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Cristiane Soares Ramos

Academic and Professional Profile

Academic Background

Professional Activities and Teaching

Contributions and Acknowledgments

Research Areas

Current Projects

Supervisory Experience

Current Supervisions

Undergraduate Research

Bachelor’s Thesis

Former Supervisions

Undergraduate Research

Bachelor’s Thesis

  1. Allan Nobre, Using Learning Analytics and Educational Data Mining to Support Pedagogical Decisions During TBL Methodology Application . Senior Project (Bachelor of Software Engineering) - University of Brasília (Brazil), 2023 Advisor(s): Cristiane Ramos . Tags: Learning Analytics, Education .
  2. Gabriel Silva Helena Gourlat, Non-Functional Requirements in the Development of Serious Games for People with ADHD . Senior Project (Bachelor of Software Engineering) - University of Brasília (Brazil), 2023 Advisor(s): Cristiane Ramos . Tags: Requirements Engineering, Education .
  3. Ícaro Oliveira Augusto Silva, Usability Information Extraction from App Comments on the Play Store . Senior Project (Bachelor of Software Engineering) - University of Brasília (Brazil), 2023 Advisor(s): Cristiane Ramos . Tags: Natural Language Processing, Software Quality .
  4. Amanda Emilly Muniz de Menezes, Identifying Factors Affecting Dropout in Higher Education . Senior Project (Bachelor of Software Engineering) - University of Brasília (Brazil), 2022 Advisor(s): Cristiane Ramos, Sergio Freitas . Tags: Education, Learning Analytics .
    Identifying factors that influence academic dropout is crucial for coordinators to take preventive actions against student desertion. This thesis aims to explore the current state of research in the field, seeking to understand the main factors associated with academic dropout and how they can be applied in predictive models of dropout. A quantitative, exploratory, and explanatory applied research was conducted, using case study techniques and bibliographic research. The systematic literature review indicated that dropout could be predicted by academic, demographic, and learning factors. Using these factors, predictive models were developed with the help of machine learning algorithms, whose predictions proved sensitive to changes in course flows. It concludes that academic, demographic, and learning factors are effective in predicting dropout, with the selection and application method of these factors being crucial for achieving accurate predictive results. Moreover, the application of academic factors in predictive models requires consideration of the specific context of the analyzed data.
  5. Ateldy Brasil Filho, Gamification Evaluation - A Practical Application in a Junior Company . Senior Project (Bachelor of Software Engineering) - University of Brasília (Brazil), 2022 Advisor(s): Cristiane Ramos . Tags: Gamification, Education .
  6. Filipe Silva, The State of Software Maintenance Practice in Early Stage Software Startups . Senior Project (Bachelor of Software Engineering) - University of Brasília (Brazil), 2022 Advisor(s): Cristiane Ramos . Tags: Software Architecture, Digital Transformation .
  7. Letícia Karla Soares Rodrigues de Araújo, Identifying Factors Affecting Dropout in Higher Education . Senior Project (Bachelor of Software Engineering) - University of Brasília (Brazil), 2022 Advisor(s): Cristiane Ramos, Sergio Freitas . Tags: Education, Learning Analytics .
    Identifying factors that influence academic dropout is crucial for coordinators to take preventive actions against student desertion. This thesis aims to explore the current state of research in the field, seeking to understand the main factors associated with academic dropout and how they can be applied in predictive models of dropout. A quantitative, exploratory, and explanatory applied research was conducted, using case study techniques and bibliographic research. The systematic literature review indicated that dropout could be predicted by academic, demographic, and learning factors. Using these factors, predictive models were developed with the help of machine learning algorithms, whose predictions proved sensitive to changes in course flows. It concludes that academic, demographic, and learning factors are effective in predicting dropout, with the selection and application method of these factors being crucial for achieving accurate predictive results. Moreover, the application of academic factors in predictive models requires consideration of the specific context of the analyzed data.
  8. Sannya Arvelos, Software Process Improvement Planning Based on Issue Analysis . Senior Project (Bachelor of Software Engineering) - University of Brasília (Brazil), 2022 Advisor(s): Cristiane Ramos . Tags: Requirements Engineering, Verification, Validation and Testing .
  9. Victor Deon, PGTBL - Team-Based Learning Management Platform . Senior Project (Bachelor of Software Engineering) - University of Brasília (Brazil), 2020 Advisor(s): Cristiane Ramos . Tags: Teamwork, Active Learning .
  10. Antonio Silva Júnior, Tool Support for the Quality Assurance Process - QUASAR – Software Quality and Results Evaluation . Senior Project (Bachelor of Software Engineering) - University of Brasília (Brazil), 2013 Advisor(s): Cristiane Ramos . Tags: Software Quality, Verification, Validation and Testing .
  11. Giulia Lobo Barros, Improvement of a software requirements engineering process . Bachelor of Software Engineering - University of Brasília (Brazil), 2023 Advisor(s): Cristiane Ramos, Ricardo Ajax . Tags: Requirements Engineering .
  12. Ricardo de Castro Loureiro, Technologies for inclusion: Metrics for evaluating web page accessibility for visually impaired people . Bachelor of Software Engineering - University of Brasília (Brazil), 2023 Advisor(s): Cristiane Ramos, Ricardo Ajax . Tags: Software Quality .
  13. Nathalia Lorena Cardoso Dias, Why are already defined software improvement processes not being used by companies? A literature review . Bachelor of Software Engineering - University of Brasília (Brazil), 2024 Advisor(s): Cristiane Ramos, Fabiana Mendes . Tags: Software Quality .

