Presentation at the Graph-based Educational Data Mining workshop (G-EDM) during the 2014 Educational Data Mining conference (EDM 2014) at Institute of Education, University of London, London, UK on July 4th, 2014.
Towards Automated Classification of Discussion Transcripts: A Cognitive Prese...Vitomir Kovanovic
LAK'16 Conference paper presentation:
abstract:
In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen’s kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features
than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.
Topic Modeling for Learning Analytics Researchers LAK15 TutorialVitomir Kovanovic
Slides from the introductory tutorial to topic modeling with R and LSA, pLSA and LDA algorithms organized at LAK15 conference in Poughkeepsie, NY March 17, 2015
Digital examination, forms and tools for aggregation of information and cogni...Johan Thorbiörnson
Plattformar för e-learning har huvudsakligen varit teknologier för att organisatoriskt kunna hantera stora studentgrupper som är skilda åt i tid och rum. Detta görs fortfarande men sådan undervisning sker inte alltid enligt den traditionella hierarkiska formen, utan iscensätts även i nya undervisningsformer. Denna presentation tar utgångspunkt i James Surowieckis begrepp "the wisdom of crowds" för att diskutera formerna för aggregering av kunskap och den dynamik som uppstår i kunskapssökande grupper. Webbplattformen Math.se komer att presenteras liksom nya initiativ inom e-learning på KTH såsom KTH Virtuellt campus.
Litteratur:
James Surowiecki "The wisdom of crowds"
http://www.randomhouse.com/features/wisdomofcrowds/
http://en.wikipedia.org/wiki/The_Wisdom_of_Crowds
Lilac 2012 Essential information skills for researchers: A collaborative proj...Chris Bark
This presentation gives an overview of a project involving four institutions: Loughborough, Nottingham, De Montfort and Coventry Universities to create an open source repurposable information skills tutorial appropriate for early career researchers. It covers the rationale for undertaking the project, the proposed content, the research conducted and methodologies used which informed the design and final content of the online module.
The module that has been developed is called: Dissemination of your research and includes the following units:
Journals and journal articles
Other forms of publishing
Journal bibliometrics
Author bibliometrics
Networking
The presentation then moves on to look at in some depth the benefits of working in a consortium but also the challenges the group faced as a result of working as a collaboration.
Presentation from 'Future Technology' strand at the CDE’s Research and Innovation in Distance Education and eLearning conference, held at Senate House London on 1 November 2013. Conducted by Dr Clare Sansom (Birkbeck College, University of London).
2015 03 19 (EDUCON2015) eMadrid UPM Towards a Learning Analytics Approach for...eMadrid network
2015 03 19 (EDUCON2015) eMadrid UPM Towards a Learning Analytics Approach for Supporting discovery and reuse of OER. An approach based on Social Networks Analysis and Linked Open Data
The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners. We focus on building models to find the characteristics and features involved in context-agnostic engagement (i.e. population-based), a seldom researched topic compared to other contextualised and personalised approaches that focus more on individual learner engagement. Learner engagement, is arguably a more reliable measure than popularity/number of views, is more abundant than user ratings and has also been shown to be a crucial component in achieving learning outcomes. In this work, we explore the idea of building a predictive model for population-based engagement in education. We introduce a novel, large dataset of video lectures for predicting context-agnostic engagement and propose both cross-modal and modality-specific feature sets to achieve this task. We further test different strategies for quantifying learner engagement signals. We demonstrate the use of our approach in the case of data scarcity. Additionally, we perform a sensitivity analysis of the best performing model, which shows promising performance and can be easily integrated into an educational recommender system for OERs.
Authors: Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz and John Shawe-Taylor
On Using Learning Analytics to Track the Activity of Interactive MOOC VideosMohammad Khalil
J. Wachtler, M. Khalil, B. Taraghi, and M. Ebner. “On Using Learning Analytics to Track the Activity of Interactive MOOC videos”. In Proceedings of the LAK 2016 Workshop on Smart Environments and Analytics in Video-Based Learning, Edinburgh, Scotland, 2016, pp.8–17.
