Sequential Pattern Mining Model of Performing Video Learning History Data to Extract the Most Difficult Learning Subjects

Edona Doko, Lejla Abazi Bexheti and Visar Shehu

A chapter in Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Split, Croatia, 6-8 September 2018, 2018, pp 342-348 from IRENET - Society for Advancing Innovation and Research in Economy, Zagreb

Abstract: The paper aim is to define a method for performing video learning data history of learner's video watching logs, video segments or time series data in consistency with learning processes. To achieve this aim, a theoretical method is introduced. Sequential pattern mining with learning histories are used to extract the most difficult learning subjects. Based on this method, it is designed a model for understanding and learning the most difficult topics of students. The performed video learning history data of learner's video watching logs makeup of stop/replay/backward data activities functions. They correspond as output of sequence of the learning histories, extraction of significant patterns by a set of sequences, and findings of learner's most difficult/important topic from the extracted patterns. The paper mostly aim to devise the model for understanding and learning the most difficult topics through method of mining sequential pattern.

Keywords: Sequential Pattern Mining (SPM); Video; Learning; Keyword Topic (KT) (search for similar items in EconPapers)
JEL-codes: O33 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:entr18:183844

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