1. MCMS Mining Course Management Systems
Samia Oussena
Thames Valley University

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

3. Project Overview BB etc. MCMS student information data sources events
records
etc.
MCMS
student information data sources
events
reports
advice
MCMS monitors student information sources and generates events, reports, advice etc. to identify potential divergence from and to recommend remedial actions for prescribed educational processes.
educational
process
descriptions

4. Project Objectives
Conduct a detailed survey of the stakeholder’s 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.

5. Collecting Data Sources
Process
Collecting Data Sources
Data Integration
Data Mining
Student Intervention

6. TVU Data Sources UNIT-E TALIS-LIST MSG BLACK BOARD PROGRESS FILE PS
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

7. MCMS Data Warehouse

8. Model Driven Data Merging
Data Source
Merging
Data Target
Meta Model
AWM
(ATLAS Weaving Model)
Logical Model
UML Based
DataSource Model
UML Based
Merging Model
UML Based
Integrated Model
Physical
Model
OWB TM
(Oracle Warehouse Builder
Transformation Model)
Flat Files
/DB Data
-DB Model
-OWB TM
DB Model
Real
Code
SQL Loader
PL/SQL
- DDL
- DDL

9. Design of the course and module cubes
Course Cube
Module Cube

10. Example of a Cube Sample Query Results Dropout Rates Study Mode School
Year
Semester
Study Mode
School

11. 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.
Pre-Processing
Feature Importance/
Associate Rules
Classification/
Clustering
Regression/
Prediction
1. Pre-Process the data
2. Find feature relations
4. Predict feature behaviours
3. Group data and extract possible rules

12. 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

13. 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?”

14. 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..

15. Finding Patterns: student data
“Do part time students behave differently from full-time students?”
Part time students enroll with higher certificate, get higher mark, have less resit, dropout less, but use library and BB less.

16. Prediction Result “Will Student A drop out or not?” Naïve Bayes
The Naive Bayes algorithm is based on conditional probabilities. It uses Bayes' Theorem( which expresses the posterior probability of a hypothesis in terms of the prior probabilities)

17. 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