MCMS Mining Course Management Systems

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MCMSMining Course Management Systems
Samia Oussena Thames Valley University Samia.ouss
ena_at_tvu.ac.uk
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Project Aim

• MCMS is a JISC funded project which aims to use
  data mining to support TVU strategy on students
  retention and course monitoring.

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Project Overview
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Project Objectives

• Conduct a detailed survey of the stakeholders
  main areas of concerns and good intervention
  practices.
• Conduct a data analysis of the institution
  database systems relevant to the problem areas.
• Propose and implement a data integration model.
• Build and evaluate data mining models.
• Build an application that will use the data
  mining model and implement intervention
  requirements.

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Process
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TVU Data Sources
Student Background Profile, Course/Module,
Enrolment, Assessment Results
Reading List Loan History
Module Profile
UNIT-E
TALIS-LIST
MSG
Student Online Activities, Module Online Content
Size
Student Basic Skills on English, Math and IT
Course Profile
BLACK BOARD
PROGRESS FILE
PS
E-Resource Access Log
Student Loan History
Course Offering Details
TALIS
Marketing System
Shibboleth
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MCMS Data Warehouse
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Model Driven Data Merging
Data Source
Merging
Data Target
Meta Model
AWM (ATLAS Weaving Model)
Logical Model
UML Based Merging Model
UML Based DataSource Model
UML Based Integrated Model
Physical Model
OWB TM (Oracle Warehouse Builder Transformation
Model)
Flat Files /DB Data
-DB Model -OWB TM
DB Model

• SQL Loader
• PL/SQL

Real Code
- DDL
- DDL
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Design of the course and module cubes
Course Cube
Module Cube
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Example of a Cube

• Sample Query Results

Dropout Rates
Study Mode
School
Dropout Rates
Dropout Rates
Year
Semester
Study Mode
School
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Data Mining Process
Transfer data to fit the data mining models
first. Apply feature importance and associate
rules to find the relation among data features.
Then classify data and extract human friendly
rules and patterns. Regression is then applied to
predict future behaviours.
Feature Importance/ Associate Rules
Classification/ Clustering
Regression/ Prediction
Pre-Processing
2. Find feature relations
4. Predict feature behaviours
1. Pre-Process the data
3. Group data and extract possible rules
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Data Mining Pre-Processing

• Summarize data on different levels (e.g. overall
  module average mark , total number of resits,
  total book loans and etc)
• Discard Short Courses data (150 courses ?100)
• map the entry Certificate into numeric value

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Finding relations Student Data
Is the student performance, such as average
mark, drop out, pass/fail related to student
background profile?
Is the student performance, such as average
mark, drop out, pass/fail related to Blackboard
System and Library Usage?
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Finding Relations student data

• Student performance is not related to the gender,
  race, age, disability, nationality etc.
• But is related to which year he/she is studying
  (Current_StudyYear), BlackBoard Usage
  (BB_Usage) and slightly related to Library Usage
  (Library_Usage)

However, the frequency of BB access is not
related to the student academic performance. Even
for the same module, there are students with
very high marks that use BB very rarely, whereas
some frequent users have very low marks..
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Finding Patterns student data
Part time students enroll with higher
certificate, get higher mark, have less resit,
dropout less, but use library and BB less.
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Prediction Result
Will Student A drop out or not?
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Conclusion and Future work

• The JISC funded MCMS project at Thames Valley
  University aims to apply Data Mining technology
  to institution data sources in order to identify
  predictive rules that can be used to detect and
  improve issues related to student retention
• The project has addressed data integration issues
  including technical, organizational and legal
  issues
• The project built and evaluated data mining
  models that identify student patterns and would
  predict behaviour
• Future Work
• Build a personalised intervention system
• Run a pilot in the next academic year