Análise de Dados e Serviços Inteligentes Aplicados na Educação à Distância: um mapeamento sistemático
DOI:
https://doi.org/10.5753/rbie.2021.29.0.331Keywords:
Educação a Distância, Análise de aprendizagem, Mapeamento sistemático, Ciência de Dados, Mineração de dados educacionaisAbstract
A utilização de ambientes virtuais contribui na geração de dados educacionais e a aplicação de métodos e técnicas de análise nesses dados gera informações valiosas aos gestores educacionais. Estas informações possibilitam tomadas de decisões direcionadas e personalizadas, de forma a melhorar o aprendizado do aluno. A oferta de serviços inteligentes ajuda as instituições a minimizarem as reprovações escolares e as evasões nos cursos online. Este artigo apresenta os resultados de um mapeamento sistemático da literatura que visa identificar como a Learning Analytics (LA) e os serviços inteligentes vêm sendo aplicados em ambientes de educação à distância (EAD). Foram realizadas buscas de 2010 até junho de 2020 nas bases IEEE Xplore Digital Library, ACM Digital Library, Scopus, Springer e Sciencedirect. A busca inicial resultou em 55.832 artigos e após aplicação dos critérios de inclusão e exclusão foram selecionados 51 artigos para leitura completa com o intuito de responder as questões de pesquisa. Os principais resultados obtidos são: dos 51 artigos selecionados foi constatado que 39% aplicaram métodos e técnicas de análise em ambientes de EAD; 39% ofereceram serviços inteligentes no âmbito da EAD e 18% aplicaram métodos e técnicas de análise focados nos históricos de registros de logs deixados pelos alunos quando interagiram em ambientes virtuais de aprendizagem.
Downloads
Referências
A. Chanaa and N. E. Faddouli, "Deep learning for a smart e-Iearning system," 2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech), Brussels, Belgium, 2018, pp. 1-8. DOI: 10.1109/CloudTech.2018.8713335 [GS Search]
Altaf, Saud. Waseem, Soomro and Izani, Mohd. Rawi, Mohamed. (2019). Student Performance Prediction using Multi-Layers Artificial Neural Networks: A Case Study on Educational Data Mining. In Proceedings of the 2019 3rd International Conference on Information System and Data Mining (ICISDM 2019). Association for Computing Machinery, New York, NY, USA, 59–64. DOI: 10.1148/3325917.3325919 [GS Search]
Anaya, Antonio R. Luque, Manuel. García-Saiz, Tomás (2013). Recommender system in collaborative learning environment using an influence diagram, Expert Systems with Applications, Volume 40, Issue 18, 2013, Pages 7193-7202, ISSN 0957-4174. DOI: 10.1016/j.eswa.2013.07.030 [GS Search]
Anaya V. Kolekar, Radhika M. Pai, Manohara Pai M.M. Adaptive User Interface for Moodle based E-learning System using Learning Styles, Procedia Computer Science, Volume 135, 2018, Pages 606-615, ISSN 1877-0509. DOI: 10.1016/j.procs.2018.08.226 [GS Search]
Balderas, Antonio. Iván Ruiz-Rube, Manuel Palomo-Duarte, and Juan M. Dodero. (2013). A generative computer language to customize online learning assessments. In Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality (TEEM ’13). Association for Computing Machinery, New York, NY, USA, 141–147. DOI: 10.1145/2536536.2536558 [GS Search]
Brown, M. (2011). Learning analytics: The coming third wave. Washington, DC: Learning Initiative. Disponível em: [link].Acesso em: 20 jan. 2020.
