Media Research Center

About: Media Research Center is a based out in . It is known for research contribution in the topics: Collaborative learning & Educational technology. The organization has 491 authors who have published 950 publications receiving 28581 citations. The organization is also known as: MRC.

Neither fish nor fowl: A perceived mismatch in norms and values between oneself, other students, and people back home undermines adaptation to university

Abstract: Students from low social-class background often struggle to adapt to university. Previous research shows that perceived incompatibility between social-class background identity and student identity is one reason, but little is known about the underlying causes of identity incompatibility. In three studies, we expected and found that students with low subjective social-class background perceived their values differently from other students, but also differently from people back home, and both increased identity incompatibility. Identity incompatibility negatively affected the student identity. Additionally, the current research also identifies specific patterns of norm and value differences that are prone to perceived identity incompatibility. The findings demonstrate that perceived differences in values from both groups are important mechanisms for identity incompatibility induced by the transition to university that may affect student identities and potentially their university trajectories.

Towards the keyboard of oz: learning individual soft-keyboard models from raw optical sensor data

Abstract: Typing on a touchscreen display usually lacks haptic feedback which is crucial for maintaining finger to key assignment, especially for touch typists who are not looking at their keyboard. This leads to typing being substantially more error prone on these devices. We present a soft keyboard model which we developed from typing data collected from users with diverging typing behavior. For data acquisition, we used a simulated perfect classifier we refer to as The Keyboard of Oz. In order to draw near to this classifier we used the complete sensor data of each keystroke and applied supervised machine learning techniques to learn and evaluate an individual keyboard model. The model not only accounts for individual keystroke distributions but also incorporates a classifier based on the images obtained from an optical touch sensor. The resulting highly individual classifier has remarkable classification accuracy. Additionally, we present an approach to compensate for hand drift during typing utilizing a Kalman filter. We show that this filter performs significantly better with the keyboard model which takes raw sensor data into account.

Fostering collaborative knowledge construction in a video-based learning setting: effects of a shared workspace and a content-specific graphical representation

Abstract: This study examined means of fostering videoconference-based collaborative learning. An experiment was conducted with 15 learning dyads divided into three conditions of videoconference-based learning: without shared workspace, with shared workspace and with shared workspace plus a content-specific graphical representation. Compared with those with a shared workspace, learning dyads without a shared workspace in the videoconference-based setting tended to make more effort at verbal coordination. This coordination effort did not affect the quality of collaboratively written texts or individual knowledge acquisition. Without shared workspace, participants’ coordinative utterances involved more frequent referrals to the learning content, which might have facilitated the cognitive processing of the learning content and might have compensated for the potential disadvantage of verbal coordination load. The content-specific graphical representation led to more coherent content-related dialogues and had positive effects on the quality of the collaboratively written texts and on individual knowledge acquisition. The study shows that content-specific graphical representation can be a meaningful support measure in videoconference-based learning settings, whereas the effects of a shared workspace should be further investigated.

Integral imaging-based large-scale full-color 3-D display of holographic data by using a commercial LCD panel.

Abstract: We propose a new type of integral imaging-based large-scale full-color three-dimensional (3-D) display of holographic data based on direct ray-optical conversion of holographic data into elemental images (EIs). In the proposed system, a 3-D scene is modeled as a collection of depth-sliced object images (DOIs), and three-color hologram patterns for that scene are generated by interfering each color DOI with a reference beam, and summing them all based on Fresnel convolution integrals. From these hologram patterns, full-color DOIs are reconstructed, and converted into EIs using a ray mapping-based direct pickup process. These EIs are then optically reconstructed to be a full-color 3-D scene with perspectives on the depth-priority integral imaging (DPII)-based 3-D display system employing a large-scale LCD panel. Experiments with a test video confirm the feasibility of the proposed system in the practical application fields of large-scale holographic 3-D displays.