Examining the Role of Learning Presence in Online and Blended Courses
Listen and learn : Research paper
Saturday, December 5, 9:00–9:45 am PST (Pacific Standard Time)
Presentation 1 of 2
The Confluence of K12 Education Technology Leadership
Catherine Bacos Dr. Elham Arabi Elizabeth Barrie Brian Bullock Dr. Karen Grove
The authors examined the role of learning presence as an emerging construct in the Community of Inquiry framework. Learning presence was measured as the metacognitive and motivational traits and activities of preservice teachers in online and blended courses.
|Audience:||Coaches, Teacher education/higher ed faculty|
|Attendee devices:||Devices not needed|
|Topic:||Distance, online & blended learning|
|Grade level:||Community college/university|
|Subject area:||Preservice teacher education, Inservice teacher education|
|ISTE Standards:||For Coaches:
Teaching, Learning and Assessments
|Additional detail:||Session recorded for video-on-demand, Graduate student|
Community of Inquiry Framework. Shea and Bidjerano (2010) proposed an enhanced CoI framework with a focus on the learner role. The original model represents relationships between teaching, social, and cognitive presence (Garrison, Anderson, & Archer, 2001); whereas, the enhanced model adds learning presence to the cycle. These elements include teaching presence, social presence, cognitive presence, and learning presence.
The CoI framework is centered on the development of a collaborative online learning community outlining the behaviors and processes to nurture an epistemic engagement in learners (Garrison et al., 2001; Shea & Bidjerano, 2010). According to the framework, the design and facilitation of social and cognitive processes to achieve instructional outcomes represent teaching presence (Anderson, Rourke, Garrison, & Archer, 2001). The element of social presence highlights the ability of learners to identify with the learning community and develop interpersonal skills to project a cohesive and collaborative online setting (Rourke, Anderson, Garrison, & Archer, 1999). Cognitive presence is the extent to which learners construct knowledge through discourse and reflection (Garrison, Anderson, & Archer, 2001). Lastly, learning presence consists of a combination of the metacognitive and motivational traits and activities of learners, and is characterized by self-regulation and self-efficacy that learners can develop through active collaboration and reflection (Shea & Bidjerano, 2010).
Self-regulation is a multidimensional process in which learners set goals, plan, and adapt their behavior to accomplish goals (Cleary, Dembitzer, & Kettler, 2015). Research on self-regulation finds it an important skill in self-directed learning and online education (Zimmerman, 2008).
Self-efficacy is one’s belief in their competence level to perform and achieve specific outcomes (Bandura, 2010). The evidence suggests a strong correlation between self-regulation and self-efficacy in that self-regulation is contingent on positive self-efficacy motivating learners to view failure as a stimulus to make more efforts to achieve their goals (Strunk & Steele, 2011; Winne, 2005; Zimmerman & Schunk, 2001). Both self-regulation and self-efficacy characterize the elements of learning presence in CoI framework (Shea & Bidjerano, 2010).
1. What are the relationships among the four constructs in the enhanced Community of Inquiry (CoI) model (i.e., teaching presence, social presence, cognitive presence, and learning presence)?
2. What are the differences in the four CoI constructs between students in online and blended courses?
3. To what extent do the constructs of learning presence (i.e., self-efficacy, self-regulation, and motivation) and course factors (i.e., course format, course format preference, and usefulness of course assignments) influence constructs of the CoI framework (i.e., teaching presence, social presence, and cognitive presence)?
4. To what extent do constructs of the CoI framework and course factors influence learning presence?
Students enrolled in multiple sections of a preservice educational technology course were recruited for the study through a subject pool at a college of education in the southwestern United States. Two sections of the undergraduate-level course were fully online and three sections were blended. All participants provided informed consent to participate in the study and received 1 research credit towards their course requirements.
An 86-item survey instrument was administered at the end of the course. Questions from several sources (Arbaugh et al., 2008; Duncan & McKeachie, 2005; Garrison, Anderson, & Archer, 2000; Pintrich, Smith, Garcia, & McKeachie, 1993; Liaw et al., 2007; Wang, Etmer, & Newby, 2004) were used and adapted to construct a survey that was applicable to students enrolled in the undergraduate courses and the goals of this study. The items used a 5-point Likert scale and a 7-point Likert scale. The following instruments were used to construct the survey.
