Abstract
The information overload is becoming increasingly serious, and the current search engine is still unable to reach the optimization of information filtering. Many previous studies prove that the recommendation learning can reduce time cost and increase efficiency to learn. People pay more attention on the multiple intelligences because diversified thoughts were developed. Through statistical analysis of the multiple intelligences, we discuss issues on the learner’s strong and non-strong intelligent influence for learning efficiency. By doing this, we try to understand what kind of intelligence suits our recommendation learning system. Students in the experimental group that utilize intelligent recommendation learning system are more prominent than students in control groups with traditional teaching. This means that the learning efficiency with intelligent recommendation learning system is better than the learning efficiency with traditional teaching. In the strong intelligences, there exist prominent differences of logical-mathematical intelligence and intrapersonal intelligence in eight intelligences of learning efficiency. This means that learners who have logical-mathematical intelligence or intrapersonal intelligence are the best suited for the teaching with intelligent recommendation learning system.
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Chen, HR., Chang, YH. (2016). Design and Analysis of Recommendation Learning System Based on Multiple Intelligences Theory. In: Spector, M., Lockee, B., Childress, M. (eds) Learning, Design, and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-17727-4_26-1
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DOI: https://doi.org/10.1007/978-3-319-17727-4_26-1
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