Interactions and Outcomes in an Online Course for Environmental Educators

Online courses have become key tools in professional development for environmental educators. Course designers seek innovative approaches to expand course outcomes beyond content acquisition. One such approach uses the course web platform to stimulate interactions among participants, as well as between participants and instructors. However, scholars and educators know little about the interactive processes of environmental educators' online learning and how those processes influence learning outcomes. In this study, the researchers addressed this knowledge gap by analyzing three types of participant interactions and their relationships to learning outcomes in an online course on urban environmental education.

Environmental Education in Urban Communities is a 12-week professional development course offered online by Cornell University as part of EECapacity, a U.S. Environmental Protection Agency (EPA)-funded professional-development program for environmental educators in North America. The course's learning goals are to: (1) learn diverse methods and outcomes of environmental education in an urban context, (2) understand the ways environmental education research can enhance practice, (3) develop and exchange (among participants) research-based activity plans that can be used in practice with various audiences, (4) contribute to developing the (new) national Guidelines for Excellence in Environmental Education (North American Association for Environmental Education [NAAEE], 1994– 2004), and (5) develop a peer network of urban environmental educators.

The researchers, two of whom were also course designers and instructors, selected 25 educators (5 males and 20 females) to participate in the course. Criteria for selection included geographic representation across the United States, interest and expressed commitment, and potential to bring diverse perspectives to the discussion on urban environmental education. Participants represented a range of experience working in education: they had between 2 and 30 years of experience, with a mean of 12 years; and they represented a variety of institutions, including community organizations, nature centers, city park departments, K–12 schools, and colleges/universities. Overall, 24 participants completed the course.

The course required certain online interactions and encouraged other interactions as well, including discussing research findings among participants, commenting (by peers and instructors) on course assignments, drafting guidelines for environmental education in urban areas, and exchanging lesson plans drafted by participants. Participants completed all assignments individually, with the exception of one group exercise on guideline development, which they completed in a small group. Weekly assignments consisted of videos and readings about environmental education in cities, environmental justice, audience diversity, behavior change, and the Guidelines for Excellence. Every week, participants were required to post reflections on how course materials related to their own practice and leave at least two comments on their peers' posts. The instructors also commented on participants' posts.

Researchers collected participants' posts and comments throughout the course, and they conducted a content analysis of the data. They coded for three types of interactions: (1) participant–participant, (2) participant–content, and (3) participant–instructor. They also coded for four outcomes: (1) participant motivation to learn, (2) intent to adapt ideas and information in their own practice, (3) actual adaptation in their practice, and (4) professional network development. The researchers coded the data per participant and per week, and they used regression analysis to build models that established (non-causal) relationships between interactions and outcomes. The models defined “interactions” as the independent variables and “outcomes” as the dependent variables. They also included other variables that might influence the results, such as frequency of receiving comments on posts from the previous week, number of years of experience in environmental education, and course progress (e.g., week two of the course), among others.

The results showed that participant–participant interaction was significantly and positively associated with “participant motivation to learn” and “professional network development.” The positive correlations are congruent with the literature that recognizes interactions among participants as conducive to enhanced content learning. However, participant– participant interaction was not associated with “intent to adapt ideas” or “actual adaptation of ideas.” This negative finding was surprising, as it is counter to the EECapacity program's foundational hypothesis that innovations in environmental education derive from educators networking and exchanging knowledge.

Researchers also found positive correlations between participant–content interaction and “motivation to learn,” “intent to adapt ideas in practice,” and “actual adaptation of ideas.” This finding highlighted the importance of attention to content, even in online courses that focus on interactive social learning.

Participant–instructor interaction was significantly positively associated with only one outcome, “professional network development,” which is consistent with previous studies of online courses that examined interactions using network analysis. This result may also be due to the pedagogical style of the instructor, who actively encouraged participant– participant interactions.

Analyses revealed that participants were more motivated to learn during the first few weeks of the course, probably due to the initial excitement and curiosity about each other's programs. In contrast, participants' intention to adapt ideas into practice was higher toward the end of the program, which is intuitive, as adaptation takes time. The time-rank results showed that the later a participant posted on the course website, the higher his/her “motivation to learn,” potentially because participants who posted later in the week were more likely to be influenced by others' posts. Finally, participants with more work experience were less likely to adapt new ideas than their less-experienced peers.