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

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?



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)



CurriM UI Demo



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)



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



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



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,


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



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



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,


http://www.processmining.org/prom/start

http://www.processmining.org/prom/start

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,


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,


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”.



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”.



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



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,


… data may suggest that students show

somewhat more diverse behaviour:



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



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



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



Process Discovery Example



Which Courses Are Difficult/Easy for Which Students?



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/

Learning Analytics @Surf 29 Febnuary 2012, Utrecht,


http://www.processmining.org/

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:

http://www.win.tue.nl/~mpechen/



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



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

Education

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