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Semantic Text Theme Generation in Collaborative Online Learning Environments
http://tinyurl.com/SPU-AACE2015
Andrew Lumpe (Seattle Pacific University, USA)David Wicks (Seattle Pacific University, USA)
Robin Henrikson (Seattle Pacific University, USA)Nalline Baliram (Seattle Pacific University, USA)
October 20, 2015AACE E-Learn Conference
AbstractOnline students' ability to self-regulate led to focused attention and time on-task. Given a need for more theoretical work in this area, as well as the potential practical benefits, we sought to compare differences between high versus low-collaboration teams in an online assignment to determine if higher levels of student-to-student collaboration lead to higher levels of semantic writing. Specifically, we explored how the use of collaboration technologies such as Google Docs and Google Hangouts impacted the level of ideas generated while participating in a group project. It was found that in terms of total generated semantic themes, low collaboration groups developed significantly more than their high collaboration counterparts in both online discussions and post course meta-reflective blog writings. Learning presence was the only significant predictor of unique theme generation on the individually generated meta-reflection blog post.
Context Part of comprehensive study on online collaboration.
Explored how the use of high-collaboration technologies (Google Docs and Google Hangouts)
High collaboration groups displayed higher learning presence (self-regulation) characteristics
Wicks, D., Craft, B., Lee, D., Lumpe, A., Henrikson, R., Baliram, N., Bian, X., Mehlberg, S., & Wicks, K. (2015). An Evaluation of Low Versus High-Collaboration in Online Learning. Online Learning Journal, 19(4). http://olj.onlinelearningconsortium.org/index.php/olj/article/view/552
IntroductionQuality continues to be a concern with online learning. Isolation and disconnectedness can lead to student dissatisfaction and attrition (Angelino, Williams, & Natvig, 2007; Kanuka & Jugdev, 2006)
LiteratureOnline learning can be enhanced by giving learners control of their interactions
with media and prompting learner reflection (Means et al., 2009)Students' ability to self-regulate leads to more focused attention, time on-task,
and in turn, these skills could lead to better learning (Shea et al., 2014)
How can learning activities be improved to give learners more control of planning, performance, and reflection?
Community of Inquiry“An educational community of inquiry is a group of individuals who collaboratively engage in purposeful critical discourse and reflection to construct personal meaning and confirm mutual understanding.”
● Social Presence○ Affective Expression○ Open Communication○ Group Cohesion
● Teaching Presence○ Design○ Facilitation○ Instruction
● Cognitive Presence○ Triggering Event○ Exploration○ Integration○ Resolution https://coi.athabascau.ca/coi-model/
Learning Presence“Iterative processes of forethought and planning, monitoring and adapting strategies for learning, and reflecting on results that successful students use to regulate their learning in online, interactive environments”
(Shea et al., 2014)
Shea, P., Hayes, S., Uzuner Smith, S., Vickers, J., Bidjerano, T., Gozza-Cohen, M., Jian, S., Pickett, A., Wilde, J., & Tseng, C. (2013). Online learner self-regulation: Learning presence viewed through quantitative content- and social network analysis. The International Review Of Research In Open And Distance Learning, 14(3), 427-461. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1466
Course InformationCourse Name: Learners in Context
Course Purpose: 1. Explore characteristics of a ‘brain friendly’ lesson,
2. Learn the developmental theories of adolescence
3. Subject matter under investigation-
biology, psychology, sociology, language, motivation, and peer relations, as they relate to child and adolescent development.
Participants: Graduate level Alternate Route to Certification (one year program) or Master in Arts for Teacher (two-year program) all combined into two different sections
ParticipantsParticipants (N = 47) were randomly assigned to
one of two graduate courses. In the low collaboration group (males = 7,
females = 17), participants had a mean age of 32. 09 (SD = 9.47).
In the high collaboration group (males = 5, females = 18), participants had a mean age of 29.65 (SD = 8.04).
Instructional StrategyDesigned for online learnersBoth sections received identical instruction and resources:
Weekly reading assignmentsWeekly discussion topicsWeekly lectures via screencast
Required text for both sections:Medina, J. (2008). Brain rules. Seattle, WA: Pear Press.Pressley, M. & McCormick, C. B. (2007). Child and adolescent development
for educators. New York, NY: Guilford Press.
Difference in Instructional MethodsLow Collaboration Section High Collaboration Section
1) Discussion Forum Participation● Students choose one of the questions to respond to within
the discussion forum. ● Students respond to two follow-up responses.
