gefördert durch das Kompetenzzentrenprogramm

Sebastian Maximilian Dennerlein

9 September, 2013

Social Computing Team & LL Design Team „Bits and Pieces“ © Know-Center 2013

www.know-center.at, www.kti.tugraz.at

Understanding & Supporting

Intersubjective

Meaning Making (IMM)

in Socio-Technical Systems

A Cognitive Psychology Perspective

© Know-Center 2013

3

Introduction - Motivation

Situation of self-directed learners in the context of

hypertext/ -media systems:

Increased meta-cognitive load in addition to demands due to

learning (Narciss et. al., 2007)

Absence of support has been found detrimental in self

directed learning (Mayer, 2004)

Nowadays collaborative contexts in socio-technical systems

increase the situation‘s complexity and necessary cognitive

demands

Reason: Need for Intersubjective Meaning Making (IMM)

in addition to the precondition of the individual situation to be

able to collaboratively construct knowledge

Consequence: Support is crucial!!

© Know-Center 2013

4

Introduction – Research Objective

To be able to assist self directed learners in collaborative

contexts with the needed support, intersubjective meaning

making has to be understood

IMM is the essence of collaboration: „Collaboration is

primarily conceptualized as a process of shared meaning

construction.” (Stahl et. al., 2006)

IMM def.: „Intersubjective meaning-making takes place when

multiple participants contribute to a composition of inter-

related interpretations.“ (Suthers, 2006)

Gaps:

No clearly defined concept

No adequate support regarding the manifold process

Goal: Bridging of these gaps!

© Know-Center 2013

5

Introduction - Research Questions

Can the process of Intersubjective Meaning Making (IMM) be

described and explained by an integrated model through

combining more theoretical and more formal approaches of

cognitive psychology?

Can this integrated model be empirically validated in

different settings by the means of achieving a higher

correspondence of the cognitive and the social system and

a shared understanding respectively?

How can these insights be used to develop support services

including appropriate affordances to support the process of

IMM in socio-technical systems?

© Know-Center 2013

6

Introduction – Framework

Artifact-Actor Network (Reinhardt, 2009) „Social Semantic

Server“ as basis of EU-IP „Learning Layers“: Scaling up

Technologies for Informal Learning (e.g., Ley et al., 2013)

LL Design Team Bits & Pieces (B&P) – Application Scenario:

Health care employees are usually under great time pressure while

managing their daily work

Quick (symbolic) representations and the affordance to visually make

sense of them afterwards are needed

stimulation of IMM would provide essential support!

+ Actor Network: Artifact Network:

Artifact-Actor

Network (AAN)

© Know-Center 2013

7

Introduction – Example application

scenario (B&P)

Realization of IMM support by revealing differences between the

individual and collective understanding :

e.g., highlighting of the most helpful documents of the collective

(green) or emphasizing different categorization approaches of the

collective (yellow).

© Know-Center 2013

8

Theoretical Background

Distributed Cognition

Formal Aspects of Socio-Cognitive

Processes

Uptake of Meaning

…

Stochastic Modelling of

Mental Categories

Conceptual Aspects

Co-evolution

Model

...

SECI Model

but: vague description of contained processes

© Know-Center 2013

9

Theoretical Background

Basis: Distributed Cognition (DC; Hutchins, 2000)

distributed cognitive systems can be analysed in terms of how

knowledge is distributed

BUT: DC remains vague in describing socio-cognitive processes

Theoretical Aspects: e.g. Coevolution model (Cress et. al., 2008;

Kump et al., 2013)

description of interaction of internal and external knowledge

representations as a form of co-evolution between the cognitive and

social system

Formal Aspects: e.g. Uptake of Meaning (Suthers, 2005)

uptake appears in the sequences of collaborative interactions, „in

which one participant‘s action on a representation [=interpretation]

is taken up by another participant in a manner that indicates

understanding of its meaning“ (Suthers, 2006)

© Know-Center 2013

10

Theoretical Background

Practical literature on relevant experiments and methods

Assessment of internal and external knowledge

representations (Ley et al., 2011; Kump et. al., 2013):

External: qualitative content analysis of wiki articles

Internal: SNA of concept maps and association tests

Method for assessment of uptake – uptake graph (Suthers,

2005)

© Know-Center 2013

11

Method - Basis

iterative methodological triangulation of ethnography,

experiment and design (Hollan et.al., 2000)

Qualitative analysis of application

domain and materials

Assessment of

cognitive and social

system to analyse

coevolution and

shared

understanding

IMM support services

Domain dependent:

e.g. Health Care –

home visit,

GP practice etc.

© Know-Center 2013

12

Method – 1. Research Question

Design of an integrated model based on literature work

consisting out of:

Visual representation of IMM

Process description

Additionally literature work serves the need to distinguish

IMM from other similar concepts

E.g. Sense Making

Hypotheses:

Intersubjective Interpretations

IMM

Subjective Interpretations

SM

Can the process of IMM be described and explained by an integrated model

through combining theoretical and formal approaches of cognitive psychology?

© Know-Center 2013

13

1.RQ IMM Model – Visual Representation

(Fu et. al., 2008)

Sketch of visual representation:

Visual representation of

IMM in collaboration

Detailed definition of

internalization and

externalization (ass &

acc)

Inclusion of different

kinds of interpretations

© Know-Center 2013

14

1.RQ IMM Model – Process Description (B&P)

Experience Remember Experience

Re-Experience

Sense Making

of Learning Episode

Share

Awareness

Imitation/ Uptake

Aggregation

Exploitation

Negotiation

(Informal) Sense Making Intersubjective Meaning Making

IMM not necessarily a linear process!

