Introduction of my dissertation progress

1. Scaffolding collaborative learning
with cognitive tools based on
mobile computers
Jari Laru
University of Oulu
Department of Educational Sciences and Teacher Education
Research Unit for Learning & Educational Technology (LET)
KTK235, Snellmania
Po.Box 2000, 90014 University of Oulu

2. Methodological insights:
Case study research & Design Based Research

3. Kurti, Arianit, Exploring the multiple dimensions of context: Implications for the design and development of
innovative technology-enhanced learning environments. - Växjö : Växjö University Press, 2009. - (Acta
Wexionensia ; 180/2009)
Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage Publications

4. Peterson, R. & Herrington, J. (2005). The State of the Art of Design-Based Research. In G. Richards (Ed.), Proceedings of World Conference on E-
Learning in Corporate, Government, Healthcare, and Higher Education 2005 (pp. 2302-2307). Chesapeake, VA: AACE. Retrieved from
http://www.editlib.org/p/21540.
-- Additional readings ---
Sandoval, W. A., & Bell, P. L. (2004). Design-Based Research Methods For Studying Learning In Context: Introduction. Educational Psychologist,
39(4), 199-201.
http://www.designbasedresearch.org/publications.html

5. My research:
Scaffolding collaborative learning with cognitive
tools based on mobile computers

6. AIMS from past to today
This thesis work focuses on developing and analyzing
innovative ways of supporting applying the framework of
distributed scaffolding for learning activities in authentic
real world contexts.
In this study theoretical ideas of cognitive tools,
collaborative learning and scaffolding are applied for
designing light-weight mobile software and pedagogical
models for learning in authentic real world contexts.
This is done in order to generate new knowledge and
solutions that advance collaborative learning in mobile
computer supported collaborative learning

7. Introduction
Earli SIG
Mobile computers Everyday contexts
Scaffolding collaborative Master’s programme,
University, Professional
learning with cognitive
Community, K-12 students,
tools based on Higher Education students,
Nature school
mobile computers
Case I Case II Case III
workplace (n=10) Nature (N=22) University (N=22)
EMI ILE INTHIG

8. Social patterns in mobile technology mediated collaboration among members of the professional distance
education community
The aim of this study was to identify social patterns in mobile technology mediated collaboration among distributed members of the
professional distance education community. Ten participants worked for twelve weeks designing a master’s programme in Information
Sciences. The participants’ mobile technology usage activity and interview data were first analyzed to get an overview of the density
and distribution of collaboration at individual and community levels. Secondly, the results of the social network analyses were
interpreted to explore how different social network patterns of relationships affect online and offline interactions. Thirdly, qualitative
descriptions of participant teamwork were analysed to provide practical examples and explanations. Overall, the analyses revealed
nonparticipative behaviour within the online community. The social network analysis revealed structural holes and sparse collaboration
among participants in the offline community. It was found that due to their separated practices in the offline community, they didn’t
have a need for mobile collaboration tools in their practices.
Laru, J. & Järvelä, S. (2008). Social patterns in mobile technology mediated collaboration among members of the professional distance education
community. Educational Media International Journal, 45(1),17-3.
Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a
case study with K-12 learners
This study explores how collaborative inquiry learning can be supported with multiple scaffolding agents in a real-life field trip context. In
practice, a mobile peer-to-peer messaging tool provided meta-cognitive and procedural support, while tutors and a nature guide provided
more dynamic scaffolding in order to support argumentative discussions between groups of students during the cocreation
of knowledge claims. The aim of the analysis was to identify and compare top- and low-performing dyads/triads in order to reveal the
differences regarding their co-construction of arguments while creating knowledge claims. Although the results revealed several shortcomings
in the types of argumentation, it could be established that differences between the top performers and low performers were statistically
significant in terms of social modes of argumentation, the use of warrants in the mobile tool and in overall participation. In
general, the use of the mobile tool likely promoted important interaction during inquiry learning, but led to superficial epistemological quality
in the knowledge claim messages.
Laru, J., Järvelä, S. & Clariana, R. (2010). Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for
learning: a case study with K-12 learners. Interactive Learning Environments, Online first, 1-15. doi:10.1080/10494821003771350
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
In this single-case study, small groups of learners were supported by use of multiple social software tools and face-to-face activities in the context of
higher education. The aim of the study was to explore how designed learning activities contribute to students’ learning outcomes by studying
probabilistic dependencies between the variables. The participants (n=22) worked in groups of four to five students for 12 weeks. Groups were
required to complete a wiki project by the end of the semester. In order to complete the wiki project, students needed to participate in recurrent
solo and collective phases mediated by the use of social software tools and face-to-face meetings in their respective sessions. The data for
multivariate Bayesian analysis was composed of video recordings, social software usage activity and pre- and post-tests of students’ conceptual
understanding. In our case, we found that using social software tools together to perform multiple tasks likely increased individual knowledge
acquisition during the course. Bayesian classification analysis revealed that the best predictors of good learning outcomes were wiki-related
activities. In addition, according to the Bayesian dependency model, students who monitored their peers’ work via syndication services and who
were active by adding, modifying or deleting text in their group’s wiki obtained higher scores. The model also shows that many other learning
activities were indirectly related to learning outcome.
Laru, J., Näykki, P. & Järvelä, S. (2011). Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education
context. Special issue on Web 2.0 on Higher Education. Journal of Internet and Higher Education.

