Exploring Pre-service Teachers’ Awareness on Using Social Networking Sites: Are they ready for digital citizenship?
Undergraduate students have been widely-adopted Social Networking Sites (SNSs) such as Facebook. Some issues were discussed among users concerning the users’ netiquette. For users like pre-service teachers who may or may not use SNSs for their communication tools in near future, however, there is a need for teacher education institutes to prepare their digital citizenship. This study examined three hundred and seventy seven pre-service teachers of the awareness of SNSs communication behaviors. Quantitative data indicated that the number of network friends was the most significant factors effects to SNSs communication behaviors and awareness. Qualitative data from the open-ended question revealed the need to revise the ED-TECH course for pre-service teachers to have more deep understanding on digital citizenship.
Teachers' Perceptions of Digital Citizenship Development in Middle School Students Using Social Media and Global Collaborative Projects
Teachers’ Perceptions of Digital Citizenship Development in Middle School Students Using Social Media and Global Collaborative Projects by Shane E. Snyder MS, Penn State University, 2000 BS, Lock Haven University, 1996 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Educational Technology
Automatic classification of TV news articles based on telop character recognition
The purpose of this study is to develop a multimedia database system for TV news video data. TV news video data consist of speech, characters and images. In this study, telop characters are recognized and given to the news articles as indices for classification. At first, telop frames which include telop characters are detected and then the telop characters are extracted and recognized. Through morphological analysis of the recognized telop characters, keywords are extracted which consist of more than two characters. Their keywords are used as indices to classify the TV news articles. We carried out experiments for 30 days of NHK 5 minutes news and obtained 95.4% telop character extraction rate, 81.4% character recognition rate and 83.8% article classification rate. We improved the article classification rate by 43.4% through character recognition improvement from 44.1% to 81.4%.