PREDICTIVE ANALYTICSfor education

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Challenges facing online education

Students learn in digital Islands

Online learners learn in digital islands

Even though there is scope for group interaction(wiki, forum) but is limited unlike in a physical classroom. Also you cannot discuss some of the novel ideas Students may get

Feedback

The strength of Online education is to reach out and teach a bigger audience but on the flip side there is no single way that a teacher can reach out to a Single Student and give the required feedback on his performance

Tutor's Blindspot

Tutors can assess the difficulty of a subject in different ways and may not be aware of the Students' struggles in a particular course

Procrastination

Since it is On-line learning there is a tendency for Students to delay in finishing the course/assignments

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Overview

With Massive Open Online Courses (MOOCs) such as Coursera, FutureLearn or any other Online learning platform becoming the new way OF learning, there is an urgent need for Universities to identify the Students At-Risk of dropouts or failures from courses so that timely intervention can bring these Students back on the right track.

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Students at Risk – A case study

Featured as one of the Top 4 case studies at NASSCOM Tech series on Analytics 2017

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Students at Risk – A case study

Business Problem

To predict at-risk students within a course so

that pro-active steps could be taken to avoid

drop-outs/failures

Benefits

Students would successfully complete the courses and score better marks too.

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Data Processing …

The below figure shows the typical processes of Data analysis of a Dataset.

Receive the Datasets (.csv)

Process the Datasetsfor Analysis

Analyse the DatasetsBuild the Model

Visualize the Analysed data

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Data Processing …

The data was received as a set of .csv files which gave the complete details. The processing of the data included the following activities:

• uploading .csv files to Postgresql

• Created joins, tables, views to aggregate the data

• The result was refined datasets

The refined datasets are passed on to Data Analysis team for analysis

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Predictive Analysis Process

Identify theSuitable Algorithm

Visualize

Build themodel

Evaluate/Deploythe model

Monitor/Refactorthe model

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Datasets

30%

70%

Train Test

The refined datasets are divided into train and test datasets in order to build the Model

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Tools and Technology

R

Postgresql

ACL UI Framework

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About the Dataset

The Dataset had a sample of

Courses offered in the month of February and October of 2013 and 2014

A total of 8 courses/Modules were offered

Total number of students – 323K in 2013 and 250K in 2014

The data mainly consisted of

Demographic Data:

gender,age,region,highest education …etc

Online activities-related Data:

OLE(Online Learning Environment), Student Assessment (tests,score,) etc.

Aggregated OLE data available daily.

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Approach

Revalidating the model 2 –assessment angle (Conditional probability)

Results are based on Assessment type

At-Risk

Not at Risk

Bayesian stats

(assessment)

At-Risk

Not at Risk

M3- Model based

on Clustering –

based on OLE data

At-Risk

Not at Risk

M2 – Model2

based in Demo+

OLE

M1 - Model1

based on

demographics

Model 1 - Demographics

Student enrols to various courses in a given semester. Since no online data is available in the beginning the model is built on the demographics data only. This is just the first sneak-peek.

Model 2 – Demographic + OLE (Online Learning Environment)

After the completion of Week 0, the system start capturing the online activities of the Students starts predicting the Risk from week 1.As an extra step, the output of the Model 2 is further assessed based on assessment type.

Model 3 – using clustering techniques

The OLE data is fed and then analysed as a clustering technique based on the online activities of a student

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Model 1 – based on Demographics

The 9 predictors considered are mainly based on demographics and few parameters like number of previous attempts etc..

The Decision Tree algorithm gave an accuracy of 50%

Model

Decision Tree for Demographic Data 51.70% 64.90% 18.50%

Accuracy Precision Recall

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Model 2 – based on Demographics + OLE (online learning

The 8 more predictors are mainly focussed on the data on Students online activities/tasks etc..

Model

Decision Tree for Demographic+OLE 96.00% 97.20% 95.64%

Accuracy Precision Recall

As an extra step, we are passing the output of the model 2 and is viewed with assessment angle.

Assessment by Tutor (TMA), Assessment by Computer(online assessment), Final Exam using Baye’s

rule

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Model 2 – Output of model 2 is passed through Bayes rule

Assessments - 2013

Total number of students: 323419

Pass or distinction: 276897

Fail or withdrawn: 46522

Prior Probabilities:

P(at risk) = 0.144 , P(not at risk) = 0.856

After TMA (Baye's rule)

P(AT-Risk|Model_at_risk) = 0.97, P(NOT-Risk | Model_at_risk) =

0.03

P(AT-Risk|Model_not_at_risk) = 0.15,P(NOT-

Risk|Model_not_at_risk) = 0.98

97% students who fail at TMA (Tutor marked assessment) fail in

2013

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Model 2 – Output of model 2 is passed through Bayes rule

Assessments - 2014

Total number of students: 250065

Pass or distinction: 173959

Fail or withdrawn: 76106

Prior Probabilities:

-P(at risk) = 0.303 , P(not at risk) = 0.696

After TMA (Baye's rule)

P(AT-Risk|Model_at_risk) = 0.97, P(NOT-Risk |

Model_at_risk) = 0.03

P(AT-Risk | Model_not_at_risk) = 0.15, P(NOT-

Risk|Model_not_at_risk) = 0.98

97% students who fail at TMA fail in 2014

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Model 2 – Output of model 2 is passed through Bayes rule

Assessments - 2014

The model 3 data is built on the online learning data (OLE) activities per week.

Using Clustering Students at Risk and Not at Risk are grouped.

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Model 3 – Clustering view Students at Risk

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Model 3 – Clustering view of Students at Not risk

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Final risk – calculated based on the sum of all the three models

0 - Not at Risk1 - At Risk

If the sum is =>2 the UI alerts are generated

Sum of all the

3 results

Model 1 Model 2 Model 3

0/1

0/10/1

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UI –Dashboard/Alerts – Student view

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UI - Dashboard/Alerts - Tutor’s Input

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Conclusions – Clear indicators

88% of the students who were involved in the forum activities are not at risk

Courses D and F in 2013 and 2014 have a higher of percentage of Students At Risk.

5% of the Students who were active in the quiz, sub content were at Risk.

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Conclusions – Interesting revelations …

Students who have completed degree program also have a higher chance of not

completing the exams falling under Students-At risk. They enrol just for Knowledge

purposes.

Assessment type(Tutor, Computer) stood out as an important factor. Students were at

high risk for the tutor monitored assessment type.

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Summary

Identifying or alerting the faculty on the students who are at-risk

Helping Students to perform better by giving out recommendations

Completing the courses and thus guiding the students

Overall increasing the revenues

Helping in provide a good feedback on the courses/faculty

Improving methods in teaching/add pre-requisites

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