owd2012 - 2,3 - studiesucces verhogen met learging analytics - mykola pechenizkiy
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CurriM: Curriculum Mining
Mykola PechenizkiyTU Eindhoven
Learning Analytics Innovation10 October 2012
SURFfoundation, Utrecht, the Netherlands
Initial Motivation for CurriM• Current practice:
– We think we know what our curriculum is and how the students study. But is this true?
• CurriM aims at providing tools to analyze – how the students actually study
• Who would benefit from our tool?– Directors of education, study advisers, students
• Goal: showcase the potential and feasibility– Data mining and process mining techniques– 10 years of TUE administrative data; exam grades
Learning Analytics @Surf 10 October 2012, Utrecht,
2CurriM: Curriculum MiningMykola Pechenizkiy, Eindhoven University of Technology
Questions for CurriM to Answer• What is the real academic curriculum (study
program)?• How do students really study?• Is there a typical (or the best) way to study?• Do current prerequisites make sense?• Is the particular curriculum constraint obeyed? • How likely is it that a student will finish the studies
successfully or will drop out?• What is my expected time to finish?• Should I now take courses A & B & C or C & D?
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3CurriM: Curriculum MiningMykola Pechenizkiy, Eindhoven University of Technology
Refocused to Target Students as Users
Awareness tool supporting interactive querying:• How does a course relate to the program?
– Prerequisites, follow up dependencies• How am I doing wrt the averages, top 10%?
– Aggregates/OLAP• What is my expected time to finish?
– Predictive modeling• Should I now take courses A & B & C or C & D?
– Collaborative filtering style recommendations
(based on the received feedback)
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4CurriM: Curriculum MiningMykola Pechenizkiy, Eindhoven University of Technology
CurriM UI Demo
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5CurriM: Curriculum MiningMykola Pechenizkiy, Eindhoven University of Technology
Where is EDM/LA?Curriculum model:• Codified constraints with Colored Petri net and LTL
– Prerequisites, follow up dependencies, 3 out of 5 selection, number of attempts, mandatory courses etc.
– Input: domain knowledge and output of patters mining• Awareness and automated conformance checking
– Is the currently chosen path compliant with the official guidelines and follows data driven recommendations
– Computed aggregates and mined pattern from the data• Data driven recommendations and predictions
– What is my expected time to finish?– Should I take now courses A & B & C or C & D?
(hidden from the users behind GUI)
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6CurriM: Curriculum MiningMykola Pechenizkiy, Eindhoven University of Technology
Main Results• Software prototype – CurriM as ProM plugin,
– Focus on GUI + architecture/interfaces– Demonstrates the concept
• Experiments with TUE dataset– Prerequisites, bottleneck/predictive courses– Recommendations– Data quality is the key
• Clear motivation and need for a continuation– The concept is found to be promising– Potential and feasibility is shown– Roadmap
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7CurriM: Curriculum MiningMykola Pechenizkiy, Eindhoven University of Technology
Why Do Students Like the Concept?CurriM is a tool that • Provides orientation:
– Curriculum as a guide and motivation– See the connections and dependencies
• Provides awareness and recommendations– Global: how good is their personal education route,
where they currently are, where they are heading,how well they do in comparison with others
– Local: what would it mean to take course X• Enables better planning and regular monitoring
– Focus on what looks important, not just interesting
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8CurriM: Curriculum MiningMykola Pechenizkiy, Eindhoven University of Technology
Main Lessons LearntData quality is the key• Administrative DBs and existing data collection
organization do not keep EDM/LA in mind • Lots of preprocessing and reorganization is requiredMeta-data is the other key (lacking codifiability)• Everything that is scattered in study guides and minds of
study advisors should become easy to codifyCurriculum changes more often than we tend to think• Semesters-trimesters-quartiles, courses & course idsBeing “flexible” (written vs. unwritten rules) too much• Effectively means no formal curriculumLearning Analytics @Surf 10 October 2012, Utrecht,
9CurriM: Curriculum MiningMykola Pechenizkiy, Eindhoven University of Technology
Conclusions• CurriM can become a big success
