FML-based intelligent adaptive assessment platform for learning materials recommendation

Lee, Chang-Shing; Wang, Mei-Hui; Yu, Jianlin; Lin, Koun-Hong; Lin, Ting-Tzu; Yang, Sheng-Chi; Cho, Sheng-Lun

doi:10.1109/fuzz-ieee.2015.7338100

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…The purpose of Part 2 of the experiments is to recommend learning contents for next students' learning by feeding the learned knowledge from Part 1 into the robot. We categorize the learning contents into four levels, including elementary, The range of RLCR is between -4 and +4 and it is the same as student's ability [20]. Table III shows partial knowledge base and rule base of learning content recommendation which is constructed according to the learned knowledge of PSO learning mechanism.…”

Section: B Part 2: Learning Content Recommendationmentioning

confidence: 99%

“…Additionally, the proposed PSO learning method has a better performance than GA learning. The range of RLCR is between -4 and +4 and it is the same as student's ability [20].…”

Section: Machine Learning For Knowledge Base Optimalizationmentioning

confidence: 99%

“…There exists some FML-based real-world applications to persons' learning. For example, Lee et al proposed a FMLbased intelligent adaptive assessment platform for learning materials recommendation [20], and an online self-learning platform construction based on genetic FML (GFML) and item response theory (IRT) agent [21]. They also proposed a FMLbased dynamic assessment agent for human-machine cooperative system on game of Go [11].…”

Section: Introductionmentioning

confidence: 99%

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Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

Lee

Wang

Huang

et al. 2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.

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Section: B Part 2: Learning Content Recommendationmentioning

confidence: 99%

“…Additionally, the proposed PSO learning method has a better performance than GA learning. The range of RLCR is between -4 and +4 and it is the same as student's ability [20].…”

Section: Machine Learning For Knowledge Base Optimalizationmentioning

confidence: 99%