The document discusses the future possibilities and challenges of using digital tools in education from three perspectives: 1) Today, where educational institutions follow current practices in the field. 2) Tomorrow, looking at predictions from research about areas like adaptive learning, smart learning environments, and educational data mining. 3) A distant future, where the possibilities are unknown since technology is changing rapidly. Overall, the document emphasizes that technology should be used to support new educational designs that help address 21st century skills, rather than seeing it as the answer on its own.

1. Digitaaliset välineet opetuksessa ja
oppimisessa opettajankoulutuksen
kontekstissa – tasapainoilua tulevaisuuden
mahdollisuuksien ja teknologian opetuskäytön
arkipäiväistymisen arkirealismin välillä
Jari Laru, KT, yliopistonlehto, teknologiatuettu oppiminen ja opetus, oppimisen ja
koulutusteknologian tutkimisysikikkö (LET), kasvatustietieden tiedekunta, Oulun yliopisto
@Larux, @LetOulu, @UniOulu

2. 1997
20101999
2003
KM 2013Yliopisto-opettaja Yliopistonlehtori
Opettajaopinnot
Tietojenkäsittelytieteen opinnot
Koulutusteknologia
Tutkijakoulutettava
Opetuksen ja opetusolosuhteiden
kehittäminen alkoi 2010…
Opiskelija Opettaja

3. Tämä hetki: kaupalliset tuotteet ja
opettajien kokeilut
Tutkimushankkeen kesto +
kaupallistaminen
Kaukainen tulevaisuus
Technology
Enhanced Learning
Technology Enhanced &
Augmented
Learning Processes
No idea 
Tänään ”Huomenna” ”Ei aavistustakaan”
ESITYKSEN RAKENNE

4. DIGI ON ARKEA: TYÖVÄLINE
1. TÄNÄÄN
Opettajankoulutus seuraa ”kenttää”

5. Verkot ovat hitaita tai
puutteellisia
Ei laitteita tai
ohjelmistoja
Ei vapautta valita
mitä sovelluksia
/järjestelmiä käyttää
Erittäin rajoitettu
budjetti
Liikaa
mahdollisuuksia
Ei ole aikaa ottaa
välineitä
käyttöön
Hyvin rajallinen
täydennyskoulutus

6. https://www.slideshare.net/la
rux/digitaalisen-oppimisen-
vlineit-yhdess-kehittmss-
varhainen-omaksuja-uhka-vai-
mahdollisuus-tietohallinnolle-
137649224

7. Kovat taidot: opetettavat
aineet
Pehmeät taidot
● Yhteisöllinen työskentely ja
oppiminen
● Luovuus ja mielikuvitus
● Kiirittinen ajattelu ja
ongelmanratkaisu
● Elämä muuttuvassa
yhteiskunassa
● Informaatiolukutaito ja ICT skilsl

8. Ideaalikuva digiaikakauden opettajasta

9. Pehmeä osaaminen:
● Yhteisöllinen työskentely
ja oppiminen
● Luovuus ja mielikuvitus
● Kiirittinen ajattelu ja
ongelmanratkaisu
● Elämä muuttuvassa
yhteiskunassa
● Informaatiolukutaito ja
ICT skilsl
Kognitiiviset taidot
Monialaiset + sivuaineet
Digikurssit + monialaiset

10. OPS:n laaja-alaisen osaamisen taidot

11. ”Ensimmäistä kertaa joku
sanoo, että pelaamisesta
on hyötyä”
”Mahtavaa, että saan
käyttää aiempaa
harrastustani gradussa ja
opettajankoulutuksessa”
Osa
opettaja(n/opiskelijan)
identiteettiä

12. Uudet oppimisympäristöt
FabLab / Makers
aktiviteetit
Tilat ovat myös osa
opettajan identiteettiä ja
sen murrosta

14. DIGILUOKKA:
• 15 chromebook kannettavaa
• 15 windows kannettavaa
• 30 iPadia
• Robotiikkaa,kameroita yms
• 4x Greenscreen
• Kattava esitystekniikka
• Laajennettavissa aulaan

15. Computater literacy (oppii
perustaitoja)
Computational fluency (oppii
aikakaudelle tärkeitä taitoja ja
käytänteitä)
Computational thinking
(ohjelmallinen ajattelu, integroituu
digitaaliseen elämäntapaan)
Computational Design
(digitaalisten tuotosten
suunnittelu)
Focus on tools like word processors &
spreadsheets
Fluency with language and practises of
computing. Asset in emerging digitalized world.
Wing’s essay on computational thinking (2006).
A lot of CT framworks, but generally computing
practises are taught by using computing
concepts and practises through games, magic
tricks, and activites

