AAOU 2022 - Spatial Learning Analytics

Hello everyone.

Thank you very much for giving me the opportunity to present

my research at 35th asian Association of Open Universities conference.

My name is Cameron mir and I'm working in Islamic

Open University as assistant director I. T.

From past 9.5 years and I'm also doing my PhD in remote sensing and G. I. S.

From National University of Science and Technology.

And my research area revolves around educational

technology special learning analytics and model.

I'm an active member of asian Association of Open Use this conference

and I'm also individual member of modal user association and I.

C. D. E. As well.

So the topic of my presentation is design and

implementation of special learning analytics in distance education.

As you know like learning analytics is one of the

popular terminology and research area in which the researchers are working

what I've included.

The new thing in it is the special component which

is coming from the geographical information and system feel.

So it will make it special learning analytics.

So in this research we will see how we can

use special learning analytics to improve the quality of education.

So my research is backed by

the quality of education U. N.

Goal number four which says to ensure inclusive and equitable

quality of education and promote lifelong learning opportunities for all.

And the statistics which are backed by S. D.

G4 are very alarming as we can see there are

more than 22.8 million Children who are out of school

And also around 617 million Children and adults

who lack minimum professions in reading and mathematics.

Also, if you look at the indicators in our area of Asia,

all of them are very alarming for all of us so that

we need to really need to work in improving the quality of education

so how we can solve this quality problem.

This picture is depicting like the first one,

if you can see the formal education system is just like a small car,

it can only accommodate few persons, few people who can afford

or few facilities that government can afford

in both sides.

So

through this

left side we cannot solve the problem of masses.

There comes the need of the conventional

of classical distance education which are being

like many open new cities of Asia and and all all around the world.

They're working hard to uh to improve and to

provide the quality of education at the doorstep.

But again

the problems in conventional distance education are

like the efficiency and the slowness.

So without using technology we cannot give

the quality of education in minimum time.

So the only option left here is the online learning or the learning,

you can say using technology or we can say the blended learning using technology.

So the distance education as all of you know that it is playing very vital role

in promoting education and my research area or

my application of this special learning analytics.

I have applied this on Islamic

open University.

So

city as an introduction as you know,

it's one of the mega Open University having more than one million students in a year

A. I. O. has more than 240,000 teachers which belong to like all over the country.

And it has more than 2000 courses and more than 50 regional campuses.

So there's a lot of special components special data which

can be analyzed um for improving the quality of education.

The study area for this specific research is the province of punjab.

This is the one of the province of one of the highly populated province of Pakistan.

And the data is from

In autumn 20 there around more than 239,000 students which belong to Punjab region.

And this map you can see the clusters of enrollment in each region of Islamic

University.

So the objective of my research was to map the scoring

patterns of distance learning students of punjab province and to see

if there is any data,

if there is any insight which can give

us insight to improve the quality of education.

The second objective was to identify the special correlation between

education and socioeconomic indicators and scoring patterns in each region.

Province of punjab province.

So the third objective was to apply classical and

special regulation to explain the relationship between these indicators

coming to the methodology.

So initially the data was collected for the scoring patterns which was the

primary data and the secondary data included

explanatory variables included the literacy rate,

the socioeconomic variables and the and the

other indicators of uh of that province.

So data standardization was done.

Then the data with geo visualization was applied in different G. I. S.

Softwares to see the visual patterns among

all the regions.

In the third step the special auto

correlations and different pattern analysis was analyzed.

And in last it was observed there was an inflated scoring patterns.

And to predict the scoring. The scoring model

variables included in my study were the average

assignment marks which which I'm calling it a

scoring student scores and the independent variables which

are the external variables which included educational indicators,

socioeconomic indicators, covid indicator and H.

D. I. Human development index

within the educational indicators.

It included the literacy rate, the resource persons,

the number of workshops and the retention score.

The socio economic parameters included a lot

of parameters for example the primary enrollment,

the secondary enrolment, household access to the water hospital bears.

And it was it was having a cumulative score which which was called a Z. Score.

