yhteisöllisen oppimisen tukeminen mobiililaitteiden avulla: kolme tapaustutkimusta kolmessa eri...

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Jari Laru, yliopisto-opettaja, Oulun yliopistoJari Laru, University teacher, University of Ouluhttp://www.mendeley.com/profiles/jari-laru/

http://farm4.static.flickr.com/3175/2961226120_61c51497b4_z.jpg?zz=1

Mobiilin oppimisen ja ohjauksen mahdollisuudet ammatillisessa koulutuksessa, mobiiliseminaari 7.12.2011. Helsingin yliopisto

Yhteisöllisen oppimisen tukeminen mobiililaitteiden avulla: Kolme tapaustutkimusta kolmessa eri kontekstissa

http://vodpod.com/watch/15508281-alan-kays-dynabook-rare-nhk-video-ipad-1960-1970-lukujen-taitteessa?u=larux&c=mobiililuento

http://www.slideshare.net/larux/1-luento-tieto-ja-viestinttekniikan-perusteet-opintojaksolla-tvt-opetuskytn-historia

http://vodpod.com/watch/15508281-alan-kays-dynabook-rare-nhk-video-ipad-1960-1970-lukujen-taitteessa?u=larux&c=mobiililuento

http://www.slideshare.net/larux/1-luento-tieto-ja-viestinttekniikan-perusteet-opintojaksolla-tvt-opetuskytn-historia

http://www.tietokone.fi/uutiset/anssi_vanjoki_alypuhelin_kuoleehttp://www.slideshare.net/philrawcliffe/wac-press-conference-mwc-2011?from=ss_embed

http://chrome.angrybirds.com/

http://www.tietokone.fi/uutiset/anssi_vanjoki_alypuhelin_kuolee

http://chrome.angrybirds.com/

http://www.slideshare.net/philrawcliffe/wac-press-conference-mwc-2011?from=ss_embed

http://www.slideshare.net/larux/3-luento-tieto-ja-viestinttekniikan-pedagogiset-perusteet-tietokoneavusteinen-yhteisllinen-oppiminen-cscl


Hutchins, E. (1995). Cognition in the wild. Cambridge, Mass: MIT Press.

Roschelle, J., & Pea, R. (2002). A walk on the WILD side: how wireless handhelds may change CSCL, 51-60. Retrieved from http://dl.acm.org/citation.cfm?id=1658616.1658624

2002“His 2002 paper with Roy Pea, "Walk on the Wild Side," has been influential in understanding the future possibilities for wireless handheld learning devices”

MLEARNING – MOBIILIOPPIMINEN. 1:1

Traditional approach / Perinteinen näkökuma

ONE-TO-ONE TECHNOLOGY ENHANCED LEARNING

http://www.flickr.com/photos/olpc/3038680654/

Chan, T.-W., Roschelle, J., Hsi, S., Kinshuk, K., BROWN, T., Brown, T., Patton, C., et al. (2006). One-to-one technology-enhanced learning: an opportunity for global research collaboration. Research and Practice in Technology Enhanced Learning, 1(1), 1-26. Retrieved from http://www.worldscinet.com/abstract?id=pii:S1793206806000032

http://www.flickr.com/photos/olpc/3038680654/

ULEARNING – U-OPPIMINEN N:1Future trend


http://www.fkaplan.com/file/caif-interpersonal.doc

Kaplan, F., DoLenh, S., Bachour, K., Kao, G. Y.-ing, Gault, C., Dillenbourg, P., Huang, J., et al. (2009). Interactive Artifacts and Furniture Supporting Collaborative Work and Learning (Vol. 10, pp. 1-17). Boston, MA: Springer US. Retrieved from http://www.springerlink.com/content/uxr3q7t022751275/

2008: interpersonal computers





http://www.youtube.com/watch?v=cN3ltvIERD4

http://www.youtube.com/watch?v=5snuQnoAAsE

TANGIBLES,ROOMWARE,PHIDGETS,WEARABLES = ULEARNING/U-OPPIMINEN

1. AKSELI/1st. axis

1.”U-oppiminen” ”uLearning”

RoomwareTangibles

http://www.youtube.com/watch?v=I5GWUx3ZSMw

Tangibles

Phidgets Wearables

http://www.youtube.com/watch?v=4hXM6paYOME

http://www.youtube.com/watch?v=5UPiFeJhwS4

http://www.youtube.com/watch?v=RmLU4GS7zAI

http://www.youtube.com/watch?v=5UPiFeJhwS4

http://www.youtube.com/watch?v=RmLU4GS7zAI

LOCATION BASED MOBILE TECHNOLOGIES, MOBILE SOCIAL MEDIA = MLEARNING/M-OPPIMINEN

2. AKSELI/2nd. axis

2. ”mLearning” ”mOppiminen”

YHTEISÖLLISEN OPPIMISEN TUKEMINEN MOBIILILAITTEILLA/ SUPPORTING COLLABORATIVE LEARNING WITH MOBILE COMPUTERS

Jari Laru

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

Quick and dirty solutions

EMI ILE INTHIG

Case Iworkplace (n=10)

Case IIIUniversity (N=22)

Introduction

Earli SIG

Case IINature (N=22)

Mobile computers Everyday contexts

Scaffolding collaborative

learning with cognitive tools based on

mobile computers

Master’s programme, University, Professional Community, K-12 students, Higher Education students, Nature school

Collaborative learning, Cognitive toolsScaffolding, Structuring

idiosyncratic (very little coercion)

scripted (low coercion)

stringent (high coercion)

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.