Budgen, D. Turner, M. Brereton, P. and Kitchenham, B. (2008). Using mapping studies in software engineering in Proceedings of PPIG, vol. 8. Lancaster University, pp. 195–204. [GS Search]
Cambruzzi, Wagner L.; Rigo, Sandro J.; Barbosa, Jorge L. V. (2015). Dropout Prediction and Reduction in Distance Education Courses with the Learning Analytics Multitrail Approach. Journal of Universal Computer Science (Print), v. 21, p. 23-47, 2015. [GS Search]
Chaffai, Abdelmajid. Hassouni, Larbi and Anoun, Houda. (2017). E-Learning Real Time Analysis Using Large Scale Infrastructure. In Proceedings of the 2nd international Conference on Big Data, Cloud and Applications (BDCA’17). Association for Computing Machinery, New York, NY, USA, Article 23,1– 6. DOI: 10.1145/3090354.3090378 [GS Search]
Chang, Yi-Chun and Chu, Chih-Ping. (2010). Applying learning behavioral Petri nets to the analysis of learning behavior in web-based learning environments, Information Sciences, Volume 180, Issue 6, 2010, Pages 995-1009, ISSN 0020-0255. DOI: 10.1016/j.ins.2009.11.022 [GS Search]
Chen, H. Yin, C. Li, R. Rong, W. Xiong Z. and David, B. (2020). Enhanced learning resource recommendation based on online learning style model, in Tsinghua Science and Technology, vol. 25, no. 3, pp. 348-356, June 2020. DOI: 10.26599/TST.2019.9010014 [GS Search]
Clarizia, Fabio. Francesco Colace, Massimo De Santo, Marco Lombardi, Francesco Pascale, and Antonio Pietrosanto. (2018). E-learning and sentiment analysis: a case study. In Proceedings of the 6th International Conference on Information and Education Technology (ICIET ’18). Association for Computing Machinery, New York, NY, USA, 111–118. DOI: 10.1145/3178158.3178181
Dahdouh, K., Dakkak, A., Oughdir, L. et al. (2018). Big data for online learning systems. Education and Information Technologies. DOI: 10.1007/s10639-018-9741-3
Dahdouh, K., Dakkak, A., Oughdir, L. et al. Large-scale e-learning recommender system based on Spark and Hadoop. J Big Data 6, 2 (2019). DOI: 10.1186/s40537-019-0169-4] [GS Search]
Dias, Robson Santos. Caracterização do Learning Analytics na educação a distância. v.1 n.1, junho, (2017): Anais do I Seminário de Pesquisa e Inovação Tecnológica. [GS Search]
Dimopoulos, Ioannis. Petropoulou, Ourania and Retalis, Symeon. (2013). Assessing students’ performance using the learning analytics enriched rubrics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK ’13). Association for Computing Machinery, New York, NY, USA, 195–199. DOI: 10.1145/2460296.2460335 [GS Search]
El Fouki, Mohammed. Aknin, Noura and El.Kadiri. K. Ed (2017). Intelligent Adapted e-Learning System based on Deep Reinforcement Learning. In Proceedings of the 2nd International Conference on Computing and Wireless Communication Systems (ICCWCS’17). Association for Computing Machinery, New York, NY, USA, Article 85, 1–6. DOI: 10.1145/3167486.3167574 [GS Search]
El Moustamid, E. En-Naimi, and J. El Bouhdidi. (2017). Integration of data mining techniques in e-learning systems: Clustering Profil of Lerners and Recommender Course System. In Proceedings of the 2nd international Conference on Big Data, Cloud and Applications (BDCA’17). Association for Computing Machinery, New York, NY, USA, Article 97, 1– 4. DOI: 10.1145/3090354.3090453 [GS Search]
Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., Vuorikari, R. (2016). Research Evidence on the Use of Learning Analytics - Implications for Education Policy. R. Vuorikari, J. Castaño Muñoz (Eds.). Joint Research Centre Science for Policy Report; EUR 28294 EN. DOI: 10.2791/955210 [GS Search]
Florian, Beatriz & Glahn, Christian & Drachsler, Hendrik & Specht, Marcus & Fabregat, Ramón. (2011). Activity-Based Learner-Models for Learner Monitoring and Recommendations in Moodle. Business Strategy and The Environment. 6964. 111-124. DOI: 10.1007/978-3-642-23985-4_10 [GS Search]
Graf, Sabine. Ives, Cindy. Rahman, Nazim. Ferri, Arnold. (2011). AAT - A tool for accessing and analysing students' behaviour data in learning systems. JO - ACM International Conference Proceeding Series. isbn 9781450309448, Association for Computing Machinery, New York, NY, USA. DOI: 10.1145/2090116.2090145 [GS Search]
