Sequential Pattern Mining Model of Performing Video Learning History Data to Extract the Most Difficult Learning Subjects - Edona Doko; Lejla Abazi Bexheti; Visar Shehu (Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Split, Croatia, 6-8 September 2018, 2018)
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: O33; 330; Sequential Pattern Mining (SPM); Video; Learning; Keyword Topic (KT)