MCMSMining Course Management Systems

Samia OussenaThames Valley UniversitySamia.oussena@tvu.ac.uk

Project Aim

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

Project Overview

BBstudentrecords etc.

MCMS

student information data sources

eventsreportsadviceetc.

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.

educationalprocess

descriptions

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.

Process

TVU Data Sources

UNIT-EUNIT-E

BLACK BOARDBLACK BOARD

TALISTALIS

TALIS-LISTTALIS-LIST

PROGRESS FILEPROGRESS FILE

MSGMSG

PSPS

Marketing SystemMarketing System ShibbolethShibboleth

Student Background Profile, Course/Module, Enrolment, Assessment Results

Student Online Activities, Module Online Content Size

Student Loan History

Reading List Loan History

Student Basic Skills on English, Math and IT

Course Offering Details

Module Profile

Course Profile

E-Resource Access Log

MCMS Data Warehouse

Model Driven Data Merging

AWM(ATLAS Weaving Model)

UML Based Merging Model

- SQL Loader- PL/SQL

Flat Files/DB Data

UML Based DataSource Model

UML Based Integrated Model

- DDL

OWB TM (Oracle Warehouse Builder

Transformation Model)

Data SourceData Source MergingMerging Data TargetData Target

Meta ModelMeta

Model

Logical ModelLogical Model

Physical Model

Physical Model

Real CodeReal Code

-DB Model -OWB TM

- DDL

DB Model

Design of the course and module cubes

Course Cube Module Cube

Example of a Cube

Dropout RatesDropout Rates

• Sample Query Results

Study Mode School

Dropout RatesDropout RatesDropout RatesDropout Rates

YearSchoolSemester Study Mode

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.

2. Find feature relations

4. Predict feature behaviours

Feature Importance/Associate Rules

3. Group data and extract possible rules

Classification/Clustering

Regression/PredictionPre-Processing

1. Pre-Process the data

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

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?”

Finding Relations : student data

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..

•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)

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.

Prediction Result

“Will Student A drop out or not?”

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