Publications (14)

  1. MARSICANO, G. C.,CANEDO, EDNA,RAMOS, C. S.,FIGUEIREDO, R. M. C.,PEDROSA, G. V., Digital Transformation of Public Services in a Startup-Based Environment: Job Perceptions, Relationships, Potentialities and Restrictions , JOURNAL OF UNIVERSAL COMPUTER SCIENCE , 30(6)(720-757), 2024 . DOI: 10.3897/jucs.106979 . Tags: Digital Transformation .
  2. Sergio Antônio Andrade Freitas,Cristiane S. Ramos,Eduardo Bessa Pereira da Silva,Marcia Renata Mortari,Dianne Magalhaes Viana, Implementing Neuroscientific Principles in Gamified Software Engineering Courses , in Frontiers in Education 2024 , pTo appear, 2024 . Tags: Active Learning, Learning Analytics, Gamification .
  3. Sergio Antônio Andrade Freitas,Mylena Angélica S. Farias,Cristiane S. Ramos,Marcus Vinícius Paiva Martins,Juan Mangueira Alves,Leda Cardoso S. Pinto, Crafting Personalized Learning Environments Through Motivational Profiling , in Frontiers in Education 2024 , pTo appear, 2024 . Tags: Active Learning, Gamification .
  4. Cristiane S. Ramos,Mylena Angélica S. Farias,Sergio Antônio Andrade Freitas,Marcus Vinícius Paiva Martins,Juan Mangueira Alves,Leda Cardoso S. Pinto, A Process to Identify Players’ Motivational Profiles for Designing a Gamification Project , in 24th International Conference on Computational Science and Its Applications - ICCSA 2024 , p49-67, 2024 . DOI: 10.1007/978-3-031-64608-9_4 . Tags: Active Learning, Gamification .
    This article presents an innovative process for identifying the motivational profiles of a specific target audience using three distinct strategies: assessment by judges, application of game dynamics, and the Intrinsic Motivation Inventory (IMI) questionnaire. Each strategy aims to align with the Octalysis framework, emphasizing the importance of personalization and adaptability in gamification. The process is tested on a gamification project within a Brazilian government portal for scientific dissemination, focusing on engaging Brazilian students in science. The analysis reveals differences in strategy outcomes, highlighting the requirement of a multifaceted approach to accurately capture the target audience’s motivational profiles and improve gamification effectiveness.
  5. Cristiane S. Ramos,Dianne M. Viana,Eduardo Bessa,Márcia R. Mortari,Sergio Antônio Andrade Freitas, Learning Indicators as Tools for Continuous Improvement in the Educational Environment , in PAEE/ALE 2024 - International Conference on Active Learning in Engineering Education , p245-254, 2024 . DOI: 2183-1378 . Tags: Active Learning .
    In today's fast-paced world characterized by rapid knowledge evolution and increasing information valuation, the education sector faces unprecedented challenges. These challenges require a continuous commitment to the evaluation and enhancement of pedagogical practices, adapting them to emerging needs and challenges. In this context, learning indicators emerge as fundamental instruments, offering detailed insights into the efficiency of active learning methods and strategies, and student progress in the development of essential competencies. This article is dedicated to exploring the implementation of two key indicators in the educational environment, aiming at optimizing the quality of teaching and learning in engineering course disciplines. The first indicator analyzed focuses on identifying students' prior knowledge and assimilating new concepts, fundamental for meaningful learning. The second indicator seeks to evaluate the consolidation of engrams, that is, the formation and reinforcement of memories related to learned concepts, allowing the identification of gaps in the teaching-learning process. It is argued that the adoption of these indicators, among others, is necessary for effective educational management, allowing not only the monitoring and evaluation of pedagogical practices but also the planning of precise and well-founded interventions to establish an inclusive, efficient, and motivating learning environment.
