D293 – DETERMINING LEARNING ANALYTICS STRATEGY – SECTION 2 OF 4
Section 2: Determining Learning Analytics Strategy
Lesson 1: The Purpose of Learning Analytics
1.1: Introduction to Learning Analytics
1.2: Learning Analytics in K-12
1.3: Learning Analytics in Higher Education
1.4: Learning Analytics in Workforce Development
Lesson 2: Aligning Learning Analytics Types to Goals
2.1: Leaning Analytics Types and Techniques
2.2: Selecting Learning Analytics Types to Support Goals
Lesson 3: Using Data to Inform Decision-Making
3.1: Types of Data Measures
3.2: Data Collected by Assessment Method
3.3: Data-Informed Decision Making
Lesson 4: Ethical Implications of Learning Analytics
4.1: Ethical Collection of Data
4.2: Ethical Analysis of Data
4.3: Ethical Interpretation of Data
Lesson 1: The Purpose of Learning Analytics
Lesson 1.1: Introduction to Learning Analytics
Learning Objectives
By the end of this lesson, you should be able to:
Define learning analytics and explain its purpose in education.
Differentiate learning analytics from assessment.
Explain how learning analytics supports evidence-based instructional decisions.
Identify the major components of the learning analytics process.
Recognize classroom scenarios that demonstrate learning analytics.
Apply foundational learning analytics concepts to WGU D293 Objective Assessment
(OA) questions.
Introduction
Every day, educators collect enormous amounts of information about their learners. They record
attendance, assign quizzes, evaluate projects, monitor participation, review discussion posts, and
observe classroom behaviors. While these activities generate valuable information, the
information alone does not improve learning.
Learning improves only when educators analyze the information, identify meaningful patterns,
and use those insights to make informed instructional decisions.
This process is known as learning analytics.
Learning analytics has become one of the most important practices in modern education because
it enables educators to make decisions based on evidence rather than assumptions. Instead of
waiting until students fail a final examination, instructors can recognize learning trends early,
identify students who may need additional support, and adjust instruction before learning gaps
become larger.
Page 1 of 180
,In today's digital learning environments, learning analytics combines information from
assessments, learning management systems (LMS), attendance records, classroom participation,
discussion boards, assignments, and many other sources. When interpreted correctly, these data
provide a clearer understanding of how students learn, where they struggle, and what
instructional strategies are most effective.
Rather than asking only,
"Did students learn?"
learning analytics asks,
"How can we help students learn better?"
This shift from measuring learning to improving learning is the foundation of learning analytics.
What Is Learning Analytics?
Learning analytics is the systematic collection, measurement, analysis, interpretation, and
use of learning data to understand and improve learning and the environments in which
learning occurs.
Unlike traditional assessment, which primarily measures what students know or can do, learning
analytics uses multiple sources of information to support instructional decisions that enhance
teaching and learning.
Learning analytics is not simply about collecting data—it is about transforming data into
meaningful actions.
Why Learning Analytics Matters
Educational decisions have traditionally relied on teacher observations, intuition, and end-of-unit
assessments. Although professional judgment remains essential, today's educators have access to
far more evidence than ever before.
Learning analytics allows educators to:
identify learning patterns
monitor student progress
recognize misconceptions early
personalize instruction
improve learner engagement
evaluate instructional effectiveness
support timely interventions
improve educational programs
By using data strategically, educators can make more informed decisions that increase student
success.
Learning Analytics vs. Assessment
Although assessment and learning analytics are closely related, they serve different purposes.
Assessment answers the question:
"What did students learn?"
Learning analytics answers the question:
"What should we do with the information we collected?"
Assessment produces evidence of learning.
Learning analytics analyzes that evidence—along with other information—to improve future
learning experiences.
Page 2 of 180
,Comparison Table
Assessment Learning Analytics
Measures learning Improves learning
Produces evidence Analyzes evidence
Often focuses on one assessment Uses multiple sources of data
Determines achievement Supports instructional decisions
Answers What did students learn? Answers What should we do next?
The Learning Analytics Process
Learning analytics is a continuous cycle rather than a one-time event.
Step 1 – Collect Data
Gather information from multiple learning sources.
Examples include:
quizzes
assignments
attendance
LMS activity
classroom participation
surveys
discussion boards
Step 2 – Analyze the Data
Look for patterns and trends.
Examples:
declining quiz scores
reduced participation
missed assignments
increased absenteeism
Step 3 – Interpret the Results
Determine what the patterns mean.
Ask questions such as:
Why are students struggling?
What misconceptions exist?
Which students need support?
Which instructional strategies are working?
Step 4 – Take Action
Use the findings to improve learning.
Possible actions include:
reteaching difficult concepts
modifying instruction
providing interventions
changing assessments
Page 3 of 180
, improving course design
Step 5 – Evaluate the Results
Determine whether the instructional changes improved learning.
The cycle then begins again.
Sources of Learning Data
Learning analytics uses many different sources rather than relying on a single assessment.
Common data sources include:
formative assessments
summative assessments
attendance records
LMS login activity
assignment completion
discussion participation
surveys
student reflections
classroom observations
The more relevant the evidence, the more accurate instructional decisions become.
Stakeholders Who Use Learning Analytics
Learning analytics benefits many educational stakeholders.
