TY - GEN
T1 - An educational video scoring scheme for level and topic-based recommendation
AU - Ji, Seonmi
AU - Kim, Yongsung
AU - Jung, Seungwon
AU - Hwang, Eenjun
N1 - Funding Information:
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. R0190-16-2012, High Performance Big Data Analytics Platform Performance Acceleration Technologies Development) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1A09919590).
PY - 2018/1/5
Y1 - 2018/1/5
N2 - Recently, a huge amount of educational video contents have become available due to diverse IT technologies and rapidly growing demand for online learning. Popular resources for various high quality educational contents include OCW (OpenCourseWare), MOOC (Massive Open Online Course) and TED (Technology, Entertainment, Design). Moreover, learners can easily access such contents via the Internet by using common video sharing platforms such as YouTube and Vimeo. However, one critical problem that many users experience in this environment is that it is difficult and time consuming to find out educational contents that is appropriate to them in terms of topic and difficulty level. To solve this problem, in this paper, we propose an educational contents scoring scheme for topic and difficulty level based contents recommendation. The scoring is based on the linguistic difficulty of the video and the topic similarity. To measure the linguistic difficulty, we apply various readability metrics to the video script. To measure the topic similarity between videos, we extract keywords from each video script and then calculate their cosine similarity. We show the effectiveness of our proposed scheme by various experiments.
AB - Recently, a huge amount of educational video contents have become available due to diverse IT technologies and rapidly growing demand for online learning. Popular resources for various high quality educational contents include OCW (OpenCourseWare), MOOC (Massive Open Online Course) and TED (Technology, Entertainment, Design). Moreover, learners can easily access such contents via the Internet by using common video sharing platforms such as YouTube and Vimeo. However, one critical problem that many users experience in this environment is that it is difficult and time consuming to find out educational contents that is appropriate to them in terms of topic and difficulty level. To solve this problem, in this paper, we propose an educational contents scoring scheme for topic and difficulty level based contents recommendation. The scoring is based on the linguistic difficulty of the video and the topic similarity. To measure the linguistic difficulty, we apply various readability metrics to the video script. To measure the topic similarity between videos, we extract keywords from each video script and then calculate their cosine similarity. We show the effectiveness of our proposed scheme by various experiments.
KW - Educational video
KW - Level and topic-based recommendation
KW - Ranking score
UR - http://www.scopus.com/inward/record.url?scp=85048396591&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048396591&partnerID=8YFLogxK
U2 - 10.1145/3164541.3164636
DO - 10.1145/3164541.3164636
M3 - Conference contribution
AN - SCOPUS:85048396591
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication, IMCOM 2018
PB - Association for Computing Machinery
T2 - 12th International Conference on Ubiquitous Information Management and Communication, IMCOM 2018
Y2 - 5 January 2018 through 7 January 2018
ER -