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Forschung 2018
(2018)
Eye tracking is a powerful technique that helps reveal how people process visual information. This paper discusses a novel metric for indicating expertise in visual information processing. Named the Gaze Relational Index (GRI), this metric is defined as the ratio of mean fixation duration to fixation count. Data from two eye-tracking studies of professional vision and visual expertise in using 3D dynamic medical visualizations are presented as cases to illustrate the suitability and additional benefits of the GRI. Calculated values of the GRI were higher for novices than for experts, and higher in non-representative, semi-familiar / unfamiliar task conditions than in domain-representative familiar tasks. These differences in GRI suggest that, compared to novices, experts engaged in more knowledge-driven, top-down processing that was characterized by quick, exploratory visual search. We discuss future research aiming to replicate the GRI in professional domains with complex visual stimuli and to identify the moderating role of cognitive ability on GRI estimates.
Eye tracking is a powerful technique that helps reveal how people process visual information. This paper discusses a novel metric for indicating expertise in visual information processing. Named the Gaze
Relational Index (GRI), this metric is defined as the ratio of mean fixation duration to fixation count.
Data from two eye-tracking studies of professional vision and visual expertise in using 3D dynamic medical visualizations are presented as cases to illustrate the suitability and additional benefits of the
GRI. Calculated values of the GRI were higher for novices than for experts, and higher in non-representative, semi-familiar / unfamiliar task conditions than in domain-representative familiar tasks.
These differences in GRI suggest that, compared to novices, experts engaged in more knowledge-driven, top-down processing that was characterized by quick, exploratory visual search. We discuss future
research aiming to replicate the GRI in professional domains with complex visual stimuli and to identify the moderating role of cognitive ability on GRI estimates.
In the field of software engineering, graph-based models are used for a variety of applications. Usually, the layout of those graphs is determined at the discretion of the user. This article empirically investigates whether different layouts affect the comprehensibility or popularity of a graph and whether one can predict the perception of certain aspects in the graph using basic graphical laws from psychology (i.e., Gestalt principles). Data on three distinct layouts of one causal graph is collected from 29 subjects using eye tracking and a print questionnaire. The evaluation of the collected data suggests that the layout of a graph does matter and that the Gestalt principles are a valuable tool for assessing partial aspects of a layout.
Code reviews are an essential part of quality assurance in modern software projects. But despite their great importance, they are still carried out in a way that relies on human skills and decisions. During the last decade, there have been several publications on code reviews using eye tracking as a method, but only a few studies have focused on the performance differences between experts and novices. To get a deeper understanding of these differences, the following experiment was developed: This study surveys expertise-related differences in experts’, advanced programmers’, and novices’ eye movements during the review of eight short C++ code examples, including correct and erroneous codes. A sample of 35 participants (21 novices, 14 advanced and expert programmers) were recruited. A Tobii Spectrum 600 was used for the data collection. Measures included participants’ eye movements during the code review, demographic background data, and cued retrospective verbal comments on replays of their own eye movement recordings. Preliminary results give proof for experience-related differences between participants. Advanced and expert programmers performed significantly better in case of error detection and the eye tracking data implies a more efficient reviewing strategy.
Although there has been much speculation about the potential of Augmented Reality (AR) in teaching for learning material, there is a significant lack of empirical proof about its effectiveness and implementation in higher education. We describe a software to integrate AR using the Microsoft Hololens into UML (Unified Modeling Language) teaching. Its user interface is laid out to overcome problems of existing software. We discuss the design of the tool and report a first evaluation study. The study is based upon effectiveness as a metric for students performance and components of motivation. The study was designed as control group experiment with two groups. The experimental group had to solve tasks with the help of the AR modeling tool and the control group used a classic PC software. We identified tendencies that participants of the experimental group showed more motivation than the control group. Both groups performed equally well
This paper deals with the identification of learning ob-stacles using the questionnaire method. Therefore, two iterations were proceeded: The first one was part of a survey that was carried out at four lo-cations at universities of applied sciences. We asked students about obstructive facts in general providing items for five learning ob-stacle dimensions that were set up before; emotional/motivational, epistemological/cognitive, didactical, resource-related and meta-cognitive learning obstacle dimensions. After the general part, we asked them to answer the same question, but in relation to the – in their opinion – most difficult learning content. With this question, we aim to get indications regarding to epistemological obstacles. In a second step, we used the “Motivated Strategies for Learning Questionnaire”, which was developed by Pintrich [1] as a basis to develop a questionnaire that extracts learning obstacles. In its original version, the “Motivated Strategies for Learning Question-naire” was intended to measure students’ learning strategies, but, as the obstacle dimensions were partly derived from learning strategy classification, we chose this already validated question-naire [2]. Within this iteration, we could confirm a five-factor structure of the questionnaire that could be mapped to the five be-fore set learning obstacle dimensions.
This study is based on the work of Uwano, Nakamura, Monden and Matsumoto (2006) who tried to identify programmers’ eye movements in source code reviews by using eye tracking technology. The researchers were able to identify certain eye movement patterns but due to the technical limitations of earlier eye tracking systems and a small sample they could not find a valid proof for their existence. Now, twelve years later, the eye tracking technology has made significant improvements and is able to capture programmers’ reading behavior in an unobtrusive and precise way. Now the goal is to verify the described patterns by using eye tracking data from expert and novice programmers. In the experiment they have to detect errors in six different codes and take part in a retrospective interview. At the moment, data collections are ongoing. At the time of the conference, we will present the results of our analyses.
We investigated the potential of augmented reality (AR) to enable visualization of abstract concepts and present the first iteration of a teaching experiment that evaluates the use of AR as support for abstraction skills. Students were confronted with the task to present and explain information to different groups of stakeholders at the example of a coffee machine. Results show that students find it helpful to have a visual app-prototype and especially one that can be disassembled in different levels. The main goal was to sensitize students for the need to think about and to abstract information for certain roles and perspectives.