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).
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?
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
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, …
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 2020See 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 2020See 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.