1. Student Characteristics and Attitudes (16 items)
a. Researcher-created survey items: Gender, age, undergraduate major area of concentration, college-level online courses completed, and course format enrolled (i.e., online or blended)
b. Attitudes toward e-learning (i.e., degree of preference for online, blended, and in-person formats; and perceived usefulness of course assignments to build technology knowledge and skills teachers need)
i. Adapted survey items from Attitudes Toward E-Learning – Technology Acceptance Model (Liaw et al., 2007)
2. Self-Efficacy for Technology Integration (7 items)
a. Selected survey items from this instrument (Wang, Ertmer, & Newby, 2004)
3. Motivated Strategies for Learning Questionnaire (MSLQ)
a. Selected survey items from this instrument (Duncan & McKeachie, 2005; Pintrich, Smith, Garcia, & McKeachie, 1993)
i. Motivation Subscales: Task Value, Control of Learning Beliefs (10 items)
ii. Learning Strategies Subscales: Metacognitive Self-Regulation, Effort Regulation, and Peer Learning (19 items)
4. Community of Inquiry (CoI) Instrument (34 items)
a. Adapted items from the original instrument (Garrison, Anderson, & Archer, 2000) and validated instrument (Arbaugh et al., 2008)
Participants. A total of 101 students participated in the study (see Table 1 for student characteristics). The average age of the sample was 22.30 (SD = 4.84). Among the course factors, 45.5% of participants (n = 46) were enrolled in the fully online course and 54.5% of participants (n = 55) were enrolled in the blended course.
RQ1. A correlation analysis revealed statistically significant relationships among the four constructs in the enhanced Community of Inquiry (CoI) model (i.e., teaching presence, social presence, cognitive presence, and learning presence) (see Table 2).
RQ2. Independent samples t-tests revealed statistically significant differences in social presence between the online and blended courses (see Table 3). Social presence was significantly higher in the blended courses compared to the online courses. However, there were no significant differences between online and blended courses for the other CoI constructs (i.e., teaching presence, cognitive presence, and learning presence).
RQ3. Multiple linear regression analyses were applied to the data to predict each of the original CoI constructs (i.e., teaching presence, social presence, and cognitive presence) from the model of predictor variables: learning presence measures (i.e., self-efficacy, self-regulation, and motivation); course format (i.e., online compared to blended), format preference (i.e., online, hybrid, and face-to-face), and course assignment usefulness. Significant regression equations were found in the regression model for each of the CoI constructs (see Table 4). The model of predictor variables explained 39% of variance in teaching presence, 51% of variance in social presence, and 62% of variance in cognitive presence.
For teaching presence, only one measure of learning presence, (i.e., students’ levels of motivation) was found to be a statistically significant predictor. The higher the participants’ level of motivation, the higher their level of teaching presence.
For social presence, two measures of learning presence (i.e., students’ level of self-regulation and motivation), students’ levels of preference for the online format, and students’ perceptions of the usefulness of course assignments were statistically significant predictors. Higher levels of these variables predict higher levels of social presence. In addition, students in the blended course format scored statistically significantly higher in levels of social presence compared to students in the online format.
For cognitive presence, two measures of learning presence (i.e., students’ level of self-regulation and motivation) and students’ perceptions of the usefulness of course assignments were statistically significant predictors. Higher levels of these variables predict higher levels of cognitive presence.
RQ4. Multiple linear regression analyses were applied to the data to predict learning presence from the model of predictor variables: CoI constructs (i.e., teaching presence, social presence, cognitive presence); course format (i.e., online compared to blended), format preference (i.e., online, hybrid, and face-to-face), and course assignment usefulness. A significant regression equation was found in the regression model for learning presence (see Table 5). The model of predictor variables explained 58% of variance in learning presence.
For learning presence, one CoI construct (i.e., cognitive presence), students’ levels of preference for the hybrid format, and students’ perceptions of the usefulness of course assignments were found to be statistically significant predictors. Higher levels of these variables predicted higher levels of learning presence. In addition, students in the blended course format scored statistically significantly lower in levels of learning presence compared to students in the online format.
With the prevalence of online education and a shift towards online learning, the number of courses offered in online or blended formats is increasing at universities. Extensive research on online or blended learning suggests that students could outperform those of face-to-face courses provided a sound pedagogy and learning design are implemented (Means, Toyama, Murphy, Bakia, & Jones, 2009; Zhao, Lei, Yan, Lai, & Tan, 2005). In addition to instructional design and pedagogy, learner role is an integral determinant of students’ academic success (Shea & Bidjerano, 2010). The enhanced community of inquiry (CoI) framework (Shea & Bidjerano, 2010) demonstrates a multifaceted relationship among teaching presence, social presence, and learning presence, all of which lead to cognitive presence. Furthermore, the evidence indicates that self-regulation, which allows students to set goals and manage their time, is an important factor in their academic success (Zimmerman, 2008).
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