Oppimisanalytiikka – Mahdollisuudet ja edessä olevat haasteet Euroopan koulumaailmalle
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Esitys pohjautuu Euroopan komission Yhteinen tutkimuskeskuksen (JRC) selvitykseen Oppimisanalytiikan käytöstä opetuksen ja oppimisen tukena, se listaa mahdollisuudet ja edessä olevat haasteet Euroopan koulumaailmalle.
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Oppimisanalytiikka – Mahdollisuudet ja edessä olevat haasteet Euroopan koulumaailmalle
- 1. The European Commission’s science and knowledge service Joint Research Centre Oppimisanalytiikka – Mahdollisuudet ja edessä olevat haasteet Euroopan koulumaailmalle Aulanko, Finland April 6 2017 Dr. Riina Vuorikari DG JRC – Directorate Innovation and Growth Unit B4 Human Capital and Employment
- 2. 2 Yhteinen tutkimuskeskus on komission oma tutkimusyksikkö Sen riippumattomat, näyttöön perustuvat tieteelliset lausunnot tukevat EU:n toimintapolitiikan suunnittelua The Joint Research Centre (JRC) Directorate Growth & Innovation Seville
- 3. Riina Vuorikari • Tutkijana Sevillassa vuodesta 2013 • 2000-2013 European Schoolnet as Senior Research Analyst and Project Manager • Tausta: • Savonlinnan OKL (MEd) • Studying abroad (exchange and postgraduate studies) e.g. hypermedia (DEA) web, use of ICT in education • Doctoral 2009 from the Dutch School Information and Knowledge System • https://www.slideshare.net/vuorikari ulkosuomalainen
- 4. The European Commission’s science and knowledge service Joint Research Centre . Osa 1: Oppimisanalytiikka, JRC:n uusin selvitys . Osa 2: Mahdollisuudet . Osa 3: Edessä olevat haasteet . Outline:
- 5. 6 Oppimisanalytiikalla Tarkoitetaan oppijasta kertyvien tietojen keräämistä, mittaamista, analysointia ja raportointia siten, että tarkoituksena on ymmärtää ja optimoida oppimista ja oppimisympäristöjä. (source) Learning analytics have their roots in many fields of educational and technical research, including assessment, personal learning and social learning, but also in business intelligence and data mining. The field draws on theory and methodologies from disciplines as statistics, artificial intelligence and computer science (Dawson et al., 2014).
- 6. 7
- 7. 10 Mitä raportti sisältää? • Inventaatio tämän hetkisistä käytänteistä, mitä on tarjolla ja miten sitä käytetään: • Työkaluja, käytänteitä, koulutusohjelmia (60 esimerkkiä) • 5 case studies • Tavoite: kriittisesti miettiä oppimisanalytiikan vaikutuksia, potentiaalia ja haasteita koulutuksen alalla • “The Action List for Learning Analytics”
- 8. The European Commission’s science and knowledge service Joint Research Centre . Osa 1: Oppimisanalytiikka, JRC:n uusin selvitys . Osa 2: Mahdollisuudet . Osa 3: Edessä olevat haasteet . Outline:
- 9. 13 Esimerrki 1: Inventory of Tools 1. 2. 3. 4. 5. Specific models of domain knowledge (in math) and on the learner responses (cognitive models ) Stand alone application that generates its own data. What does this data tell us about learning? Is it actionable?
