UFCG - SAC2014 - Lessons Learned from an Online Open Course: A Brasilian Case Study

LESSONS LEARNED FROM AN ONLINE OPEN
COURSE: A BRAZILIAN CASE STUDY
Ranilson Oscar Araújo Paiva
Dr. Ig Ibert Bittencourt Santana Pinto
SYMPOSIUM OF APPLIED COMPUTING 2014
GYEONGJU, SOUTH KOREA

Agenda
• The Online Open Course
• Problem Found
• Investigation
• Lessons Learned
• Conclusion
• References
2/27Ranilson Paiva | SAC2014 - GyeongJu | Lessons Learned from an Online Open Course: A Brazilian Case Study

• Online Environment for
Language Learning
• Developed by the Federal
University of Alagoas – Brazil
• Offers Online free courses
• Gamified Environment
• Used Ontologies
3/27
The Online Open Course
Ranilson Paiva | SAC2014 - GyeongJu | Lessons Learned from an Online Open Course: A Brazilian Case Study

• UFAL Línguas Espanhol
– 2075 registration forms
• 200 submissions were accepted
– 100 Students from the University (UFAL)
– 100 Students from public schools (High School)
– 5 monthes course (October 2012 to February 2013)
– 3 Modules (With 2 Units Each)
• All classes were online
• Some exams were held in classroom
• 1 Teacher + 8 Tutors
– 780 megabytes of interactional data
• 1,200,000 RDF Triples

Problem Found

Investigation
Research Questions
– What kind of interactions led students to fail?
– What kind of interactions led students to succeed?
– How can we detect these interactions?
– How can we help teachers reacting to them?

Investigation
According to [MORAN, 2006], students’
participation can be evaluated by
analyzing their interactions with forum,
chat, blogs and lists.

Investigation
Educational Resources
• Exercises
– Check topics’ understanding
– No points awarded
• Mock Tests (Tests)
– Check unit’s learning
– No points awarded
• Activities
– Proposed by the teachers
– Awarded points
• Exams
– In-classroom and Mandatory
– Awarded points

Investigation
Interactional Points
(Educational Resources)
• Accesses*
• Videos*
• Forum Interactions
• Chat Interactions
• Exercises
– Answered and Correct
– Weights (easy = 1, medium = 3 and difficult = 5)
• Tests
• Activities
• In-Classroom Exams*

Investigation

Investigation
• Low Performance Interactions
– 0 to 1499 points
– 120 Students
– All failed
• Medium Performance Interactions
– 1500 to 3499 points
– 63 Students
– 40 failed / 23 succeeded
• High Performance Interactions
– 3500 to 5000 points
– 14 Students
– All succeeded

Investigation
According to [ROMERO, 2011],
Educational Data Mining (EDM) has the
objective of finding relevant information
(detect significant patterns) from big
amounts of data (educational context).

Investigation

Lessons Learned
• What kind of interactions led students to fail?
– Low quality interactions
• Avoiding more difficult exercises
• Avoiding tests
• Avoiding activities proposed by the teacher
• Avoiding social interactions (chat and forum)
• What kind of interactions led students to succeed?
– Good quality interactions
• Just the opposite of low quality interactions
• How can we detect these interactions?
– Classify students according to the way their interactions with educational resources
• How can we help teachers reacting to them?
– Warn teachers/tutors when these behaviors are detected
– Identify why students’ interaction quality is low
– Recommend appropriate actions to overcome the pedagogical issues found

Conclusion
• Teachers need assistance to help students
– Considerable amount of data
– Reports and graphs do not provide quick help
• Students need help
– Quick
– Personalized to their needs
• The learning environment
– Prepared to identify pedagogial issues
– Prepared to take reactive or preventive measures

References
1. BAYER, Jaroslav; BUDZOVSKA, Hana; GERYK, Jan; OBSIVAC, Tomás;
POPELINSKY, Lubomir. Predicting drop-out from social behaviour of students.
Educational Data Mining Conference, 2012.
2. HUANG, Shaobo; FANG, Ning. A Work in Progress: Early Prediction of
Students' Academic Performance in an Introductory Engineering Course
Through Different Mathematical Modeling Techniques. Frontiers in
Education, 2012.
3. KANELLOPOULOS, Dimitris; KOTSIANTIS, Sotiris. Towards an ontology-based
system for intelligent prediction of student dropouts in distance education.
International Journal of Management in Education. Vol 2, No. 2, 2008.
4. MORAN, José Manoel. O que aprendi sobre avaliação em cursos semi-
presenciais. In: SILVA, Marco; SANTOS, Edméa (Orgs). Avaliação da
Aprendizagem em Educação Online. São Paulo: Loyola, 2006. Available at:
http://www.metodista.br/atualiza/conteudo/cursos/docentes/semana-de-
capacitacao-docente-fev-20111/arquivos/avaliacao-cursos-semi.pdf.
5. PAIVA, Ranilson O. A.; BITTENCOURT, Ig Ibert; SILVA, Alan Pedro, 2013. Uma
Ferramenta para a Recomendação Pedagógica Baseada em Mineração de
Dados Educacionais. Federal University of Alagoas- Programa de Pós-
Graduação em Modelagem Computacional do Conhecimento. Maceió. Brasil.
6. ROMERO, Cristóbal; VENTURA, Sebastian; PECHENIZKIY; BAKER, Ryan.
Handbook of Educational Data Mining. Florida: CRC Press, 2011.

Authors
Daniel Borges
Federal University of Alagoas
Jário Santos
Dr. Ig Ibert Bittencourt
Dr. Alan Pedro da Silva

Place
Maceió, Alagoas - Brazil

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UFCG - SAC2014 - Lessons Learned from an Online Open Course: A Brasilian Case Study

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