Big data in education
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Big data in education has the potential to disrupt existing systems through personalization, evidence-based decision making, and continuous innovation. However, it also poses threats such as privacy issues, oversimplification of learners, and reducing the role of teachers. Learning analytics uses data from online learning platforms to provide insights into learning processes. Examples from Tallinn University include an open educational resources platform and a tool for visualizing pedagogical scenarios. Policies are needed to ensure the ethical use of student data and learning analytics.
Big data in education
- 1. Big Data in Education Mart Laanpere, Ph.D. Senior researcher Centre for Educational Technology, Tallinn University
- 2. Disruptive change in education • Disruptive innovation (Christensen): creates a new market and value network and eventually disrupts an existing market and value network, displacing established market leading firms, products, and alliances • Models of disruptive change: • Napster, iTunes and Spotify disrupting the music industry • Uber disrupting the taxi business • Predicting disruptive change in education: • De-schooling society (Ivan Illich, Seymour Papert) • Steve Jobs: iPads will change schools • The promise of MOOCs • Big Data?
- 5. Two change processes in education • Datafication: transformation of different aspects of education (such as test scores, school inspection reports, or clickstream data from an online course) into digital data • Digitization: transition of diverse educational practices into software code, it is most obvious in the ways that aspects of teaching and learning are digitized as e-learning software products (Learning Management Systems, student information systems, e-assessment tools, interactive learning resources, educational games, recommender systems etc) Williamson, 2017
- 6. What comes to your mind when you think of Big Data in education? • Go to Menti.com and enter the code 13 53 04 • Enter three keywords that you associate with Big Data in education
- 7. Big Data in Education • What? • Why? • How? • Where? • Who?
- 8. Who: two communities • International Educational Data Mining Society (EDM) • First event: EDM workshop in 2005 • First conference: EDM2008 • Publishing JEDM since 2009 • http://educationaldatamining.org • Society for Learning Analytics Research (SOLAR) • First conference: LAK2011 • Journal of Learning Analytics (founded 2012) • http://solaresearch.org
- 9. Hot, interdisciplinary field in RDI • HackingEDU: 100 000 USD prize for disruptors (Uber for education) • Education policy and governance • Commercial interests in the educational technology market • Philanthropic and charitable goals around supporting alternative pedagogic approaches • Emerging forms of scientific expertise such as that of psychology, biology and neuroscience • Practical knowledge of innovative practitioners in education
- 10. A vision of Data-Driven Education • Personalization: Educators dynamically adjust instruction to accommodate students’ individual strengths and weaknesses rather than continue to utilize a mass production-style approach. • Evidence-Based Learning: Teachers and administrators make decisions about how to operate classrooms and schools informed by a wealth of data about individual and aggregate student needs, from both their own students as well as those in comparable schools across the nation ... rather than by intuition, tradition, and bias. • School Efficiency: Educators and administrators use rich insight from data to explore the relationships between student achievement, teacher performance, and administrative decisions to more effectively allocate resources. • Continuous Innovation: Researchers, educators, parents, policymakers, tech developers, and others can build valuable and widely available new education products and services to uncover new insights, make more informed decisions, and continuously improve the education system. US Center for Data Innovation, 2016
- 11. Threats of relying on Big Data in education • Privacy (GDPR) • Validity: picture is based on only one, narrow facet • Cultural/linguistic issues • Learners are programmed by machine • Simplified computable models, biased towards average • Reducing the role of teacher • Any other concerns?