Towards Automated Classification of Discussion Transcripts: A Cognitive Prese...Vitomir Kovanovic
LAK'16 Conference paper presentation:
abstract:
In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen’s kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features
than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.
Topic Modeling for Learning Analytics Researchers LAK15 TutorialVitomir Kovanovic
Slides from the introductory tutorial to topic modeling with R and LSA, pLSA and LDA algorithms organized at LAK15 conference in Poughkeepsie, NY March 17, 2015
Digital examination, forms and tools for aggregation of information and cogni...Johan Thorbiörnson
Plattformar för e-learning har huvudsakligen varit teknologier för att organisatoriskt kunna hantera stora studentgrupper som är skilda åt i tid och rum. Detta görs fortfarande men sådan undervisning sker inte alltid enligt den traditionella hierarkiska formen, utan iscensätts även i nya undervisningsformer. Denna presentation tar utgångspunkt i James Surowieckis begrepp "the wisdom of crowds" för att diskutera formerna för aggregering av kunskap och den dynamik som uppstår i kunskapssökande grupper. Webbplattformen Math.se komer att presenteras liksom nya initiativ inom e-learning på KTH såsom KTH Virtuellt campus.
Litteratur:
James Surowiecki "The wisdom of crowds"
http://www.randomhouse.com/features/wisdomofcrowds/
http://en.wikipedia.org/wiki/The_Wisdom_of_Crowds
Lilac 2012 Essential information skills for researchers: A collaborative proj...Chris Bark
This presentation gives an overview of a project involving four institutions: Loughborough, Nottingham, De Montfort and Coventry Universities to create an open source repurposable information skills tutorial appropriate for early career researchers. It covers the rationale for undertaking the project, the proposed content, the research conducted and methodologies used which informed the design and final content of the online module.
The module that has been developed is called: Dissemination of your research and includes the following units:
Journals and journal articles
Other forms of publishing
Journal bibliometrics
Author bibliometrics
Networking
The presentation then moves on to look at in some depth the benefits of working in a consortium but also the challenges the group faced as a result of working as a collaboration.
Presentation from 'Future Technology' strand at the CDE’s Research and Innovation in Distance Education and eLearning conference, held at Senate House London on 1 November 2013. Conducted by Dr Clare Sansom (Birkbeck College, University of London).
2015 03 19 (EDUCON2015) eMadrid UPM Towards a Learning Analytics Approach for...eMadrid network
2015 03 19 (EDUCON2015) eMadrid UPM Towards a Learning Analytics Approach for Supporting discovery and reuse of OER. An approach based on Social Networks Analysis and Linked Open Data
The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners. We focus on building models to find the characteristics and features involved in context-agnostic engagement (i.e. population-based), a seldom researched topic compared to other contextualised and personalised approaches that focus more on individual learner engagement. Learner engagement, is arguably a more reliable measure than popularity/number of views, is more abundant than user ratings and has also been shown to be a crucial component in achieving learning outcomes. In this work, we explore the idea of building a predictive model for population-based engagement in education. We introduce a novel, large dataset of video lectures for predicting context-agnostic engagement and propose both cross-modal and modality-specific feature sets to achieve this task. We further test different strategies for quantifying learner engagement signals. We demonstrate the use of our approach in the case of data scarcity. Additionally, we perform a sensitivity analysis of the best performing model, which shows promising performance and can be easily integrated into an educational recommender system for OERs.
Authors: Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz and John Shawe-Taylor
On Using Learning Analytics to Track the Activity of Interactive MOOC VideosMohammad Khalil
J. Wachtler, M. Khalil, B. Taraghi, and M. Ebner. “On Using Learning Analytics to Track the Activity of Interactive MOOC videos”. In Proceedings of the LAK 2016 Workshop on Smart Environments and Analytics in Video-Based Learning, Edinburgh, Scotland, 2016, pp.8–17.
I want to use our online presence as a way to help us think through one big idea: who we are when we are online as educators. What do professors do online? Is there anything special about faculty members who are online? Does their use of social media differ from the general population? Do they also post pictures of their children food, and cats? In this presentation, I will discuss how/why academics use social media and online networks, and explore aspects of online participation that is unique to scholars. I will discuss the opportunities and tensions that exist in online spaces, and share recent original research that shows how small data, as well as big data, can help us make sense of professors’ (and thereby students’) participation in online spaces.
