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2017 12-15-iv jornadas innovación psicología

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Slide deck from IV Jornadas Innovación Psicología. 15th Dec 2017. Madrid

Publicado en: Datos y análisis
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2017 12-15-iv jornadas innovación psicología

  2. 2. Investigación + Desarrollo + innovación en Ingeniería del Conocimiento Entidad privada sin ánimo de lucro creada en 1989 Productos y servicios únicos y adaptables Nos autofinanciamos con nuestros productos y servicios Reinvertimos en I+D+i Quiénes somos N U E S T R O S S O C I O S
  3. 3. Definition Learning Analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, for the purpose of understanding and optimizing learning and the environments in which it occurs. (LAK’11)
  4. 4. Analytics everywhere Web analytics Human Resource analytics Business analytics Social media analytics Marketing analytics Sports analytics Text analytics …
  5. 5. Definition XXX Analytics is the measurement, collection, analysis, and reporting of data about XXX and their contexts, for the purpose of understanding and optimizing XXX and the environments in which it occurs. (LAK’11)
  6. 6. Nihil sub sole novum
  7. 7. Drivers ● Data informing teaching and learning are increasingly extensive and accessible ● Innovative new analytic approaches to digesting, visualizing, and acting on these data emerge every day
  8. 8. Growth of MOOCs
  9. 9. MOOC Popularity
  10. 10. MOOC providers by registered users ● Coursera – 23 million ● edX – 10 million ● XuetangX – 6 million ● FutureLearn – 5.3 million ● Udacity – 4 million
  11. 11. LMS/VLE Popularity
  12. 12. Who benefits? Siemens, G., & Long, P. (2011) Micro-level (students, Instructors) Macro-level (Policy-makers) Meso-level (Dpt. leader, Administrators)
  13. 13. Micro-level benefits ● Identify at-risk learners and provide interventions. ● Provide learners with insight into their own learning habits and give recommendations for improvement. Shum, S. B. (2012)
  14. 14. Meso-level benefits ● Improve administrative decision-making and organizational resource allocation. ● More transparent data and analysis could create a shared understanding of the institution’s successes and challenges. ● Increase organizational productivity by providing up-to-date information and allowing rapid response to challenges. ● Help leaders determine the hard (e.g. patents, research) and soft (e.g. reputation, profile, quality of teaching) value generated by faculty activity Shum, S. B. (2012)
  15. 15. Macro-level benefits ● Ultimately the above might transform the college/university system, as well as academic models and pedagogical approaches. Shum, S. B. (2012)
  16. 16. Who Does What in a Massive Open Online Course? 6.002x in spring 2012 154,000 registrants 46,000 never accessed the course (~30%) 76% of participants were browsers 7% of participants earned certificate 230 million interactions (Seaton, D. T., et al 2014)
  17. 17. Percentage use of course components
  18. 18. How video production affects student engagement: an empirical study of MOOC videos ● 4 edX courses, Fall 2012: CS1, A.I., Stat, Chem ● 862 videos, 128k students, 6.9 million sessions ● Engagement metric: video watching session length ● Interviews with edX video producers & program managers (Guo P.J., Kim J., & Rubin, R., 2014)
  19. 19. Recording styles a) a recorded classroom lecture, b) an instructor’s talking head, c) a Khan-style digital tablet drawing (popularized by Khan Academy), d) a PowerPoint slideshow. (Guo P.J., Kim J., & Rubin, R., 2014)
  20. 20. Which kind of videos lead to most engagement at a reasonable cost? 1. Short: Shorter videos much more engaging. Engagement drops sharply after 6 minutes. 2. Pre-planned: Pre-production improves engagement 3. Talking head: Talking head is more engaging than showing only slides 4. Personal: Informal shots can beat expensive studios 5. Khan-style: Khan-style tutorials beat slides/code (Guo P.J., Kim J., & Rubin, R., 2014)
  21. 21. Social learning analytics: five approaches ● social network analytics: interpersonal relationships define social platforms ● content analytics: user- generated content is one of the defining characteristics of social learning platform ● discourse analytics: language is a primary tool for knowledge negotiation and construction ● disposition analytics: intrinsic motivation to learn is a defining feature of online social media and lies at the heart of engaged learning, and innovation ● context analytics: mobile computing is transforming access to both people and content (Ferguson, R., & Shum, S. B. ,2012).
  22. 22. Example: video-based learning
  23. 23. Social network analytics (Castellanos, J., Haya, P.A., Urquiza-Fuentes, J, 2017)(Haya, P.A., et al., 2015)
  24. 24. New career opportunities in psychology Strengths: Analytical skills Know the right questions Make decisions Weaknesses: Big Data: Volume, Velocity, Varity Machine learning models Opportunities: Cloud services Black-box approaches R/Python programming languages Data science process flowchart from "Doing Data Science", Cathy O'Neil and Rachel Schutt, 2013
  25. 25. Resources and references
  26. 26. Open University Learning Analytics dataset ● The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries) ● Other datasets: Stamper, J., Niculescu-Mizil, A., Ritter, S., Gordon, G. J. & Koedinger, K. R. Algebra I 2008-2009. Challenge data set from KDD Cup 2010 Educational Data Mining Challenge (2010). Cao, L. & Zhang, C. KDD Cup 2015—Predicting dropouts in MOOC (2015)
  27. 27. Research Community ● The Society of Learning Analytics Research (SoLAR). ○ LAK conferences ○ Journal of Learning Analytics ○ Learning Analytics Summer Institutes ● International Educational Data Mining Society ○ Journal of Educational Data Mining ○ Educational Data Mining (EDM) Conference ● International Artificial Intelligence in Education Society ○ International Journal of Artificial Intelligence in Education (IJAIED) ○ Artificial Intelligence in Education (AIED) Conference ● Learning at Scale conference ● The International Society of the Learning Sciences ○ Journal of Learning Sciences
  28. 28. References ● (LAK11) 1st International Conference on Learning Analytics and Knowledge, Banff, Alberta, February 27– March 1, 201 ● Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30. ● Shum, S. B. (2012) UNESCO Policy Brief: Learning Analytics, UNESCO, ● Seaton, D. T., Bergner, Y., Chuang, I., Mitros, P., & Pritchard, D. E. (2014). Who does what in a massive open online course?. Communications of the ACM, 57(4), 58-65. ● Philip J. Guo, Juho Kim, and Rob Rubin. 2014. How video production affects student engagement: an empirical study of MOOC videos. In Proceedings of the first ACM conference on Learning @ scale conference (L@S '14). ACM, New York, NY, USA, 41-50. DOI= ● Ferguson, R., & Shum, S. B. (2012, April). Social learning analytics: five approaches. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 23-33). ACM. ● Castellanos, J., Haya, P.A., Urquiza-Fuentes, J. IEEE Transactions on Learning Technologies, 10(3), July-Sept. 1 2017, pp.306-317, DOI: 10.1109/TLT.2016.2582164 ● Haya, P. A., Daems, O., Malzahn, N., Castellanos, J., Hoppe, H. U. (2015) Analysing content and patterns of interaction for improving the learning design of networked learning environments. British Journal of Educational Technology, 46 (2), 300–316 ● Papamitsiou, Z. & Economides, A. A. Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educational Technology & Society 17, 49–64 (2014).