- 12. Learning analytics With LMS Without LMS xAPI statement: Actor-Verb-Object-Context-Result
- 14. Examples of our Big Data/ Learning Analytics projects in Tallinn University
- 15. Configurations of digital textbook 2.0 Planetary system model Linux model Lego model Stabile core Dynamic core No core at all
- 16. e-Schoolbag: the heart of Educational Cloud Publisher e-Exam system EIS Koolielu.ee OER repository Startups Collection of DLR e-Schoolbag eKool (online Gradebook service) Learning analytics LePlanner (learning scenarios)
- 17. E-Koolikott.ee
- 18. DigiÕppeVaramu: Open Educational Resources • Estonian Ministry of Education and Research procured a set of web- based Open Educational Resources that cover the whole curriculum of Grades 10 - 12 • From June 2017 til August 2018: 80+ expert teachers hired, 10 000 learning objects created, currently piloted in 20+ schools • Each Learning Object creates a stream of xAPI data that is recorded in Learning Record Store • In the future: Single Sing-On allows aggregation of events for one learner in various digital platforms (anonymisation, masking needed) • Multimodal learning analytics: online + offline data https://vara.e-koolikott.ee
- 19. Innovative pedagogical scenarios • Mainstream practices within 2nd generation e-learning systems (LMS) follow the conservative pedagogy: presentation-practice-test • Innovative pedagogical scenarios from LEARNMIX project (learners and teachers as co-authors of “e-textbooks”): • Flipped classroom • Project-based learning • Problem-based learning • Inquiry-based learning • Game-based learning Http://learnmix.tlu.ee
- 20. LePlanner.net: a tool for visualising and sharing innovative pedagogical scenarios
- 21. Levels of interaction (or co-authorship) Level Learner’s contribution Examples of tools 6: Creating Creates a new resource from scratch GeoGebra, iMovie, Aurasma, PhotoStory, GarageBand, iBooksAuthor 5: Remixing Rips, mixes, cuts, adds visuals or subtitles “Hitler gets angry” video, 9gag, samples, GeoGebra, GDocs 4: Expanding Curates, adds external resources to collection Scoop.it, blog 3: Submitting Solves a task, submits to teacher for the feedback Kahoot, Khan Academy, online tests, worksheets made with Gdocs 2: Interacting Self-test, simple game LearningApps, HotPotatoes, SCORM 1: Annotating Likes, bookmarks, comments Youtube video, ePub, PDF, Web page 0: Consuming Views, listens, reads PowerPoint, PDF, video
- 22. Observata – xAPI-compliant lesson observation and analytics tool
- 23. Digital Mirror Self-assessment: • By the principal • By digi-team • By peer team Data-driven decision-making: • Benchmarking • Strategic goals • Action plan • School-owners’ digital strategy An online tool for self-assessment of school’s digital maturity, Creating digital strategy
- 24. Samsung Digi Pass: Open Badges for digital skills profile for disadvantaged youth • Collect - stuff, tools, memories, friends • Make sense - annotate, systematize • Share – know what and how and with whom • Create – digital production, social skills • Collaborate – teamwork, social skills • Show yourself – digital identity, portfolio, pitching • Be safe, be nice – licenses, privacy, health, ethics • Fix it - problem solving, troubleshooting • Improve it – innovation, entrepreneurial mindset
- 25. SHEILA: Learning Analytics policies in HE http://sheilaproject.eu interviews e interviews, 21 out of 51 institutions were already implementing centrally-supported learning s, 9 of which had reached institution-wide level, 7 partial-level (including pilot projects), and 5 loration and cleaning stage. Meanwhile, 18 institutions were in preparation to roll out ning analytics projects, and 12 did not have any concrete plans for an institutional learning yet. uestion in the survey revealed that 15 institutions had implemented learning analytics, of which ll implementation and 13 were in small scale testing phases. Sixteen institutions were in earning analytics projects, and 15 were interested but had no concrete plans yet. N O P L A N S I N P R E P A R A T I O N I M P L E M E N T E D 9 7 5 12 18 The adoption of learning analytics (interviews) Institution-wide Partial/ Pilots Data exploration/cleaning IMP LE ME NTE D 2 13 The adoption of learning analytics (survey) Institution-wide Small scale The results show that topics about “privacy and transparency” are considered as both the most impor easiest to address, whereas “research and data analysis” is comparatively less important than other th “objectives of learning analytics” is less easy to address than other themes. The overall scores of the im ranking are higher than the overall scores of the ease-ranking. 4. Survey and interviews At the time of the interviews, 21 out of 51 institutions were already implementing centrally-supported analytics projects, 9 of which had reached institution-wide level, 7 partial-level (including pilot project were at data exploration and cleaning stage. Meanwhile, 18 institutions were in preparation to roll ou Motivators: • To improve student learning performance (16%) • To improve student satisfaction (13%) • To improve teaching excellence (13%) • To improve student retention (11%) • To explore what learning analytics can do for our institution/ staff/ students (10%)
- 26. Conclusion • Technology is the answer! • … but what was the QUESTION?