From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning
Name
11280_2019_730_ReferencePDF.pdf
Size
890.04 KB
Format
Adobe PDF
Checksum (MD5)
d59cf39c8577f7e1aef14a18e27d0443
Author(s)
Lin, Jiayin
Sun, Geng
Cui, Tingru
Shen, Jun
Xu, Dongming
Beydoun, Ghassan
Yu, Ping
Pritchard, David
Li, Li
Chen, Shiping
Date Issued
October 23, 2019
Publisher
Springer US
Version
Author's final manuscript
Abstract
Abstract
The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s11280-019-00730-9
