Digital case-based learning system in school

• Published in: PloS one Vol. 12; no. 11; p. e0187641
• Main Authors: Gu, Peipei, Guo, Jiayang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06.11.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial intelligence; Audiovisual materials; Biochemistry; Biology and Life Sciences; Case based; Case studies; Computer and Information Sciences; Computer engineering; Distance learning; Efficiency; Engineering and Technology; Genetic algorithms; Humans; Interactive learning; Internet; Learning; Media; Multimedia; Mutation; Online education; Online instruction; Physical Sciences; Problem based learning; Recommender systems; Research and Analysis Methods; Researchers; School environment; Schools; Science education; Social Sciences; Students; Studies; Teacher education; Video compression
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0187641

With the continuing growth of multi-media learning resources, it is important to offer methods helping learners to explore and acquire relevant learning information effectively. As services that organize multi-media learning materials together to support programming learning, the digital case-based learning system is needed. In order to create a case-oriented e-learning system, this paper concentrates on the digital case study of multi-media resources and learning processes with an integrated framework. An integration of multi-media resources, testing and learning strategies recommendation as the learning unit is proposed in the digital case-based learning framework. The learning mechanism of learning guidance, multi-media materials learning and testing feedback is supported in our project. An improved personalized genetic algorithm which incorporates preference information and usage degree into the crossover and mutation process is proposed to assemble the personalized test sheet for each learner. A learning strategies recommendation solution is proposed to recommend learning strategies for learners to help them to learn. The experiments are conducted to prove that the proposed approaches are capable of constructing personalized sheets and the effectiveness of the framework.