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Online video-based learning has been increasingly used in educational settings. However, students usually do not have enough cognitive capacity and metacognition skills to diagnose and record their...

Online video-based learning has been increasingly used in educational settings. When viewing video content, students may not always maintain a high degree of attention and may neglect or ignore important content critical for effective learning. This study thus presents an attention-based video lecture review mechanism (AVLRM) that can generate video segments for review based on students' sustained attention status, as determined using brainwave signal detection technology. A quasi-experiment nonequivalent control group design was utilized to divide 55 participants from two classes of an elementary school in New Taipei City, Taiwan, into two groups. One class was randomly assigned to the experimental group, and used video lectures with the AVLRM support for learning. The other class was assigned to the control group, and used video lectures with autonomous review for learning. Analytical results indicate that students in the experimental group exhibited significantly better review effectiveness than did the control group, and this difference was especially marked for students who had a low attention level, were field-dependent, or were female. The findings show that AVLRM based on brainwave signal detection technology can precisely identify video segments that are more useful for effective review than those picked by student themselves.

In the recent years, due to the prevalence of 3D printers, there have been many applications of 3D printing developed for education such as creating custom-made teaching aids or student art work. Although there are web sites hosting 3D models created by students and allowing them to share and search in the database, these systems usually lack functions of collaborative learning. On the other hand, most learning management systems do not provide functions needed for sharing and viewing 3D models. In this work, we aim to design a system called 3D Model Co-learning Space (3D MCLS), dedicated to collaborative learning allowing students to share 3D models as well as performing blind peer assessment. The system allows a user to store, share, display, and discuss 3D models and allows a teacher to manage a group of students in a flexible way. More specifically, the system can automatically create a thumbnail by computing an optimized view of an uploaded 3D model. It uses tags to organize models into groups according to their attributes or teams in a class for easier access through search. In addition, it can provide blind assignments of peer review and compare the results with self-assessment. We have implemented such a system and conducted a pilot study to obtain a preliminary evaluation on the usability of the system.