Knowledge Enriched Learning by Converging Knowledge Object & Learning Object

被引:0
|
作者
Sabitha, Sai [1 ]
Mehrotra, Deepti [1 ]
Bansal, Abhay [1 ]
机构
[1] Amity Univ, Noida, Uttar Pradesh, India
来源
ELECTRONIC JOURNAL OF E-LEARNING | 2015年 / 13卷 / 01期
关键词
LMS; KMS; Learning Object; Knowledge Object; Classification; Decision Tree; Knowledge Driven Learning Objects; Knowledge Driven Learning Management System; e-Learning;
D O I
暂无
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
The most important dimension of learning is the content, and a Learning Management System (LMS) suffices this to a certain extent. The present day LMS are designed to primarily address issues like ease of use, search, content and performance. Many surveys had been conducted to identify the essential features required for the improvement of LMS, which includes flexibility and a user centric approach. These features can suffice the need of all learners, when they have different learning requirements. For a true learning, knowledge should also be delivered along with the domain information. There is a need to design an architecture for user centric Knowledge Driven Learning Management System (KDLM). Thus, for holistic learning, knowledge enriched teaching skills are required, which can enhance and increase the thinking skills of the learner to a higher level. The current LMS needs an improvement in the direction of knowledge discovery, exploration so that knowledge enriched learning can be provided to the learner. It can be based on knowledge engineering principles like ontology, semantic relationship between objects, cognitive approach and data mining techniques. In this paper, we are proposing an idea of an enhanced Learning Object (LO) called Knowledge Driven Learning Object (KDLO), which can be delivered to the user for better learning. We had used a data mining approach, classification to harness, exploit and classify these objects according to their metadata, thereby strengthening the content of objects delivered through the LMS.
引用
收藏
页码:3 / 13
页数:11
相关论文
共 50 条
  • [31] Active Object Detection Based on PPO Learning Algorithm with Decision Knowledge Guidance
    Yao, Fujing
    Tian, Guohui
    Wang, Yuhao
    Yang, Ning
    MACHINE INTELLIGENCE RESEARCH, 2025, : 386 - 396
  • [32] Incremental Deep Learning Method for Object Detection Model Based on Knowledge Distillation
    Fang W.
    Chen A.
    Meng N.
    Cheng H.
    Wang Q.
    Gongcheng Kexue Yu Jishu/Advanced Engineering Sciences, 2022, 54 (06): : 59 - 66
  • [33] Relationship Matters: Relation Guided Knowledge Transfer for Incremental Learning of Object Detectors
    Ramakrishnan, Kandan
    Panda, Rameswar
    Fan, Quanfu
    Henning, John
    Oliva, Aude
    Feris, Rogerio
    2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2020), 2020, : 1009 - 1018
  • [34] Learning through sharing and distributing knowledge with application to object recognition and information retrieval
    Mignon, Alexis
    Bronisz, Alban
    Le Hy, Ronan
    Mekhnacha, Kamel
    Santos, Luis
    2017 26TH IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (RO-MAN), 2017, : 1273 - 1278
  • [35] KURL: A Knowledge-Guided Reinforcement Learning Model for Active Object Tracking
    Liu, Xin
    Tan, Jie
    Ren, Xiaoguang
    Ren, Weiya
    Dai, Huadong
    ASIAN CONFERENCE ON MACHINE LEARNING, VOL 222, 2023, 222
  • [36] One-stage object detection knowledge distillation via adversarial learning
    Na Dong
    Yongqiang Zhang
    Mingli Ding
    Shibiao Xu
    Yancheng Bai
    Applied Intelligence, 2022, 52 : 4582 - 4598
  • [37] Deep Learning for Object Detection and Segmentation in Videos: Toward an Integration With Domain Knowledge
    Ilioudi, Athina
    Dabiri, Azita
    Wolf, Ben J.
    De Schutter, Bart
    IEEE ACCESS, 2022, 10 : 34562 - 34576
  • [38] One-stage object detection knowledge distillation via adversarial learning
    Dong, Na
    Zhang, Yongqiang
    Ding, Mingli
    Xu, Shibiao
    Bai, Yancheng
    APPLIED INTELLIGENCE, 2022, 52 (04) : 4582 - 4598
  • [39] Deep Learning for Object Detection and Segmentation in Videos: Toward an Integration With Domain Knowledge
    Ilioudi, Athina
    Dabiri, Azita
    Wolf, Ben J.
    De Schutter, Bart
    IEEE Access, 2022, 10 : 34562 - 34576
  • [40] THE OBJECT, KNOWLEDGE, TRANSFER
    Larose, Myriame
    JEU-REVUE DE THEATRE, 2012, 143 : 126 - 131