{"title":"The Structural Way for Binding a Learning Material with Personal Preferences of Learners","authors":"R. Yoshinov, O. Iliev","doi":"10.15622/sp.60.7","DOIUrl":null,"url":null,"abstract":"Yoshinov R., Iliev O. The Structural Way for Binding a Learning Material with Personal Preferences of Learners. Abstract. Learning content creation process requires more than just collection and presentation of set of information. In order to gain knowledge, the learning content should be designed in such a way to meet predefined learning goals. Learning goals determine the entire process of learning. Bloom’s Taxonomy provides a description of a cognitive process with six hierarchical levels, each containing specific learning goal to achieve. It could be adapted into a model by which tutors create learning materials. However, when it comes to productivity of learning, it is important to consider the personalization of the presented content according to the learning style of the individual. This article analyzes the correlation between Bloom’s Taxonomy and Honey & Mumford’s learning cycle, providing a way to bind the structure of learning material to the personal preferences of learners. This novel way of creating learning materials is integrated into a model that is used for automatic generation of personalized learning materials. The effectiveness of the model is further verified through an experiment with real participants. The results of the experiment show promising potential in the way of how a learner’s capabilities may be enriched. However, while experimenting and rest of the work on the model outline some challenges before the model’s application and future work.","PeriodicalId":53447,"journal":{"name":"SPIIRAS Proceedings","volume":"246 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SPIIRAS Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15622/sp.60.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 3
Abstract
Yoshinov R., Iliev O. The Structural Way for Binding a Learning Material with Personal Preferences of Learners. Abstract. Learning content creation process requires more than just collection and presentation of set of information. In order to gain knowledge, the learning content should be designed in such a way to meet predefined learning goals. Learning goals determine the entire process of learning. Bloom’s Taxonomy provides a description of a cognitive process with six hierarchical levels, each containing specific learning goal to achieve. It could be adapted into a model by which tutors create learning materials. However, when it comes to productivity of learning, it is important to consider the personalization of the presented content according to the learning style of the individual. This article analyzes the correlation between Bloom’s Taxonomy and Honey & Mumford’s learning cycle, providing a way to bind the structure of learning material to the personal preferences of learners. This novel way of creating learning materials is integrated into a model that is used for automatic generation of personalized learning materials. The effectiveness of the model is further verified through an experiment with real participants. The results of the experiment show promising potential in the way of how a learner’s capabilities may be enriched. However, while experimenting and rest of the work on the model outline some challenges before the model’s application and future work.
期刊介绍:
The SPIIRAS Proceedings journal publishes scientific, scientific-educational, scientific-popular papers relating to computer science, automation, applied mathematics, interdisciplinary research, as well as information technology, the theoretical foundations of computer science (such as mathematical and related to other scientific disciplines), information security and information protection, decision making and artificial intelligence, mathematical modeling, informatization.