Presentation shared by author at the 9th EDEN Research Workshop "Forging new pathways of research and innovation in open and distance learning: Reaching from the roots" held on 4-6 October 2016, in Oldenburg, Germany.
Find out more on #EDENRW9 here: http://www.eden-online.org/2016_oldenburg/
Kaleidoscope conference slides - Academic networkingKaty Jordan
Jordan, K. (2013) Reshaping the Higher Education network? Analysis of academic social networking sites. Presentation given at the Kaleidoscope Conference, Faculty of Education, University of Cambridge, 31st May 2013.
ePortfolios and Metacognition in Online Basic Research Methods - SPSJiyeon Lee
ePortfolios and Metacognition in Online Basic Research Methods - SPS
Web 2.0 tools facilitate shared knowledge, reflection, and metacognition among CUNY Online Baccalaureate students in a research methods learning community. E-Portfolio
modeling encourages transfer of ownership and thoughtful self-navigation of program learning goals targeted in course objectives and assignments. Examples highlight the first tier of a scaffolded series of competencies across two research methods courses that are prerequisites to capstone projects.
• Barbara Walters, Consortial Faculty, CUNY Online
• Ellen Smiley, Academic Director of the CUNY Online
• Sarah Morgano, ePortfolio Communication Coordinator
• Kate Noel Moss, Learning Community Instructor, CUNY Online
• William Bernhardt, Consorti al Faculty, CUNY Online
Research dissemination within and beyond the curriculumSimon Haslett
Author: Dr Helen Walkington, Oxford Brookes University.
Keynote Presentation at the Research - Teaching in Wales 2011 Conference, 13th - 14th September, Gregynog Hall, Newtown (Powys)
Application of Significance Tests to Massive Open Online Courses (MOOCs)FutureLearn FLAN
Presented by Simon Coton and Steve Cayzer of the University of Bath at The Open University, Milton Keynes, UK on 15 June 2017. This presentation formed part of the FutureLearn Academic Network section (FLAN Day) of the 38th Computers and Learning Research Group (CALRG) conference. For full details, see http://cloudworks.ac.uk/cloudscape/view/3004
Research in Distance Education: impact on practice conference, 27 October 2010. Presentation in Assessment Strand by Dr Stylianos Hatzipanagos, Lecturer in Higher education/Head of e-learning, King’s College London. Teaching and Research Award Holder.
More details at www.cde.london.ac.uk.
With the advent of Internet technologies, online communities have proliferated over the last three decades. People from dispersed locations are constantly coming together on virtual spots and are enabled by a wide range of software technologies to share common interests and concerns. With early emergent examples, online communities have received intensive study across various academic disciplines. This presentation aims at introducing the basic framework for understanding the specificities of online communities. The first section tries to construct an understanding of these communities by analysing their components. The second section exposes some of the influence spheres of this new virtual space. electronic media
Introduction to Learning Analytics for High School Teachers and ManagersVitomir Kovanovic
Presentation at the first Learning Analytics Learning Network (LALN) Event in Adelaide, Australia on Oct 22, 2019.
Abstract:
With the increased adoption of technology, institutions have unprecedented opportunities to continuously improve the quality of their services through data collection and analysis. Schools and universities now have data about learners and their contexts that can provide valuable insight into how they learn. Early attempts were directed towards mining educational data to identify students-at-risk and develop interventions. Recently, more sophisticated approaches are being deployed by researchers and practitioners. These include analysis of learner behaviour that leads to various learning outcomes, social networks and teams, employability, creativity, and critical thinking. Analysing digital traces generated through learning processes requires a broad suite of methods from data science, statistics, psychometrics, social and learning sciences.
This workshop aims to introduce teachers and educators to the fast growing and promising field of learning analytics. How digital data can be used for the analysis and improvement of student learning will be explored. First, we will provide an overview of learning analytics, its key methods and approaches, as well as problems for which it can be used. Secondly, attendees will engage in group learning activities to explore ways in which learning analytics could be used within their institutions. The focus will be on identifying learning-related challenges that are relevant to their particular context and exploring how learning analytics can be used to practically and effectively.
