Connectivity for Knowledge Building: A Framework of Socio-Semantic Network Motif Analysis

Transcript

1. Connectivity for Knowledge Building: A Framework
   of Socio-Semantic Network Motif Analysis
   Bodong Chen
   ISLS Annual Meeting • June 6, 2022
   Link to slides: https://bit.ly/isls2022-motif
   

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2. Collaborative discourse
   Learners are engaged in discussing substantive content related to a domain
   ● social interaction, mediated communication, group awareness
   ● intersubjective meaning-making, transactivity, uptakes...
   Knowledge-building discourse engages students in productive discourse
   that leads towards continual improvement of ideas within a community
   (Scardamalia & Bereiter, 2014)
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3. Source
   See Chen & Hong, 2016, for a review
   Knowledge Forum
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4. Indicators of productive KB discourse
   Social network analysis; communication
   networks (e.g., Dawson, 2008; Haythornthwaite, 2015)
   Computational lexical, semantic, content
   analysis (Kovanović et al., 2017; Teplovs & Fujita, 2013)
   The social, cognitive, and integrated domains (Y Chen et al., 2019)
   Knowledge Connections Analyzer
   (Yang et al., 2021)
   Knowledge Building Discourse Explorer
   (KBDeX; Oshima et al., 2012)
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5. KBDeX, and its socio-semantic network analysis
   Learners
   Words
   Discourse
   Units
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6. We need more integrative
   approaches to the analysis of
   collaborative discourse,
   as well as knowledge-building
   discourse in particular
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7. “The social and the cultural orders are
   dual – that is, they constitute each other……
   Socio-semantic network analysis brings
   together the study of relations among
   actors (social networks), relations among
   elements of actors’ cultural structures
   (their semantic networks), and relations
   among these two orders of networks.”
   — Basov et al. (2020)
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8. A Socio-Semantic Network Motifs Framework
   Socio-semantic networks (SSNs):
   ● the actors (learners) and semantic entities (words), along with
   their connections, are modeled as nodes and edges in a
   two-mode, dual-layer socio-semantic network
   Source 8
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9. Socio-semantic networks
   Network motifs:
   ● Recurring, significant patterns of
   interconnections in the network
   (Milo et al., 2004)
   ● In network science, network
   motifs have been widely used to
   examine a variety of networks
   Socio-Semantic Network Motifs
   (Milo et al., 2002)
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10. Network motifs in
    socio-semantic
    networks
    Socio-Semantic Network Motifs
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11. Situate socio-semantic
    network motifs in
    collaborative discourse
    Socio-Semantic Network Motifs
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12. Situate socio-semantic
    network motifs in
    collaborative discourse
    Socio-Semantic Network Motifs
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13. Analyzing Socio-Semantic Network Motifs
    1) Construct the network
    ○ A range of analytical decisions
    (Chen & Poquet, 2022)
    ■ Learners: all learners in a class
    ■ Words: the top 100 high frequency
    words (after removing stopwords)
    which have appeared for at least 5
    times
    • Only writing behaviors
    • Threshold: edges with
    a weight >= 3
    Learner-Word
    • φ correlation
    coefficient >= .4
    Word-Word
    • Undirected edges, for
    simplicity
    Learner-Learner
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14. Analyzing Socio-Semantic Network Motifs
    1) Construct the network
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15. Analyzing Socio-Semantic Network Motifs
    1) Construct the network
    2) Count SSN motifs
    ○ count the occurrences of SSN motifs in the
    empirical network using the motifr R package
    (version 0.5.0)
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16. Analyzing Socio-Semantic Network Motifs
    1) Construct the network
    2) Count SSN motifs
    3) Compute significance of each motif
    ○ Generate 1,000 refined Erdos-Rényi random graphs
    ○ Compare the empirical network’s motif frequencies with the
    random graphs
    ○ A Z-score (-1, 1) is calculated for each SSN motif to show its
    over- or under-representation in the empirical network
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18. Example
    SSN motifs
    profile of a
    discourse
    segment:
    Analyzing Socio-Semantic Network Motifs
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19. ● A secondary dataset of knowledge-building discourse in 9th science
    ● Two contrasting classes:
    ○ Both classes showed progress across two phases around two topics
    ○ Class B engaged with more web resources, showed more intensive
    collaboration, and achieved greater progress on related topics
    Data
    Class Phase Posts Interactions Words/post
    Class A
    (n=22)
    1 68 33 77.2
    2 72 38 102.0
    Class B
    (n=26)
    1 97 40 65.4
    2 83 35 60.8
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20. Phase 1
    Phase 2
    Class A Class B 20
    

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22. Summary of findings
    Knowledge-building discourse in both classes showed considerable
    socio-semantic connectivity in both phases.
    SSN motifs showing exclusive access to words, e.g. (01,1a) and (11,1a),
    decreased, while the “hyper connectivity area” (upper-right corner) increased.
    Class A showed more room to improve socio-semantic connectivity in their
    discourse.
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23. Discussion
    We proposes a nascent socio-semantic network (SSN)
    motifs framework for the analysis of collaborative discourse.
    SSN motifs provide nuanced information about discourse.
    Ongoing work
    ● Adapt and evaluate the framework in different
    discourse contexts (e.g., Chen, Zhu, & Hong, 2022)
    ● Further combine SSN analysis with other methods
    ● Action-taking based on network motifs
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24. Thank You! Acknowledgement
    This material is based upon work supported
    by the National Science Foundation under
    Grant No. 1657009.
    @bod0ng
    bodong.chen@gmail.com
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