MCMS Mining Course Management Systems Samia Oussena Thames Valley University...
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Transcript of MCMS Mining Course Management Systems Samia Oussena Thames Valley University...
![Page 2: MCMS Mining Course Management Systems Samia Oussena Thames Valley University Samia.oussena@tvu.ac.uk.](https://reader035.fdocuments.us/reader035/viewer/2022062307/551613e2550346a2308b5426/html5/thumbnails/2.jpg)
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
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
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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.
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Process
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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
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MCMS Data Warehouse
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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
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Design of the course and module cubes
Course Cube Module Cube
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Example of a Cube
Dropout RatesDropout Rates
• Sample Query Results
Study Mode School
Dropout RatesDropout RatesDropout RatesDropout Rates
YearSchoolSemester Study Mode
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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.
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
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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
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)
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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.
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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