Extending video interactions to support self-regulated learning in an online ...Vitomir Kovanovic
Slides from our presentation at ASCILITE'18 conference in Geelong, Victoria. Full paper is available in ASCILITE conference proceedings at http://ascilite.org/wp-content/uploads/2018/12/ASCILITE-2018-Proceedings.pdf
Analysing social presence in online discussions through network and text anal...Vitomir Kovanovic
The slides from our presentation at IEEE ICALT'19 conference.
Abstract:
This paper presents an approach to studying relationships between students' social presence and course topics from transcripts of asynchronous discussions in online learning environments. Specifically, the paper uses topic modelling and epistemic network analysis to investigate how students' social presence is expressed across different course topics. Finally, we show how this method can be adopted to examine how students' social presence changed due to an instructional intervention. The results of this study and its implications are further discussed.
Automated Analysis of Cognitive Presence in Online Discussions Written in Por...Vitomir Kovanovic
Slides from our EC-TEL'18 Paper presentation. Full paper is available at https://dx.doi.org/10.1007/978-3-319-98572-5_19
Abstract:
This paper presents a method for automated content analysis of students’ messages in asynchronous discussions written in Portuguese. In particular, the paper looks at the problem of coding discussion transcripts for the levels of cognitive presence, a key construct in a widely used Community of Inquiry model of online learning. Although there are techniques to coding for cognitive presence in the English language, the literature is still poor in methods for others languages, such as Portuguese. The proposed method uses a set of 87 different features to create a random forest classifier to automatically extract the cognitive phases. The model developed reached Cohen’s κ of .72, which represents a “substantial” agreement, and it is above the Cohen’s κ threshold of .70, commonly used in the literature for determining a reliable quantitative content analysis. This paper also provides some theoretical insights into the nature of cognitive presence by looking at the classification features that were most relevant for distinguishing between the different phases of cognitive presence.
Validating a theorized model of engagement in learning analyticsVitomir Kovanovic
Slides from our paper presentation at LAK'19 conference in Tempe, AZ. The full paper is available at https://dx.doi.org/10.1145/3303772.3303775
Abstract:
Student engagement is often considered an overarching construct in educational research and practice. Though frequently employed in the learning analytics literature, engagement has been subjected to a variety of interpretations and there is little consensus regarding the very definition of the construct. This raises grave concerns with regards to construct validity: namely, do these varied metrics measure the same thing? To address such concerns, this paper proposes, quantifies, and validates a model of engagement which is both grounded in the theoretical literature and described by common metrics drawn from the field of learning analytics. To identify a latent variable structure in our data we used exploratory factor analysis and validated the derived model on a separate sub-sample of our data using confirmatory factor analysis. To analyze the associations between our latent variables and student outcomes, a structural equation model was fitted, and the validity of this model across different course settings was assessed using MIMIC modeling. Across different domains, the broad consistency of our model with the theoretical literature suggest a mechanism that may be used to inform both interventions and course design.
Examining the Value of Learning Analytics for Supporting Work-integrated Lear...Vitomir Kovanovic
Slides from our presentation at the Seventh National Conference
on Work-Integrated Learning (ACEN’18).
The full paper is available at https://www.researchgate.net/publication/328578409_Examining_the_value_of_learning_analytics_for_supporting_work-integrated_learning
Unsupervised Learning for Learning Analytics ResearchersVitomir Kovanovic
Slides from my 2.5-day workshop organised at 2018 Learning Analytics Summer Institute (LASI'18) organised at Teachers College, Columbia University on July 11, 2018.
Introduction to R for Learning Analytics ResearchersVitomir Kovanovic
The slides from my 2hr tutorial organised at 2018 Learning Analytics Summer Institute (LASI) at Teachers College, Columbia University on June 11, 2018.
