International Peer-Reviewed Journal•Open Access•ISSN 2456-8880
irejournals@gmail.com•+91-7433024337

Home / Current Issue / Paper 1704401

1704401 Vol 6 · Issue 11 Download Paper

Personalize Learning Management System Platform Using Artificial Intelligence Rule-Based Technique

Anthony U. Concepcion Joseph D. Espino

Subject area: Science,Engineering and Technology  ·  Area of research: Machine Learning

Abstract

This mixed-method research utilizing descriptive-developmental design is about designing and evaluating Personalize Learning Management System Platform using Artificial Intelligence Rule-Based Techique employsan incremental prototyping model in thedevelopment. Consultative meetings,interview and the used of survey questionaires were held to obtain data from 10 information technology experts as the alpha evaluators and 10 teachers who is practicing ICT education as the beta evaluators chosen using purposive sampling. Results show that personalize learning management system is excellent in terms of functional suitability (M=4.70), performance efficiency (M=4.80), compatibility (M=4.85), usability (M=4.80), reliability (M=4.80), security (M=4.71), maintainability (M=4.69), portability (M=4.76) recording a grand mean of 4.76 interpreted as excellent. This means that the system satisfies both software quality standards and end-user requirements. Thus, it is ready for adoption. Along with its implementation, it is recommended to gather feedback regularly conduct and conduct an impact analysis of the effectiveness of using the personalize learning management system platform using artificial intelligence rule-based technique.

Keywords

Prototyping model, LMS, Artificial Intelligence, Rule-based technidue, Philippines, Platform

References

[1] Akanksha Jaiswal & C. Joe Arun. (2021). Potential of Artificial Intelligence for transformation of the education system in India. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 2021, Vol. 17, Issue 1, pp. 142-158

[2] Fürnkranz, Johannes & Gamberger, Dragan & Lavrac, Nada. (2012). Foundations of Rule Learning. 10.1007/978-3-540-75197-7.

[3] Fürnkranz, Johannes & Gamberger, Dragan & Lavrač, Nada. (2012). Machine Learning and Data Mining. Cognitive Technologies. 1-351. 10.1007/978-3-540-75197-7-1.

[4] Grosan, C., & Abraham, A. (2011). Intelligent Systems - A Modern Approach. Intelligent Systems Reference Library.

[5] Hsu, S. & Kuan, P. Y. (2013). The impact of multilevel factors on technology integration: The case of Taiwanese grade 1-9 teachers and schools. Educational Technology Research & Development, 61, 25-50. DOI: 10.1007/s11423-012-9269-y

[6] Irina M. VARZARU, Bianca E. NICA and Antonela TOMA (2022)," ViTeach: Artificial Intelligence Algorithms to Improve E-learning through Virtual Teachers ", Journal of e-Learning and Higher Education, Vol. 2022 (2022), Article ID 343795, DOI: 10.5171/2022.343795

[7] Jaiswal, Akanksha & Arun, C.. (2021). Potential of Artificial Intelligence for transformation of the education system in India. The International Journal of Education and Development using Information and Communication Technology. 17. 142-158.

[8] Khaled, M. (2021). Learning styles, Personalization, and Learning Management Systems: Towards a Student-Centred LMS Approach (Dissertation). Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447989

[9] Knote, Robin & Janson, Andreas & Söllner, Matthias & Leimeister, Jan Marco. (2019). Classifying Smart Personal Assistants: An Empirical Cluster Analysis.

[10] M. Krishnaveni, P. Subashini, T. T. Dhivyaprabha and A. S. Priya, "Optimized classification based on Particle Swarm Optimization algorithm for Tamil Sign Language digital images," 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), 2016, pp. 53-57, doi: 10.1109/CSITSS.2016.7779439.

[11] Nuangpolmak, Apiwan. (2014). Multilevel Materials for Multilevel Learners. 10.1057/9781137023315_8.

[12] Nuangpolmak, Apiwan. (2014). Multilevel Materials for Multilevel Learners. 10.1057/9781137023315_8.

How to cite this paper

Anthony U. Concepcion, Joseph D. Espino "Personalize Learning Management System Platform Using Artificial Intelligence Rule-Based Technique" Iconic Research And Engineering Journals Volume 6 Issue 11 2023 Page 108-115
Anthony U. Concepcion, Joseph D. Espino "Personalize Learning Management System Platform Using Artificial Intelligence Rule-Based Technique" Iconic Research And Engineering Journals, vol. 6, no. 11, May. 2023
Anthony U. Concepcion, Joseph D. Espino (2023). Personalize Learning Management System Platform Using Artificial Intelligence Rule-Based Technique. Iconic Research And Engineering Journals, 6(11).
Anthony U. Concepcion, Joseph D. Espino "Personalize Learning Management System Platform Using Artificial Intelligence Rule-Based Technique" Iconic Research And Engineering Journals, vol. 6, no. 11, May. 2023.
@article{1704401,
      author = {Anthony U. Concepcion, Joseph D. Espino},
      title = {Personalize Learning Management System Platform Using Artificial Intelligence Rule-Based Technique},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {11},
      pages = {108-115},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704401.pdf},
      abstract = {This mixed-method research utilizing descriptive-developmental design is about designing and evaluating Personalize Learning Management System Platform using Artificial Intelligence Rule-Based Techique employsan incremental prototyping model in thedevelopment. Consultative meetings,interview and the used of survey questionaires were held to obtain data from 10 information technology experts as the alpha evaluators and 10 teachers who is practicing ICT education as the beta evaluators chosen using purposive sampling. Results show that personalize learning management system is excellent in terms of functional suitability (M=4.70), performance efficiency (M=4.80), compatibility (M=4.85), usability (M=4.80), reliability (M=4.80), security (M=4.71), maintainability (M=4.69), portability (M=4.76) recording a grand mean of 4.76 interpreted as excellent. This means that the system satisfies both software quality standards and end-user requirements. Thus, it is ready for adoption. Along with its implementation, it is recommended to gather feedback regularly conduct and conduct an impact analysis of the effectiveness of using the personalize learning management system platform using artificial intelligence rule-based technique.},
      keywords = {Prototyping model, LMS, Artificial Intelligence, Rule-based technidue, Philippines, Platform},
      month = {May},
  }