Sequential Pattern Mining Model to Identify the Most Important or Difficult Learning Topics via Mobile Technologies

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 understanding 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.

Hacia el desarrollo de un sistema de evaluación adaptativa computarizada basada en reglas de asociación secuenciales

“…Tabla 1: Trabajos relacionados. Artículos 1 2 3 4 5 (Tarus et al, 2018) X X (Anwar y Uma, 2022) X (Zheng et al, 2019) X X (Wong et al, 2019) X X (Liu et al, 2018) X X X (Qu et al, 2019) X X (Klašnja-Milićević et al, 2018) X X (Zhu et al, 2019) (Deeva y De Weerdt, 2019) (Taub et al, 2018) X X (Wan y Niu, 2020) X X (Al-Twijri et al, 2022) (Taub y Azevedo, 2018b) X X (Norm Lien et al, 2020) X X (Yildirim y Usluel, 2022) X X X (Zhang et al, 2022) X X X (Shih, 2018) X X (Song et al, 2022) X X (Wang y Zaïane, 2018) (Mudrick et al, 2018) X X (Taub y Azevedo, 2018a) X X (Yang, 2021) (Bermudez et al, 2020) (Cheng Tan et al, 2020) X X (Malekian et al, 2020) X X (Kong y Pollock, 2020) X X (Latypova, 2022) X X X (Fatahi et al, 2018) X X X (Pogorskiy y Beckmann, 2022) X (He et al, 2021) X (Chen y Wang, 2020) X (Czibula et al, 2019) X X (Doko et al, 2018) X X (Real et al, 2021) X (Song y Ye, 2021) (Cheng et al, 2021) X (Niemeijer et al, 2020) X (Aktas y Aktas, 2021)…”

“…De la misma forma, Wang y Zaïane. ( 2018) trató el tema de una correcta recomendación de recursos educativos, al igual que Mudrick et al (2018) quien estudio el monitoreo metacognitivo para el desarrollo de un sistema de recomendación de cursos, por último, en Doko et al (2018) se recomendó un modelo y la arquitectura de un sistema para la visualización de videos educativos. 2019), ya que su área de estudio fue STEM (Science, Technology, Engineering, and Mathematics, Ciencia, Tecnología, Ingeniería y Matemáticas), el resto de los trabajos se centraron en áreas como sociales (Wong et al, 2019) o biología (Mudrick et al, 2018), sin embargo, hubo algunos trabajos que no mencionaron su área de estudio, este es el caso de Anwar y Uma.…”

Considering temporal aspects in recommender systems: a survey

“…In the learning/tutoring domain, temporal aspects are often used to identify the strategy of learners by finding sequential patterns in the users' learning activity (Maldonado et al, 2010;Doko et al, 2018;Wong et al, 2019;Uto et al, 2020). Some researchers, for example, consider the time it took for a student to solve a problem (Shen and Chi, 2016).…”

Modeling micro-interactions in self-regulated learning: A data-driven methodology

“…SPM is frequently used in the area of educational data mining; to identify sequences of events that can distinguish stronger/weaker groups in an online collaboration environment [67], and to extract learning features to classify learner groups [81]. It has been used in recent e-learning studies to improve online learning platforms, investigate online collaboration, and explore self-regulated learning [26,67,25]. Previous work has also used SPM to mine student behaviours from length and correctness of steps taken in online programming courses [45].…”