Virtual Communities of Practice in Academia: An Automated Discourse Analysis

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Nicolae Nistor, Beate Baltes, George Smeaton, Mihai Dascalu, Dan Mihaila, & Stefan Trausan-Matu.. Talk at DCLA13 Leuven 2013, co-located with LAK13

Education Technology

Virtual Communities of Practice in Academia:
An Automated Discourse Analysis

Nicolae Nistor, Beate Baltes, George Smeaton,
Mihai Dascălu, Dan Mihailă & Ștefan Trăușan-Matu

LAK13 – DCLA13

1. Rationale

•  Increasing use of virtual communities of practice (vCoPs)
in academia
•  Available technology acceptance and CoP models
•  Models are methodologically limited and
insufficiently tested in vCoPs
•  Participation in vCoP = technology use? If so,
the combined acceptance x CoP model should be valid

Ø  Validation of automated discourse analysis
Ø  Verification of the acceptance x CoP model in an
academic vCoP

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

2. Theoretical background

Communities of practice (Lave & Wenger, 1991;
Wenger, 1998)
•  Groups of people sharing goals, practice and knowledge
over lengthy periods of time
•  Environment for knowledge construction/creation
•  Practice and knowledge are reflected in dialogue
•  Main factors
•  expertise
•  participation
•  expert status

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

2. Theoretical background

Communities of practice
Conceptual model (Nistor & Fischer, 2012)

Knowledge
domain
Expert status
Participation
(centrality)
Time in the
CoP

Role in CoP
Expertise

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

2. Theoretical background

Educational technology acceptance
•  Unified Theory of Acceptance and Use of Technology
(UTAUT; Venkatesh et al., 2003, 2012)

Performance
expectancy

Effort Technology Technology
expectancy use intention use behavior

Social Facilitating Technology
influence conditions anxiety

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

3. Research model
CoP model
Role in CoP

Domain
knowledge
Expert status
Participation
(centrality)
Time in CoP

Expertise

Technology Facilitating Technology
use intention conditions anxiety

Performance Effort Social
expectancy expectancy influence
Acceptance model

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

4. Methodology

Design: Correlation study
Sample: N = 129 members of academic vCoP at US
American online university (20 full-time, 500 part-time staff)
Setting: Asynchronous discussion forum
Variables:
•  Acceptance
•  Expertise, as reflected in the quality of interventions
•  Expert status/Centrality
Methods:
•  Acceptance: UTAUT questionnaire
•  CoP: Automated content analysis
•  Centrality: Social Network Analysis

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

4. Methodology

Automated content analysis

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

4. Methodology

Automated content analysis – Validation
•  Manual content analysis:
Critical thinking framework
•  Categories: initiation of discussion, exploration
of the problem, solution, judgment, resolution
•  Argumentation quality rating
Ø Strong correlation (r = .79, p < .000) between
automated and manual content analysis

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

4. Findings

Partial verification of UTAUT model

Performance
expectancy .30***
R2 = .36 R2 = .06
Effort .22** Technology n.s. Technology
expectancy use intention use behavior
n.s. -.28**
Social .23** Facilitating Technology
influence conditions anxiety

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

4. Findings

Successful verification of CoP model

Role in CoP
Domain
knowledge .99***
.87***
n.s. Participation Expert status

Time in the
n.s. R2 = .98 R2 = .76
CoP significant mediation effect

Expertise

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

5. Discussion

•  Automated content analysis is useful for assessing vCoP activity
•  Technology acceptance develops use intention
•  However, use behavior is influenced by CoP factors

Role in CoP

Expertise Participation Expert status

Technology
anxiety

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

6. Conclusions

Consequences for educational research
•  CoP model was confirmed
•  Acceptance models need reconceptualization for
complex educational environments

Consequence for educational practice
•  Development of assessment tools for
collaboration in vCoP

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

Thank you for your attention!

nic.nistor@lmu.de

Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

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This Dissertation explores the particular circumstances of Mirzapur, a region located in the core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal environment for investigating the changes in vegetation cover dynamics. Our study utilizes advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to analyze the transformations that have taken place over the course of a decade. The complex relationship between human activities and the environment has been the focus of extensive research and worry. As the global community grapples with swift urbanization, population expansion, and economic progress, the effects on natural ecosystems are becoming more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for these activities. As the most crucial natural resource, its utilization by humans results in different 'Land uses,' which are determined by both human activities and the physical characteristics of the land. The utilization of land is impacted by human needs and environmental factors. In countries like India, rapid population growth and the emphasis on extensive resource exploitation can lead to significant land degradation, adversely affecting the region's land cover. Therefore, human intervention has significantly influenced land use patterns over many centuries, evolving its structure over time and space. In the present era, these changes have accelerated due to factors such as agriculture and urbanization. Information regarding land use and cover is essential for various planning and management tasks related to the Earth's surface, providing crucial environmental data for scientific, resource management, policy purposes, and diverse human activities. Accurate understanding of land use and cover is imperative for the development planning of any area. Consequently, a wide range of professionals, including earth system scientists, land and water managers, and urban planners, are interested in obtaining data on land use and cover changes, conversion trends, and other related patterns. The spatial dimensions of land use and cover support policymakers and scientists in making well-informed decisions, as alterations in these patterns indicate shifts in economic and social conditions. Monitoring such changes with the help of Advanced technologies like Remote Sensing and Geographic Information Systems is crucial for coordinated efforts across different administrative levels. Advanced technologies like Remote Sensing and Geographic Information Systems 9 Changes in vegetation cover refer to variations in the distribution, composition, and overall structure of plant communities across different temporal and spatial scales. These changes can occur natural.

