Interpreting Data Mining Results with Linked Data for Learning Analytics
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The document discusses the integration of linked data in learning analytics to enhance the interpretation of data mining results, emphasizing the necessity of background knowledge for effective analysis. It proposes an approach that builds a navigation structure from sequential patterns of student course enrollments, leveraging linked data to provide context and meaning. The conclusion highlights the value of using linked data to refine analytics and supports the iterative process of data interpretation in educational contexts.
Interpreting Data Mining Results with Linked Data for Learning Analytics
- 1. Interpreting Data Mining Results with Linked Data for Learning Analytics: Motivation, Case Study and Directions Mathieu d’Aquin Knowledge Media Institute, The Open University mdaquin.net - @mdaquin mathieu.daquin@open.ac.uk Nicolas Jay Université de Lorraine, LORIA, nicolas.jay@loria.fr
- 2. My super naïve view of learning analytics Insight! Tada! Some kind of data processing Visualisation Data (from some education related system)
- 3. But actually… Insight! Tadada! Some kind of data processing Visualisation Interpretation Data (from some education related system)
- 4. Needs more data/information Insight! Tadada Some kind of data dou! processing Visualisation Background Interpretation Data (from some knowledge education related system)
- 5. The challenge for learning analytics Most of the time, background knowledge needs to be in the head of the people looking at the analytics. How to find/obtain background information for interpretation to support him/her considering that: – The data we are analysing and insight we are trying to obtain can cover a wide range of things, topics, domains, subjects… – We might not know in advance we background information is needed for interpretation Our approach: Integrate linked data sources at the time of interpretation
- 6. What’s linked data See the “Using Linked Data in Learning Analytics” tutorial yesterday http://linkedu.eu/event/lak2013-linkeddata- tutorial/
- 7. Linked Data Open University Person: Mathieu Website Publication: Pub1 author workFor Open University VLE Course: M366 offers KMi Website M366 Course page Organisation: The Open University Mathieu’s Homepage availableIn setBook Mathieu’s List of Mathieu’s Publications Twitter Country: Belgium Book: Mechatronics The Web The Web of Linked Data
- 8. rNews Music Ontology Geo Ontology SIOC Media Ontology Dublin Core DBPedia FOAF Ontology DOAP FMA BIBO Ontology LODE Gene Ontology
- 10. Use case: student enrolment data From the Open University’s Course Profile Facebook Application: Examples: Who enrolled to what Student ID Course Code Status Date 112 dse212 Studying 2007 course at 112 d315 Intend to study 2008 what time 109 a207 Completed 2005
- 11. Sequence mining We can represent each student’s trajectory by a sequence of courses, e.g. (DD100) (D203, S180) (S283) Applying sequence mining makes it possible to find frequent patterns in these sequences, i.e., courses often taken together in a certain order.
- 12. The results (and again, why they need background knowledge for interpretation) Out of 8,806 sequences (students), we obtained 126 different sequential patterns with a support threshold of 100* i.e. filtering out patterns included in less than 100 sequences. Sequential pattern Support (DD100) (DSE212) 232 Examples: (DSE212) (ED209) (DD303) 150 (B120) (B201) 122 How to know what that means? We need background information about the courses (DD100, DSE212, ED209 ,etc.)
- 13. The approach to interpretation: Building a navigation structure in the patterns using dimensions obtained in linked data
- 14. Making the results linked data compliant Use a simple ontology of sequences to represent the patterns And use linked data URIs to represent the items, e.g. DSE212 http://data.open.ac.uk/course/dse212
- 16. Selecting a dimension in linked data Propose relations that apply to the items of the patterns Then relations that apply to the objects of these relations Etc. i.e. follow the links to build a chain of relationships.
- 17. Building a hierarchy of patterns The end-values of the chain of relations built out of following links of linked data form attributes of the patterns Build a lattice (hierarchy) of concepts representing groupings of these attributes, using formal concept analysis
- 20. Benefits (see following examples) Provides an overview of the patterns obtained along a custom dimension Helps identifying gaps and issues in the original data/process Helps identifying areas in need of further exploration Generic: can be straightforwardly applied to other source data, other linked data and other mining methods
- 22. Examples • Subjects of books Subjects of related course material
- 24. Discussion Limitations of the approach: – Requires the results to be linked data and the items to connect to linked data – Sources of linked data needs to be available to support interpretation) http://data.linkededucation.org/linkedup/catalog
- 25. Discussion: It’s a loop Views and Data selection dimensions mining Background Interpretation Data (from some knowledge education related system)
- 26. Conclusion Linked data can be used to enrich and bring some meaningful structure to the patterns from an analytics/mining process Introducing linked data not only in input of the process, but also in support of more analytical tasks Promising, considering the growth of education-related linked data Should become part of an iterative process, where patterns and data get refined through interpretation and the introduction of background information from linked data
- 27. Thank you! More info at: http://mdaquin.net @mdaquin http://linkedup-project.eu http://linkedup-challenge.org http://linkedu.eu/event/lak2013-linkeddata- tutorial/