Guo, Chenkai., Yan, Xiaoyu., and Li, Yan. (2020). Prediction of Student Attitude towards Blended Learning Based on Sentiment Analysis. In Proceedings of the 2020 9th International Conference on Educational and Information Technology (ICEIT 2020). Association for Computing Machinery, New York, NY, USA, 228–233. DOI: 10.1145/3383923.3383930 [GS Search]
Hamada M. (2012) Learning Style Model for e-Learning Systems. In: Huang R., Ghorbani A.A., Pasi G., Yamaguchi T., Yen N.Y., Jin B. (eds) Active Media Technology. AMT 2012. Lecture Notes in Computer Science, vol 7669. Springer, Berlin, Heidelberg. DOI: 10.1007/9783- 642-35236-2_19 [GS Search]
Heidrich, Leonardo; Barbosa, Jorge; Rigo, Sandro; Cambruzzi, Wagner Luiz; Ribeiro, Giovane. (2014). Diagnóstico do Comportamento dos Aprendizes na Educação a Distância com Base no Estilo de Aprendizagem. Disponível em: [link]. DOI: 10.5753/cbie.sbie.2014.412 [GS Search]
Huang, Zhenya., Liu, Qi., Zhai, Chengxiang., Yu Yin, Chen, Enhong., Gao, Weibo and Hu, Guoping. (2019). Exploring Multi-Objective Exercise Recommendations in Online Education Systems. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM ’19). Association for Computing Machinery, New York, NY, USA, 1261– 1270. DOI: 10.1145/3357384.3357995 [GS Search]
Hussain, Mushtaq. Hussain, Sadiq, Wu Zhang, Wenhao Zhu, Paraskevi Theodorou, and Syed Muhammad Raza Abidi. (2018). Mining Moodle Data to Detect the Inactive and Low performance Students during the Moodle Course. In Proceedings of the 2nd International Conference on Big Data Research (ICBDR 2018). Association for Computing Machinery, New York, NY, USA, 133-140. DOI: 10.1145/3291801.3291828 [GS Search]
Iqbal, M. et al. (2019). Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm, in IEEE Access, vol. 7, pp. 24719-24737, 2019. DOI: 10.1109/ACCESS.2019.2897003 [GS Search]
Islam, Muazzam. Siddiqui and Naif Radi Aljohani. (2019). Identifying Online Profiles of Distance Learning Students Using Data Mining Techniques. In Proceedings of the 2019 The 3rd International Conference on Digital Technology in Education (ICDTE 2019). Association for Computing Machinery, New York, NY, USA, 115–120. DOI: 10.1145/3369199.3369249 [GS Search]
Jeevamol Joy, Nisha S Raj, and Renumol V G. (2019). An ontology model for content recommendation in personalized learning environment. In Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems (DATA’19). Association for Computing Machinery, New York, NY, USA, Article 9, 1–6. DOI: 10.1145/3368691.3368700 [GS Search]
Jovanović, Jelena. Dawson, Shane. Joksimović, Srećko and Siemens, George. (2020). Supporting actionable intelligence: reframing the analysis of observed study strategies. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (LAK ’20). Association for Computing Machinery, New York, NY, USA, 161–170. DOI: 10.1145/3375462.3375474 [GS Search]
Kapembe, Samuel Stallin and Quenum, José, Ghislain. (2019). A Personalised Hybrid Learning Object Recommender System. 2019, isbn 9781450362382, Association for Computing Machinery, New York, NY, USA. DOI: 10.1145/3297662.3365810 [GS Search]
Kim, W and Kim, J. (2020). "Individualized AI Tutor Based on Developmental Learning Networks," in IEEE Access, vol. 8, pp. 27927-27937, 2020. DOI: 10.1109/ACCESS.2020.2972167 [GS Search]
Kotsiantis, S. Patriarcheas, Xenos, K. M. (2010). A combinational incremental ensemble of classifiers as technique for predicting students’ performance in distance education, Knowledge-Based Systems, Volume 23, Issue 6, 2010, Pages 529-535, ISSN 0950-7051. DOI: 10.1016/j.knosys.2010.03.010 [GS Search]
Kozierkiewicz-Hetmańska A., Zyśk D. (2013) A Method for Determination of an Opening Learning Scenario in Intelligent Tutoring Systems. In: Selamat A., Nguyen N.T., Haron H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science, vol 7803. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-36543-0_14 [GS Search]
Lagman, Ace C. and Mansul, Danna May. (2017). Extracting Personalized Learning Path in Adaptive E-Learning Environment Using Rule Based Assessment. In Proceedings of the 2017 International Conference on Information Technology (ICIT 2017). Association for Computing Machinery, New York, NY, USA, 335– 340. DOI: 10.1145/3176653.3176679 [GS Search]