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Chapter wise All Notes of First year Basic Civil Engineering Syllabus Chapter-1 Introduction to objective, scope and outcome the subject Chapter 2 Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country. Chapter 3 Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements. Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station. Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps. Chapter 4 Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation Chapter 5 Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures. Chapter 6 Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems. Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect Text Books: 1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers. 2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers. 3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House. 4. BCP, Surveying volume 1

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Virtual Communities of Practice in Academia: An Automated Discourse Analysis

1. Virtual Communities of Practice in Academia: An Automated Discourse Analysis Nicolae Nistor, Beate Baltes, George Smeaton, Mihai Dascălu, Dan Mihailă & Ștefan Trăușan-Matu LAK13 – DCLA13

2. 1. Rationale •  Increasing use of virtual communities of practice (vCoPs) in academia •  Available technology acceptance and CoP models •  Models are methodologically limited and insufficiently tested in vCoPs •  Participation in vCoP = technology use? If so, the combined acceptance x CoP model should be valid Ø  Validation of automated discourse analysis Ø  Verification of the acceptance x CoP model in an academic vCoP Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

3. 2. Theoretical background Communities of practice (Lave & Wenger, 1991; Wenger, 1998) •  Groups of people sharing goals, practice and knowledge over lengthy periods of time •  Environment for knowledge construction/creation •  Practice and knowledge are reflected in dialogue •  Main factors •  expertise •  participation •  expert status Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

4. 2. Theoretical background Communities of practice Conceptual model (Nistor & Fischer, 2012) Knowledge domain Expert status Participation (centrality) Time in the CoP Role in CoP Expertise Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

5. 2. Theoretical background Educational technology acceptance •  Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003, 2012) Performance expectancy Effort Technology Technology expectancy use intention use behavior Social Facilitating Technology influence conditions anxiety Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

6. 3. Research model CoP model Role in CoP Domain knowledge Expert status Participation (centrality) Time in CoP Expertise Technology Facilitating Technology use intention conditions anxiety Performance Effort Social expectancy expectancy influence Acceptance model Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

7. 4. Methodology Design: Correlation study Sample: N = 129 members of academic vCoP at US American online university (20 full-time, 500 part-time staff) Setting: Asynchronous discussion forum Variables: •  Acceptance •  Expertise, as reflected in the quality of interventions •  Expert status/Centrality Methods: •  Acceptance: UTAUT questionnaire •  CoP: Automated content analysis •  Centrality: Social Network Analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

8. 4. Methodology Automated content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

9. 4. Methodology Automated content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

10. 4. Methodology Automated content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

11. 4. Methodology Automated content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

12. 4. Methodology Automated content analysis – Validation •  Manual content analysis: Critical thinking framework •  Categories: initiation of discussion, exploration of the problem, solution, judgment, resolution •  Argumentation quality rating Ø Strong correlation (r = .79, p < .000) between automated and manual content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

13. 4. Findings Partial verification of UTAUT model Performance expectancy .30*** R2 = .36 R2 = .06 Effort .22** Technology n.s. Technology expectancy use intention use behavior n.s. -.28** Social .23** Facilitating Technology influence conditions anxiety Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

14. 4. Findings Successful verification of CoP model Role in CoP Domain knowledge .99*** .87*** n.s. Participation Expert status Time in the n.s. R2 = .98 R2 = .76 CoP significant mediation effect Expertise Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

15. 5. Discussion •  Automated content analysis is useful for assessing vCoP activity •  Technology acceptance develops use intention •  However, use behavior is influenced by CoP factors Role in CoP Expertise Participation Expert status Technology anxiety Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

16. 6. Conclusions Consequences for educational research •  CoP model was confirmed •  Acceptance models need reconceptualization for complex educational environments Consequence for educational practice •  Development of assessment tools for collaboration in vCoP Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

17. Thank you for your attention! nic.nistor@lmu.de Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

Virtual Communities of Practice in Academia: An Automated Discourse Analysis

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