This document provides an introduction to technology enhanced learning. It discusses how the modern knowledge economy requires lifelong learning. It describes key findings from learning sciences, including the importance of deep conceptual understanding, connected knowledge, and collaborative learning. It also discusses motivational competence, self-regulated learning, and how technology can support deep learning and collaboration through representing knowledge concretely and allowing learners to build knowledge together. Examples are given of learning, collaboration, and technology use in the author's research and teaching.

1. INTRODUCTION TO
TECHNOLOGY ENHANCED
LEARNING
Hanna Järvenoja
Learning and Educational Technology Research Unit - LET
Department of Educational Sciences and Teacher Education
University of Oulu, Finland

2. Learning is ”a hot topic” today!
1) There is a transformation of industrial economy
to knowledge economy and creative economy.
The knowledge economy is based on “the
production and distribution of knowledge and
information, rather than the production and
distribution of things”
(Drucker, 1993, p. 182).
2) Knowledge economy is also a learning society.
3) It is no longer possible to imagine that
education ends by a certain age, after which
learning is no longer necessary.

3. CLAIMS ABOUT LEARNING AND
THINKING

4. 21st century skills
Diginatives With
Students are
Information is not enough – Skills and Will

5. WHY DO WE NEED LEARNING SCIENCE?
• Because, the Standard Model of Schooling
• Has been based on common-sense assumptions that had
never been tested scientifically.
• Knowledge is a collection of facts about the world and
procedures for how to solve problems.
• The goal of schooling is to get these facts and procedures
into the student’s head.
• Teachers know these facts and procedures, and their job is to
transmit them to students.
• The schools don’t teach the deep knowledge that underlies
knowledge work.

6. WHAT IS LEARNING SCIENCES?
(SAYWER, 2006)
• Is an interdisciplinary field that studies teaching and
learning. The sciences of learning include cognitive science,
educational psychology, computer science, sociology,
neuroscience and other fields.
• Educators can use the learning science to design more
effective learning environments, including schools,
classrooms and informal settings, as well as learning
practices.

7. KEY FINDING FROM THE
LEARNING SCIENCES (SAYWER, 2006)
The importance of
• learning deeper conceptual understanding, rather
than superficial facts and procedures.
• learning connected and coherent knowledge, rather
than bits of facts and knowledge.
• learning authentic knowledge in its context of use,
rather than decontextualized classroom exercises.
• learning in collaboratively, rather than in isolation.

8. Developing Future Education:
Deep learning instead of transfer of facts

9. LET WANTS TO TACKLE
THE 21ST CENTURY LEARNING CHALLENGES
Learning in and for the 21st century requires –
beyond knowledge - motivational competence
and effective learning strategies in individual
and collaborative work in formal education and
informal learning settings.

10. MOTIVATION
METACOGNITION
SKILLS WILL
EMOTIONS
COGNITION
THINKING
TAKING CHARGE OF
OWN LEARNING

11. WHAT IS COLLABORATIVE LEARNING?
(Dillenbourg, 1999; Baker Et Al., 1999; Fischer Et Al., 2007; Roschelle & Teasley,
1995, Stahl, 2006; Webb, Nemer & Ing, 2006)
”A coordinated synchronous activity that is the result of
continued attempt to construct and maintain a shared
conception of a problem”
Learning does not emerge because of a group of people, but
because collaboration stimulates and activates knowledge
acquisition mechanisms in individual learners!
Asking questions, clarifying, explaining, constructing
arguments, comparing opinions, creating new ideas…..

12. COLLABORATIVE LEARNING
IS NOT A RECIPE
• Collaborative learning often leads to better results than
learning individually (Johnson & Johnson, 1999), but not
systematically.
• Asking students to work together is not enough (Järvelä &
Häkkinen, 2001).
• Collaboration per se does not produce learning
outcomes; its results depend upon the extent to which
groups actually engaged in productive interactions.

13. SELF-REGULATED LEARNING
(Pintrich, 2001; Zimmerman, 2008; Winne, 2005)
• Planning, maintaining and controlling goal oriented activity
• Includes cognitive & metacognitive,
motivational & emotional and
contextual & behavioral aspects
• Requires both SKILL and WILL!!!
• Is not automatic or easy all the time

14. TECHNOLOGY FOR DEEP LEARNING
(Dillenbourg, Järvelä & Fischer, 2009; Brandsford, Et Al.,2000)
• A context, tool and support for learning and
collaboration
e.g.
• Computers can represent abstract knowledge in
concrete form.
• Computer tools can allow learners to articulate
their developing knowledge in a visual and verbal
way.
• Virtual forums offer opportunities for sharing and
building knowledge together.
• Social interactions will be enriched.

15. examples of
LEARNING, COLLABORATION and
the use of TECHNOLOGY in
LET RESEARCH and TEACHING

16. Malmberg, J., Järvenoja, H. & Järvelä, S. (2010). Tracing elementary school students’ study
tactic use in gStudy by examing a strategic and self-regulated learning. Computers in Human
Behavior, 26, 5, 1034-1042
Hyvönen, P., Kronqvist, E-L., Järvelä, S., Määttä, E., Mykkänen, A. & Kurki, K. (2014,
submitted). Interactive and child-centred research methods for investigating the efficacious
agency of children.
Mykkänen, A., Järvelä, S. & Perry, N. (2014, submitted). Children Making causal attributions in
situations that support self-regulated learning.

17. Laru, Malmberg, Järvenoja, Sarenius & Järvelä (2014). Designing
simple tools for socially shared regulation: experiences of using
google doc and mobile SRL tool in maths education

18. Kontturi, H., Juuso, H., Kangas, V., Kumpulainen, K., Tuominen, T.,
Järvelä S. (2013). UBIKO - School unit as an inspiring learning
environment.

19. Multimodal dataHTML5 apps for learning & data collection Videodata
”LeaForum on uudenlainen
tutkimusinfrastruktuuri ja -yhteisö,
joka tarjoaa mahdollisuuden
tuotekehitystyöhön ja monitieteiseen
vuorovaikutustutkimukseen sekä
yhteiset laiteresurssit observointiin ja
aineistojen tallentamiseen.”
http://leaforum.fi

20. Kirschner, Järvelä, Malmberg, Drachsler & Darvasi,
(2014). Strategic Regulation of Learning through
Learning Analytics and Mobile clouds for Individual
and Collaborative Learning Success