| Title |
Personalized Subject Learning Based on Topic Detection and Canonical Correlation Analysis |
| ID_Doc |
44587 |
| Authors |
Shi, ZZ; Shi, SK; Shi, LL |
| Title |
Personalized Subject Learning Based on Topic Detection and Canonical Correlation Analysis |
| Year |
2015 |
| Published |
International Journal Of Advanced Computer Science And Applications, 6, 10 |
| DOI |
|
| Abstract |
To keep pace with the time, learning from printed medium alone is no longer a comprehensive approach. Fresh digital contents can definitely be the complement of printed education medium. Although timely access to fresh contents is becoming increasingly important for education and gaining such access is no longer a problem, the capacity for human teachers to assimilate such huge amounts of contents is limited. Topic Detection (TD) is then a promising research area that addresses speedy access of desired contents based on topic or subject. On the other hand, personalized education is getting more attention because it facilitates the improvement of creativity and subject learning of the students. This paper reveals a patented Personalized Subject Learning (PSL) system that caters for the need of personalized education and efficiently provides subject based contents. An efficient topic detection algorithm for providing subject content is presented. Moreover, since education contents are multimedia based ones with multimodal, PSL introduces Canonical Correlation Analysis (CCA) method to detect multimodal correlations across different types of media. Due to its novelty, PSL has been used as the key engine in a real world application of personalized education system as the smart education module sponsored by a Smart City project. |
| Author Keywords |
Topic Detection; Canonical Correlation Analysis; Personalized Education; Subject Learning; Multimodality |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:000369851700018 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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