© Know-Center 2013

15

Method – 1. Research Question

Iterative refinement and enrichment of the model by insights

gained in (field-) studies:

qualitative analysis of realistic work practices to elaborate on

the model and elicit the necessary context for uptake – method:

qualitative analysis of existing socio-technical systems to break

down the available services (& affordances) useful for IMM

Can the process of IMM be described and explained by an integrated model

through combining theoretical and formal approaches of cognitive psychology?

© Know-Center 2013

16

1. RQ IMM Model – Mapping of IMM

steps, Affordances & Services

Visualization Recommendation …

Experience

Remember

Re-Experience

Sense Making

Share

Awareness

Imitation

Aggregation

Exploitation

Negotiation

Sense M

akin

g

IMM

Affordances

Timeline

visualization

Visualization of personalized

recommendations

support services

© Know-Center 2013

17

Method – 2. & 3. Research Question

Inferred model will be transferred into assumptions and

hypotheses

Testing of corresponding hypotheses in controlled (field-)

experiments

Can this integrated model be empirically validated in different settings by the

means of achieving a higher correspondence of the cognitive and the social

system and a shared understanding respectively?

Improvement of old or design of new support services to

selectively support steps of IMM

How can these insights be used to develop support services including

appropriate affordances to facilitate the process of IMM in socio-technical

systems?

© Know-Center 2013

18

Discussion

Motivation to socially analyse collaboration and its drivers

based on Stahl et.al. (2006)

claim that theorizing about mental models in individuals'

heads does not help regarding IMM

BUT: analysis of mental models in comparison to the

collective representation is helpful to analyse meaning

making (Cress et al., 2013; Fu et.al., 2012)

Important CAVEATS:

do not limit IMM to one single kind of interpretation

do not constrain unit of analysis and corresponding log files

© Know-Center 2013

19

Literature Cress, U., & Kimmerle, J. (2008). A systemic and cognitive view on collaborative

knowledge building with wikis. International Journal of Computer-Supported

Collaborative Learning, 3(2), 105-122.

Cress, U., Held, C., & Kimmerle, J. (2013). The collective knowledge of social tags:

Direct and indirect influences on navigation, learning, and information processing.

Computers & Education, 60(1), 59-73.

Fu, W.-T. (2008). The microstructures of social tagging: a rational model.. In B.

Begole & D. W. McDonald (eds.), CSCW, 229-238.

Fu, W.-T., & Dong, W. (2012). Collaborative Indexing and Knowledge Exploration: A

Social Learning Model. IEEE Intelligent Systems, 27(1), 39-46.

Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: toward a new

foundation for human-computer interaction research. ACM Transactions on

Computer-Human Interaction, 7(2), 174-196.

Hutchins, E. (2000). Distributed Cognition. Society, 34(6), 1-10.

Kump, B., Moskaliuk, J., Dennerlein, S., & Ley, T. (2013). Tracing knowledge co-

evolution in a realistic course setting: A wiki-based field experiment. Computers &

Education, in press.

Ley, T., Cook, J., Dennerlein, S., Kravcik, M., Kunzmann, C., Laanpere, M., Pata,

K., Purma, J., Sandars, J., Santos, P., & Schmidt, A. (2013). Scaling Informal

Learning: An Integrative Systems View on Scaffolding at the Workplace. In D.

Hernández-Leo, T. Ley, R. Klamma, & A. Harrer (Eds.), Proceedings of the 8th

European Conference on Technology-enhanced Learning, 484–489. Heidelberg:

Springer.

© Know-Center 2013

20

Literature Nonaka, I., Toyama, R., & Konno, N. (2000). SECI, Ba and Leadership: a Unified

Model of Dynamic Knowledge Creation. Long range planning, 33(1), 5-34.

Ley, T., Schweiger, S., & Seitlinger, P. (2011). Implicit and Explicit Memory in

Learning from Social Software: A dual-process account. In C. Delgado-Kloos, D.

Gillet, R. M. Crespo-Garcia, F. Wild, & M. Wolpers (Eds.), Proceedings of the 6th

European Conference on Technology Enhanced Learning, 449–454. Heidelberg:

Springer.

Mayer, R.E. (2004). Should there be a three-strikes rule against pure discovery

learning? The case for guided methods of instruction. American Psychologist, 59(1),

14–19.

Narciss, S., Proske, A. & Koerndle, H. (2007). Promoting self-regulated learning in

web-based environments. Computers in Human Behavior, 23(3), 1126–1144.

Reinhardt, W., Moi, M., & Varlemann, T. (2009). Artefact-Actor-Networks as tie

between social networks and artefact networks. 5th International Conference on

Collaborative Computing Networking Applications and Worksharing, 1-10. IEEE.

Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative

learning: An historical perspective. Computer, 409-426.

Suthers, D. D. (2005). Collaborative knowledge construction through shared

representations. 38th Hawaii International Conference on System Science ,110.

Suthers, D. D. (2006). Technology Affordances for intersubjective Meaning-making:

A research agenda for CSCL. The International Journal of Computer-Supported

Collaborative Learning, 1(3), 315-337.

© Know-Center 2013

21

Thanks for your attention!

Sincere thanks are given to my mentor Tobias Ley and my consultant Paul

Seitlinger.

Stefan Schweiger, Peter Kraker, Gudrun Wesiak, Dieter Theiler, Rene Kaiser,

Marina Bratic, Vladimir Tomberg, Christoph Trattner and Stefanie Lindstaedt may

be thanked, too, for helping me with their valuable input.

sdennerlein@know-center.at

twitter

linkedin

researchgate