Learning Analytics

LEARNING ANALYTICS

DEFINITION

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LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs

• Siemens & Gazevic, 1st International Conf in Learning Analytics and Knowledge, Alberta, Canada, 2012

• The Horizon Report: 2019 Higher Education Edition, produced by the EDUCAUSE Learning Initiative, identifies learning analytics as one of the digital strategies and technologies expected to enter mainstream use in the near future.

• The main objective of Learning Analytics is Continuous Improvement

LEARNING ANALYTICS STEPS

learning analytics requires 3 steps:

Collect

During the student pathway the Learning Management System is recording data from the interaction between the student and his/her pedagogical environment

Analyze

These raw data could then be used to set up more advanced metrics

Vizualize

An important part of Learning Analytics is to make the data visually understandable. Dashboards with relevant metrics give the opportunity to highlight the data with the help of graphs

TYPES OF LEARNING ANALYTICS

There are many types of learning analytics but we can segment these analytics into three categories:

Learning experience analytics, Learner analytics, Learning program analytics.

LEARNING EXPERIENCE ANALYTICS

Learning experience analytics seek to understand more about a specific learning activity. The learning experience category often answers questions about usage patterns for a specific activity, such as:

often an item is used?

and for how long?

resources or topics do learners search for most?

do learners navigate into the course platform?

ANALYTICS

analytics seek to understand more about a specific person or group of people engaged in a course. It covers questions about usage patterns and performance for specific learners, such as:

Has everyone in this group completed compliance training?

are the strengths of this group? Where are the gaps?

Who need more support? Who is the most engaged/ performing?

LEARNING PROGRAM ANALYTICS

program analytics seek to understand how an overall learning program is performing; A learning program typically encompasses many learners and many learning experiences; Learning program analytics answer questions about strategic decisions, such as:

learning methodology is more effective?

this program meet the educational objectives?

learners acquire the required skills?

THE

LEARNING ANALYTICS METHODOLOGIES

We can define three methodologies for the learning analytics:

Descriptive Analytics

Uses data aggregation and data mining to understand trends and evaluative metrics over time such as student satisfaction surveys, general figures

Diagnostic Analytics

This form of advanced analytics is characterised by techniques such as drill-down, data discovery, data mining and correlations to examine data or content to answer the question ‐ “Why did it happen?” (such as KPI, LMS metrics to improve student engagement, …)

Predictive and Prescriptive Analytics

Combines historical data to identify patterns in the data and applies statistical models and algorithms to capture relationships between various data sets such as preventing difficulties for students at risk, focusing on elements where small changes could have a big impact on improving student engagement,

SOMES EXAMPLES OF LEARNING ANALYTICS APPLICATIONS

Identify students at risk

system uses simple combinations of student grades and frequency of logins to the course site

identify students at risk of failure in advance.

Know your audience’s profile

for a MOOC for example with more information about your participants you can adapt parts of your course to better meet their expectations

Improving your course content

better how students interact with your material to focus your effort to modify only the most important, assure material fit with the students level and expectation

OF LEARNING ANALYTICS APPLICATIONS

Students’ dashboard

These dashboards can, for example, take the form of competency cards allowing students to visualize their achievements

Create personalized recommendation of learning resources

Recommend to each learner additional content or activities, according to their background, level and objective. The objective is to provide resources adapted to each learner, based on their evaluations performance, their course activity , and on the choices made by learners with similar profiles.

Measure Social Interaction (social learning analytics)

The frequency of exchanges, their density and the affinities that emerge from them make it possible to identify the accompanying persons and thus to characterize the links between the various actors (for example if you want to create a student tutoring system)

Coursera

SOME EXAMPLE OF DATAVISUALIZATION: STUDENT PROFILE

SOME EXAMPLE OF DATAVISUALIZATION: STUDENT ENGAGEMENT

Coursera

SOME EXAMPLE OF DATAVISUALIZATION: STUDENT'S BEHAVIOR

Imperial College Londonc Learning