Concept of Learning Analytics

transcript

LEARNING ANALYTICS

Learning – Do we require it?

A world without learning is a world without light!

God when asked would certainly have said, “Let there be learning,” as he said, “Let there be light.”

Learning – Need we analyze it?

We employ our brains on what to eat everyday, what to wear everyday, etc. So why should not we employ our brains on what to learn everyday?

How do we do it? We conduct an analysis.

It lets us know our learning requirements which helps us learn better!

Learning – Was it ever analyzed?

Analysis was born at the time when the realization to learn was born.

So yes, analysis of learning has been continuing for quite a long time.

Learning – We should analyze it.

A good analysis of our learning requirements not

only tells us our own requirements but also our

required methods of learning and the tools of testing the

knowledge gained.

So you see, Learning Analytics is quite an important concept.

Hi, I am Chifro!I shall be your host for

the rest of this presentation.

Before we proceed ahead…

Let’s have a look at what will be discussed!

Table of Contents

1. Definition

2. Core Belief

3. Learning Theories

4. Bloom’s Taxonomy

5. Leaning Analytics – The Two Steps

6. Who All Get the Feedback

7. Summary

Note the topics before we proceed.

Table of

Contents!

Definition

The measurement, collection, analysis,

and reporting of data about learners for

understanding and optimizing the learning

and the environment where it occurs.

Ah! Here is the definition.

Definition

Core Belief

Learning Analytics is centered around

the learners!

You have to know the underlying belief first!

Learning Theories

Over the years, different scientists,

thinkers, and researchers have put

forth many interesting learning

theories. These theories are key to

understanding the method in which

Learning Analytics is carried out. Some

of the theories are:

• Gagne’ Nine Events of Instruction

• Bloom’s Taxonomy

• ADDIE Model

Learning theories never go waste!

Bloom’s Taxonomy

Learning Analytics bases itself around

Bloom’s Taxonomy hence before we

proceed ahead it is important to know

about it.

Perhaps, the most important theory!

Evaluation

Synthesis

Analysis

Application

Comprehension

Knowledge

Bloom’s Taxonomy

Benjamin Bloom formulated a

classification of learning objectives

and this classification is referred as the

Bloom’s Taxonomy.

Evaluation

Synthesis

Analysis

Application

Comprehension

Knowledge

Bloom’s Taxonomy

Learning objectives are the objectives

that teachers set for their students.

Bloom’s Taxonomy segregates the

learning objectives in three domains:

• Cognitive

• Affective

• Psychomotor

Bloom’s Taxonomy

The cognitive domain talks about

knowledge, comprehension, and

critical thinking. This domain houses

the learning objectives aimed at

improving knowledge grasping and

retention and learning through critical

thinking and deductive reasoning.

Critical

Thinking

Bloom’s Taxonomy

The affective domain talks about the

ability to comprehend and recognize

attitudes, emotions, and feelings. This

domain houses the learning objectives

based on subjects likes morals and

ethics.

Bloom’s Taxonomy

The psychomotor domain talks about

the ability to physically manipulate

objects and tools. This domain houses

the learning objectives aimed at

improving hand-eye coordination, fine

motor-skills etc.

Understanding learning will give you the reason

behind analyzing it.

Here we go!

Leaning Analytics – The Two Steps

Learning Analytics, broadly put, is

carried out by undertaking the

following steps in a sequential manner.

1. Data Gathering

2. Data AnalyzingLearningAnalytics

Data Gathering

The gathering of data is undertaken

using multiple methods. Different

types of data Is gathered on

measurable and non-measurable

attributes of the learner. Let’s have a

look at some of the main data types.

Here we go!

Data Types – Performance

How do you increase the learning

abilities of a learner unless you know

about his current learning level and

capability? This extremely important

questions is answered using surveys

that ask learners close-ended

questions pertaining to their marks,

percentages, grades etc. Hence,

performance of the learner can be

adjudged by looking at the progression

of the metrics mentioned with the

passage of time.

Here we go!

Data Types – Attitude

It is important to understand the

attitude of the learner. Hence, data

has to be taken pertaining to the

degree of self-belief in learners. But

how can it be measured. A possible

example is that of gathering data

pertaining to the time taken by the

learner to complete tasks assigned to

Here we go!

Data Types – Learning Environment

The pedagogical scenario today is one

which extensively makes use of

technologies. Hence, the learning

environment need not be necessarily

the school alone.

Here we go!

Data Types – Teaching Style

Data also needs to be taken regarding

the different teaching styles that are in

place. The analysis of this data and its

comparison with the other data types

mentioned in the above frames will

identify the preferable mode-of-

interaction for the learner. There are

primarily three modes of interaction:

• Learner-to-content

• Learner-to-instructor

• Learner-to-learner

Here we go!

Data Types – Family

The family members do play an

important role in the education of

human beings. Hence, data pertaining

to family-details of the learners needs

to be gathered as well.

Here we go!

Data Types – Demographics

DEMOGRAPHICS is also an important

part of data collection for Learning

Analytics.

Here we go!

Data-Gathering Tools

Data is gathered in different ways.

Some examples are given in the next

frames.

Here we go!

Data-Gathering Tools – Tests

Learners are encouraged to undertake

self-surveys through short tests.

