Learning path personalization and recommendation methods: A survey of the state-of-the-art

被引:67
|
作者
Nabizadeh, Amir Hossein [1 ]
Leal, Jose Paulo [2 ]
Rafsanjani, Hamed N. [3 ]
Shah, Rajiv Ratn [4 ]
机构
[1] Univ Lisbon, Inst Super Tecn, INESC ID, P-1000029 Lisbon, Portugal
[2] Univ Porto, Fac Ciencias, P-4169007 Porto, Portugal
[3] Univ Georgia, Sch Environm Civil Agr & Mech Engn, Athens, GA 30602 USA
[4] Indraprastha Inst Informat Technol, Delhi Okhla Ind Estate,Phase 3, New Delhi 110020, India
关键词
Long term goal oriented recommender systems (LTRS); E-learning recommender; Learning path recommenders; Intelligent tutoring systems; Personalization; MODEL; OPTIMIZATION; HYPERMEDIA; FRAMEWORK; STRATEGY; BEHAVIOR; SYSTEMS;
D O I
10.1016/j.eswa.2020.113596
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A learning path is the implementation of a curriculum design. It consists of a set of learning activities that help users achieve particular learning goals. Personalizing these paths became a significant task due to differences in users' limitations, backgrounds, goals, etc. Since the last decade, researchers have proposed a variety of learning path personalization methods using different techniques and approaches. In this paper, we present an overview of the methods that are applied to personalize learning paths as well as their advantages and disadvantages. The main parameters for personalizing learning paths are also described. In addition, we present approaches that are used to evaluate path personalization methods. Finally, we highlight the most significant challenges of these methods, which need to be tackled in order to enhance the quality of the personalization. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:20
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