  6. LACERDA, A. R. T.,FREITAS, S. A. A.,RAMOS, C. S., Gamified Chatbot Management Process: A way to build gamified chatbots , in 10th Intelligent Systems Conference , 2024 . DOI: 10.1007/978-3-031-66428-1_2 . Tags: Gamification, Machine Learning .
    This paper proposes the incorporation of gamification with machine learning for the development of chatbots. The Gamified Chatbot Development Process (GCMP), is a process for the development of gamified chatbots, it comprises eight activities, arranged into four steps, emphasizing gamification implementation. This process includes gamification planning, gamification management, updating chatbot content, chatbot behavior implementation, chatbot behavior validation, chatbot behavior analysis, chatbot delivery, and chatbot usage analysis. GCMP provides a clear and structured guide, allowing flexibility to accommodate each project’s specific requirements. This article describes the methodology employed, which includes the application of an experiment with software engineering students. The experiment is conducted by providing documents, holding weekly meetings, and collecting pertinent data. The applicability of GCMP in gamified chatbot projects is examined, and a new version of the process is proposed to resolve the gaps found, and conclusions are drawn based on experiments.
  7. RAMOS, C. S.,VIANNA, D. M.,BESSA, E.,MORTARI, M. R.,FREITAS, S. A. A., Indicadores de aprendizagem , Biblioteca Central da Universidade de Brasília, 2023 . Tags: Learning Analytics, Active Learning .
  8. MARSICANO, GEORGE,CANEDO, EDNA,PEDROSA, GLAUCO,RAMOS, CRISTIANE,FIGUEIREDO, REJANE, Digital Transformation of Public Services from the Perception of ICT Practitioners in a Startup-Based Environment , in 25th International Conference on Enterprise Information Systems , p490, 2023 . DOI: 10.5220/0011826600003467 . Tags: Digital Transformation .
  9. SILVA, E. C.,FREITAS, S. A. A.,RAMOS, C. S.,MENEZES, A. E. M.,ARAUJO, L. K. S. R., A systematic review of the factors that impact the prediction of retention and dropout in higher education , in 56th Hawaii International Conference on System Science , 2023 . Tags: Learning Analytics, Machine Learning .
  10. CALAZANS, Angelica Toffano Seidel,MASSON, E.,SOUZA, M.,BRITO, I.,PALDES, R.,KOSLOSKI, RICARDO AJAX DIAS,RAMOS, C. S.,GUIMARAES, F., Ensino SUPERIOR com metodologias ativas: na prática, como se faz , Clube dos autores, 2019 . DOI: 10.29327/57070 . Tags: Active Learning .
  11. KOSLOSKI, RICARDO AJAX DIAS,RAMOS, CRISTIANE SOARES,CANEDO, E. D.,GOULART, H. B., Aprendizagem baseada em projetos aplicada em uma disciplina de integração de Engenharias: desafios e benefícios , in Simpósio Brasileiro de Informática na Educação (SBIE) , 2019 . DOI: 10.5753/cbie.sbie.2019.89 . Tags: Active Learning .
  12. RAMOS, CRISTIANE SOARES,KOSLOSKI, Ricardo Ajax Dias,VENSON, ELAINE,DA COSTA FIGUEIREDO, REJANE M.,DEON, VICTOR HUGO A., TBL as an active learning-teaching methodology for software engineering courses , in the XXXII Brazilian Symposium , p289, 2018 . DOI: 10.1145/3266237.3266253 . Tags: Active Learning .
  13. RAMOS, C. S.,OLIVEIRA, K. M.,ROCHA, A. R. C., Critical issues in SPI Programs: A Holistic View , in The First International Conference on Advances and Trends in Software Engineering , p60-66, 2015 .
  14. RAMOS, C. S.,OLIVEIRA, K. M.,ROCHA, A. R. C., Planejamento de Programa de Melhoria Abordagem Multimodelo , in Simpósio Brasileiro de Qualidade de Software , p79-93, 2015 . DOI: 10.5753/sbqs.2015.15215 . Tags: Software Architecture .

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