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
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) 2 Link to slides: https://bit.ly/isls2022-motif
3. Source See Chen & Hong, 2016, for a review Knowledge Forum 3 Link to slides: https://bit.ly/isls2022-motif
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) 4 Link to slides: https://bit.ly/isls2022-motif
5. KBDeX, and its socio-semantic network analysis Learners Words Discourse Units 5
6. We need more integrative approaches to the analysis of collaborative discourse, as well as knowledge-building discourse in particular 6 Link to slides: https://bit.ly/isls2022-motif
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) 7 Link to slides: https://bit.ly/isls2022-motif
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 Link to slides: https://bit.ly/isls2022-motif
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) 9 Link to slides: https://bit.ly/isls2022-motif
10. Network motifs in socio-semantic networks Socio-Semantic Network Motifs 10 Link to slides: https://bit.ly/isls2022-motif
11. Situate socio-semantic network motifs in collaborative discourse Socio-Semantic Network Motifs 11 Link to slides: https://bit.ly/isls2022-motif
12. Situate socio-semantic network motifs in collaborative discourse Socio-Semantic Network Motifs 12 Link to slides: https://bit.ly/isls2022-motif
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 13 Link to slides: https://bit.ly/isls2022-motif
14. Analyzing Socio-Semantic Network Motifs 1) Construct the network 14 Link to slides: https://bit.ly/isls2022-motif
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) 15 Link to slides: https://bit.ly/isls2022-motif
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 16 Link to slides: https://bit.ly/isls2022-motif
18. Example SSN motifs profile of a discourse segment: Analyzing Socio-Semantic Network Motifs 18 Link to slides: https://bit.ly/isls2022-motif
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 19 Link to slides: https://bit.ly/isls2022-motif
20. Phase 1 Phase 2 Class A Class B 20
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. 22 Link to slides: https://bit.ly/isls2022-motif
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 Link to slides: https://bit.ly/isls2022-motif
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 Link to slides: https://bit.ly/isls2022-motif