Article Sidebar

Download
PDF
Statistic
Read Counter : 193 Download : 107

Downloads

Download data is not yet available.

Main Article Content

Abstract

The use of technology is often used to support the learning process, one of which is the German Learning language learning media. The use of learning media in the actions of the student learning process that has been carried out can store data in the form of a log database. A database log is a sequence of student learning processes that have been generated that need to be processed and analyzed to identify the same learning patterns to help lecturers provide appropriate input. This study aims to utilize a log database of German Learning learning media using the sequential pattern method to explore useful information from the student's learning process when working on German Learning language problems. The algorithm used is Sequential Pattern Discovery using the Equivalent class (SPADE). Furthermore, this study uses an algorithm to identify the same learning patterns based on student learning activities when using MONSAKUN learning media. Moreover, the learning pattern that has been successfully obtained will be carried out by extracting information to analyze the same learning pattern based on the category of the student's ability to solve problems. Based on the results of the implementation and analysis that has been carried out on 20 German Learning language questions that have been successfully processed in the MONSAKUN learning media, six patterns of learning activities that vary on each question and level are produced, namely: pattern 1, pattern 2, pattern 3, pattern 4, pattern 5, and pattern 6. Each pattern requires further feedback and responses to optimize students' learning progress in Learning German Learning

Keywords

Pattern, Exploration, Learning

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Adekitan, A. I., & Salau, O. (2019). The impact of engineering students’ performance in the first three years on their graduation result using educational data mining. Heliyon, 5(2), e01250. https://doi.org/10.1016/j.heliyon.2019.e01250
  2. Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49. https://doi.org/10.1016/j.tele.2019.01.007
  3. Alp Christ, A., Capon-Sieber, V., Grob, U., & Praetorius, A.-K. (2022). Learning processes and their mediating role between teaching quality and student achievement: A systematic review. Studies in Educational Evaluation, 75, 101209. https://doi.org/10.1016/j.stueduc.2022.101209
  4. Ashraf, M., Zaman, M., & Ahmed, M. (2020). An Intelligent Prediction System for Educational Data Mining Based on Ensemble and Filtering approaches. Procedia Computer Science, 167, 1471–1483. https://doi.org/10.1016/j.procs.2020.03.358
  5. Brammer, J., Lutz, B., & Neumann, D. (2021). Solving the mixed model sequencing problem with reinforcement learning and metaheuristics. Computers & Industrial Engineering, 162, 107704. https://doi.org/10.1016/j.cie.2021.107704
  6. Brown, G. (2022). Proposing Problem-Based Learning for teaching future forensic speech scientists. Science & Justice, S1355030622000429. https://doi.org/10.1016/j.scijus.2022.03.006
  7. Byusa, E., Kampire, E., & Mwesigye, A. R. (2022). Game-based learning approach on students’ motivation and understanding of chemistry concepts: A systematic review of literature. Heliyon, 8(5), e09541. https://doi.org/10.1016/j.heliyon.2022.e09541
  8. Dabhade, P., Agarwal, R., Alameen, K. P., Fathima, A. T., Sridharan, R., & Gopakumar, G. (2021). Educational data mining for predicting students’ academic performance using machine learning algorithms. Materials Today: Proceedings, 47, 5260–5267. https://doi.org/10.1016/j.matpr.2021.05.646
  9. Dahiya, V., & Dalal, S. (2022). EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data. International Journal of Information Management Data Insights, 2(1), 100055. https://doi.org/10.1016/j.jjimei.2021.100055