16. ”However, a critical gap remains in
understanding how to best integrate
CT into NGSS (next generation
science standardards)-aligned science
instruction beyond simply including
more and bigger datasets or adding
technological instruments.
Instead, the goal is to provide
students with ongoing experiences
to advance their CT skills and
support their understanding of how
those skills are a fundamental aspect
of contemporary scientific inquiry”

17. Computational Thinking in STEM classes
David Weintrop, Elham Beheshti, Michael Horn, Kai Orton, Kemi Jona, Laura Trouille, and Uri Wilensky. 2016. Defining computational
thinking for mathematics and science classrooms. Journal of Science Education and Technology 25, 1 (2016), 127–147.
https://link.springer.com/article/10.1007/s10956-015-9581-5 (LINKIN TAKANA TARKEMMAT KUVAUKSET)

18. Asare, K. O., Leikanger, T., Schuss, C., Klakegg, S., Visuri, A., &
Ferreira, D. (2018, September). S3: environmental fingerprinting
with a credit card-sized NFC powered sensor board.
In Proceedings of the 20th International Conference on Human-
Computer Interaction with Mobile Devices and Services
Adjunct(pp. 298-305). ACM.
http://www.akuvisuri.com/wordpress/wp-
content/uploads/2018/09/mhci18.pdf

19. ESIMERKKI
A B C D
Gendreau Chakarov, A., Recker, M., Jacobs, J., Van Horne, K., & Sumner, T. (2019, February). Designing a Middle School Science Curriculum that Integrates Computational
Thinking and Sensor Technology. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 818-824). ACM.
Vaiheet, sensoreiden hyödyntäminen

20. Computational thinking is not only
something programmers must know, but it
is also a thinking tool to for understanding
our technology infused social world
• It increases our awaraness of how our everyday digital tools work
• Improves our resilience against diff threats:
• Algorithm guided attemps to guide our behavior
• Personally tailored fake news
• Viral powers of social media
• Massive, data-intensive analysis of our movements

21. Computing as pervasive information
processes: nothing to automate
COMPUTATIONAL INTERPRETATION OF THE
WORLD
• Natural processes of DNA transcription
are computational
• Many brain processes can be seen as
computational
• Simulations & mathematical models
MANY NEW TECHNOLOGICAL innovations
* blogging, blockchain, image
recognizitioon, artificial intelligence etc.
https://commons.wikimedia.org

22. DIGI (ON) HYPEÄ: TUTKIMUS
2. HUOMENNA
Tutkijoiden kammioista..

23. https://www.slideshare.
net/larux/predicting-
future-of-edtech-2030-
v2

24. To adress 21st centyry challenges and opportunities,
Woolf (2010) suggests..
● User modeling
● Mobile and network tools
● Rich interfaces and
environments, including
gamification and
intelligent systems
● Educational data mining
● Personalizing education
● Assessing student learning
● Diminishing boundaries
● Developing altenative
teaching strategies
● Enhancing the role of
stakeholders
● Adressing policy changes
Technology should be used for:New designs that include:
Woolf B.P., A roadmap for education technology, National
Science Foundation, Washington, DC, 2010, https://hal.
archives-ouvertes.fr/hal-00588291.
Technology is not answer, unless it
can be used for
?

25. Example of smart Learning Environment [metatutor]
Adaptive learning materials: early steps
Chew, S. W., Cheng, I. L., & Chen, N. S. (2018). Exploring challenges faced by different
stakeholders while implementing educational technology in classrooms through expert
interviews. Journal of Computers in Education, 5(2), 175-197.
Metatutor Environment (left side:) Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-
Behnagh, R., Bouchet, F., & Landis, R. (2013). Using trace data to examine the complex roles of
cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent
systems. In International handbook of metacognition and learning technologies, Springer New
York, p. 431
..towards developing ”smart learning environment”
• That monitors learners’ learning process and
their progress,
• adapting to their learning patterns and needs,
• suggesting and feeding learners with relevant
information what they need in different forms
that suits each learner’s learning preference
and style
Future: Automated real-time adaptive learning
environment?

