TY - GEN
T1 - An Instructive Video Locating System for Hybrid Teaching with MOOC
AU - Song, Tian
AU - Li, Mengdie
AU - Zhao, Wentian
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - The rapid growth of Massive Open Online Courses (MOOCs) has revolutionized the education landscape by providing accessible and flexible learning opportunities. However, with the emergence of hybrid learning models, how to integrate MOOC into traditional classroom environments and effectively utilize MOOC video content in face-to-face teaching has become a current challenge. To solve this problem, we propose an instructive video locating system for MOOC hybrid teaching. The system analyzes teacher instructions and leverages technologies such as speech recognition, text segmentation, and natural language processing to easily locate relevant video clips in MOOC courses, enhancing the blended teaching experience. We conducted experiments using educational videos in MOOC online courses. The results show that the system can accurately locate video clips based on text queries. Compared with manual searches, the accuracy rate exceeds 85%, which significantly improves the efficiency of merging and supplementing multimedia content. The video locating system proposed in this article seamlessly integrates MOOC resources into the physical classroom through intelligent information retrieval, which not only enhances teaching flexibility and enriches hybrid teaching, but also lowers the technical threshold for teachers to use video assistance. This innovative application has great potential to promote the development and application of hybrid learning models in the education field.
AB - The rapid growth of Massive Open Online Courses (MOOCs) has revolutionized the education landscape by providing accessible and flexible learning opportunities. However, with the emergence of hybrid learning models, how to integrate MOOC into traditional classroom environments and effectively utilize MOOC video content in face-to-face teaching has become a current challenge. To solve this problem, we propose an instructive video locating system for MOOC hybrid teaching. The system analyzes teacher instructions and leverages technologies such as speech recognition, text segmentation, and natural language processing to easily locate relevant video clips in MOOC courses, enhancing the blended teaching experience. We conducted experiments using educational videos in MOOC online courses. The results show that the system can accurately locate video clips based on text queries. Compared with manual searches, the accuracy rate exceeds 85%, which significantly improves the efficiency of merging and supplementing multimedia content. The video locating system proposed in this article seamlessly integrates MOOC resources into the physical classroom through intelligent information retrieval, which not only enhances teaching flexibility and enriches hybrid teaching, but also lowers the technical threshold for teachers to use video assistance. This innovative application has great potential to promote the development and application of hybrid learning models in the education field.
KW - MOOC
KW - educational technology
KW - educational video retrieval
KW - hybrid teaching
KW - speech recognition
KW - text segmentation
UR - http://www.scopus.com/inward/record.url?scp=85187805217&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-0791-1_29
DO - 10.1007/978-981-97-0791-1_29
M3 - Conference contribution
AN - SCOPUS:85187805217
SN - 9789819707904
T3 - Communications in Computer and Information Science
SP - 336
EP - 346
BT - Computer Science and Education. Teaching and Curriculum - 18th International Conference, ICCSE 2023, Proceedings
A2 - Hong, Wenxing
A2 - Kanaparan, Geetha
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Computer Science and Education, ICCSE 2023
Y2 - 1 December 2023 through 7 December 2023
ER -