Presentation on theme: "MCMS Mining Course Management Systems"— Presentation transcript:
1MCMS Mining Course Management Systems Samia OussenaThames Valley University
2Project AimMCMS is a JISC funded project which aims to use data mining to support TVU strategy on students retention and course monitoring.
3Project Overview BB etc. MCMS student information data sources events recordsetc.MCMSstudent information data sourceseventsreportsadviceMCMS monitors student information sources and generates events, reports, advice etc. to identify potential divergence from and to recommend remedial actions for prescribed educational processes.educationalprocessdescriptions
4Project ObjectivesConduct 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.
5Collecting Data Sources ProcessCollecting Data SourcesData IntegrationData MiningStudent Intervention
6TVU Data Sources UNIT-E TALIS-LIST MSG BLACK BOARD PROGRESS FILE PS Student Background Profile, Course/Module, Enrolment, Assessment ResultsReading List Loan HistoryModule ProfileUNIT-ETALIS-LISTMSGStudent Online Activities,Module Online Content SizeStudent Basic Skills on English, Math and ITCourse ProfileBLACK BOARDPROGRESS FILEPSE-Resource Access LogStudent Loan HistoryCourse Offering DetailsTALISMarketing SystemShibboleth
9Design of the course and module cubes Course CubeModule Cube
10Example of a Cube Sample Query Results Dropout Rates Study Mode School YearSemesterStudy ModeSchool
11Data Mining ProcessTransfer 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-ProcessingFeature Importance/Associate RulesClassification/ClusteringRegression/Prediction1. Pre-Process the data2. Find feature relations4. Predict feature behaviours3. Group data and extract possible rules
12Data 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
13Finding 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?”
14Finding 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..
15Finding 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.
16Prediction 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)
17Conclusion 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 retentionThe project has addressed data integration issues including technical, organizational and legal issuesThe project built and evaluated data mining models that identify student patterns and would predict behaviourFuture Work:Build a personalised intervention systemRun a pilot in the next academic year