Analyzing the Main Paths of Knowledge Evolution and Contributor Roles in an Open Learning Community

Halatchliyski, Iassen; Hecking, Tobias; Goehnert, Tilman; Hoppe, H. Ulrich

doi:10.18608/jla.2014.12.5

Cited by 21 publications

(10 citation statements)

References 33 publications

(30 reference statements)

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“…This encourages us to look further into generally relevant conditions and processes and to embrace the challenge of the dynamics of knowledge, which is difficult to grasp (see, for example, [55], [56]). Series of ideas and actions are said to lead to the stabilization of historical trajectories and structural patterns over time [27].…”

Section: Discussionmentioning

confidence: 99%

How Structure Shapes Dynamics: Knowledge Development in Wikipedia - A Network Multilevel Modeling Approach

Halatchliyski¹,

Creß

2014

PLoS ONE

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Using a longitudinal network analysis approach, we investigate the structural development of the knowledge base of Wikipedia in order to explain the appearance of new knowledge. The data consists of the articles in two adjacent knowledge domains: psychology and education. We analyze the development of networks of knowledge consisting of interlinked articles at seven snapshots from 2006 to 2012 with an interval of one year between them. Longitudinal data on the topological position of each article in the networks is used to model the appearance of new knowledge over time. Thus, the structural dimension of knowledge is related to its dynamics. Using multilevel modeling as well as eigenvector and betweenness measures, we explain the significance of pivotal articles that are either central within one of the knowledge domains or boundary-crossing between the two domains at a given point in time for the future development of new knowledge in the knowledge base.

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Section: Discussionmentioning

confidence: 99%

How Structure Shapes Dynamics: Knowledge Development in Wikipedia - A Network Multilevel Modeling Approach

Halatchliyski¹,

Creß

2014

PLoS ONE

Self Cite

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“…In other cases, however, the term temporality is used to refer to the order of events without explicit reference to the duration of passage of time between them (e.g. Halatchliyski, Hecking, Goehnert, & Hoppe, 2014;Biswas, Jeong, Kinnebrew, Sulcer, & Roscoe, 2010). Thus, we recognize that in education studying sequences of change and progression can provide insight that is complementary to research that addresses the measured flow and passage of time.…”

Section: Conceptual Challengesmentioning

confidence: 99%

Time for Change: Why Learning Analytics Needs Temporal Analysis

2017

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Learning is a process that occurs over time: We build understanding, change perspectives, and develop skills over the course of extended experiences. As a field, learning analytics aims to generate understanding of, and support for, such processes of learning. Indeed, a core characteristic of learning analytics is the generation of high-resolution temporal data about various types of actions. Thus, we might expect study of the temporal nature of learning to be central in learning analytics research and applications. However, temporality has typically been underexplored in both basic and applied learning research. As Reimann (2009) notes, although "researchers have privileged access to process data, the theoretical constructs and methods employed in research practice frequently neglect to make full use of information relating to time and order" (p. 239). Typical approaches to analysis often aggregate across data due to a collection of conceptual, methodological, and operational challenges. As described below, insightful temporal analysis requires (1) conceptualising the temporal nature of learning constructs, (2) translating these theoretical propositions into specific methodological approaches for the capture and analysis of temporal data, and (3) practical methods for capturing temporal data features and using analyses to impact learning contexts. There is a pressing need to address these challenges if we are to realize the exciting possibilities for temporal learning analytics.

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“…Results attributable to the discourse moves tool were limited and apparently did not extend to influencing the overall rated quality of notes. There are already research tools that can identify discourse topics and epistemic roles (e.g., Jeong 2009;Halatchliyski et al 2014). The challenge is to make their underlying logic sufficiently transparent and comprehensible that they overcome their Bblack box^character and to find readily comprehensible visualizations of results (comparable to comparative word clouds) that give students a meaningful perspective on their own work that they cannot get from ordinary observation.…”

Section: Irections Of Further Development Of Group-level Feedback In mentioning

confidence: 99%

Group-level formative feedback and metadiscourse

Resendes

Scardamalia

Bereiter

et al. 2015

Intern. J. Comput.-Support. Collab. Learn

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This research explores the ability of grade 2 students to engage in productive discussion about the state of their knowledge building using group-level feedback tools to support their metadiscourse. Two aspects of knowledge work were common to the comparison and experimental classes: BKnowledge Building talk^(KB talk) involving teacher-student discussions and the use of Knowledge Forum, an online environment optimized to support Knowledge Building/knowledge creation and to represent and support student work and KB talks. Students in experimental conditions additionally reviewed visualizations of vocabulary use and discourse patterns during KB talk time. Two formative feedback visualization tools were co-developed by the classroom teacher and researchers to show (a) overlaps and discrepancies between words students used in their Knowledge Forum notes and words used by writers more knowledgeable in the field and (b) frequency of discourse moves indicated by students' use of epistemic discourse markers in Knowledge Forum.These visualizations served as grounding for KB talk concerned with interpreting the visualizations and considering their implications. A comparison of two classes similar except for presence or absence of these visualizations showed significant effects favoring the experimental class in domain-specific vocabulary, repertoire of discourse moves, scientific understanding, epistemic complexity of ideas, and interpersonal connectedness of online discourse.

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Analyzing the Main Paths of Knowledge Evolution and Contributor Roles in an Open Learning Community

Cited by 21 publications

References 33 publications

How Structure Shapes Dynamics: Knowledge Development in Wikipedia - A Network Multilevel Modeling Approach

How Structure Shapes Dynamics: Knowledge Development in Wikipedia - A Network Multilevel Modeling Approach

Time for Change: Why Learning Analytics Needs Temporal Analysis

Group-level formative feedback and metadiscourse

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