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From Local Patterns to Global Models: Towards Domain Driven Educational Process Mining

From Local Patterns to Global Models: Towards Domain Driven Educational Process Mining. Nikola Tr č ka Mykola Pechenizkiy. Motivation. ?. What is the real curriculum (study program)? How do students really study? Is there a typical/best way to study? Do current prerequisites make sense?

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From Local Patterns to Global Models: Towards Domain Driven Educational Process Mining

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  1. From Local Patterns to Global Models: Towards Domain Driven Educational Process Mining Nikola Trčka MykolaPechenizkiy

  2. Motivation ? What is the real curriculum (study program)? How do students really study? Is there a typical/best way to study? Do current prerequisites make sense? What is my expected time to finish? Should I take course A or course B now? … Student database with exam records Process: Standard techniques YES/NO 80% ISDA’09 - EDM

  3. Proposed approach and architecture Approach: Isolate a set of standard curriculum patterns and based on this patterns mine the curriculum as an executable quantified formal model and analyze it, or (first) manually devise a formal model of the assumed curriculum and test it against the data. Event Log - MXML format supported by ProM Typical forms of requirements in the curriculum Colored Petri net ISDA’09 - EDM

  4. Colored Petri nets ISDA’09 - EDM

  5. token place arc transition (task) Classical Petri nets • Well known and established formalism • Supports all routing constructs (choice, parallelism, sequence, etc.) • No explicit support for data • Example - Complaints handling workflow: ISDA’09 - EDM

  6. Colored Petri nets • Extend Petri nets with data information • Data in tokens - Places typed ISDA’09 - EDM

  7. Modeling Academic Curriculum Patterns ISDA’09 - EDM

  8. Course - Exam construct • Models an exam for course C for every student id • Firing of C adds a new grade to the grade list • There is a maximum number of attempts ISDA’09 - EDM

  9. Start and End pattern • Models courses that must be taken first • Starting place of the model • Graduation is always the last course • Example: Start with either C1 or C2 ISDA’09 - EDM

  10. M-out-of-N pattern • M courses out of a group of N courses must be passed before some other course can be taken • Example: Two from {C1,C2,C3} before D ISDA’09 - EDM

  11. Dependency pattern • Result of C is automatically also a result for some other (weaker) course D • Firing of D adds a grade to the list for course C ISDA’09 - EDM

  12. Expiration pattern • Grades stay valid only for some time, i.e. they can expire • Expiration condition arbitrary • Firing of GradesExpire remove all the grades of id for course C ISDA’09 - EDM

  13. Application: Conformance checking ISDA’09 - EDM

  14. Conformance checking 80% Check whether the (manually constructed) model complies with the log (observed behavior) Has a curriculum pattern always been respected? Possible use: Fraud detection Supported in ProM for classical Petri nets ISDA’09 - EDM

  15. Example 2-out-of-3 pattern check At least 2 courses from { 2Y420,2F725,2IH20 } must be taken before graduation. ISDA’09 - EDM

  16. Conclusions A framework for mining and analysis of educational data is proposed. Main idea: Model/Mine a curriculum as a Colored Petri net using some standard (predefined) patterns Applied in a real-world case study using ProM. Future work: • Implement the actual mining algorithm, and • enable online monitoring support. ISDA’09 - EDM

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