TY - GEN
T1 - Predicting pre-knowledge on vocabulary from e-learning assignments for language learners
AU - Zou, Di
AU - Xie, Haoran
AU - Wong, Tak Lam
AU - Rao, Yanghui
AU - Wang, Fu Lee
AU - Wu, Qingyuan
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - In the current big data era, we have witnessed the prosperity of emerging massive open online courses, user-generated data and ubiquitous techniques. These evolving technologies and applications have significantly changed the ways for people to learn new knowledge and access information. To find users’ desired data in an effective and efficient way, it is critical to understand/model users in applications involving in such a large volume of learning resources. For instance, word learning systems can be promoted significantly in terms of learning effectiveness if the preknowledge on vocabulary of learners can be predicted accurately. In this research, we focus on the issue of how to model a specific group of users, i.e., language learners, in the context of e-learning systems. Specifically, we try to predict the pre-knowledge on vocabulary of learners from their previous learning documents such as writing assignments and reading essays. The experimental study on real participants shows that the proposed predicting model is very effective and can be exploited for various applications in the future.
AB - In the current big data era, we have witnessed the prosperity of emerging massive open online courses, user-generated data and ubiquitous techniques. These evolving technologies and applications have significantly changed the ways for people to learn new knowledge and access information. To find users’ desired data in an effective and efficient way, it is critical to understand/model users in applications involving in such a large volume of learning resources. For instance, word learning systems can be promoted significantly in terms of learning effectiveness if the preknowledge on vocabulary of learners can be predicted accurately. In this research, we focus on the issue of how to model a specific group of users, i.e., language learners, in the context of e-learning systems. Specifically, we try to predict the pre-knowledge on vocabulary of learners from their previous learning documents such as writing assignments and reading essays. The experimental study on real participants shows that the proposed predicting model is very effective and can be exploited for various applications in the future.
KW - Learner profile
KW - Vocabulary pre-knowledge
KW - Word learning
UR - http://www.scopus.com/inward/record.url?scp=84981185383&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-32865-2_12
DO - 10.1007/978-3-319-32865-2_12
M3 - Conference contribution
AN - SCOPUS:84981185383
SN - 9783319328645
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 111
EP - 117
BT - Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers
A2 - Zou, Di
A2 - Gong, Zhiguo
A2 - Chiu, Dickson K.W.
T2 - International Conference on Web Based Learning, ICWL 2015
Y2 - 5 November 2015 through 8 November 2015
ER -