  10. Daoudi, I., Chebil, R., Tranvouez, E., Lejouad Chaari, W., & Espinasse, B. (2021). Improving Learners’ Assessment and Evaluation in Crisis Management Serious Games: An Emotion-based Educational Data Mining Approach. Entertainment Computing, 38, 100428. https://doi.org/10.1016/j.entcom.2021.100428
  11. Geng, X., Liang, Y., & Jiao, L. (2021). EARC: Evidential association rule-based classification. Information Sciences, 547, 202–222. https://doi.org/10.1016/j.ins.2020.07.067
  12. Görgen, R., Huemer, S., Schulte-Körne, G., & Moll, K. (2020). Evaluation of a digital game-based reading training for German children with reading disorder. Computers & Education, 150, 103834. https://doi.org/10.1016/j.compedu.2020.103834
  13. Gunawan, R. (2021). Online Retail Pattern Quality Improvement: From Frequent Sequential Pattern to High-Utility Sequential Pattern. 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 242–246. https://doi.org/10.1109/ISRITI54043.2021.9702782
  14. Höyng, M. (2022). Encouraging gameful experience in digital game-based learning: A double-mediation model of perceived instructional support, group engagement, and flow. Computers & Education, 179, 104408. https://doi.org/10.1016/j.compedu.2021.104408
  15. Jääskä, E., Lehtinen, J., Kujala, J., & Kauppila, O. (2022). Game-based learning and students’ motivation in project management education. Project Leadership and Society, 3, 100055. https://doi.org/10.1016/j.plas.2022.100055
  16. Johnson, J., Parakkal, R., Bartz, S., Ren, G., & Ogihara, M. (2019). Survival of the Fastest: Using Sequential Pattern Analysis to Measure Efficiency of Complex Organizational Processes. 2019 International Conference on Data Mining Workshops (ICDMW), 830–837. https://doi.org/10.1109/ICDMW.2019.00122
  17. Lemay, D. J., Baek, C., & Doleck, T. (2021). Comparison of learning analytics and educational data mining: A topic modeling approach. Computers and Education: Artificial Intelligence, 2, 100016. https://doi.org/10.1016/j.caeai.2021.100016
  18. Nowak, J., Korytkowski, M., & Scherer, R. (2020). Discovering Sequential Patterns by Neural Networks. 2020 International Joint Conference on Neural Networks (IJCNN), 1–6. https://doi.org/10.1109/IJCNN48605.2020.9207461
  19. Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications, 41(4), 1432–1462. https://doi.org/10.1016/j.eswa.2013.08.042
  20. Pushpalatha, K., & Ananthanarayana, V. S. (2020). Multimedia Document Mining using Sequential Multimedia Feature Patterns. 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), 231–238. https://doi.org/10.1109/BigMM50055.2020.00040
  21. Ralla, A., Reddy, P. K., & Mondal, A. (2019). An Incremental Technique for Mining Coverage Patterns in Large Databases. 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 211–220. https://doi.org/10.1109/DSAA.2019.00036
  22. Supianto, A. A., & Hafis, M. (2018). GTRAS: Graphical Tracking Activity System for Problem-Posing Learning Process Insights. 2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 231–235. https://doi.org/10.1109/ICACSIS.2018.8618216
  23. Supianto, A. A., Hayashi, Y., & Hirashima, T. (2017). Designing scaffolding system in a problem-posing learning environment. 2017 3rd International Conference on Science in Information Technology (ICSITech), 546–551. https://doi.org/10.1109/ICSITech.2017.8257173
  24. Tang, H., Fang, B., Liu, R., Li, Y., & Guo, S. (2022). A hybrid teaching and learning-based optimization algorithm for distributed sand casting job-shop scheduling problem. Applied Soft Computing, 120, 108694. https://doi.org/10.1016/j.asoc.2022.108694
  25. Wang, L., Gui, L., & Xu, P. (2022). Incremental sequential patterns for multivariate temporal association rules mining. Expert Systems with Applications, 207, 118020. https://doi.org/10.1016/j.eswa.2022.118020
  26. Xie, T., Zheng, Q., & Zhang, W. (2018). Mining temporal characteristics of behaviors from interval events in e-learning. Information Sciences, 447, 169–185. https://doi.org/10.1016/j.ins.2018.03.018