Kovanović et al. 2017 - developing a mooc experimentation platform: insight...Vitomir Kovanovic
LAK'17 Conference paper presentation:
Abstract:
In 2011, the phenomenon of MOOCs had swept the world of education and put online education in the focus of the public discourse around the world. Although researchers were excited with the vast amounts of MOOC data being collected, the benefits of this data did not stand to the expectations due to several challenges. The analyses of MOOC data are very time-consuming and labor-intensive, and require a highly advanced set of technical skills, often not available to the education researchers. Because of this MOOC data analyses are rarely done before the courses end, limiting the potential of data to impact the student learning outcomes and experience.
In this paper we introduce MOOCito (MOOC intervention tool), a user-friendly software platform for the analysis of MOOC data, that focuses on conducting data-informed instructional interventions and course experimentations. We cover important design principles behind MOOCito and provide an overview of the trends in MOOC research leading to its development. Although a work-in-progress, in this paper, we outline the prototype of MOOCito and the results of a user evaluation study that focused on system’s perceived usability and ease-of-use. The results of the study are discussed, as well as their practical implications.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
What is the source of social capital? The association between social network position and social presence in communities of inquiry
1. What is the source of social capital?
The association between social network position
and social presence in communities of inquiry
Vitomir Kovanovic1
Srecko Joksimovic1
vitomir kovanovic@sfu.ca sjoksimo@sfu.ca
Dragan Gasevic2
Marek Hatala1
dgasevic@acm.org mhatala@sfu.ca
1
School of Interactive Arts and Technology 2
School of Computing Science
Simon Fraser University Athabasca University
Burnaby, Canada Edmonton, Canada
July 4, 2014,
London, UK
4. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Final goal
For instructors
Provide instructors with information on student’s learning progress
within a learning community so that appropriate instructional
interventions can be planned and implemented.
For students
Provide learners with the real time feedback of their own progress, and
progress of their peers so that they can self-regulate their learning
activities more successfully.
For researchers
Use data to better operationalize current Community of Inquiry model
of online learning.
V. Kovanovic et al. What is the source of social capital? 2 / 23
5. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Asynchronous online discussions -
“gold mine of information” Henri [9]
• Frequently used in both blended and
fully online learning [11],
• Their use produced large amount of
data about learning processes [4],
• Particularly important in
social-constructivist pedagogies [1].
• Frequently used for constructing
students’ social networks.
V. Kovanovic et al. What is the source of social capital? 3 / 23
6. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Social network analysis
• Social capital: value resulting from occupying a particularly
advantageous position within a social network [2]
• Many studies indicated importance of students’ social capital on
many important aspects of learning and educational experience:
• Academic performance,
• Retention,
• Persistance,
• Program satisfaction,
• Sense of community,
• . . .
V. Kovanovic et al. What is the source of social capital? 4 / 23
7. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Social network analysis
However,
• Typically isolated studies focusing on a single aspect of particular
interest,
• Typically not explaining what might be the cause of observed
differences in network positions,
• Lack of well-established learning theories which explicitly address
social network position.
V. Kovanovic et al. What is the source of social capital? 5 / 23
8. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Social network analysis
However,
• Typically isolated studies focusing on a single aspect of particular
interest,
• Typically not explaining what might be the cause of observed
differences in network positions,
• Lack of well-established learning theories which explicitly address
social network position.
Can we leverage existing comprehensive models of online learning to
provide insight into the nature of the observed differences in social
networks?
V. Kovanovic et al. What is the source of social capital? 5 / 23
9. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Community of Inquiry (CoI) model
Conceptual model outlying the important constructs that define
worthwhile educational experience in online education setting.
• Social presence: relationships and
social climate in a community.
• Cognitive presence: phases of
cognitive engagement and knowledge
construction.
• Teaching presence: instructional
role during social learning.
CoI model is:
• Extensively researched and validated,
• Adopts content analysis for
assessment of presences.
V. Kovanovic et al. What is the source of social capital? 6 / 23
10. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Social presence
Social presence
“Ability of participants in a community of inquiry to project themselves
socially and emotionally, as “real” people (i.e., their full personality),
through the medium of communication being used.” [7, p. 89]
V. Kovanovic et al. What is the source of social capital? 7 / 23
11. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Social presence
Social presence
“Ability of participants in a community of inquiry to project themselves
socially and emotionally, as “real” people (i.e., their full personality),
through the medium of communication being used.” [7, p. 89]
Three different dimensions of communication:
1 Affectivity and expression of emotions: defined as “the ability
and confidence to express feelings related to the educational
experience.” [7, p. 99]
2 Interactivity and open communication: defined as ““reciprocal
and respectful exchanges of messages” [7, p. 100].