Stakeholder How Learning Analytics Helps
Teachers Improve instruction and monitor progress
Students Receive timely feedback and interventions
School Leaders Evaluate programs and allocate resources
Instructional Designers Improve course design
Colleges and Universities Increase student retention and success
Workforce Trainers Improve employee learning and performance
Benefits of Learning Analytics
Learning analytics helps educators:
make evidence-based decisions
identify learning trends
personalize instruction
improve learner engagement
increase student achievement
monitor learning continuously
support struggling learners early
evaluate instructional effectiveness
Page 4 of 180
Section 2: Determining Learning Analytics Strategy
Lesson 1: The Purpose of Learning Analytics
1.1: Introduction to Learning Analytics
1.2: Learning Analytics in K-12
1.3: Learning Analytics in Higher Education
1.4: Learning Analytics in Workforce Development
Lesson 2: Aligning Learning Analytics Types to Goals
2.1: Leaning Analytics Types and Techniques
2.2: Selecting Learning Analytics Types to Support Goals
Lesson 3: Using Data to Inform Decision-Making
3.1: Types of Data Measures
3.2: Data Collected by Assessment Method
3.3: Data-Informed Decision Making
Lesson 4: Ethical Implications of Learning Analytics
4.1: Ethical Collection of Data
4.2: Ethical Analysis of Data
4.3: Ethical Interpretation of Data
Lesson 1: The Purpose of Learning Analytics
Lesson 1.1: Introduction to Learning Analytics
Learning Objectives
By the end of this lesson, you should be able to:
Define learning analytics and explain its purpose in education.
Differentiate learning analytics from assessment.
Explain how learning analytics supports evidence-based instructional decisions.
Identify the major components of the learning analytics process.
Recognize classroom scenarios that demonstrate learning analytics.
Apply foundational learning analytics concepts to WGU D293 Objective Assessment
(OA) questions.
Introduction
Every day, educators collect enormous amounts of information about their learners. They record
attendance, assign quizzes, evaluate projects, monitor participation, review discussion posts, and
observe classroom behaviors. While these activities generate valuable information, the
information alone does not improve learning.
Learning improves only when educators analyze the information, identify meaningful patterns,
and use those insights to make informed instructional decisions.
This process is known as learning analytics.
Learning analytics has become one of the most important practices in modern education because
it enables educators to make decisions based on evidence rather than assumptions. Instead of
waiting until students fail a final examination, instructors can recognize learning trends early,
identify students who may need additional support, and adjust instruction before learning gaps
become larger.
Page 1 of 180
,In today's digital learning environments, learning analytics combines information from
assessments, learning management systems (LMS), attendance records, classroom participation,
discussion boards, assignments, and many other sources. When interpreted correctly, these data
provide a clearer understanding of how students learn, where they struggle, and what
instructional strategies are most effective.
Rather than asking only,
"Did students learn?"
learning analytics asks,
"How can we help students learn better?"
This shift from measuring learning to improving learning is the foundation of learning analytics.
What Is Learning Analytics?
Learning analytics is the systematic collection, measurement, analysis, interpretation, and
use of learning data to understand and improve learning and the environments in which
learning occurs.
Unlike traditional assessment, which primarily measures what students know or can do, learning
analytics uses multiple sources of information to support instructional decisions that enhance
teaching and learning.
Learning analytics is not simply about collecting data—it is about transforming data into
meaningful actions.
Why Learning Analytics Matters
Educational decisions have traditionally relied on teacher observations, intuition, and end-of-unit
assessments. Although professional judgment remains essential, today's educators have access to
far more evidence than ever before.
Learning analytics allows educators to:
identify learning patterns
monitor student progress
recognize misconceptions early
personalize instruction
improve learner engagement
evaluate instructional effectiveness
support timely interventions
improve educational programs
By using data strategically, educators can make more informed decisions that increase student
success.
Learning Analytics vs. Assessment
Although assessment and learning analytics are closely related, they serve different purposes.
Assessment answers the question:
"What did students learn?"
Learning analytics answers the question:
"What should we do with the information we collected?"
Assessment produces evidence of learning.
Learning analytics analyzes that evidence—along with other information—to improve future
learning experiences.
Page 2 of 180
,Comparison Table
Assessment Learning Analytics
Measures learning Improves learning
Produces evidence Analyzes evidence
Often focuses on one assessment Uses multiple sources of data
Determines achievement Supports instructional decisions
Answers What did students learn? Answers What should we do next?
The Learning Analytics Process
Learning analytics is a continuous cycle rather than a one-time event.
Step 1 – Collect Data
Gather information from multiple learning sources.
Examples include:
quizzes
assignments
attendance
LMS activity
classroom participation
surveys
discussion boards
Step 2 – Analyze the Data
Look for patterns and trends.
Examples:
declining quiz scores
reduced participation
missed assignments
increased absenteeism
Step 3 – Interpret the Results
Determine what the patterns mean.
Ask questions such as:
Why are students struggling?
What misconceptions exist?
Which students need support?
Which instructional strategies are working?
Step 4 – Take Action
Use the findings to improve learning.
Possible actions include:
reteaching difficult concepts
modifying instruction
providing interventions
changing assessments
Page 3 of 180
, improving course design
Step 5 – Evaluate the Results
Determine whether the instructional changes improved learning.
The cycle then begins again.
Sources of Learning Data
Learning analytics uses many different sources rather than relying on a single assessment.
Common data sources include:
formative assessments
summative assessments
attendance records
LMS login activity
assignment completion
discussion participation
surveys
student reflections
classroom observations
The more relevant the evidence, the more accurate instructional decisions become.
Stakeholders Who Use Learning Analytics
Learning analytics benefits many educational stakeholders.
Stakeholder How Learning Analytics Helps
Teachers Improve instruction and monitor progress
Students Receive timely feedback and interventions
School Leaders Evaluate programs and allocate resources
Instructional Designers Improve course design
Colleges and Universities Increase student retention and success
Workforce Trainers Improve employee learning and performance
Benefits of Learning Analytics
Learning analytics helps educators:
make evidence-based decisions
identify learning trends
personalize instruction
improve learner engagement
increase student achievement
monitor learning continuously
support struggling learners early
evaluate instructional effectiveness
Page 4 of 180
