Paper—Sequential Pattern Mining Model to Identify the Most Important or Difficult Learning Topics via… Sequential Pattern Mining Model to Identify the Most Important or Difficult Learning Topics via Mobile Technologies https://doi.org/10.3991/ijim.v12i4.9223 Edona Doko ! ! " , Lejla Abazi Bexheti, Mentor Hamiti, Blerta Prevalla Etemi SEE University, Tetovo, Macedonia ed15197@seeu.edu.mk Abstract—The paper aim is to come up with methodology for performing video learning data history of learner’s video watching logs, video segments or time series data in accordance with learning processes via mobile technologies. To reach this goal, it is introduced a theoretical method of sequential pattern mining specialized for learning histories in identifying the most important or difficult learning. Based on this method, it is designed a model for understand- ing and learning the most difficult topics of students topics. The user will be able to use and access the model through mobile technologies when and where he/she wants. The performed video learning history data of learner’s video watching logs consists of functions that are responsible for collection of stop/replay/backward data activities, generation of sequence from the collected learning histories, extraction of important patterns from a set of sequences, and findings of learner’s most difficult/important topic from the extracted patterns. The paper mainly describes the model for understanding and learning the most difficult topics through the sequential pattern mining method. Implementing the method to use in mobile phones is considered as future aim. Keywords—Sequential Pattern Mining (SPM); Video; Learning; Most Im- portant/Difficult Learning Topics (MIDLT); Mobile 1 Introduction Providing students high quality, stable technology and rich environments is the challenge in improved accessibility and enhanced applications embedded in emerging mobile technologies. The late progress in technology and ideology has opened a com- pletely new direction for education research. The areas of Learning Analytics and Education Data Mining explore the use of data to increase insight about learning envi- ronments and improve the overall quality of experience for students. Mobile technol- ogies in learning systems are influenced from several areas to research and build models of educational data mining and learning analytics [1, 21, 24]. Smart mobile technologies, such as tablet computers and smartphones, offer advanced computing abilities as well as access to internet-based resources without the constraints of time or place. This has resulted in devices that enable the provision of ubiquitous learning iJIM ‒ Vol. 12, No. 4, 2018 109