3 Cohesiveness: Activities that “build and sustain a sense of group
commitment” [7, p. 101]
V. Kovanovic et al. What is the source of social capital? 7 / 23
12. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Social presence coding scheme
• Content analysis scheme for analysis of discussion messages,
• Use of whole message as unit of analysis,
• Look for particular indicators of different sociocognitive processes,
Social presence categories and indicators as defined by Rourke et al. [12].
Category Code Indicator
Affective A1 Expression of emotions
A2 Use of humor
A3 Self-disclosure
Interactive or Open
Communication
I1 Continuing a thread
I2 Quoting from others’ messages
I3 Referring explicitly to others’ messages
I4 Asking questions
I5 Complementing, expressing appreciation
I6 Expressing agreement
Cohesive C1 Vocatives
C2 Addresses or refers to the group using inclusive pronouns
C3 Phatics, salutations
V. Kovanovic et al. What is the source of social capital? 8 / 23
14. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Proposed approach
General idea
Investigate the relationship between students’ social capital
and social climate in the course.
More specificially,
We looked at the relationship between social network
centrality measures and social presence, one of the three
main components of Community of Inquiry model of online
learning.
V. Kovanovic et al. What is the source of social capital? 9 / 23
16. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Proposed approach
Measure three dimensions of social presence for each student and see
how they relate to their network centrality measures.
• Are three dimensions of social presence statistically significant
predictors of network centrality measures?
• What is the relative importance of different dimensions of social
presence?
V. Kovanovic et al. What is the source of social capital? 10 / 23
17. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Data set
• Six offerings of graduate level course in software engineering at
distance learning university.
• Total of 1747 messages by 81 students.
Course offering statistics.
Student count Message count Graph density
Winter 2008 15 212 0.52
Fall 2008 22 633 0.69
Summer 2009 10 243 0.84
Fall 2009 7 63 0.58
Winter 2010 14 359 0.84
Winter 2011 13 237 0.77
Average 13 291 0.71
Total 81 1747
V. Kovanovic et al. What is the source of social capital? 11 / 23
18. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Social network centrality measures
• Directed social network graph based on post-reply activity.
• Extracted popular centrality measures.
Descriptive statistics of social network metrics.
Mean SD Min Max
Betweenness 9.04 14.51 0.00 74.20
In-degree 19.84 8.62 4.00 42.00
Out-degree 19.86 9.37 3.00 44.00
In-closeness 0.09 0.04 0.04 0.17
Out-closeness 0.08 0.04 0.03 0.18
V. Kovanovic et al. What is the source of social capital? 12 / 23
19. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Social presence message coding
• Each message manually coded by two coders on the levels of
indicators (Percentage agreement = [84.1 − 98.9]%).
Social presence indicators.
Category Code Indicator Count Percentage Percent Agreement
Affective A1 Expression of emotions 288 16.5% 84.4
A2 Use of humor 44 2.5% 93.1
A3 Self-disclosure 322 18.4% 84.1
Interactive I1 Continuing a thread 1664 95.2% 98.9
I2 Quoting from others messages 65 3.7% 95.4
I3 Referring explicitly to other’s messages 91 5.2% 92.7
I4 Asking questions 800 45.8% 89.4
I5 Complementing, expressing appreciation 1391 79.6% 90.7
I6 Expressing agreement 243 13.9% 96.6
Cohesive C1 Vocatives 1433 82.0% 91.8
C2 Addresses or refers to the group using
inclusive pronouns
144 8.2% 88.8
C3 Phatics, salutations 1281 73.3% 96.1
V. Kovanovic et al. What is the source of social capital? 13 / 23
20. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Social presence message coding
• Some indicators way too frequent.
• Limiting discriminatory power of the whole category.
• We removed indicators occurring in more than 75% of the messages.
Social presence categories.
