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

7 Pages Posted: 19 Nov 2018

See all articles by Edona Doko

Edona Doko

South East European University (SEEU)

Lejla Abazi-Bexheti

South East European University (SEEU)

Visar Shehu

South East European University (SEEU)

Date Written: September 6, 2018

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)

JEL Classification: O33

Suggested Citation

Doko, Edona and Abazi-Bexheti, Lejla and Shehu, Visar, Sequential Pattern Mining Model of Performing Video Learning History Data to Extract the Most Difficult Learning Subjects (September 6, 2018). 2018 ENTRENOVA Conference Proceedings. Available at SSRN: https://ssrn.com/abstract=3283714 or http://dx.doi.org/10.2139/ssrn.3283714

Edona Doko (Contact Author)

South East European University (SEEU) ( email )

Tetovo
Macedonia

Lejla Abazi-Bexheti

South East European University (SEEU) ( email )

Ilindenska nn
Tetovo, 1200
Macedonia

Visar Shehu

South East European University (SEEU) ( email )

Ilindenska nn
Tetovo, 1200
Macedonia

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
8
Abstract Views
197
PlumX Metrics