26. https://www.oulu.fi/eudaimonia/node/50067

27. https://www.slamproject.org/uploads/5/7/5/1/57512023/j%C3%A4rvel%C3%A4
-keynote_lak_2017-final_optimized.pdf
101 hours of video, 266 216 000 data points of
physiological data, 236 000 EdX log events…
Collaboration with LA, data-mining and signal
processing experts => Methodological
development (LA) => Data vizualisation
SLAM PROJECT https://www.slamproject.org/
Järvelä, S. , Kirschner, P. A., Hadwin, A., Järvenoja, H., Malmberg, J. Miller, M. & Laru, J. (2016, in
press). Socially shared regulation of learning in CSCL: Understanding and prompting individual- and
group-level shared regulatory activities. International Journal of Computer Supported Collaborative
Learning.
Järvelä, S., Malmberg, J. & Koivuniemi, M. (2016). Recognizing socially shared regulation by using
the temporal sequences of online chat and logs in CSCL. Learning and Instruction, 42, 1-11.
DOI: 10.1016/j.learninstruc.2015.10.006
Järvelä, S., Järvenoja, H., Malmberg, J., Isohätälä, J. & Sobocinski, M. (2016). How do types of
interaction and phases of self-regulated learning set a stage for collaborative engagement? Learning
and Instruction 43, 39-51. DOI:10.1016/j.learninstruc.2016.01.005
Järvelä, S., Malmberg, J., Sobocinski, M., Haataja, E., & Kirschner, P. (2016). What multimodal data
can tell us about the self-regulated learning process? Submitted.
Malmberg, J., Järvelä, S., Holappa, J., Haataja, E., & Siipo, A. (2016). Going beyond what is visible
–What physiological measures can reveal about regulated learning in the context of collaborative
learning. Submitted.
Malmberg, J., Järvelä, S., & Järvenoja, H. (2016). Capturing temporal and sequential patterns of
self-, co-, and socially shared regulation in the context of collaborative learning. Submitted.
Pijeira-Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2016). Investigating collaborative
learning success
with physiological coupling indices based on electrodermal activity. Proceedings of the Sixth
International Conference on Learning Analytics and Knowledge. ACM.
DOI:10.1145/2883851.2883897
Pijeira-Díaz, H. J., Drachsler, H., Kirschner, P. A., & Järvelä, S. (2018). Profiling sympathetic arousal
in a physics course: How active are students? Journal of Computer Assisted Learning, (April), 1–12.
DOI:10.1111/jcal.12271
Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2018). Linking Learning
Behavior Analytics and Learning Science Concepts: Designing a Learning Analytics Dashboard for
Feedback to Support Learning Regulation. Computers in Human Behavior.
DOI:10.1016/j.chb.2018.05.004
Sobocinski, M., Malmberg, J. & Järvelä, S. (2016). Exploring temporal sequences of regulatory
phases and associated interaction types in collaborative learning tasks. Submitted.

28. Educational robots
Educational robot is not just a tool used in the
class, but more general learning companion
• Ability to have fully context aware whereby it
would be to feed learner’s preference (Mishra,
2015)
• Ability to understand and attain learning
patterns and characteristics of the learners
• Would be able to react to the learner’s input
• Robot would grow together with child,
learning the child’s living style and learning
habits

29. D. Hood, S. Lemaignan and P.
Dillenbourg. The CoWriter
Project: Teaching a Robot how
to Write. 2015 Human-Robot
Interaction Conference,
Portand, USA, 2015.
Educational robots: example robot which
can teach children to write
See the project: https://chili.epfl.ch/page-92073-en-html/robotics/cowriter/

30. https://medium.com/@shivsoni377/the-difference-between-augmented-reality-virtual-reality-and-mixed-reality-
a028bdd81f9d?fbclid=IwAR17d57MG4e9-ClKnGKEk8Sv9tSn87Uac2Gqx28pnw2NIM8gq_-zEwcYy_g
Rich interfaces & Environments: virtual assistants
and xReality

31. To adress 21st centyry challenges and opportunities,
Woolf (2010) suggests..
● User modeling
● Mobile and network tools
● Rich interfaces and
environments, including
gamification and
intelligent systems
● Educational data mining
● Personalizing education
● Assessing student learning
● Diminishing boundaries
● Developing altenative
teaching strategies
● Enhancing the role of
stakeholders
● Adressing policy changes
Technology should be used for:New designs that include:
Woolf B.P., A roadmap for education technology, National
Science Foundation, Washington, DC, 2010, https://hal.
archives-ouvertes.fr/hal-00588291.
Technology is not answer, unless it
can be used for
?

32. DISTANT FUTURE (No ETA,
surprise)
We have no idea about it.

33. Next generation hardware and software: tool for
living (so easy, intuitive and supportive)

34. Kiitos! Ole yhteydessä!
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