Please use this identifier to cite or link to this item:
Doko, Edona
Abazi Bexheti, Lejla
Shehu, Visar
Year of Publication: 
[Title:] Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Split, Croatia, 6-8 September 2018 [ISSN:] 2706-4735 [Volume:] 4 [Pages:] 342-348
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.
Sequential Pattern Mining (SPM)
Keyword Topic (KT)
Creative Commons License:
Document Type: 
Conference Paper

Files in This Item:

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.