- 10. 14 http://cloudworks.ac.uk/cloud/view/9633 1. 2. 3. 4. 5. 6. Esimerkki 2: Inventory of Tools Data sources include VLE; social media, “card swipes” (e.g. using student card to go to library), libraries, housing
- 11. 15 Keskeyttämisuhan alla olevien oppilaiden identifiointi: Georgia State University At the university, predictive analytics have been used to tackle the achievement gap for low income and first-generation students. GSU graduation rate rose from 32% in 2003 to 54% in 2014. In the process, the university claims to have removed the achievement gap between students from minority backgrounds or lower socioeconomic status and their peers. Example 3: Inventory of Practices Tasa-arvo, Oikeuden- mukaisuus
- 12. 16 1. 2. 3. 4. Esimerkki 4: Inventory of Tools
- 13. 17 • “stand-alone” tools; add on to an existing VLE; custom-made solutions; • Different target beneficiaries of analytics: • oppijat, • opettajat, • tuutorit, opinto-oppaat, koulutuksenohjaajat, • koulunjohto/hallintotyöntekijät, • Opetusvirasto, päättäjät,.. The Inventory: Tools (1)
- 14. 18 The Inventory: Tools (2) • Different data sources: • Student digital traces from the platform or outside of it, e.g. interaction data, social media, libraries • Data from offline sources, e.g. evaluations by the learner, demographic data, nation-wide test data/evaluations • Kuvaaminen ja tilastojen muodostaminen, visualisointi, tilastollisia päätelmiä; • Actions on data: mallintaminen, jonka pohjalta mukauttamista (adaptation), tukea, varottaminen (alerting); suosituksia • Action based on past behaviour, similarity in grades, domain knowledge, right answers, statistics, …
- 15. 19 2. 1.. Esimerkki 5: Inventory of Tools
- 16. 20 Mitä tuloksia ja perustuvatko ne tieteelliseen näyttöön (6)? • Evidence of Impact: The research evidence documented in this study shows that there is little formal validation of tools • e.g. whether the tools fulfil their intended purpose such as having a positive impact on learning; encouraging more efficient learning; or more effective learning,..
- 17. 21 Do we see real improvements in learning outcomes for learners? 37 examples
- 18. 22 Eettiset ja tietoturvaan liittyvät kysymykset (7) • Impact: The research evidence documented in this study shows that currently, most impact of learning analytics in education and training seems takes place around issues, little impact on changing practices yet: • Keskustelun teemana ovat eettiset ja tietotrvaan liittyvät kysymykset useimmin kuin esim. oppimisen prosessejen tukeminen ja uusien kehittäminen
- 19. The European Commission’s science and knowledge service Joint Research Centre . Osa 1: Oppimisanalytiikka, JRC:n uusin selvitys . Osa 2: mahdollisuudet . Osa 3: Edessä olevat haasteet . Outline:
- 20. 25 Haasteet Euroopan koulumaailmalle • Haaste 1: Luodaan eurooppalainen visio oppimisanalyytikasta, joka pohjautuu meidän arvomaailmaan • Luodaan toisenlaista tulevaisuutta, jossa pyritään luovuutta, innovaatiota, sosiaalisia taitoja ja ongelmanratkaisua tähtääviin oppimistuloksiin • Entä aktiivinen kansalaisuus ja työllistyvyys? • Syrjimättömyys ja kansalaistaitojen edistäminen?
- 21. 26 Haasteet Euroopan koulumaailmalle • Haaste 2: Kehittää oppimisanalytiikan työkaluja, jotka oikeasti auttavat opettajia ja oppijoita • Nyt on tarjontaa, mutta onko kysyntää? • Tuleeko käyttäjille se wow-efekti?
- 22. 27 Do Learning Analytics with dashboards and half-hearted visions of learning remain the lower hanging fruit of digital technology ? What about the visions for empowering learning?
- 23. The European Commission’s science and knowledge service Joint Research Centre Check the research of our team at the JRC Science Hub: https://ec.europa.eu/jrc/ New skills agenda: https://ec.europa.eu/education/ news/20160610-education-skills- factsheet_en Thank you!
Editor's Notes
- JRC-IPTS: One of the key knowledge providers for DG EAC
- Ulkomailla Suomesta kysellään aina kaikenlaista, esim. PISA tai revontulista. Hirvimerkki herättää myös hämmennystä ja naurua
- Source from web-analyics in the early 2000 Even if the field in new, there are long root in existing research such as Adaptive learning; Personalised learning and Intelligent tutoring systems; Recommender systems to support learning, etc Techniques and methods are also borrowed from statistics, artificial intelligence, computer science It is like a catch-all term, an umbrella under which many existing old things have been re-dressed with some new spice Adaptiivinen oppiminen on opetusmenetelmä, jossa hyödynnetään tietokoneita vuorovaikutteisina opetusvälineinä.selvennä Tietokoneet sovittavat (eli adaptoivat) oppisisällön oppijan vastausten perusteella pääteltyjen heikkouksien ja vahvuuksien mukaan. Tarkoitus on, että tietotekniikalla voidaan näin korvata ihmisopettajan tai tuutorin tarjoama vuorovaikutteinen opetus. It’s also a topic with lots of hype around it, so thinking of the mission of the JRC and the demand for evidence driven policy-making in Europe, it was clear that the policy-makers in Europe had a real need for better data and evidence of what is actually happening. People in the Ministries, educational boards at the national and local level do take many decisions…
- Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., Vuorikari, R., Research Evidence on the Use of Learning Analytics and Their Implications for Education Policy. (2016), Joint Research Centre Science for Policy Report.