2) bPortfolio Reflections● Students were assigned a total of four reflections
3) Theory to Practice Paper ● A four page term paper at the end of the course● Students will demonstrate their emerging knowledge of
developmental theories and their implications for practice. ● Individual Assignment
4) Partner Practice Lesson● Students work with a partner to complete the assignment.● Students were free to collaborate or use a ‘divide and
conquer’ strategy to complete the assignment.
1) Discussion Forum Participation● Students choose one of the questions to respond to within
the discussion forum. ● Students respond to two follow-up responses.
2) bPortfolio Reflections● Students were assigned a total of three reflections
3) Theory to Practice Project● Presentation completed in groups of 3 - 4 ● Students will demonstrate their emerging knowledge of
developmental theories and their implications for practice. ● Students collaborative using Google Hangout on Air
4) Peer Evaluations for Theory to Practice Project● Students evaluated members in the group for Phases 2 - 4
5) Practice Lesson Plans● Individual assignment
Research QuestionsDo students participating in high collaborative online learning environments develop more total semantic text themes than students in low collaborative environments during online discussions?
Do students participating in high collaborative online learning environments develop more unique semantic text themes than students in low collaborative environments during post course reflective blogging?
What variables predicts semantic theme generation in online reflective blog posts?
MethodsA form of text analytics was applied to the students’ discussions and blog posts in
order to analyze the content of the text corpus. The Semantria (www.semantria.com) program was used to apply semantic linguistic
algorithms to the text corpus to extract themes. An extracted theme represents noun phrases with contextual relevance scores – in
other words, “What are the students writing about”? Themes are noun phrases taken from written text and contain the main ideas of the
content.Themes represent a form of conceptual learning/understanding.Results included total extracted themes and total unique themes.Learning presence categories were coded from discussion and blog text (see
previous study)
MethodsDescriptive statistics were used to identify text themes extracted from online
discussions and end of course reflective blogs.
Using SPSS v. 23, two mixed model ANOVAs were used to determine the effect of low and high collaboration on
Total extracted text themes for discussions and blog postsTotal unique blog themes for discussions and blog posts
Multiple regression model to predict unique theme generation on post course meta-reflective blog.
Results
Total Extracted Semantic Themes
Descriptive Statistics
group Mean Std. Deviation N
Discussion themes
low collaboration 161.6472 114.61185 18
high collaboration 74.7059 81.37626 17
Blog themes low collaboration 105.0556 97.50202 18
high collaboration 52.1765 38.62841 17
● Low Collaboration greater than High Collaboration (F = 6.91, sig = .013)● Discussion higher than Blog themes (F = 8.49, sig = .006)● There was no interaction between the factors (F = 1.57, sig = .218)
Total Unique Semantic Themes
group Mean Std. Deviation N
discussion low collaboration 108.0000 54.03158 18
high collaboration 96.3529 48.14424 17
Total 102.3429 50.84633 35
blog low collaboration 29.3889 15.23594 18
high collaboration 28.5294 13.40764 17
Total 28.9714 14.17223 35
● Low Collaboration same as High Collaboration (F = .416, sig = .523)● Discussion higher than Blog themes (F = 78.7, sig = .000)● There was no interaction between the factors (F = .427, sig = .518)
ANOVAa
Model Sum of
Squares
df Mean
Square
F Sig.
1 Regression 2106.414 3 702.138 4.609 .009
Residual 4722.558 31 152.341
Total 6828.971 34
Model Unstandardized
Coefficients
Standardize
d
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 15.384 8.374 1.837 .076
learning presence .130 .042 .527 3.091 .004
UNIQUE discussion
themes
.049 .044 .176 1.128 .268
group (low or high coll) -6.325 4.695 -.226 -1.347 .188
Adjusted R square = .242
Regression model predicting unique
blog themes
DiscussionStudents develop more semantic themes during online collaboration than when writing blog posts
individually.Low and high collaboration groups developed similar numbers of unique semantic themes during
discussions and blog posts.Both low and high collaboration were well scaffolded. Symatics themes represent a form of learning analytics.Learning presence is a significant predictor of semantic theme generation. Strategies to foster learning presence in online settings should be developed.The results from this particular study call into question the nature and efficacy of online
collaboration. More research is needed to understand how semantic theme generation is related to other
measures of learning and constructs related to Community of Inquiry (CoI) and learning presence.
Further research is needed to better understand how to design collaborative online courses that enhance communities of inquiry and promote self and co-regulation that ultimately leads to higher quality courses.
Future Research - Analyze extractions from final products
Thank You
Questions? Suggestions?
For more information contact: Andrew Lumpe
Thanks to Xu Bian for extracting text into Excel
