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How could learning analytics
improve teaching and learning in
Higher Education?
The UOC Case
Dr. Josep M Duart
Universitat Oberta de Catalunya (UOC)
EDEN Executive Committe member
Learning
Analytics
“The measurement, collection,
analysis and reporting of data about
learners and their contexts, for
purposes of understanding and
optimizing learning and the
environments in which it occurs”
G. Siemens, 2011
Learning
Analytics
Learners
DataMetrics
Interventions
The Learning
Analytics
Cycle
Doug Clow, 2013
Analytics
Report
(Past)
Automated
Analytics
(Present)
Predictive
Analytics
(Future)
Open University of
Catalonia (UOC)
Who
we are
We offer people
lifelong learning
• The world's first
online university
• The only online university with
20 years' experience
• More than 58,000 graduates,
• Specialists in e-learning
and the knowledge society.
• A community of over 200,000
people (professors, tutors,
students, graduates)
• In more than 90 countries
• A unique educational model
A global
knowledge
network
60%
70%
78%
are aged 30 or
over
prior university
qualification
study and have a
job
* Data for academic year 2015/2016
54,059Students
What are we
doing in
Learning
Analytics
Analyzing
Students
Behavior
• Virtual Campus
• Academic and Social Community
• Innovative and interactive
Educational Model
• A community of over 200,000
people (professors, tutors,
students, graduates)
• In more than 90 countries
Capturing
Data
in Virtual
Learning
Enviroments
Admission and
pre-enrolment
Learner data Enrolment data Navigational and
interaction data
Other dataSurvey dataEvaluation data
Learners’
expectations and
goal
Capturing evidence-based data
Learners’
sociodemographic
data. Age, residence,
previous degree, …
Courses or subjects
per semester;
institutional
recommendations
Interaction on VC
(students,
professors, learning
materials, Ligrary,
etc.)
Partial and total
academic
performance
Institutional survey
(VC, Learning
materials,
professors, etc.)
Innovation and
research projects,
intelligent tutoring
system, etc.
* Minguillón, J; et al. (2016) “Learning
analytics in practice: Providing e-learning
researchers and practicioners with activity
data”
1. Session Level (short-term /
micro)
What does the learner do when
he/she connects to the Virtual
Campus?
Email uses; learning resources
access, etc.
2. Course level (mid-term / meso)
What does the learner do during a
course or academic semester?
Assessments; reply messages on
virtual forum, etc.
3. Institutional Level (long-term /
macro)
What does the learner do from the
time of enrolment until he/she
finishes (or abandons) the degree?)
Number of subjects enrolled,
institutional interaction, etc.
Three-level
Data
Analysis
* Minguillón, J; et al. (2016) “Learning
analytics in practice: Providing e-learning
researchers and practicioners with activity
data”
Case study:
Fighting early
dropout
Each semester almost a 30% of
the new students enrolling into a
degree take a break during their
second semester.
Only around a 10% of these
students returns after such a
break
Data used for analysing dropout
causes:
• Pre-enrolment: learner profile (age, gender,
previous academic background, admission
procedure, etc.)
• Enrolment data: Number and type of
courses enrolled
• Navigational and interaction data during the
semester
• Answers to institutional surveys
• Decision taken about enrolling or taking a
break
Predictive power data:
Navigational and interactive data
during the academic semester
Evaluation data (continuous and
final)
Early
Dropout
* Grau-Valldosera, J; et al. (2014)
“Rethinking dropout in online higher
education: The case of the Universitat
Oberta de Catalunya” IRRODL
Institutional proposal
1. To revise current processes
and data sources, detecting
omissions and inconsistencies:
Rethink and redesign enrolment and
admission process
Collect more candidates information related
to learning strategies
Tutors on enrolment and admission process
2. To convert all the institutional
know-how about dropout into a
complex alert model:
Continuous assessment activities
Alerts on interaction and assessment
activities
Evaluation and feedback
Fighting
Early
Dropout
UOC.universitat
@UOCuniversitat
UOCuniversitat
@jduart // @ETHEjournal
Thank you!
Josep M Duart
jduart@uoc.edu
What sets
us apart
The educational
model

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How could learning analytics improve teaching and learning in higher education