– The students seem to like the idea– It is promising and it is feasible; but it is a long way
from the current concept to a fully functional and usable tool
• Surf funding opportunity in LA was nice– Triggered us to take concrete practical steps, a tool
rather than techniques development; – But a more serious commitment is needed to
make a real breakthrough and bring CurriM into the educational practice
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10CurriM: Curriculum MiningMykola Pechenizkiy, Eindhoven University of Technology
Continuation Roadmap
• Working out the full cycle of the information flows including pattern mining, predictions and recommendations, and its integration/parallelization with the administrative processes
• Working out different views and functionality for students vs. educators, HCI/usability aspects
• Improve data quality collection• Facilitate knowledge base construction (meta-data,
mappings)• Facilitate curriculum formalization for faculties
(tooling)
Conditioned wrt funding opportunities
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11CurriM: Curriculum MiningMykola Pechenizkiy, Eindhoven University of Technology
Project TeamProject leader: • dr. Mykola Pechenizkiy – educational data mining expertDriving force:• Pedro Toledo – software developer, applied researcherTechnology experts:• Prof. dr. Paul De Bra – Human-computer interaction and databases
expert• dr. Toon Calders – pattern mining expert, assistant professor• dr. Nikola Trcka – collaborator on curriculum mining, postdoc• dr. Boudewijn van Dongen – process mining expert, assistant professor• dr. Eric Verbeek – ProM software expert, scientific programmerDomain experts• Several domain experts, i.e. responsible educators, are available for
CurriM on request: dr. Karen Ali (STU), Prof. dr. Mark de Berg (CSE)Learning Analytics @Surf 10 October 2012, Utrecht,
12CurriM: Curriculum MiningMykola Pechenizkiy, Eindhoven University of Technology
Additional slides
• Including some from the original proposal
Learning Analytics @Surf 29 February 2012, Utrecht,
13CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
Execution planTask 1. Developing the first software prototype for
academic curriculum modeling. As mini R&D cycles: • identifying types of curriculum specific patterns we
need to mine from the event logs (in collaboration with the domain experts) and to include in the curriculum modeling and developing corresponding pattern mining and pattern assembling techniques;
• Implementing techniques and integrating it with ProM that provides an important process mining foundation framework and many of the building blocks for curriculum modeling software;
• testing a particular piece of software.Learning Analytics @Surf 29 February2012, Utrecht,
14CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
Execution planTask 2. Case study: modeling the curriculum of the
Department of Computer Science, TUE; Goals:• Validating the correctness and usefulness (to the
end users, i.e. teachers, study advisers, students) of the developed curriculum mining techniques and their implementations.
• Developing guidelines for managing the curriculum related data to avoid the problems we will encounter or envision during the case study.
• Task 1 and Task 2 will run simultaneously ensuring timely feedback.
Learning Analytics @Surf 29 February2012, Utrecht,
15CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
Execution planTask 3. Creating a roadmap for further study and
development of the curriculum modeling toolset• Develop R&D agenda for the coming years. • This includes identification of not only research
challenges i.e. answering the question – “what kind of new data mining and process mining techniques
are needed to address the peculiarities of the curriculum mining domain?”
• but also the strategy of the smooth technology transfer to the prospective end users, i.e.– early adopters (e.g. TUE or 3TU departments) that would help
to validate the usability and usefulness of the curriculum mining software “in the wild”.
Learning Analytics @Surf 29 February2012, Utrecht,
16CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
Project TeamTask 3. Creating a roadmap for further study and
development of the curriculum modeling toolset• Develop R&D agenda for the coming years. • This includes identification of not only research
challenges i.e. answering the question – “what kind of new data mining and process mining techniques
are needed to address the peculiarities of the curriculum mining domain?”
• but also the strategy of the smooth technology transfer to the prospective end users, i.e.– early adopters (e.g. TUE or 3TU departments) that would help
to validate the usability and usefulness of the curriculum mining software “in the wild”.
