Ilker Yengin

Robot watchfulness hinders learning performance

Educational technological applications, such as computerized learning environments and robot tutors, are often programmed to provide social cues for the purposes of facilitating natural interaction and enhancing productive outcomes. However, there can be potential costs to social interactions that could run counter to such goals. Here, we present an experiment testing the impact of a watchful versus non-watchful robot tutor on childrens language-learning effort and performance. Across two interaction sessions, children learned French and Latin rules from a robot tutor and filled in worksheets applying the rules to translate phrases. Results indicate better performance on the worksheets in the session in which the robot looked away from, as compared to the session it looked toward the child, as the child was filling in the worksheets. This was the case in particular for the more difficult worksheet items. These findings highlight the need for careful implementation of social robot behaviors to avoid counterproductive effects.

Computer assisted active learning system development for critical thinking and flow

In this study, the implemented learning system(LS) explained which create an active learning environment by enhancing students critical thinking with aid of several computer applications. The developed computer tools are intending to support an active learning with dynamic cognitive mappings, movies for classroom discussion, flash cards where grouping structures within keyword learning and several quiz applications.

Investigation of New Statistical Features for BCI Based Sleep Stages Recognition through EEG Bio-signals

Electroencephalogram (EEG) is one of the oldest techniques available to read brain data. It is a methodology to measure and record the electrical activity of brain using sensitive sensors attached to the scalp. Brain’s electrical activity is visualized on computers in form of signals through BCI tools. It is also possible to convert these signals into digital commands to provide human-computer interaction (HCI) through adaptive user interfaces. In this study, a set of statistical features: mean entropy, skew-ness, kurtosis and mean power of wavelets are proposed to enhance human sleep stages recognition through EEG signal. Additionally, an adaptive user interface for vigilance level recognition is introduced. One-way ANOVA test is employed for feature selection. EEG signals are decomposed into frequency sub-bands using discrete wavelet transform and selected statistical features are employed in SVM for recognition of human sleep stages: stage 1, stage 3, stage REM and stage AWAKE. According to experimental results, proposed statistical features have a significant discrimination rate for true classification of sleep stages with linear SVM. The accuracy of linear SVM reaches to 93% in stage 1, 82% in stage 3, 73% in stage REM and 96% in stage AWAKE with proposed statistical features.

Subjective Evaluation of Prototypes and Task Complexities for Mobile Phone Usability

Different types of prototypes may have different effect on mobile phone interface design processes. Understanding these effects may help designers to evaluate design options in depth. Empirically derived research results may provide a realistic ways for evaluations. Confirming and extending our previous research study, this study investigates the effect of different mobile phone interface prototype types (paper, computer and fully operational device) on task complexities. Task complexity of mobile phone was measured by 5 scale self-evaluation instrument. Results showed that computer based prototype with complex task yielded the highest usability rate. This empirical based paper may help designers to evaluate what types of prototypes are usable for complex and non-complex tasks.

Applying mobile trends in education: A mobile learning guideline

There are many technologies available to education enabling fast and easy access to knowledge. Technologies change rapidly so the opportunities and the challenges they bring. Changing nature of some technologies may have a great impact in education. The next big technology change in education is expected to be in mobile learning platform. The next coming years are expected to offer wide range of opportunities for mobile applications. As the mobile technology grows, there will be more practices of mobile learning in formal education. In order to be ready for the change, one should understand the context. In this paper, we provided a future projection to the mobile markets and the mobile applications and their implications in education. First, we analyzed the mobile trends in the market. Then, we provided an analysis of the latest status of related practices in education. Finally, we provided a guideline, which addresses the issue from a technological and pedagogical point of view, for directing future research and applications. Regarding to our guideline we also presented a list of recommendations.