  • 1. How could learning analytics improve teaching and learning in Higher Education? The UOC Case Dr. Josep M Duart Universitat Oberta de Catalunya (UOC) EDEN Executive Committe member
  • 3. “The measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” G. Siemens, 2011 Learning Analytics Learners DataMetrics Interventions The Learning Analytics Cycle Doug Clow, 2013 Analytics Report (Past) Automated Analytics (Present) Predictive Analytics (Future)
  • 4. Open University of Catalonia (UOC) Who we are
  • 5. We offer people lifelong learning • The world's first online university • The only online university with 20 years' experience • More than 58,000 graduates, • Specialists in e-learning and the knowledge society. • A community of over 200,000 people (professors, tutors, students, graduates) • In more than 90 countries • A unique educational model A global knowledge network
  • 6. 60% 70% 78% are aged 30 or over prior university qualification study and have a job * Data for academic year 2015/2016 54,059Students
  • 7. What are we doing in Learning Analytics
  • 8. Analyzing Students Behavior • Virtual Campus • Academic and Social Community • Innovative and interactive Educational Model • A community of over 200,000 people (professors, tutors, students, graduates) • In more than 90 countries Capturing Data in Virtual Learning Enviroments
  • 9. Admission and pre-enrolment Learner data Enrolment data Navigational and interaction data Other dataSurvey dataEvaluation data Learners’ expectations and goal Capturing evidence-based data Learners’ sociodemographic data. Age, residence, previous degree, … Courses or subjects per semester; institutional recommendations Interaction on VC (students, professors, learning materials, Ligrary, etc.) Partial and total academic performance Institutional survey (VC, Learning materials, professors, etc.) Innovation and research projects, intelligent tutoring system, etc. * Minguillón, J; et al. (2016) “Learning analytics in practice: Providing e-learning researchers and practicioners with activity data”
  • 10. 1. Session Level (short-term / micro) What does the learner do when he/she connects to the Virtual Campus? Email uses; learning resources access, etc. 2. Course level (mid-term / meso) What does the learner do during a course or academic semester? Assessments; reply messages on virtual forum, etc. 3. Institutional Level (long-term / macro) What does the learner do from the time of enrolment until he/she finishes (or abandons) the degree?) Number of subjects enrolled, institutional interaction, etc. Three-level Data Analysis * Minguillón, J; et al. (2016) “Learning analytics in practice: Providing e-learning researchers and practicioners with activity data”
  • 12. Each semester almost a 30% of the new students enrolling into a degree take a break during their second semester. Only around a 10% of these students returns after such a break Data used for analysing dropout causes: • Pre-enrolment: learner profile (age, gender, previous academic background, admission procedure, etc.) • Enrolment data: Number and type of courses enrolled • Navigational and interaction data during the semester • Answers to institutional surveys • Decision taken about enrolling or taking a break Predictive power data: Navigational and interactive data during the academic semester Evaluation data (continuous and final) Early Dropout * Grau-Valldosera, J; et al. (2014) “Rethinking dropout in online higher education: The case of the Universitat Oberta de Catalunya” IRRODL
  • 13. Institutional proposal 1. To revise current processes and data sources, detecting omissions and inconsistencies: Rethink and redesign enrolment and admission process Collect more candidates information related to learning strategies Tutors on enrolment and admission process 2. To convert all the institutional know-how about dropout into a complex alert model: Continuous assessment activities Alerts on interaction and assessment activities Evaluation and feedback Fighting Early Dropout

Editor's Notes

  1. What are the benefits of this educational model for the student? The UOC student is supported at all times by specialized professors who guide them, offer advice and maintain their motivation and engagement in the learning process. This constant coaching seeks to ensure that the target skills are acquired, through continuous assessment. Personalized treatment and a close relationship with the teaching team.
  2. But who are we? What do we offer that makes us unique? How could we explain ourselves in another way?
  3. Because it is true that a) we are the world’s first online university; b) the only online university that has 20 years’ experience; c) we have more than 68,000 graduates (qualifications issued, of 30 ECTS credits or more); and that d) we are specialists in e-learning and the knowledge society. But what is most significant, what the UOC uniquely offers, is the possibility of lifelong learning. At the UOC we provide people lifelong learning and educational opportunities with a standard and non-standard offer of courses, contributing to their occupational and personal development. Moreover, we like to present ourselves by highlighting the fact that we are a GLOBAL KNOWLEDGE NETWORK, formed by more than 200,000 people from across the globe, from different fields and spheres, all connected with one another. This comprises students, graduates, teachers and researchers, who bring together their skills and expertise not only for their own benefit (personal and professional), but also for the benefit of the rest of the collective. To achieve this, we have a unique educational model and the best specialists in e-learning, internationally acknowledged for their research.
  4. At present, of the almost 50,000 students (exactly: 49,672): 80% study and work (exactly: 79.40%), 70% already have a university qualification, and 60% are aged 30 or over. These figures provide us with our own unique profile, far from a conventional university, that has mainly students aged 18, or students who are doing postgraduate studies after having finished a bachelor’s degree. Our typical student studies and works, has a prior university qualification and is aged 30 or over. She/he is a student with experience and knowledge, who knows what they want and who seeks to develop their education on an ongoing basis. [2014-2015 Academic Year Report data]
  5. What are the benefits of this educational model for the student? The UOC student is supported at all times by specialized professors who guide them, offer advice and maintain their motivation and engagement in the learning process. This constant coaching seeks to ensure that the target skills are acquired, through continuous assessment. Personalized treatment and a close relationship with the teaching team.
  6. Because it is true that a) we are the world’s first online university; b) the only online university that has 20 years’ experience; c) we have more than 68,000 graduates (qualifications issued, of 30 ECTS credits or more); and that d) we are specialists in e-learning and the knowledge society. But what is most significant, what the UOC uniquely offers, is the possibility of lifelong learning. At the UOC we provide people lifelong learning and educational opportunities with a standard and non-standard offer of courses, contributing to their occupational and personal development. Moreover, we like to present ourselves by highlighting the fact that we are a GLOBAL KNOWLEDGE NETWORK, formed by more than 200,000 people from across the globe, from different fields and spheres, all connected with one another. This comprises students, graduates, teachers and researchers, who bring together their skills and expertise not only for their own benefit (personal and professional), but also for the benefit of the rest of the collective. To achieve this, we have a unique educational model and the best specialists in e-learning, internationally acknowledged for their research.
  7. As an online university, there are three aspects that make us unique: 20 years of experience. Specialization in e-learning. Our EDUCATIONAL MODEL.
  8. As an online university, there are three aspects that make us unique: 20 years of experience. Specialization in e-learning. Our EDUCATIONAL MODEL.