Learning Analytics @Surf 29 February2012, Utrecht,
17CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
18Learning Analytics Seminar, August 30-31, Utrecht, NL
Educational Data Mining & Learning Analytics for All: Potential, Dangers, ChallengesMykola Pechenizkiy, Eindhoven University of Technology
Educational Process Mining Toolbox
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19CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
Intuition suggests that curriculum is
• Structured and easy to understand as we think there are not that many options to choose from– It may look just like this one:
• but the data may suggest that it looks different…Learning Analytics @Surf 29 February 2012, Utrecht,
20CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
… data may suggest that students show
somewhat more diverse behaviour:
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21CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
Two Different TasksIsolate a set of standard curriculum patterns and based on these 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.
Data log
Pattern mining Pattern set
Pre-authored pattern templates
Process model
Process assembling
Conformance checking Model extension
Online monitoring
Educators
Event Log -MXML format
supported by ProMTypical forms of
requirements in the curriculum
Colored Petri net
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24CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
Application ScenariosStudent Timestamp Events
A S1 2, 3, 5
A S2 6, 1
A S3 1
B S1 4, 5, 6
B S3 2
B S4 7, 8, 1, 2
B S5 1, 6
C S1 1, 8, 7
Scenario 1: Find most common types of behavior (and cluster them)
Scenario 2: Find emerging patterns: such patterns, which capture significant – differences in behavior of students who
graduated vs. those students who did not – changes in behaviour of students from year
2006-07 to 2007-08.– in both cases we search for such patters which
supports increase significantly from one dataset to another (i.e. in space in the first case and in time in the second case)
Scenario 3: After finding a bottleneck, find frequent patterns that describe it, i.e. for which students it is the bottleneck and why
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25CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
Example 2-out-of-3 Pattern Check• At least 2 courses from { 2Y420,2F725,2IH20 } must
be taken before graduation :
• An higher level abstraction can be developed on a longer run to avoid we aim at developing a
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26CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
Process Discovery Example
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27CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
Which Courses Are Difficult/Easy for Which Students?
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28CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
References• Trčka, N., Pechenizkiy, M. & van der Aalst, W. (2010) "Process Mining from
Educational Data (Chapter 9)", In Handbook of Educational Data Mining. , pp. 123-142. London: CRC Press.
• Pechenizkiy, M., Trčka, N., Vasilyeva, E., van der Aalst, W. & De Bra, P. (2009) Process Mining Online Assessment Data, In Proceedings of 2nd International Conference on Educational Data Mining (EDM'09), pp. 279-288.
• Trčka, N. & Pechenizkiy, M. (2009) From Local Patterns to Global Models: Towards Domain Driven Educational Process Mining, In Proceedings of Ninth International Conference on Intelligent Systems Design and Applications (ISDA'09), pp. 1114-1119.
• Bose, R.P.J.C., van der Aalst, W.M.P., Zliobaite, I. & Pechenizkiy, M. (2011) Handling Concept Drift in Process Mining, In Proceedings of 23rd International Conference on Advanced Information Systems Engineering CAiSE'2011, Lecture Notes in Computer Science 6741, Springer, pp. 391-405.
• Dekker, G., Pechenizkiy, M. & Vleeshouwers, J. (2009) Predicting Students Drop Out: a Case Study, In Proceedings of the 2nd International Conference on Educational Data Mining (EDM'09), pp. 41-50.
• http://www.processmining.org/
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30CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology
Short CV of the Project LeaderMykola PechenizkiyAssistant Professor at Dept. of Computer Science, TU/eResearch interests: data mining and knowledge discovery;Particularly predictive analytics for information systems serving industry, commerse, medicine and education. http://www.win.tue.nl/~mpechen/ - projects, pubs, talks etc.
Major recent EDM-related activities:
Confirmed interest in CurriM at TUE
• Dr. Karen S. Ali - Director of Education and Student Service Center, STU
• Prof. Dr. Mark de Berg - Director of the graduate program, Dept. of Computer Science
• Dr. Marloes van Lierop - Director of the bachelor program, Dept. of Computer Science
• Study advisers at different faculties
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32CurriM: Curriculum Mining Project ProposalMykola Pechenizkiy, Eindhoven University of Technology