Also we included the covid index derivations which was conducted by another study.

So it was also having like cumulative data of all the provinces,

all the districts which we correlated with the scoring patterns.

This is the covid temporal map which is based on some other study.

Another indicator which was included,

it was human development index and it is these are the factors which

were considered by the UNDP for derivation of human development index which are the

standard of living based on the grouse

national income and healthy life corresponding to

the life expectancy index and the knowledge according to the years of schooling.

Now coming to the interesting part of the study which is the result.

Now in this you can see different maps which

were generated based on the the special letter.

This map is showing you the general population, the people of population of

in each province.

And you can see there is a pattern, there's a visual pattern,

you can see that in the middle,

in the bottom and in upside you can see different patterns of population.

Again if you look at the literacy rate this is also

a secondary or external data which is published by the government

and you can see there is a patron in the provinces which belong to the southern path.

They have loaded Tracy and

regions which are in the northern side of the punjab province,

they have like high literacy rate.

Also if you look at the past passing percentage which

is like specific information of like distance learning students.

So again there is a pattern but the pattern is quite like opposite.

If you can see the southern part they have

like more passing percentage as compared to them.

Northern part.

Also if you look at

the scoring pattern

of the punjab province you can see there is a pattern in a visually

visually we can see the southern belles of the southern part of the province.

They have like high

average score of assignments, average scoring patterns.

Whereas the regions which belong to the northern side,

they have slightly less average score.

Also,

if we look at the other educational indicators

which are published by some other studies,

if you look at the learning score, overall learning score of the punjab province,

again, there is a pattern.

If you look at the retention score again you can see there is a pattern

and the gender score. Again, you can see there is a pattern.

Now let us correlate this pattern with different um

indicators like these maps are showing you the socioeconomic, the covid cases,

energy index and HD I index.

Again every factor has some pattern in in it in itself as well.

Now if we

do the scatter plot between the average assignment and literacy, you can see

there is an inverse of negative correlation

between the scoring pattern and the literacy,

the literacy rate

increasing, the average score is also

decreasing.

Also if you like that if the literacy rate is low,

the average assignment score is getting higher.

Also if you look at

the retention score and literacy,

so there is a direct or positive correlation high

highly positive correlation between literacy and the retention score.

If you look at the average score correlation chart at like 0.05 significant values.

Again there's there's a positive,

significant negative relationship between literacy

rate and the scoring patterns.

Also,

the classical regression model is showing like um the R value with more than like

0.62.

And it is giving we can predict the average score by

this model of literacy rate retention score and resource persons.

Now, coming to the conclusion of my study,

the result shows that there's a pattern in students score.

That's the first part of my study,

like the pattern exists in the school scores and to see

how it is related with other factors like we related,

correlated it with literacy rate.

So the result shows that it's negatively correlated with the literacy rate

and the retention scores and human development index and covid index,

and also socioeconomic index of that specific region.

So, what what was concluded what, what was observed?

So it seems there is an inflated scoring which exist in the region,

especially where the literacy rate is low

or the retention scare is low.

So it was it was logically expected that with the literacy rate is low,

the average score should also be low.

But in this study, the special patterns or the special analytics,

Gay was like different results.

So we can name it as an inflated scoring.

So we don't know the reason.

So there is a need to further investigate

the reasons why the inflated scoring exist,

which can ultimately help us to improve the quality of marking of

the or improve the quality of scores in the low literacy regions.

Or maybe there might be some other reasons which needs some further studies.

Yeah,

so this was all about my study which is like

designing and implementing the special

learning analytics in distance education,

especially when we have like large number of data and that

data is like belong to different area or geographical aspects.

So that makes the G. I. S. Or the special software is more accurate to work.

And it is where the

helpful to visualize such kind of data in

our maps and to relate these analytics with different

factors to make like more appropriate decisions or to

make more precise decisions according to the data.

Thank you very much Once again for giving me the opportunity.

Um If you have any question you can

email me because I think uh or if I'm available online, so I can also answer that.

Thank you very much.

AAOU 2022 - Spatial Learning Analytics

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