Category Count Percentage Percent Agreement
Affective 530 30.3% 80.8
Interactive (Excluded I1 and I5) 1030 59.0% 86.2
Cohesive (Excluded C1) 1326 75.9% 93.4
V. Kovanovic et al. What is the source of social capital? 14 / 23
21. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Statistical Analysis
Multiple regression analysis:
• DV: Social network centrality metrics.
• IVs: CoI Social presence codes.
• Backward stepwise model selection using AIC criterion [8].
• Holm-Bonferroni correction [10]:
• Guaranteed to keep family-wise error rate (FWER) α at the desired
level (i.e., α = 0.05).
• Significantly more powerful than classical Bonferroni correction [5].
V. Kovanovic et al. What is the source of social capital? 15 / 23
22. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Holm-Bonferroni correction procedure
For family of N tests and desired α significance:
• Sort all N observed p-values from smallest to largest.
• Cutoff for the smallest p-value: α/N.
• Cutoff for next p-value: α/(N − 1).
• . . .
• Cutoff for largest p-value: α.
V. Kovanovic et al. What is the source of social capital? 16 / 23
23. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Holm-Bonferroni correction procedure
For family of N tests and desired α significance:
• Sort all N observed p-values from smallest to largest.
• Cutoff for the smallest p-value: α/N.
• Cutoff for next p-value: α/(N − 1).
• . . .
• Cutoff for largest p-value: α.
Important rule
If any of the tests gets rejected, all the subsequent tests are also rejected
automatically.
V. Kovanovic et al. What is the source of social capital? 16 / 23
24. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Holm-Bonferroni correction procedure
For family of N tests and desired α significance:
• Sort all N observed p-values from smallest to largest.
• Cutoff for the smallest p-value: α/N.
• Cutoff for next p-value: α/(N − 1).
• . . .
• Cutoff for largest p-value: α.
Important rule
If any of the tests gets rejected, all the subsequent tests are also rejected
automatically.
Current study
In our study with 5 tests, cutoff p-values are
α = [0.01, 0.0125, 0.0167, 0.0250, 0.05]
V. Kovanovic et al. What is the source of social capital? 16 / 23
25. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Results
Regression results for selected centrality measures after stepwise model selection
using AIC criterion.
Betweenness In-degree Out-degree In-closeness Out-closeness
β SE p β SE p β SE p β SE p β SE p
Affective 0.27 0.12 0.024 0.18 0.054 0.001 0.23 0.059 <0.001
Interactive 0.38 0.12 0.002 0.65 0.064 <0.001 0.65 0.07 <0.001 0.27 0.11 0.015 0.37 0.15 0.017
Cohesive 0.2 0.061 0.001 0.14 0.066 0.041 -0.23 0.15 0.137
F(3, 77) 19.6 <0.001 159 <0.001 130 <0.001 6.24 0.015 3.03 0.054
Adjusted R2
0.32 0.86 0.83 0.061 0.048
V. Kovanovic et al. What is the source of social capital? 17 / 23
26. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings
• All but one regression models were significant, one marginally
significant.
• Interactive dimension of social presence is the most strongly
associated with all of the network centrality measures.
• Probable reason is the nature of social networks as a medium for
fostering collaborative and productive learning.
• According to Garrison [6], interactive social presence is dominant in
the beginning until students develop trust and sense of community,
but it decreases over time, while affective and cohesive increase over
time.
V. Kovanovic et al. What is the source of social capital? 18 / 23
27. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings
• All but one regression models were significant, one marginally
significant.
• Interactive dimension of social presence is the most strongly
associated with all of the network centrality measures.
• Probable reason is the nature of social networks as a medium for
fostering collaborative and productive learning.
• According to Garrison [6], interactive social presence is dominant in
the beginning until students develop trust and sense of community,
but it decreases over time, while affective and cohesive increase over
time.
• Practical implication: provide opportunities for focused, on task
interactions that foster open communication and collaboration.
V. Kovanovic et al. What is the source of social capital? 18 / 23
28. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings: Degree centrality
• All three categories of social presence were significantly predictive of
In-Degree and Out-Degree centrality measures.
• Affective and Cohesive are very interesting as they are not directly
affecting degree centrality.
• Interactive category was most strongly associated with degree
network centrality.