- Adaptiivinen oppiminen on opetusmenetelmä, jossa hyödynnetään tietokoneita vuorovaikutteisina opetusvälineinä.selvennä Tietokoneet sovittavat (eli adaptoivat) oppisisällön oppijan vastausten perusteella pääteltyjen heikkouksien ja vahvuuksien mukaan. Focus only on one area: mathematics and provides personalised learning activities and feedback Only certain courses which are based on specific models of domain knowledge in math and on the learner responses (cognitive models ) Information on student progress and mastery of each achievable skill Teachers get several reports on individual’s engagement and learning of skills, but also on underperformance. Class assessment
- Works directly with each institution and makes use of available student data. Is individually tailored to each institution to fit their analytics needs Data sources include VLE; social media, “card swipes” (e.g. using student card to go to library?), libraries, housing - Aggregates student data for analysis and visualisation Historic and predictive data for institutional leaders and student service providers Visualises student performance and success across modules and predicts programme completition Integrates different tools
- A learning environment tool that has a module (a feature) to make analytics available Provides analysis and reporting at individual and group level. Tools to support evaluation and improvement of pedagogical practices Data is gathered from different sources: works with several school book publishers whose modules can be used for analytics, but also student surveys and statistical data & data from national texts Also uses adaptive tools, see Knewton in tools inventory for more information
- malliscaffold, support, recommend, predict,..
- A programme that provides content Students can complete lessons, the programme analyses performance and detects gaps Teachers have dedicated tools available Runs easily in a brower HOWEVER 5. Little information about privacy 6. No information about the impact of the tools, whether the use actually guarantees any learning outcomes or effectiveness, etc.
- näyttöön perustuvat tieteelliset lausunnot
- Teaching 32 examples
- Challenge !: What does it mean for educational policy? work is needed to make links between learning analytics, the beliefs and values that underpin the area in Europe – and European priority areas for education and training 2020 See also Vuorikari, R. (2017): Can Learning Analytics help the EU achieve its strategic objectives for education and training by 2020? Learning Analytics and Policy workshop in LAK Challenge 2: - much of the current work on learning analytics concentrates on the supply side – the development of tools, data, models and prototypes. There is considerably less work on the demand side – i.e. on how analytics connect with education and the changes that school administrators, teachers and studentswant these tools to make in order to support their everyday learning, teaching and assessment work. More attention needs to be paid to the demand side - like, for example, the work carried out by Kennisnet in the Netherlands. This sought to help schools articulate what they want from ICT vendors, mediating requirements and exploring possible solutions, thus ensuring that learning analytics products have useful features for their end users.
- Challenge !: What does it mean for educational policy? work is needed to make links between learning analytics, the beliefs and values that underpin the area in Europe – and European priority areas for education and training 2020 See also Vuorikari, R. (2017): Can Learning Analytics help the EU achieve its strategic objectives for education and training by 2020? Learning Analytics and Policy workshop in LAK Challenge 2: - much of the current work on learning analytics concentrates on the supply side – the development of tools, data, models and prototypes. There is considerably less work on the demand side – i.e. on how analytics connect with education and the changes that school administrators, teachers and studentswant these tools to make in order to support their everyday learning, teaching and assessment work. More attention needs to be paid to the demand side - like, for example, the work carried out by Kennisnet in the Netherlands. This sought to help schools articulate what they want from ICT vendors, mediating requirements and exploring possible solutions, thus ensuring that learning analytics products have useful features for their end users.