Here we go!

Data-Gathering Tools – Feedback

Data is also gathered through filled-in

feedback forms.

Here we go!

Data-Gathering Tools – Classroom

Activities

Classroom activities are also important

tools of data gathering.

Here we go!

ClassroomActivities

Data-Gathering Tools – Observation

Learning takes place through

observation. Analysis can also take

place through observation.

Here we go!

Data-Gathering Tools – Online Behavior

Data pertaining to the online behavior

of learners is also collected. This data

can contain information of different

types. Notable examples have been

given below.

• Frequency: How many times do

learners access the Internet during

the day?

• Time: For how many hours at a time

do learners stay online?

• Preference: Which websites do

they revisit?

Here we go!

Data-Gathering Tools – Peers

Parents fill in survey reports for data

generation about social learning of the

learners.

Here we go!

Frequency of Data Gathering

The frequency of data gathering is

dependent on the requirement and

therefore it can be gathered:

• Immediate basis

• Daily

• Monthly

• Annually

Here we go!

Data Analysis

A good analysis features many

important activities. These activities

are key to identifying the learning

requirements to prepare the proper

action-plans for improvement in

learning. Some of the important

activities have been given in the next

frames.

Here we go!

Data-Analysis Activities – Learning

Patterns

Analysis leads to the identification of

the learning patterns for the learners.

Not every student learns the same

way. There are different learning

patterns in place and the identification

of the correct learning pattern

accentuates the learning.

Here we go!

Data-Analysis Activities – Goal

Proficiency

The goals – the learning objectives –

need to be identified for the learners.

Determination of goals is perhaps one

of the most important activities in

Data Analysis.

Here we go!

Data-Analysis Activities – Causality

Learning takes place through cause-

and-effect scenarios. Hence, relevant

data gathering methods are

undertaken to measure causality as

Here we go!

It’s midnight. Why do I

see a light?

I switched on the light-

Difficulties

The hurdles (confusions, doubts,

apprehensions) in learning need to be

understood and addressed properly.

Here we go!

Influences

Identification of the different elements

acting as learning influencers is an

important activity of Data Analysis.

Here we go!

Feedback (1 of 2)

The improvement of learning can only

take place if the results of the analysis

are shared with the relevant persons.

But who are these relevant persons?

They have been mentioned in the next

frame.

Here we go!

FEEDBACK SHEET

Feedback (1 of 2)

• Teachers

• Administrators

• Policy Makers

• Students

Here we go!

Feedback (2 of 2)

The feedback is used by teacher and

student both for identification,

improvement of different factors

associated with learning. Some

examples have been given in the next

few frames.

Here we go!

FACTORS!

Feedback (2 of 2)

Learning Levels:

• Cognitive level

• Affective level

• Psychomotor level

Here we go!

Feedback (2 of 2)

Adaptive Learning: The usage of

computers as interactive teaching

devices for children.

With learning adorning a global garb in

terms of the increase of its usage of

existing technologies, the concept of

adaptive learning is quickly put into

place on a global scale.

The outcome of Learning Analytics

features the usage of adaptive

learning.

Here we go!

That’s not the right way to do

Feedback (2 of 2)

Identification of possible pitfalls in

learning and resultant alerts.

Timely intervention in resolving those

pitfalls.

Here we go!

Feedback (2 of 2)

Personalization of learning. After all, as

mentioned earlier, learning need not

happen sitting in the classroom only.

Here we go!

Feedback (2 of 2)

Another wonderful feature is

prioritization of the feedbacks.

Prioritization means feedback

regarding:

• “What to do right now”?

• “When to provide feedback”?

Here we go!

Feedback (2 of 2)

Feedback is also important for

identification of the correct learning

strategies. This can take place on the

basis of different factors. Some of

them are given below.

• Goals of the learner

• Goals of the instructor

• Strengths and weaknesses of the

learner

• Level of learner-engagement

Here we go!

Summary (1 of 5)

Here are the key takeaways of this

presentation:

Learning Analytics is important for the

improvement of learning.

Data gathered in Learning Analytics is

based on:

• Performance

• Attitude

• Learning Environment

• Teaching Style

Visit me at:

www.chifro.com

Takeaways!

Summary (2 of 5)

• Family

• Demographics

Tools for data gathering are:

• Tests

• Feedback Forms

• Classroom Activities

• Online-Behavior Metrics

• Peer-Learning Metrics

Visit me at: www.chifro.com

Takeaways!

Summary (3 of 5)

Data can be gathered on an

immediate basis. However, it can also

be gathered daily, weekly, monthly, or

annually.

Activities involved in Data Analysis

• Identification of learning patterns

• Determination of goal proficiency

• Determination of the extent of

learning through causality

• Determination of learning difficulties

Takeaways!

Summary (4 of 5)

• Determination of learning

influencers

Feedback should be given to teachers,

administrators, policy maker, and

students

Feedback should be given at the

correct time

Feedback should address adaptive

learning, learning intervention,

improvement, and personalization of

learning

Takeaways!

Summary (5 of 5)

Feedback should address factors like

learning goals, instructors’ goals,

learners’ strengths and weaknesses,

learning engagement

Takeaways!

Concept of Learning Analytics

Education