TY - GEN
T1 - Personalized book recommendation to young readers
T2 - 2020 ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2020
AU - Hu, Xiao
AU - Ng, Jeremy T.D.
AU - Yang, Chengrui
AU - Chu, Samuel K.W.
N1 - Publisher Copyright:
© 2020. ACM ISBN.
PY - 2020/8/1
Y1 - 2020/8/1
N2 - Online learning platforms that aim to improve reading interests and proficiency of young readers, particularly students in elementary schools, rarely have automated personalized recommendation services. This study attempts to bridge this gap by developing and evaluating two book recommenders that are integrated into an online learning platform for young readers. A preliminary user experiment was conducted to measure the effectiveness and usability of the recommender prototypes. Results of think-aloud usability testing, post-test questionnaires, and a semi-structured interview verified the feasibility of adding these book recommenders to improve personalization of the online learning platform. Further improvements of the recommenders were also suggested. Th e user evaluation framework provides a reference for future studies on personalized learning material recommendation.
AB - Online learning platforms that aim to improve reading interests and proficiency of young readers, particularly students in elementary schools, rarely have automated personalized recommendation services. This study attempts to bridge this gap by developing and evaluating two book recommenders that are integrated into an online learning platform for young readers. A preliminary user experiment was conducted to measure the effectiveness and usability of the recommender prototypes. Results of think-aloud usability testing, post-test questionnaires, and a semi-structured interview verified the feasibility of adding these book recommenders to improve personalization of the online learning platform. Further improvements of the recommenders were also suggested. Th e user evaluation framework provides a reference for future studies on personalized learning material recommendation.
KW - Association rule mining
KW - Bipartite graph analysis
KW - Book recommendation
KW - Evaluation criteria
KW - User evaluation
UR - http://www.scopus.com/inward/record.url?scp=85095125762&partnerID=8YFLogxK
U2 - 10.1145/3383583.3398604
DO - 10.1145/3383583.3398604
M3 - Conference contribution
AN - SCOPUS:85095125762
T3 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
SP - 413
EP - 416
BT - JCDL 2020 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020
Y2 - 1 August 2020 through 5 August 2020
ER -