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Understanding and Supporting Intersubjective Meaning Making in Socio-Technical Systems: A Cognitive Psychology Perspective
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Understanding and Supporting Intersubjective Meaning Making in Socio-Technical Systems: A Cognitive Psychology Perspective

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This dissertation will elaborate on the understanding of intersubjective meaning making by analyzing the traces of collaborative knowledge construction users leave behind in socio-technical systems. …

This dissertation will elaborate on the understanding of intersubjective meaning making by analyzing the traces of collaborative knowledge construction users leave behind in socio-technical systems. Therefore, it will draw upon more theoretical and more formal models of cognitive psychology to describe and explain the underlying process
in detail. This is done with the goal to support intersubjective meaning
making and thus elevate informal collaborative knowledge construction
in nowadays a ordances of social media.

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  • 1. 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
  • 2. © 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!!
  • 3. © 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!
  • 4. © 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?
  • 5. © 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)
  • 6. © 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).
  • 7. © 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
  • 8. © 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)
  • 9. © 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)
  • 10. © 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.
  • 11. © 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?
  • 12. © 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
  • 13. © 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!
  • 14. © 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?
  • 15. © 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 SenseMakingIMM Affordances Timeline visualization Visualization of personalized recommendations support services
  • 16. © 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?
  • 17. © 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
  • 18. © 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.
  • 19. © 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.
  • 20. © 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