Sequential Pattern Mining Model to Identify the Most Important or Difficult Learning Topics via Mobile Technologies
| Autor: | Mentor Hamiti, Edona Doko, Lejla Abazi Bexheti, Blerta Prevalla Etemi |
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| Rok vydání: | 2018 |
| Předmět: |
Sequence
Information retrieval Video learning Computer Networks and Communications Computer science Most Important/Difficult Learning Topics (MIDLT) Video TK5101-6720 Mobile Computer Science Applications Set (abstract data type) Telecommunication Learning Mobile technology Sequential Pattern Mining Time series Sequential Pattern Mining (SPM) |
| Zdroj: | International Journal of Interactive Mobile Technologies, Vol 12, Iss 4, Pp 109-122 (2018) International Journal of Interactive Mobile Technologies (iJIM); Vol. 12 No. 4 (2018); pp. 109-122 |
| ISSN: | 1865-7923 |
| DOI: | 10.3991/ijim.v12i4.9223 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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