl, C., Pivec, M., Trummer, C., García-Barrios, V.M., Mödritscher, F., Pripfl, J., and Umgeher, M. 2004. “AdELE: A Framework for Adaptive E-Learning through Eye Tracking.” InProceedings of IKNOW.

3. Poole, A. and Ball, L. J. 2005. Eye Tracking in Human-Computer Interaction and Usability Research: Current Status and Future Prospects. In Ghaoui, C. (ed.) Encyclopedia of Human Computer Interaction. Hershey, PA: Idea Group. 211-219. [PDF]

4. Wang, H., Chignell, M., and Ishizuka, M. 2006. “Empathic Tutoring Software Agents using Real-Time Eye Tracking.” In Proceedings of the 2006 symposium on Eye tracking research & applications. 73-78.

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