The document discusses learning analytics and outlines an agenda for an OER policy roundtable. It defines learning analytics and academic analytics. It discusses benefits like reducing attrition and personalizing learning. Examples from universities in Australia are provided. The document outlines what teaching and learning data is needed, strategies for acquiring this data, and potential actionable insights around retention, at-risk students, and learning effectiveness. It concludes with recommendations for next steps like funding research projects, establishing an open analytics platform, and pilot studies.

1. Learning Analytics
OER Policy Roundtable, May 26, 2014, New Delhi
Session Facilitators: Akanksha Bapna, Manish Upadhyay and Viplav Baxi

2. Agenda
 Learning Analytics (10 minutes)
 Teaching Learning Data (30-35 minutes)
 Data Acquisition Strategies (30-35 minutes)
 Actionable insights (30-35 minutes)
 Way Forward (10 minutes)

3. Learning Analytics
What are Learning Analytics?

4. Definition
 Learning Analytics (LA) is 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 Learning analytics are largely concerned with improving learner
success
 Academic Analytics is the improvement of organizational
processes, workflows, resource allocation, and institutional
measurement through the use of learner, academic, and institutional
data Academic analytics, akin to business analytics, are concerned with
improving organizational effectiveness

5. BIG DATA
SOCIAL NETWORK
ANALYSIS
WEB ANALYTICS
OPERATIONS
RESEARCH
NETWORK SCIENCE
STATISTICS
ARTIFICIAL
INTELLIGENCE INFORMATION
VISUALIZATION
SEMANTIC CONTEXT
ANALYSIS

6. Benefits
 REDUCE ATTRITION: Reduce attrition through early detection of at-risk students
 PERSONALIZATION: Personalize and adapt learning process and content
 IMMEDIATE FEEDBACK: Extend and enhance learner achievement, motivation, and confidence by
providing learners with timely information about their performance and that of their peers, as well as
providing suggestions on activities and content that address identified knowledge gaps
 TEACHER EFFICIENCY: Makes better use of teacher time and effort by providing information on which
students need additional help, which students are candidates for mentoring others, and which teaching
practices are making the biggest impact
 BETTER DESIGN: Higher quality learning design and improved curriculum development processes
through the utilization of data generated during real- time instruction and learning activities
 RAPID ACHIEVEMENT: More rapid achievement of learning goals by giving learners access to tools
that help them to evaluate their progress and determine which activities are producing the best results
 INTERACTIVE VISUALIZATIONS: Interactive visualizations of complex information will give learners
and educators the ability to “zoom in” or “zoom out” on data sets, depending on the needs of a
specific teaching or learning context

9. FORUM A FORUM B
SNAPP: Realising the affordances of real-time SNA within networked learning environments, Networked Learning Conference 2010

10. FORUM A FORUM B
•No student interaction
•All interaction via Tutor/Lecturer
•Student to student social
interaction beginning
SNAPP: Realising the affordances of real-time SNA within networked learning environments, Networked Learning Conference 2010

11. • Dense interactions between central
nodes
• Instances of no interaction occurring
among isolated individuals.
• Early warning indicator for
teaching staff investigate lack of
interaction
• Intervention may be necessary
to ensure isolated learners are included
in the emerging community
SNAPP: Realising the affordances of real-time SNA within networked learning environments, Networked Learning Conference 2010

12. Examples
 University of New England: The UNE Early Alert Program (EAP) commenced in 2010 as a
means of identifying students at risk of disengaging from studies, a known contributing
factor to retention rates This program developed the Automated Wellness Engine (AWE) to
analyse student information using 34 triggers with varying weights, which the student
support team used to contact the 200 most at risk students.
 University of Technology, Sydney: UTS has been engaged in a variety of learning
analytics projects to assess scale and impact UTS have used analytics to identify
why students continue to fail subjects or why there are low pass rates in particular
subjects. These findings have led to interventions around the order in which
subjects were taken in particular courses. The early identification of students at risk
of failure or attrition led to a prioritisation of contact made with students in their
first year of study to offer support tools and mechanisms. Additionally, UTS has
piloted a student dashboard, measuring effort and engagement, which then provides
a ‘help yourself’ toolkit/front end
 Many more applications such as for User knowledge modelling, user behaviour
modelling, user experience modelling, user profiling domain modelling, learning
component/ instructional principle analysis, Trend analysis

13. Teaching Learning Data
What is the data that we need on teaching and learning?

14. Teaching Learning Data - Starting Points
In-class vs. out-of-class
Alone or with others
Synchronous or Asynchronous
Learning or Assessment
On various devices
Open or Closed networks
Social Media and Networks
Nodes as well as Edges
Patterns

15. Data Acquisition Strategies
How should we acquire this data?

16. Data Acquisition Strategies - Starting Points
Location (source)
Implicit Vs Explicit
Granularity
Frequency
Storage (sink)
Privacy
Offline data
Encryption/Security
Data Licensing & Fair Use

17. Actionable Insights
What outcomes can we expect?

18. Actionable Insights - Starting Points
Retention
At-Risk (scores)
Capability to self learn
Curricular effectiveness
Content effectiveness
Interaction effectiveness
Learning Difficulties
Learning Trends identification

19. Way Forward
Immediate Next Step suggestions for MHRD

20. Data
 Awareness Generation
 Research (benchmarking, models, predictive models, taxonomies
etc)
 Standards
 Data
 User Profiles
 Usage
 Learning Effectiveness
 Pedagogical Data
 Employer feedback (behavioral level post learning transfer)

21. Acquisition
Multi device, multi platform, multi location
Capture Tools (including clicker technology)
Offline data and digitization
Standards for storage and API for communications
Framework for data to be shared (Open Data Policy), privacy,
security
Evaluation (regular, evidence based learning)
Unified Data Storage

22. Insights
Educational Data Analysts certification/courses
Predictive models (and standards)
Time Series Analysis
Content Analysis
Adaptive Learning engines
Feedback system (online & immediate -> closed
and offline

23. Recommendations
Research projects be funded in learning analytics
Open Analytics Platform (data, software)
Open Data Policy
Use of feedback methods (through predictive modeling) to
continuously guide and achieve desired outcomes
Learning Analytics and Knowledge Workshops and
International Conference
Association with international players like SoLAR
Initiate pilot studies
Appropriate norms for funding be identified
Implement Learning Analytics @ NMEICT across the board

24. Data Acquisition
Recommendation
Engine
Data Store
Content Library
Workshops
Standards based API
for Acquisition
Standards based API
for Extraction
Learning Analytics
Engine
Dashboard
visualization tools
Users & Groups
Prediction
Engine
Standards based
metadata
Assessments
LA Platform Architecture

25. Thanks!