Applying machine learning for tag classification in a collaborative knowledge system


  • Bruno Zolotareff dos Santos
  • Sandra dos Santos Vales
  • Jorge Rady de Almeida Junior



knowledge, metrics, metadata, recommendation, learning


The use of technological resources to enhance learning has grown exponentially in recent decades, mainly with the advent of mobile communication devices that increased the interactivity of users who collaborate to form a network of collective intelligence. In this technology environment that uses the Web, there is a large volume of data that is commonly disorganized, which is a challenge to use this data in the learning process in a continuous way to complete the knowledge. This study proposes using metrics capable of measuring knowledge aggregated in metadata shared in a collaborative system to be used in a recommendation system in the tagging process in continuous learning.


AGRA, Glenda et al. Análise do conceito de Aprendizagem Significativa à luz da Teoria de Ausubel. Revista Brasileira de Enfermagem, v. 72, p. 248-255, 2019.

AHMED, Mohiuddin. Data summarization: a survey. Knowledge and Information Systems, v. 58, n. 2, p. 249-273, 2019.

AL-JARF, Reima. Online vocabulary tasks for engaging and motivating EFL college students in distance learning during the pandemic and post-pandemic. International Journal of English Language Studies (IJELS), v. 4, n. 1, p. 14-24, 2022.

ARDIANTO, Rian et al. Sentiment analysis on E-sports for education curriculum using naive Bayes and support vector machine. Jurnal Ilmu Komputer dan Informasi, v. 13, n. 2, p. 109-122, 2020.

BLANCO-FERNÁNDEZ, Yolanda et al. Exploring synergies between content-based filtering and spreading activation techniques in knowledge-based recommender systems. Information Sciences, v. 181, n. 21, p. 4823-4846, 2011.

BRANSFORD J. et al. How People Learn: Brain, Mind, Experience, and School: Expanded Edition. Washigton.D.C., National Academy Press, National Research Coucil, 2000.

CHELMIS, Charalampos; PRASANNA, Viktor K. Social networking analysis: A state of the art and the effect of semantics. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing. IEEE, p. 531-536, 2011.

CONRAD, Dianne; OPENO, Jason. Estratégias de avaliação para a aprendizagem online. São Paulo: Artesanato Educacional, 2019.

CORDEIRO, Karolina Maria de Araújo. O impacto da pandemia na educação: a utilização da tecnologia como ferramenta de ensino. IDAAM, C3, v. 87, p. C3, 2020.

CORLEY, Courtney D. et al. Text and structural data mining of influenza mentions in web and social media. International journal of environmental research and public health, v. 7, n. 2, p. 596-615, 2010.

CORRELL, Joshua et al. Avoid Cohen’s ‘small’,‘medium’, and ‘large’for power analysis. Trends in cognitive sciences, v. 24, n. 3, p. 200-207, 2020.

DAGGER, Declan et al. Service-oriented e-learning platforms: From monolithic systems to flexible services. IEEE internet computing, v. 11, n. 3, p. 28-35, 2007.

ELAHI, Mehdi; RICCI, Francesco; RUBENS, Neil. A survey of active learning in collaborative filtering recommender systems. Computer Science Review, v. 20, p. 29-50, 2016.

KASIM, Nurul Nadirah Mohd; KHALID, Fariza. Choosing the right learning management system (LMS) for the higher education institution context: A systematic review. International Journal of Emerging Technologies in Learning, v. 11, n. 6, 2016.

KOTTNER, Jan et al. Guidelines for reporting reliability and agreement studies (GRRAS) were proposed. International journal of nursing studies, v. 48, n. 6, p. 661-671, 2011.

KRAFT, Matthew A. Interpreting effect sizes of education interventions. Educational Researcher, v. 49, n. 4, p. 241-253, 2020.

LIMA, Edivania Barros et al. HQ´ S VIRTUAIS: UMA PROPOSTA LÚDICA PARA O ENSINO DE TEMAS AMBIENTAIS. Redin-Revista Educacional Interdisciplinar, v. 7, n. 1, 2018.

MCHUGH, M. L. Interrater reliability: the kappa statistic. Biochemica Médica, 22 (3), 276–282. 2012.

PETER, Sophie et al. Tagging learning objects in Moodle for personalisation and re-use. In: EdMedia+ Innovate Learning. Association for the Advancement of Computing in Education (AACE), 2011. p. 2259-2266.

PIERCE, Marlon E. et al. Social networking for scientists using tagging and shared bookmarks: a Web 2.0 application. In: International Symposium on Collaborative Technologies and Systems. IEEE, 2008. p. 257-266.

SANTOS, B. Z. “data_tweets2022-2023”, Mendeley Data, V1, doi: 10.17632/bd5trxccvp.1, 2023.

SINGH, Smita et al. Evaluation of Tweet Sentiments Using NLP. In: International Symposium on Intelligent Informatics. Singapore: Springer Nature Singapore, 2022. p. 225-238.

TANTAM, Digby. The machine as psychotherapist: impersonal communication with a machine. Advances in Psychiatric treatment, v. 12, n. 6, p. 416-426, 2006.

TILLAEV, Azamat. Ways to use modern information technologies in education. In: AIP Conference Proceedings. AIP Publishing, 2023.

VANBELLE, Sophie. A new interpretation of the weighted kappa coefficients. Psychometrika, v. 81, n. 2, p. 399-410, 2016.

VIANA, Josué et al. Aprendizagem Baseada em Equipe Aplicada no Ensino Remoto na Disciplina de Interação Humano-Computador. In: Anais Estendidos do XX Simpósio Brasileiro de Fatores Humanos em Sistemas Computacionais. SBC, 2021. p. 35-40.

WILSON, B. D. et al. Mememxgate: Unearthing latent content features for improved search and relevancy ranking across scientific literature. In: AGU Fall Meeting Abstracts. 2015. p. IN33A-1794.

XAVIER, Lídia Maria Ferreira da Silva. O uso das TIC em salas de aula inclusivas: atitudes e práticas de professores do 1º ciclo. Tese de Doutorado apresentada na ESELx - Escola Superior de Educação de Lisboa, Instituto Politécnico de Lisboa, 2011.

ZHONG, Wenfeng et al. Design and Construction of the New Web Learning System of Tsinghua University. In: 2022 12th International Conference on Information Technology in Medicine and Education (ITME) v. IEEE, 2022. p. 53-56.




How to Cite

dos Santos, B. Z., Vales, S. dos S., & de Almeida Junior, J. R. (2023). Applying machine learning for tag classification in a collaborative knowledge system. OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA, 21(8), 10439–10460.