De zes dimensies van learning analytics

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Dr. Hendrik Drachsler, UHD, Pre-Con. #OWD2015 09.11.20151

Learning Analytics

http://www.flickr.com/photos/traftery/4773457853Picture Pic. by Tom Raftery:

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• Hendrik DrachslerAssociate Professor Learning Technologies

• Research topics:Personalization, Recommender Systems, Learning Analytics, Mobile devices

• Application domains: Schools, HEI, Medical education

WhoAmI

2006 - 2009

@HDrachsler

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Research activitesSURF

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@HDrachsler, #LASI_NL, Zeist, NetherlandsSlide 4 / 29 June 2014

1. Why LA2. LA Framework

Lecture structure

3. Conclusions

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More ICT = More DATA

Deze data zijn waardevol en informatief.

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The Big Data Economy Voorbeeld:

The Google Flue trendtechnology …

… kun nu ook voor onderwijs toegepast worden.

Learning Analytics = Data Science voor educatie

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New MIT study

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Onderwerp via >Beeld >Koptekst en voettekstPagina 9

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DATA vanLearning Profiles

DATA vanLMS en MOOCs

DATA vanLearning Resources (Apps, Games,..)

DATA vanassessments

Wat zijn educatief data?

De eerste keer dat wij de leerling in het leerproces kunnen volgen.

- On Demand Learning Measures -

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Reinhardt, W., Meier, C., Drachsler, H., & Sloep, P. B. (2011). Analyzing 5 years of EC-TEL proceedings. In C. D. Kloos, D. Gillet, R. M. Crespo García, F. Wild, & M. Wolpers (Eds.), Towards Ubiquitous Learning: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011 (pp. 531-536). September, 20-23, 2011, Palermo, Italy. LNCS 6964; Heidelberg, Berlin: Springer.

Nieuwe inzichten

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Nieuwe inzichten

Dawson, S., Bakharia, A., & Heathcote, E. (2010, May). SNAPP: Realising the affordances of real-time SNA within networked learning environments. In

Proceedings of the 7th International Conference on Networked Learning (pp. 125-133). Denmark, Aalborg.

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17Graph by Rob Koper. Data science voor de realisatie van online activerend onderwijs.

Presentation given at Dag van het Onderwijs (5 November 2015). Heerlen. The Netherlands

Nieuwe inzichten

LearningActivities

Studytime in days

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1. Why LA 2. LA Framework

3. Conclusions

Lecture structure

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Greller, W. & Drachsler, H. (2012). Turning Learning into Numbers. Toward a Generic Framework for Learning Analytics. Journal of Educational Technology & Society.

http://ifets.info/journals/15_3/4.pdf

Visualiz.

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Stakeholdersdata subjects

data clients

Greller, W. & Drachsler, H. (2012). Turning Learning into Numbers. Toward a Generic Framework for Learning Analytics. Journal of Educational Technology & Society.

http://ifets.info/journals/15_3/4.pdf

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Stakeholders

Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist.

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ObjectivesReflection Prediction

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Educational Data

Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning. 1st Workshop Recommnder Systems in Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28, 2010, Barcelona, Spain.

Verbert, K., Manouselis, N., Drachsler, H., and Duval, E. (2012). Dataset-driven Research to Support Learning and Knowledge Analytics. Journal of Educational Technology & Society. www.ifets.info/journals/15_3/10.pdf

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Edu. Data Storage

Berg, A., Scheffel, M., Ternier, S., Drachsler, H., and Specht, M. (submitted). Dutch cooking with xAPI recipes and the flavour of various Learning Record Stores.

Learning Analytics and Knowledge conference 2016, Edinburgh, UK.

• Various heterogonous data sources

• No metadata standards • No proper description of

data fields• No unique user ID in the

different systems• Not intended for

evaluation and educational interventions

• No comparison of effective methods

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Edu. Data Storage

Berg, A., Scheffel, M., Ternier, S., Drachsler, H., and Specht, M. (submitted). Dutch cooking with xAPI recipes and the flavour of various Learning Record Stores.

Learning Analytics and Knowledge conference 2016, Edinburgh, UK.

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Data Standards

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TechnologiesPrediction

Manouselis, N., Drachsler, H., Verbert, K., and Duval, E. (2012). Recommender Systems for Learning. Berlin:Springer

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TechnologiesReflection

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Constraints

1.Data security

2.Ethics & Privacy

3.Transparency

4.Ownership

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Constraints

• $100 million investment • Aim: Personalized learning in public schools, through data &

technology standards • 9 US states participated, In 2013 data about millions of children

have been stored

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Constraints

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Constraints

http://www.open.ac.uk/students/charter/essential-documents/ethical-use-student-data-learning-analytics-policy#

https://www.jisc.ac.uk/sites/default/files/jd0040_code_of_practice_for_learning_analytics_190515_v1.pdf

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Constraints

Learning analytics onder de Wet bescherming persoonsgegevens

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Competences

1.E-literacy

2.Interpretation skills

3.Agency

4.Privacy understanding

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Competences

Drachsler, H., Stoyanov, S., d'Aquin, M., Herder, E., Dietze, S., & Guy, M. (2014, 16-19 September). An Evaluation Framework for Data Competitions in TEL. 9th European Conference on Technology-Enhanced Learning (EC-TEL 2014), Graz, Austria.

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1. Why LA2. LA Framework

3. Conclusions

Lecture structure

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LA driven instructional Design

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Sophistican model

Siemens, G., Dawson, S., & Lynch, G. (2014). Improving the Quality and Productivity of the Higher Education Sector – Policy and Strategy for Systems-Level Deployment of Learning Analytics. Canberra, Australia: Office of Learning and Teaching, Australian Government. Retrieved from http://solaresearch.org/Policy_Strategy_Analytics.pdf

LA Sophistication Model

http://solaresearch.org/Policy_Strategy_Analytics.pdf

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Creative data sourcing,

necessary IT support

Question-driven, not data or IT

driven

Participatory design of analytics tools

One–size-fits-all does not work in LA and is no innovation

Suggestions to do own LA

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Join us: www.laceproject.eu

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De zes dimensies van learning analytics

Education

Transcript of De zes dimensies van learning analytics