9. Social patterns in mobile technology mediated collaboration among members of the professional distance
education community
1. What is the density and the distribution of the collaboration at
individual and community levels in the online and offline communities?
Questions 2. How do different social network patterns of relationships affect online
and offline interactions?
3. How do participants describe teamwork and the technologies used to
support it?
Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a
case study with K-12 learners
1. What were the differences between top and low performers in regards
to collaborative inquiry learning during the field trip? groups?
2. What was the difference between top and low performers in regards to
the structural quality of knowledge claim messages?
3. How much did the top and low performers learn about biology during
the field trip?
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
1. How much did students learn during the course?
2. Which social software and face-to-face variables were the best predictors
for identifying differences between high- and low-performing groups of
students?
3. What was the impact of social software and face-to-face sessions on
individual students' learning gain?

10. Social patterns in mobile technology mediated collaboration among members of the professional distance
education community
• 1st generation: mobile
versions of desktop tools:
Tools FLE3mobile
• wlan
Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a
case study with K-12 learners
• 2nd generation: context-
aware peer-to-peer mobile
tools: flyers
• mobile encounter network
(bluetooth)
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
• 3nd generation: mobile social
media: mobile clients + flickr
+ wordpress + wikispaces +
google reader
• 3G connectivity

11. Social patterns in mobile technology mediated collaboration among members of the professional distance
education community
• No groups designed (participants worked in
three teams though)
Design ”Let’s try it” .. • No clear task, work related activities (no formal
learning)
• Knowledge building
• Metacognitive scaffolding
Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a
case study with K-12 learners
• Dyads/Triads
• Ill-structured task
• Argumentative collaboration
• Procedural scaffolding & metacognitive
scaffolding
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
• 4-5 students per group
• Ill-structured tasks
• Small groups of learners were supported by
multiple social software tools and face-to-face
activities
• Recurrent individual and collaborative phases
• Multiple scaffolds

12. Social patterns in mobile technology mediated collaboration among members of the professional distance
education community
• Quantitative analysis of FLE3mobile’s log-files
Methods SNA
Social network analysis
•
(log file analyzer)
Qualitative-Quantitative Interview analysis (SNA
analysis)
Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a
case study with K-12 learners
Mann-whitney • Quantititative Mindmap analysis (pre-post-test)
• Qualitative analysis of recorded argumentative
U-test •
discussions (Mann-whitney U-test)
Qualitative analysis of the flyers (Mann-whitney
U-test)
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
• Quantitative analysis of conceptual knowledge
Bayes
Classification analysis
•
test (normalized gain, t-test)
Qualitative+Quantitative analysis of social
software activities (Bayesian classification
Dependency modeling analysis + Bayesian dependency modeling)

13. Social patterns in mobile technology mediated collaboration among members of the professional distance
education community
• Overall, the analyses revealed nonparticipative behaviour within the online
community. The social network analysis revealed structural holes and sparse
collaboration among participants in the offline community.
Results • It was found that due to their separated practices in the offline community, they
did not have a need for mobile collaboration tools in their practices.
Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a
case study with K-12 learners
• Although the results revealed several shortcomings in the types of argumentation, it
could be established that differences between the top performers and low
performers were statistically significant in terms of social modes of argumentation,
the use of warrants in the mobile tool and in overall participation.
• In general, the use of the mobile tool likely promoted important interaction during
inquiry learning, but led to superficial epistemological quality in the knowledge
claim messages.
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
• Explorative Bayesian classification analysis revealed that the best predictors of
good learning outcomes were wiki-related activities.
• In general, the results indicated that interaction between individual and collective
actions likely increased individual knowledge acquisition during the course.

14. Similar
• Cognitive tools; Generic cognitive tools
• Mobile computer supported collaborative learning
• Can be considered as example: development of ”mobile learning” (from
past to today)
• Design can be considered as example: learning from => learning with
Different
• Study 1 is socio-cultural (COP) while others are socio-cognitive
• Methodological designs are quite different
• No explicit design cycles from study 1 to study 3, instead studies are
independent cases. Development cycles are in design etc.

15. Theory Theory
Learning design Learning design
Techno trends Techno trends
Methodology Methodology
Context Context
Tool Tool