Lara, Juan A, David Lizcano, María A. Martínez, Juan Pazos, Teresa Riera, (2014). A system for knowledge discovery in e-learning environments within the European Higher Education Area – Application to student data from Open University of Madrid, UDIMA, Computers & Education, Volume 72, 2014, Pages 23-36, ISSN 0360-1315. DOI: 10.1016/j.compedu.2013.10.009 [GS Search]
Lavoie, Francis B. and Proulx, Pierre. (2019). A Learning Management System for Flipped Courses. In Proceedings of the 2019 The 3rd International Conference on Digital Technology in Education (ICDTE 2019). Association for Computing Machinery, New York, NY, USA, 73–76. DOI: 10.1145/3369199.3369216 [GS Search]
Manhães, Laci Mary Barbosa. Da Cruz, Sérgio Manuel Serra and Zimbrão, Geraldo. (2014). WAVE: an architecture for predicting dropout in undergraduate courses using EDM. In Proceedings of the 29th Annual ACM Symposium on Applied Computing (SAC ’14). Association for Computing Machinery, New York, NY, USA, 243–247. DOI: 10.1145/2554850.2555135 [GS Search]
McAfee, Andrew; Brynjolfsson, Erik. Big Data: The Management Revolution. (2012). Harvard Business Review. Disponível em: [link]. Acesso em: 20 mar. 2020.
Thai-Nghe, Nguyen. Drumond, Lucas. Krohn-Grimberghe, Artus. Schmidt-Thieme, Lars. (2010). Recommender system for predicting student performance, Procedia Computer Science, Volume 1, Issue 2, 2010, Pages 2811-2819, ISSN 1877-0509. DOI: 10.1016/j.procs.2010.08.006 [GS Search]
Okubo, F. Yamashita, T. Shimada, A. and Ogata. H. (2017). A neural network approach for students’ performance prediction. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (LAK ’17). Association for Computing Machinery, New York, NY, USA, 598– 599. DOI: 10.1145/3027385.3029479 [GS Search]
Olivé, David Monllaó, Du Q. Huynh, Mark Reynolds, Martin Dougiamas, and Damyon Wiese. (2018). A supervised learning framework for learning management systems. In Proceedings of the First International Conference on Data Science, E-learning and Information Systems (DATA ’18). Association for Computing Machinery, New York, NY, USA, Article 18, 1–8. DOI: 10.1145/3279996.3280014 [GS Search]
Omar, L. and Abdesselam, B. (2017). Applying Clustering Algorithms to Solve E-learning Problems. SIG Proceedings Paper in word Format. In Proceedings of In Proceedings of ACM ICCES conference, Istanbul, Turkey, July 2017 (ICCES '17), 5 pages. DOI: 10.1145/3129186.3129195 [GS Search]
Peñafiel, Myriam. Stefanie Vásquez, Diego Vásquez, Juan Zaldumbide, and Sergio Luján-Mora. (2018). Data Mining and Opinion Mining: A Tool in Educational Context. In Proceedings of the 2018 International Conference on Mathematics and Statistics (ICoMS 2018). Association for Computing Machinery, New York, NY, USA, 74–78. DOI: 10.1145/3274250.3274263 [GS Search]
Petersen, K. Vakkalanka, S. and Kuzniarz. L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology 64 (2015), 1–18. DOI: 10.1145/3375462.3375493 [GS Search]
Qu S., K. Li, S. Zhang and Y. Wang, (2018). Predicting Achievement of Students in Smart Campus, in IEEE Access, vol. 6, pp. 60264-60273, 2018. DOI: 10.1109/ACCESS.2018.2875742 [GS Search]
Rashidah, Olanrewaju & Khan, Burhan & Mir, Roohie & Baba, Asifa & Anwar, Farhat. (2016). Dfam: A distributed feedback analysis mechanism for knowledge based educational big data. Journal Teknologi. 78. 31-38. DOI: 10.11113/jt.v78.10020 [GS Search]
Rayón, Alex. Guenaga, Mariluz and Núñez, Asier. (2014). Supporting competency-assessment through a learning analytics approach using enriched rubrics. In Proceedings of the Second International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM ’14). Association for Computing Machinery, New York, NY, USA, 291–298. DOI: 10.1145/2669711.2669913 [GS Search]
Ros, Salvador and Lázaro, Juan Carlos and Robles-Gómez, Antonio and Caminero, Agustín C. and Tobarra, Llanos and Pastor, Rafael. (2017). Analyzing Content Structure and Moodle Milestone to Classify Student Learning Behavior in a Basic Desktop Tools Course., isbn 9781450353861, Association for Computing Machinery, New York, NY, USA. DOI: 10.1145/3144826.3145392 [GS Search]
Sharma, Mugdha and Ahuja, Laxmi. (2016). A Novel and Integrated Semantic Recommendation System for E-Learning using Ontology. In Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies (ICTCS ’16). Association for Computing Machinery, New York, NY, USA, Article 52, 1–5. DOI: 10.1145/2905055.2905110.
