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Alan Berg
- 1. Squeezing biggish job market data onto a laptop Alan Mark Berg BSc.MSc.PGCE. a.m.berg@uva.nl
- 2. Agenda •Overview • Who I am and what am I doing? • Area of research • Technique •Example Results: • Stereotypes • Female • IT • Discrimination •Question and Answers • Refinements • References
- 3. Who am I? A rather mature, external PhD Candidate in Learning Analytics. 2. Hard Science Background: Physics, microelectronics with computational engineering, experimental Science 3. Pragmatic: Last 17 years involved in Design and development of large scale IT systems @UvA ○ Wishes to use the simplest technique possible for a given task. 4. Author of 4 books 5. Busy with open source communities. ○ Considers the best place to curate software 6. Stephan and Gabor are my co-supervisors. Prof Robin Boast my supervisor. 7. Status: In the process of writing up the research and then finishing the PhD. 8. Initial Infrastructure, standards papers published (see references)
- 5. Technique ❑ 3 million UK job adverts – 1150 million words - Thank you Monsterboard. ❑ Simplest possible scenario ❑ Bag of words ❑ Unigrams ❑ Perl to process the text ❑ R language: Inferential Statistic and visualization ❑ CATA: Frequency of words ❑ Mapped job dataset to SOC 2010 occupation categories ❑ From SOC 2010 categories merged UK Labour force survey
- 6. UK Labour Market Survey
- 7. Representation in adverts ❏ Qualifications + ❏ Male + ❏ Head hunting - ❏ Female - Under Represented Over Represented Male Dominated Female Dominated
- 9. Dispersion IT Skills - Monitor skill dispersion - Has implications for policy and training - Has implications for risks within occupations such as the deployment of IT projects.
- 11. Is discrimination wording attracted to female worded job adverts?
- 12. Discrimination Diffusion process into the central region where men and women are more equally represented. Color: Red = Highest percentage of female wording Notice the large amount of green (less wording) in 2013
- 13. Questions
- 14. Refinements ❏Inferential Statistics ❏From Unigram to Bigram ❏Cleaner data sources ❏Multiple languages ❏Compare to specific surveys ❏Generation of many dictionaries ❏From dictionary to taxonomies ❏From research to practice
- 15. References Motivation: We can develop large scale systems without using sensitive data. Berg, A. M., Mol, S. T., Kismihók, G., & Sclater, N. (2016). The role of a reference synthetic data generator within the field of learning analytics. Journal of Learning Analytics, 3, 107–128. http://doi.org/10.18608/jla.2016.31.7 Motivation: We need to add new xAPI profiles and be consistent to avoid issues with connecting systems Berg, A., Scheffel, M., Drachsler, H., Ternier, S., & Specht, M. (2016). The dutch xAPI experience. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK ’16 (pp. 544–545). New York, New York, USA: ACM Press. http://doi.org/10.1145/2883851.2883968 Motivation: We need to add new xAPI profiles and be consistent to avoid issues with connecting systems Berg, A., Scheffel, M., Drachsler, H., Ternier, S., & Specht, M. (2016). Dutch Cooking with xAPI Recipes: The Good, the Bad, and the Consistent. In 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT) (pp. 234–236). IEEE. http://doi.org/10.1109/ICALT.2016.48 Motivation: To contribute to the discussion around LA infrastructural elements, hence providing a means to consistency Sclater, N., Berg, A., & Webb, M. (2015). Developing an open architecture for learning analytics. Proceedings of the EUNIS 2015 Congress. http://doi.org/ISSN: 2409-1340