• This is expected for In-Degree as activities such as asking
questions, addressing by name or quoting someone’s message
increase chances of ’provoking’ a response.
• For Out-Degree it more interesting.
V. Kovanovic et al. What is the source of social capital? 19 / 23
29. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings: Betweenness centrality
• 32% of variability in betweenness centrality scores explained by our
regression model. Effect size: Cohen’s f2
= 0.47 which is considered
to be a large effect size [3].
• Interactive and affective dimensions of social presence were
significantly predictive of betweenness centrality, with interactive
dimension being more strongly associated.
• Probably due to the nature of social networks and the focus on
information exchange. Also trust and sense of community develops
later in the course when student already developed open
communication.
• As a followup, we want to look at the individual indicators, as they
might contain some answers to our findings.
V. Kovanovic et al. What is the source of social capital? 20 / 23
30. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings: Closeness centrality
• Only interactive category was significantly predictive of
In-closeness centrality. Model for Out-closeness was very
marginally significant (p = 0.054).
• Probable reason might be the fact that closeness embeds the
interactive relationships, for which affectivity and cohesiveness are
not much important.
V. Kovanovic et al. What is the source of social capital? 21 / 23
31. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Limitations and future work
Limitations:
• Data from one course
• despite having data from several offerings of the course, there might
be an effect of the particular pedagogical approach.
• Not all student interactions have positive effect and increase social
capital,
• Other important factors beside social presence.
Future work:
• Replicate on new data set, with larger and more diverse subjects,
• Investigate changes in the distributions of three social presence
dimensions over time,
• Maybe look at the levels of indicators instead of categories.
V. Kovanovic et al. What is the source of social capital? 22 / 23
32. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Summary
• There is an interesting connection between social presence and
students’ social network positions.
• We can use three dimensions of social presence to predict different
network centrality metrics.
• Interactivity and open communication showed to be the most
significant component of social presence.
• Our findings indicate the need for providing student with
opportunities for the development of social capital through
collaboration with other students on focused tasks.
• Educational theories suggest that development of trust and sense of
community follows from on-task interactions. Our data shows some
preliminary support for this.
V. Kovanovic et al. What is the source of social capital? 23 / 23
34. References I
Terry Anderson and Jon Dron. “Three generations of distance education pedagogy”. In:
The International Review of Research in Open and Distance Learning 12.3 (2010),
pp. 80–97.
Ronald S. Burt. “Structural Holes versus Network Closure as Social Capital”. In: Social
Capital: Theory and Research. 2001.
Jacob Cohen. “The Analysis of Variance”. In: Statistical power analysis for the behavioral
sciences. 1988, pp. 273–406.
Roisin Donnelly and John Gardner. “Content analysis of computer conferencing
transcripts”. In: Interactive Learning Environments 19.4 (2011), pp. 303–315.
Olive Jean Dunn. “Multiple Comparisons among Means”. In: Journal of the American
Statistical Association 56.293 (1961), pp. 52–64.
D. Randy Garrison. E-Learning in the 21st Century: A Framework for Research and
Practice. 2 edition. Routledge, 2011.
D. Randy Garrison, Terry Anderson, and Walter Archer. “Critical Inquiry in a Text-Based
Environment: Computer Conferencing in Higher Education”. In: The Internet and Higher
Education 2.2–3 (1999), pp. 87–105.
35. References II
Trevor J Hastie, Robert J Tibshirani, and Jerome H Friedman. The elements of statistical
learning: data mining, inference, and prediction. Springer, 2013.
France Henri. “Computer Conferencing and Content Analysis”. In: Collaborative Learning
Through Computer Conferencing. 1992, pp. 117–136.
Sture Holm. “A Simple Sequentially Rejective Multiple Test Procedure”. In: Scandinavian
Journal of Statistics 6.2 (1979), pp. 65–70.
Rocci Luppicini. “Review of computer mediated communication research for education”.
In: Instructional Science 35.2 (2007), pp. 141–185.
Liam Rourke et al. “Assessing Social Presence In Asynchronous Text-based Computer
Conferencing”. In: The Journal of Distance Education 14.2 (1999), pp. 50–71.