De zes dimensies van learning analytics
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Maandag 9 november Plenair Titel: De zes dimensies van learning analytics Spreker(s): Hendrik Drachsler Zaal: Rotterdam Hall
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De zes dimensies van learning analytics
- 1. `` ` Dr. Hendrik Drachsler, UHD, Pre-Con. #OWD2015 09.11.2015 1 Learning Analytics http://www.flickr.com/photos/traftery/4773457853Picture Pic. by Tom Raftery:
- 2. `` ` 3 • Hendrik Drachsler Associate Professor Learning Technologies • Research topics: Personalization, Recommender Systems, Learning Analytics, Mobile devices • Application domains: Schools, HEI, Medical education WhoAmI 2006 - 2009 @HDrachsler
- 4. `` ` @HDrachsler, #LASI_NL, Zeist, Netherlands Slide 4 / 29 June 2014 1. Why LA 2. LA Framework Lecture structure 3. Conclusions
- 5. `` ` More ICT = More DATA Deze data zijn waardevol en informatief.
- 6. `` ` The Big Data Economy Voorbeeld: The Google Flue trend technology … … kun nu ook voor onderwijs toegepast worden. Learning Analytics = Data Science voor educatie
- 8. `` ` Onderwerp via >Beeld >Koptekst en voettekst Pagina 9
- 9. `` ` DATA van Learning Profiles DATA van LMS en MOOCs DATA van Learning Resources (Apps, Games,..) DATA van assessments Wat zijn educatief data? De eerste keer dat wij de leerling in het leerproces kunnen volgen. - On Demand Learning Measures -
- 10. `` ` 17 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
- 11. `` ` 17 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.
- 12. `` ` 17 Graph 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 Learning Activities Studytime in days
- 13. `` ` 17 Graph 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 Learning Activities Studytime in days
- 14. `` ` @HDrachsler, #LASI_NL, Zeist, Netherlands Slide 16 / 29 June 2014 1. Why LA 2. LA Framework 3. Conclusions Lecture structure
- 15. `` `
- 16. `` ` 18 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.
- 17. `` ` 19 Stakeholders data 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
- 18. `` ` 20 Stakeholders Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist.
- 20. `` ` 24 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
- 21. `` ` 25 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
- 22. `` ` 26 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.
- 24. `` ` 34 Technologies Prediction Manouselis, N., Drachsler, H., Verbert, K., and Duval, E. (2012). Recommender Systems for Learning. Berlin:Springer
- 26. `` ` 36 Constraints 1.Data security 2.Ethics & Privacy 3.Transparency 4.Ownership
- 27. `` ` 37 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
- 30. `` ` 41 Constraints Learning analytics onder de Wet bescherming persoonsgegevens
- 32. `` ` 43 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.
- 33. `` ` @HDrachsler, #LASI_NL, Zeist, Netherlands Slide 44 / 29 June 2014 1. Why LA 2. LA Framework 3. Conclusions Lecture structure
- 34. `` ` 45 LA driven instructional Design
- 35. `` ` 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
- 36. `` ` @HDrachsler, #LASI_NL, Zeist, Netherlands Slide 50 / 29 June 2014 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
- 38. `` ` 116
Notas do Editor
- What is Learning Analytics? Measurement, collection, analysis, and reporting of data about learners and their context. Why LA? Understanding and optimizing the learning and the env. It occurs?
- For example, when one competition asked teams to predict whether a student would drop out during the next ten days, based on student interactions with resources on an online course, there were many possible factors to consider. Teams might have looked at how late students turned in their problem sets, or whether they spent any time looking at lecture notes. But instead, MIT News reports, the two most important indicators turned out to be how far ahead of a deadline the student began working on their problem set, and how much time the student spent on the course website. These statistics weren’t directly collected by MIT’s online learning platform, but they could be inferred from data available.
- add nice pics copy and put between slides
- add nice pics copy and put between slides
- add nice pics copy and put between slides
- add nice pics copy and put between slides
- Participatory design of analytics tools for non-statics experts, Develop capabilities to exploit (big) data
- Multidisciplinary teams Learning from successful examples
- The mentioned framework was used to structure the questionnaire in order to avoid as much as possible bias toward a single perspective of learning analytics, e.g. the data technologies, and in order to get a balanced overview of the field as a whole. The questionnaire took concrete aspects into focus in the following way: The ‘stakeholders’ dimension inquired about the expected beneficiaries; ‘objectives’ tried to highlight the preference between reflective use of analytics and prediction; It also checked for the development areas where benefits are most likely or are expected; the ‘data’ section looked into stances on sharing and access to datasets; ‘methods’ investigated trust in technology and algorithmic approaches; ‘constraints’ focused on observations on ethical and privacy limitations (so-called soft-barriers); and, finally, ‘competences’ looked into the confidence for exploiting the results of analytics in beneficial ways.
- Limited response rate from Romance countries (France, Iberia, Latin America) high return from Anglo-Saxon countries. lack of responses Russia, China or India
- Effects of LA on ...
- The LinkedUp evaluation framework will be created and refined during each stage of the LinkedUp challenge to take into account both the increasing requirements of the competition, and recent progress with respect to technological developments. This framework will persist and be maintained beyond the project duration and enable the long-term evaluation and take-up of data-driven technologies according to a well-defined set of evaluation criteria, thresholds and assessment methods. It will be validated through its ability to support the evaluation of applications participating to the LinkedUp Challenge, as well as through its uptake in the evaluation of other applications, in other competitions, situations and domains.
- point map of the 103 contributed quality indicators (outcome of the multidimensional scaling analysis) multidimensional scaling analysis assigns bridging value between 0 and 1 low bridging values are grouped together statements are coherent (98, 52, 75, 99) higher bridging values grouped together but further apart (statements 95, 23, 50, 61) close together = close in meaning = clustered together by many participants
- 1 system automatically suggests a list of labels per cluster top suggestion is label of a participant’s cluster whose centroid is the closest to the centroid of the cluster formed by the aggregated cluster data from all participants 2 other way is to look at the bridging values: the lower the bridging values are, the better do those statements define the cluster 3 third way is to find the overarching theme of a cluster We combined all three methods to define the labels of the 8-cluster solution
- tool automatically applies the experts’ ratings to the cluster map system always divides the ratings into 5 equivalent layers based on the average ratings provided by the participants for the rating maps one layer indicates a low rating, whereas five layers indicate a high rating of the respective aspect
- ladder graph offers visualisation well suited to compare the clusters’ ratings according to importance and feasibility rating values are based on a cluster’s average rating Pearson product-moment correlation coefficient (r = 0.65) indicates a strong positive relationship between the two aspects of importance and feasibility
- Go-zone graph maps each statement onto a space between x- and y-axis based on the mean values of the two rating aspects of importance and feasibility lower left = neither important nor feasible upper left = feasible but not important lower right = important but not so feasible upper right = important and also feasible
- importance map on the left learning-related belt is deemed highly important all Eastern clusters also receive five layers of importance focus of importance is on the learning process-related clusters feasibility map on the right technically-oriented clusters in the North (green circle) are deemed most feasible followed by the learning-related belt in the middle (yellow circle) concluded by the human-related clusters in the South (red circle)
- suitable indicator collected from the go-zone graphs