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.

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.

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 cocreationof 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. Ingeneral, 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

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.

Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context

Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a case study with K-12 learners

Social patterns in mobile technology mediated collaboration among members of the professional distance education community




Questions1. What is the density and the distribution of the collaboration at individual and community levels in the online and offline communities?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?

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?

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?




• 1st generation: mobile versions of desktop tools: FLE3mobile

• wlan

• 2nd generation: context-aware peer-to-peer mobile tools: flyers

• mobile encounter network (bluetooth)

• 3nd generation: mobile social media: mobile clients + flickr + wordpress + wikispaces + google reader

• 3G connectivity

Tools




• Dyads/Triads• Ill-structured task• Argumentative collaboration• Procedural scaffolding & metacognitive

scaffolding

Design ”Let’s try it” ..

• No groups designed (participants worked in three teams though)

• No clear task, work related activities (no formal learning)

• Knowledge building• Metacognitive scaffolding

• 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


STUDY 2: FLYERS

Course feed

WikiworkPhototaking

A.Ground C.ConceptualizeB.ReflectD. Reflect & elaborate

E. Review & evaluate F. Co-construct knowledge

G.Monitor Tools used to merge multiple RSS feeds

Merged feedsMultiple feeds

Course blog and wiki Mobile applications

Course level tools

Group level tools

Monitoring tools

Lecture Discussion Blogging Discussion

Collaborative Solo Collaborative

Phase:

Software:

Activity:

Figure 4. Socio-technological design of the course. The idea of making use of each others’knowledge was operationalized in socio-technical design. It consisted of recurrent individual and collective phases in which students used multiple Web 2.0 tools and mobile phones in concert to perform designed tasks. Retrieved from: Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.

Figure 5. Pedagogical design of the course. 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 discussions in their respective phases. Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.




• Quantititative Mindmap analysis (pre-post-test)• Qualitative analysis of recorded argumentative

discussions (Mann-whitney U-test)• Qualitative analysis of the flyers (Mann-whitney

U-test)

Methods• Quantitative analysis of FLE3mobile’s log-files

(log file analyzer)• Qualitative-Quantitative Interview analysis (SNA

analysis)

• Quantitative analysis of conceptual knowledge test (normalized gain, t-test)

• Qualitative+Quantitative analysis of social software activities (Bayesian classification analysis + Bayesian dependency modeling)

SNA

U-test

Bayes

Mann-whitney

Classification analysisDependency modeling

Social network analysis

29LET - Oppimisen ja koulutusteknologian tutkimusyksikköJari Laru, 22.4.2009

Monimenetelmäinen (kokeileva) oteOsatutkimus I Osatutkimus II Osatutkimus III

Haastattelut

Kyselyt

Logidata (yksi sovellus)

Keskustelut

KyselytRyhmähaastattelu

Lehtiset

Nauhoitetut ryhmätilanteet

käsitekartat

KyselytHaastattelut

Videoidut ryhmätilanteet

logidata (useita sovelluksia)

onlinedata (wikit, blogit etc)

käsitetesti

Logidatan analyysi

Verkostoanalyysi (SNA) olemattoman vuorovaikutuksensyiden etsimisessä

Kevyt keskusteluanalyysi

Haastattelut tukimateriaalina

Käsitekarttojen tilastollinen analyysi, jonka avulla ryhmät jaettiin huonosti ja hyvin menestyneisiin

Mann-Whitney U-test hyvien ja huonojen ryhmien suoritusten vertailemiseksi

Ryhmätilanteiden ja lehtisten sisällönanalyysi

Bayes mallinnus, jonka avulla kyselydata, logidata ja käsitetesti kytkettiin yhteen [kokeilu]

Verkkoon tuotetun materiaalin analysointi (multilevel IA analysis)

haastatteluiden ja/tai ryhmätilanteiden analysointi

RESULTS

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.

Although the results revealed several shortcomings in the types of argumentation...

….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.

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.




Results

• 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.

• 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.

• 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 did not have a need for mobile collaboration tools in their practices.

Similar

Different

• 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

• 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.

? ?

Collaborative learning, Cognitive toolsScaffolding

Collaborative learning, Cognitive toolsScaffolding, Structuring

idiosyncratic (very little coercion)

scripted (low coercion)

stringent (high coercion)

QUESTIONS? FEEDBACK?HTTP://WWW.MENDELEY.COM/PROFILES/JARI-LARU/

Kiitos / Thank You!