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist. Vol. 57, Issue 10:1380–400. DOI: 10.1177/0002764213498851 [GS Search]
Sorour, Shaymaa. Kazumasa Goda, and Tsunenori Mine. (2015). Correlation of Topic Model and Student Grades Using Comment Data Mining. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (SIGCSE ’15). Association for Computing Machinery, New York, NY, USA, 441–446. DOI: 10.1145/2676723.2677259 [GS Search]
Spatiotis, Nikolaos. Isidoros Perikos, Iosif Mporas, and Michael Paraskevas. (2018). Evaluation of an Educational Training Platform Using Text Mining. In Proceedings of the 10th Hellenic Conference on Artificial Intelligence (SETN ’18). Association for Computing Machinery, New York, NY, USA, Article 42, 1–5. DOI: 10.1145/3200947.3201049 [GS Search]
Uzir, Nora’ayu Ahmad., Dragan Gašević, Jelena Jovanović, Wannisa Matcha, Lisa-Angelique Lim, and Anthea Fudge. (2020). Analytics of time management and learning strategies for effective online learning in blended environments. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (LAK ’20). Association for Computing Machinery, New York, NY, USA, 392–401. DOI: 10.1145/3375462.3375493 [GS Search]
Venugopalan, S., Srinath, M. V. and Rodrigues, Paul. (2016). Recommender System for ELearning through Content and Profile Based Approach. In Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies (ICTCS ’16). Association for Computing Machinery, New York, NY, USA, Article 45, 1–5. DOI: 10.1145/2905055.2905103 [GS Search]
Wang, Y. Sun, Y. and Chen Y. (2019) "Design and Research of Intelligent Tutor System Based on Natural Language Processing," IEEE International Conference on Computer Science and Educational Informatization (CSEI), Kunming, China, 2019, pp. 33-36. DOI: 10.1109/CSEI47661.2019.8939031 [GS Search]
Yang, J. Huang, Z. X., Gao Y. X. and Liu H. T. (2014). "Dynamic Learning Style Prediction Method Based on a Pattern Recognition Technique," in IEEE Transactions on Learning Technologies, vol. 7, no. 2, pp. 165-177, April-June 2014. 10.1109/TLT.2014.2307858 [GS Search]
Zakrzewska D. (2010) Building Group Recommendations in E-Learning Systems. In: Jędrzejowicz P., Nguyen N.T., Howlet R.J., Jain L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2010. Lecture Notes in Computer Science, vol 6070. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-13480-7_41 [GS Search]
Zaoudi, Mohammed and Hicham Belhadaoui. (2020). Adaptive E-learning: Adaptation of Content According to the Continuous Evolution of the Learner During his Training. In Proceedings of the 3rd International Conference on Networking, Information Systems & Security (NISS2020). Association for Computing Machinery, New York, NY, USA, Article 71, 1–6. DOI: 10.1145/3386723.3387890 [GS Search]
Zorrilla, Marta. García, Diego and Álvarez, Elena. (2010). A decision support system to improve e-learning environments. In Proceedings of the 2010 EDBT/ICDT Workshops (EDBT ’10). Association for Computing Machinery, New York, NY, USA, Article 11, 1–8. DOI: 10.1145/1754239.1754252 [GS Search]
Arquivos adicionais
Published
Como Citar
Issue
Section
Licença
![Creative Commons License](http://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png)
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.