5. • Segment, Qualify, Develop, Measure
• What is the business model here?
• Three possible markets – learning,
networking, recognition
• How can we use digital technologies to
improve the business model?
• How do we measure success?
University Business Model
Technology
6. • The use of data, analysis, and predictive
modeling to improve teaching and
learning
• Analytics models aggregate data in new
ways
• Help students and institutions
understand past, present and future
academic performance
• Impact on personalized learning,
pedagogical practices, curriculum
development, institutional planning, and
research
Learning Analytics
Technology
Learning Analytics: Challenges and Future Research
7. • Based on multiple dimensions of a learner’s
activities, including attendance and
participation in class, in co-curricular activities
• Data might reside in any number of
repositories, such as LMSs, learning tools, and
the institution’s student information system
• Applying models and algorithms designed to
produce actionable findings
• Impact on personalized learning, pedagogical
practices, curriculum development, institutional
planning, and research
How does it work?
Technology
8. • The input layer that provides the
infrastructure with the data and the
activities.
• The data layer –which is for storing
student activities carried out in the various
online learning environments (LRS)
• The business layer, which aggregates,
organizes, analyses and customizes
personal data
• The presentation layer, which provide
teachers and students insights into study
behavior
Data Infrastructure
Technology
confluence.sakaiproject.org
How to start with learning analytics?
9. • Georgia State University tailored individual
interventions to narrow the graduation gap for low-
income, first-generation, and minority students
• San Diego State University’s Instructional
Technology Servicesgoal to identify and intervene
with students who were at-risk of failing
• University of Central Florida, an Analytics Insights
and Action Team helps increase undergraduate
persistence by synthesizing insights from various
analytics tools and developing processes that identify
at-risk student
• Digital Innovation Greenhouse at the University of
Michigan works with user communities to adopt
wider use of digital engagement tools like E-Coach, a
tool that personalizes learning for students in large
classes
Whose doing it?
Technology
10. • identify which students are not learning
effectively and intervene to improve the
their educational trajectory
• help students find which academic paths are
best suited to their interests and capitalize on
their individual strength
• map their academic progress in near-real time,
without waiting for midterms or final exams,
and can inspire them to take a more active role
in their learning
• Data gleaned from analytics might help
institutions design better courses and make
better use of learning resources such as faculty
talent
What is the bottom line?
Technology
11. • Proxies of learning - it can be tempting to
mistake correlations for causation
• Requires close cooperation between campus
departments that traditionally have worked
independently (e.g., IT, academic affairs,
student affairs, and faculty).
• Distributed across campus the data is difficult
to integrate, particularly if technology vendors
format data in proprietary ways
• Ethical issues surrounding data privacy and
institutional obligations to act on analytics
findings, including by providing resources to
assist those learners
• Misapprehensions about analytics among
university administrators can result in
unrealistic expectations for resultts
What are the risks?
Technology
12. • From an optional feature to a required
component of academic technologies
• Integration of disparate data sets from a
broader range of sources, including the
Internet of Things
• Evolving learning data standards (e.g., xAPI
and Caliper) may make it possible to aggregate
much more learning data
• applications such as the LMS will increasingly
be judged on how well they integrate with or
provide learning analytics
What does the future hold ?
Technology
13. • Virani K., (2016) Data-driven Education (video)
• Chatti, M., (2016), Learning Analytics: Challenges
and Future Research
• De Wit et al., (2016?) How to start with learning
analytics?
• Smith K.,(2016) Predictive Analytics: Nudging,
Shoving, and Smacking Behaviors in Higher
Education
• Fritz J. and Whitmore J., (2017) Moving the
Heart and Head
Bibliography
Next Steps
14. • What is the organization’s business
model?
• Why does the organization focus on
data?
• How is the Data Science team
organized?
• Which data science techniques does
the organization favor ?
• What is the link between data science
and decision making?
• How does the organization use Data
Science to propel growth
Case Study Questions
Technology