Using Learning Presence to Uncover Self-Regulation in the

Community of Inquiry Model

Suzanne Hayes, Empire State College*Peter Shea, University at Albany*

Sedef Smith, Indiana University of PennsylvaniaJason Vickers, University at Albany

*State University of New York

Review of Community of Inquiry Model Learning Presence as a new construct to

explain online learner self-regulation LP in collaborative and non-collaborative

activities Using social network analysis to examine LP

patterns among students

Presentation

Community of Inquiry Model (CoI) of Garrison, Anderson & Archer, 2000

Most widely cited theory in study of online learning

Explains what takes place in an online course Based on interaction

Conceptual Framework

Shea & Bidjerano, 2010

What is the CoI Model?

All three elements needed to create a meaningful online learning experience (Garrison, Anderson & Archer, 2000)

* Triggering event* Exploration* Integration* Resolution

Looked to the literature of self-regulated learning (SRL)

Zimmerman’s research (2000, 2001) takes into account individual cognition and social interaction

SRL evident in students who exhibit agency in directing thoughts, emotions, motivations, behaviors and strategies in the service of their learning

What else does a successful online learner contribute ?

Revised CoI model with Learner Presence

Shea & Bidgerano (2010) proposed new CoI element called LP

Based on large scale survey research they found that LP was strongly correlated with SP, TP & CP

Examined two undergraduate courses Looked outside threaded discussions to

examine other learning activities Identified a problem in applying existing CoI

codes to student interaction in a series of small group debate preparation areas

Used Zimmerman and others to develop a LP coding scheme

Study 2: What do students actually do?

Shea, Hayes, Uzuner & Vickers, et al. (In press). Learning presence: additional research on a new conceptual element within the Community of Inquiry (CoI) framework.  Internet and Higher Education.

Learning Presence in Preparation Areas versus Discussion Areas

All Prep Areas Debate Discussion0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Avg Student LP - Combined Debate Prep. v. Debate Discussion - Course B

FP

MO

SU

MO

More LP in collaborative areas with group products

FP = Forethought & PlanningMO = MonitoringSU = Strategy Use

Current study

A graduate level required research methods blended course delivered during Fall 2010 term

18 doctoral students divided into teams Each team assigned to lead module on research

method of their choice Teams worked with instructor to select readings,

activities and discussion questions 1 full class discussion and option to select either

video/discussion on field notes or interviewing Reporting today on preliminary LP results

Phase 1: Quantitative Content Analysis

Focused on online discussion transcripts and learning journals from a two-week long course module

Coded all Week 2 discussions and module learning journal for LP

Refined our original LP coding scheme Realigned with Zimmerman’s (2000) three phase

cyclical model of SRL (Planning, Performance and Reflection)

LP: Forethought/Planning Indicators

FP1 Setting goals FP2 Planning FP3 Coordinating, delegating or assigning tasks

LP: Performance Indicators

Strategy Use◦ S1 Seeking/offering

help◦ S2 Recognizing a gap

in knowledge ◦ S3 Reviewing◦ S4 Noting outcome

expectations◦ S5 Seeking/offering

additional information

Monitoring ◦ M1 Checking for

understanding◦ M2 Identifying problems◦ M3 Noting completion of

tasks◦ M4 Evaluating quality of

products or process◦ M5 Monitoring and taking

corrective action◦ M6 Appraising interest or

engagement ◦ M7 Recognizing learning

behaviors of self or group◦ M8 Advocating effort or focus◦ M9 Noting use of strategies

LP: Reflection Indicators Added

R1 Change in thinking R2 Causal attribution of results to personal

or group performance

SNA measures nature of relationships between actors (students) in a network (online discussion or course)

Examines “ties” between participants Represented as a network graph i.e. “who

talks to who” and “how often” Helps us understand

How complete the network is Who is central to the network and who is isn’t Patterns of participation based on certain

characteristics

Phase 2: Social Network Analysis

Generated social network graph combining 3 discussions from Module 6 week 2 using Usenet software

Overlaid student LP measures from QCA

Phase 2: Social Network Analysis

Inter-Rater Reliability for QCA Paired coders practiced to establish baseline IRR Used Holsti’s Coefficient of Reliability (CR) Completed initial coding; Reported Initial IRR Negotiated differences; Reported Negotiated IRR

