NDLI: Individualized learning through MOOC: online automatic test system based on genetic algorithm

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Individualized learning through MOOC: online automatic test system based on genetic algorithm

Content Provider	ACM Digital Library
Author	Cao, Lufeng Zhang, Xiaoyan Yin, Yipeng
Abstract	Massive Open Online Course (MOOC) has made an unprecedented development at home and abroad. However, the problems concerning the coarse granularity services, the imbalance of the faculty-student ratio, the lower course completion ratesand other issues, have weakened the performance of MOOC service. This paper analyzes the challenges faced by MOOC in the process of rapid growth, and the learning process of students in a cell cycle, and offers learning behavior evaluation model to provide the basic data for individualized learning. In addition, according to the individual differences of learners, it designs genetic algorithm to realize online auto generating test paper system. And the verification experimental results indicate that the design of genetic algorithm has a good convergence and the generated test paper is related to the individual learners. This system not only reduces the teacher's work pressure and optimizes the MOOC platform, but also realizes the goal of individualized learning and greatly improve the learning efficiency of students. This is a great significance to the development of the future open curriculum.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450347990
DOI	10.1145/3028842.3028855
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-12-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Mooc Individualized learning Genetic algorithm Auto-generating test paper
Content Type	Text
Resource Type	Article

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