OWD2012 - 2,3 - Studiesucces verhogen met learging analytics - Mykola Pechenizkiy
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OWD2012 - 2,3 - Studiesucces verhogen met learging analytics - Mykola Pechenizkiy

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  • the curriculum based on sound formalisms and
  • Focus on education management people, like directors of education, study advisors and alike
  • Presenting poster at EDM’12, preparing for LAK’13 and journal submission
  • Motivation e.g. to do math because it is needed for many other coursesJust in line with the motivation we have had
  • Being “flexible” (written vs. unwritten rules) on too many things results in a mess, not a flexible curriculum
  • Usefulness and potential utility will be evaluated by the educators. The correctness of the tool will be done by data/process mining experts.
  • The ultimate goal of this task is to validate the usefulness of the developed mining curriculum mining techniques and their implementations in the software. In data mining and process mining it is often not enough to build the correct or sound algorithms and to implement then in a software toolkit. We want the resulting models, which are constructed with these techniques, to provide certain utility, i.e. be useful for the end users. Given the timeline of this project it is important that we have a few of such cycles during the project execution to receive a timely feedback from the analysis of the resulting models. Experimenting with the real historical data will hint us what issues have been omitted in the initial R&D sprints.Working with real data also gives an understanding how good or bad it is wrt organization, noise, redundancy, consistency, completeness etc. Obviously through the hands on experience with the data that has been collected already in the past we can developing guidelines for management of the curriculum related data to avoid the problems we will encounter or envision during the case study.
  • The color indicates how much time the students on average spend in a certain node. This awareness helps to understand bottlenecks in the curriculum and to facilitate data-driven decision making as for students (I really need to take pathB, i.e. Logic first or Logic with grade >8 or whatever semantics we put) as for study advisors or directors of education (we need to reconsider a prerequisite)This figure is about online assessment, but the principle can be explained.
  • cf. what are the bottlenecks in the curriculum

OWD2012 - 2,3 - Studiesucces verhogen met learging analytics - Mykola Pechenizkiy Presentation Transcript

  • 1. CurriM: Curriculum Mining Mykola Pechenizkiy TU Eindhoven Learning Analytics Innovation 10 October 2012 SURFfoundation, Utrecht, the Netherlands
  • 2. 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 gradesLearning Analytics @Surf CurriM: Curriculum Mining 110 October 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 3. 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?Learning Analytics @Surf CurriM: Curriculum Mining 210 October 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 4. Refocused to Target Students as Users (based on the received feedback)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 recommendationsLearning Analytics @Surf CurriM: Curriculum Mining 310 October 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 5. CurriM UI DemoLearning Analytics @Surf CurriM: Curriculum Mining 410 October 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 6. Where is EDM/LA? (hidden from the users behind GUI)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?Learning Analytics @Surf CurriM: Curriculum Mining 510 October 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 7. 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 – RoadmapLearning Analytics @Surf CurriM: Curriculum Mining 610 October 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 8. 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 interestingLearning Analytics @Surf CurriM: Curriculum Mining 710 October 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 9. 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 CurriM: Curriculum Mining 810 October 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 10. 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 practiceLearning Analytics @Surf CurriM: Curriculum Mining 910 October 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 11. Continuation Roadmap Conditioned wrt funding opportunities• 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)Learning Analytics @Surf CurriM: Curriculum Mining 1010 October 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 12. 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 CurriM: Curriculum Mining 1110 October 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 13. Additional slides• Including some from the original proposalLearning Analytics @Surf CurriM: Curriculum Mining Project Proposal 1229 February 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 14. 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 CurriM: Curriculum Mining Project Proposal 1329 February2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 15. 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 CurriM: Curriculum Mining Project Proposal 1429 February2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 16. 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 CurriM: Curriculum Mining Project Proposal 1529 February2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 17. 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 CurriM: Curriculum Mining Project Proposal 1629 February2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 18. Learning Analytics Seminar, Educational Data Mining & Learning Analytics for All: Potential, Dangers, Challenges 17August 30-31, Utrecht, NL Mykola Pechenizkiy, Eindhoven University of Technology
  • 19. Educational Process Mining ToolboxLearning Analytics @Surf CurriM: Curriculum Mining Project Proposal 1829 February 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 20. 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 CurriM: Curriculum Mining Project Proposal 1929 February 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 21. … data may suggest that students showsomewhat morediverse behaviour:Learning Analytics @Surf CurriM: Curriculum Mining Project Proposal 2029 February2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 22. 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. Event Log - MXML format Typical forms of supported by ProM requirements in the curriculum Colored Petri netLearning Analytics @Surf CurriM: Curriculum Mining Project Proposal 2329 February 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 23. Application Scenarios Scenario 1: Find most common types of Student A Timestamp S1 Events 2, 3, 5 behavior (and cluster them) A S2 6, 1 A S3 1 Scenario 2: Find emerging patterns: such B S1 4, 5, 6 B S3 2 patterns, which capture significant B S4 7, 8, 1, 2 B S5 1, 6 – differences in behavior of students who C S1 1, 8, 7 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 whyLearning Analytics @Surf CurriM: Curriculum Mining Project Proposal 2429 February 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 24. 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 aLearning Analytics @Surf CurriM: Curriculum Mining Project Proposal 2529 February 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 25. Process Discovery ExampleLearning Analytics @Surf CurriM: Curriculum Mining Project Proposal 2629 February 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 26. Which Courses Are Difficult/Easy for Which Students?Learning Analytics @Surf CurriM: Curriculum Mining Project Proposal 2729 February 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 27. 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 (EDM09), 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 (ISDA09), 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 CAiSE2011, 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 (EDM09), pp. 41-50.• http://www.processmining.org/Learning Analytics @Surf CurriM: Curriculum Mining Project Proposal 2929 Febnuary 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology
  • 28. Short CV of the Project Leader Mykola Pechenizkiy Assistant Professor at Dept. of Computer Science, TU/e Research 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:
  • 29. 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 facultiesLearning Analytics @Surf CurriM: Curriculum Mining Project Proposal 3129 February 2012, Utrecht, Mykola Pechenizkiy, Eindhoven University of Technology