Coding Initial IRR Negotiation Negotiated IRR

Inter-Rater Reliability for QCA

Holsti’s CR

Module 6 Activity Pre Post

Week 2 Discussion 1 0.73 1.00

Week 2 Discussion 2 0.81 1.00

Week 2 Discussion 3 0.88 1.00

Learning Journal 0.69 1.00

For exploratory research we targeted .70 +

Discussion and Journal LP Compared

Module 6 Week 2

Network Graph: Week 2 Discussions

Students with Highest Discussion LP

Students with Highest Journal LP

Facilitators vs. Non-Facilitators

Occurrences of LP Indicators

Average LP Indicators

Student Facilitators S02, S09, S13, S19 30 7.5

Rest of Class 14 other students 73 5.2

Student facilitators demonstrate higher average LP in discussions

Based on Combined LP in Journals and Discussions in Module 6 Week 2

Facilitator’s Discussion LPRankings byLP Occurrence

S05 8S13 8S09 7S15 6… ….S02 4S19 3

Facilitators’ Journal LPRankings byLP Occurrence

S02 5S05 3S06 3S18 3… ….S09 1S13 1S19 1

Facilitators generally at the center of the network due to strength of connections

Demonstrated higher levels of LP (monitoring and strategy use)

Facilitators appeared to have lower relative LP in learning journals

But some students who appear to be less active in discussion have higher journal LP

Not surprising to find higher levels of monitoring and reflection in journals.

Preliminary Observations

Doctoral students Preliminary data based on one week from

one module Need results from other CoI measures SP,

TP, CP Possible relationship between LP and CP, LP

and TP when instructional activity shifted to learners

Limitations/Future Research

Asking students to take on instructional responsibilities may offer promise in terms of enhancing self-regulation

Preliminary results are consistent with prior research that points to benefits of having students assume facilitator role in discussions (Baran & Correia, 2009; Gilbert & Dabbah, 2005; Seo, 2007)

Implications for Practice

May need to adjust expectations for student facilitators – 3 of their 4 learning journals had lower levels of LP – At what point do students go into overload?

Perhaps activity-based discussions (where students “do” something)* when combined with readings and learning journals may encourage LP.

Implications for Practice

Learning activity design: Make explicit expectations for individual and group use of planning, monitoring, strategy use and reflection

Learning Journals: Ask students to examine their self regulatory processes

Assessment: Incorporate elements of LP into rubrics

Implications for Practice

Baran, E., & Correria, A. (2009). Student-led facilitation strategies in online discussions. Distance Education, 30, 339–361. doi:10.1080/01587910903236510.

Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2, 87-105. doi: http://dx.doi.org/10.1016/S1096-7516(00)00016-6.

Gilbert, P. & Dabbah, N. (2005). How to structure online discussions for meaningful discourse(2005) British Journal of Educational Technology, 36 (1),5–18

Seo, K. (2007). Utilizing peer moderating in online discussions: Addressing the controversy between teacher moderation and nonmoderation. The American Journal of Distance Education, 21, 21–36. doi:10.1080/08923640701298688.

Shea, P., & Bidjerano, T. (2010). Learning presence: Towards a theory of self-efficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments. Computers & Education, 55, (4), 1721–1731. doi: http://dx.doi.org/10.1016/j.compedu.2010.07.017Shea, Hayes, Uzuner & Vickers, et al. (In press). Learning presence: additional research on a new conceptual element within the Community of Inquiry (CoI) framework.  Internet and Higher Education.

Zimmerman, B. J. (1998). Developing self-fulfilling cycles of academic regulation: An analysis of exemplary instructional models. In D. H. Schunk & B.J. Zimmerman(Eds.), Self-regulated learning: From teaching to self-reflective practice (pp. 1–19). New York: Guilford.

 Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). New York: Academic Press.

 Zimmernan, B.J. & Schunk, D.H. (2001). Theories of self-regulated learning and academic achievment: An overview and analysis. In B. J. Zimmerman and D.H. Schunk (Eds) Self regulated learning and academic achievement: Theoretical perspectives. (pp.1-36) Mahwah, NJ: Lawrence Erlbaum

Works Cited

suzanne.hayes@esc.edu

petershea@albany.edu

Thank you!