Mutual development in mass collaboration: Identifying interaction patterns in customer-initiated software product development

Andersen, Renate; Mrch, Anders I.

doi:10.1016/j.chb.2016.08.005

Cited by 12 publications

(11 citation statements)

References 13 publications

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“…First, we adopted a social network analysis approach to identify the interactions between students and teachers across the 4 weeks of online discussion. The analysis showed that some students (i.e., S13, S3, S29, and S14) were very active across the 4 weeks hence being regarded as information brokers or bridge builders [1,8]. Moreover, some weeks recorded more interactions than others (i.e., week 2, 3 and 4).…”

Section: General Discussion and Conclusionmentioning

confidence: 99%

“…More importantly, most of the previous studies are limited to the description of social networks, without analysis of the discourse dimensions of these interactions. However, the combination of social network and discourse analysis of students' artefacts could allow for a more nuanced description of student engagement and learning [1,21], and necessary to reach an overall interpretation of such complex dynamics generated among students [10]. From this background, this study aims to explore the potential of SLA (i.e.…”

Section: Identified Gaps and Research Questionsmentioning

confidence: 99%

“…Thus, the analyzed ties represent unweighted and undirected graphs which were constructed to represent the students' interactions on the Canvas platform. After the coding, we used the social network analysis measures suggested in previous studies [1] (i.e. degrees, closeness, and betweenness) to assess and determine the level of importance, strength, and influence each node/student had on the broader social network [2].…”

Section: Social Network Analysismentioning

confidence: 99%

See 2 more Smart Citations

Exploring Social Learning Analytics to Support Teaching and Learning Decisions in Online Learning Environments

Kaliisa

Mørch

Kluge

2019

Lecture Notes in Computer Science

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Most teachers to date have adopted summative assessment items as a benchmark to measure students' learning and for making pedagogical decisions. However, these may not necessarily provide comprehensive evidence for the actual learning process, particularly in online learning environments due to their failure to monitor students' online learning patterns over time. In this paper, we explore how social learning analytics (SLA) can be used as a proxy by teachers to understand students' learning processes and to support them in making informed pedagogical decisions during the run of a course. This study was conducted in a semester-long undergraduate course, at a large public university in Norway, and made use of data from 4 weekly online discussions delivered through the university learning management system Canvas. First, we used No-deXL a social network analysis tool to analyze and visualize students' online learning processes, and then we used Coh-Metrix, a theoretically grounded, computational linguistic tool to analyze the discourse features of students' discussion posts. Our findings revealed that SLA provides insight and an overview of the students' cognitive and social learning processes in online learning environments. This exploratory study contributes to an improved conceptual understanding of SLA and details some of the methodological implications of an SLA approach to enhance teaching and learning in online learning environments.

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Section: General Discussion and Conclusionmentioning

confidence: 99%

Section: Identified Gaps and Research Questionsmentioning

confidence: 99%

Section: Social Network Analysismentioning

confidence: 99%

See 1 more Smart Citation

Exploring Social Learning Analytics to Support Teaching and Learning Decisions in Online Learning Environments

Kaliisa

Mørch

Kluge

2019

Lecture Notes in Computer Science

Self Cite

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“…Li and Gu (2015) proposed an OSN link formation model from the perspective of user behavior, which reproduced degree distribution, clustering and degree correlation of OSN [56]. Andersen and Mørch (2016) classified user types through social network statistical analysis and constructed "user-topic" hybrid network with user interaction analysis of user posts [57]. Baumgartner and Peiper (2017) extended a novel method called stochastic block modeling to derive communities of cannabis consumers as part of a complex social network on Twitter [58].…”

Section: Research Methods Of Consumer Online Communitymentioning

confidence: 99%

Properties Exploring and Information Mining in Consumer Community Network: A Case of Huawei Pollen Club

et al. 2018

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Substantial changes took place in the role of consumers in the supply chain with the development of practices. They became creators from consumers of product values. More and more consumers express their consumption experiences by posting in network community. Consumer community network is an important place for feedback of product experiences and facilitating product innovation in future. Manufacturers can promote improvement and innovation of products by exploring effective information on the consumer community network, thus improving the experience level of consumers. Therefore, how to explore information in topics (posts) and their relationships becomes very important. Is it possible to describe the structure of consumer community network by complex network and explore information about products and consumers? There is important and positive significance to study the collaborative innovation in the supply chain in which consumers participate. In this paper, the consumer community network was constructed by Boolean retrieve programming and discussed in the methodology and empirical way based on the community data of Huawei P10/P10 Plus. In methodology, interaction difference and uniformity within consumer community were explored by the density of isolated nodes and generalized variance of degree of network. In empirical studies, community network users were divided into ordinary user group, intermediary user group, and enterprise user group according to empirical data, and corresponding interaction networks were constructed. A contrastive analysis on the interaction of these three groups was carried out by combining the existing properties and innovative properties. Topics in each network were put in the order according to significance. Research conclusions have important significance to enrich the network analysis methods, explore the effective information in consumer community network, facilitate product improvement and innovations, and improve the experience level of consumers.

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“…Then, if student S8 posted a message in response to S10's post, we coded it as (S8èS10). Following the SNA measures used in previous studies (Andersen & Mørch, 2016), we used degree centrality measures to determine the number of ties an individual student actor had with other student actors in the network (Smith et al, 2009). Moreover, we used betweenness centrality to identify the students occurring within the shortest path between other nodes, which represented other students, who thus facilitated the spread or control of information within the network.…”

Section: Social Network Analysismentioning

confidence: 99%

Combining Checkpoint and Process Learning Analytics to Support Learning Design Decisions in Blended Learning Environments

Kaliisa

Kluge

Mørch

2020

JLA

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Learning analytics (LA) constitutes a key opportunity to support learning design (LD) in blended learning environments. However, details as to how LA supports LD in practice and information on teacher experiences with LA are limited. This study explores the potential of LA to inform LD based on a one-semester undergraduate blended learning course at a Norwegian university. Our findings indicate that creating valuable connections between LA and LD requires a detailed analysis of student checkpoints (e.g., online logins) and process analytics (e.g., online content and interaction dynamics) to find meaningful learning behaviour patterns that can be forwarded to teachers in retrospect to support the redesign of courses. Moreover, the teachers in our study found the LA visualizations to be valuable for understanding student online learning processes, but they also requested the timely sharing of aggregated LA visualizations in a simple, easy-to-interpret format, yet detailed enough to be informative and actionable. We conclude the paper by arguing that the potential of LA to support LD is improved when multiple levels of LA are considered.

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Mutual development in mass collaboration: Identifying interaction patterns in customer-initiated software product development

Cited by 12 publications

References 13 publications

Exploring Social Learning Analytics to Support Teaching and Learning Decisions in Online Learning Environments

Exploring Social Learning Analytics to Support Teaching and Learning Decisions in Online Learning Environments

Properties Exploring and Information Mining in Consumer Community Network: A Case of Huawei Pollen Club

Combining Checkpoint and Process Learning Analytics to Support Learning Design Decisions in Blended Learning Environments

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