This document discusses learning analytics and its use in higher education. It defines learning analytics as using data, analysis, and predictive modeling to improve teaching and learning. This is done by aggregating student data from various systems to gain insights into academic performance and identify at-risk students. Examples are given of universities that have implemented learning analytics to increase graduation rates and student persistence. Challenges discussed include issues with data integration and privacy, and ensuring analytics are used appropriately. The future of learning analytics is predicted to include more data sources and standards to better support personalized learning.

1. Analytics in Action
http://DSign4.education
Higher Education
February 2019

2. Introduction
©2016 L. SCHLENKER
Agenda
Introduction
Definitions
Infrastructure
Use Scenarios
Limits

3. Inputs
Prediction
Evaluation
Actions
Outcomes

4. Education
Telecommunications
Textiles
Medicine
Leisure
Automobile
Separation, alignment, cohesion
Leading the flock Challenges

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