A National Research Model for Online Learning
By Tanya Joosten
In developing the grant proposal for the U.S. Department of Education’s Fund for Improvement in Postsecondary Education (FIPSE), several researchers from the University of Wisconsin-Milwaukee (UWM) spent a Friday afternoon discussing the types of research projects we would propose to be conducted by the new National Research Center for Distance Education and Technological Advancements (DETA). What became clear in that meeting room was evidence of a broader issue in distance education research. Individuals who are studying distance education, including eLearning, blended learning, and online learning, are heterogeneous. These individuals represent an array of disciplines, including different paradigmatic, theoretical, and methodological approaches to studying distance education, just as we were witnessing in the room that day. The opportunity of this diversity in research approaches has the potential to provide our higher education communities a greater understanding of the complexity of human interaction in distance education. The opportunity identified also presented a new problem to solve – we don’t all speak the same language about research in distance education. Evident from this discussion was a need for coherency about how to approach the study of this phenomenon.
In distance education, a common language or ground has not yet been established. Although existing scholarship attempts to establish an identity for teaching and learning on the fringe or margins (see Moore, 2013), such as distance education, there is still much work to be done. It is common in other disciplines to struggle with finding this common ground as well (e.g., Corman & Poole, 2000). Yet, unlike many other disciplines that have models illustrative of the phenomenon of interest or research models that guide the design of research, distance education has seen little traction in this area. A cohesive approach to researching distance education from a transdisciplinary lens is pertinent.
The lack of common language and work being conducted in disciplinary silos has led to a disregard or lack of acknowledgement of previous developments in the field. Furthermore, the disconnect many times between the fast moving development of practice and redundant research of already proven practices is less than helpful to developing distance education. Several authors over the last several years have noted this dilemma. Saba (2013) discusses that “authors, editors, and reviewers are not familiar with the historical origin and conceptual growth of the field of distance education…history starts from when they become interested in the field” (p. 50). Dziuban and Picciano (2015) refer to Roberts (2007) and Diamond (1999) in describing this as a type of amnesia where “we tend to trust what we have seen for ourselves and dismiss events that have occurred in the distance past…we forget anything but what we are experiencing at the moment and assume that the present is a way it has always been” (p. 179). Moore and Kiersey (2011) have discussed this tendency as a threat to good practice and good scholarship.
Our initial goal, as outlined in the grant, is to solve this problem and create a language that will have sustainability across disciplines and temporal barriers. At least in the first year, it was apparent that there was a need for grant efforts to focus on creating a language we can all understand as well as to engage distance education stakeholders from across the country in the attempt to create an interdisciplinary lens for examining distance education. In so doing, the aim is to facilitate research efforts regarding cross-institutional distance education research as a strategy for ensuring quality in teaching and learning for all students. The research fellows on the grant team felt a desire to identify a model or models that represented research in distance education, in particular, with regard to the research that would be conducted as part of the grant activities. Moreover, the development of a framework of inquiry that included detailed representations, which illustrates the varying levels of inquiry as characterized by input-throughput-output processes facilitating an interdisciplinary approach to studying distance education, was needed as well as research models.
|Goal 1: Develop National Distance Education and Technology Advancements (DETA) Research Models for Online Learning|
The first goal of the grant activities is to develop research models for online learning that provide guidance in the practice of distance education research. The models were intended to facilitate the exploration of instructional practices, inform future instructional practices, serve as a model for future research practices across educational institutions, and enhance consistency in the field. In the development process, it became clear that a more general research model was needed to represent the various research designs that would be deployed as part of the DETA research efforts rather than several specific research models. The development of this model included the following steps:
- Review of the literature on desired outcomes in distance education, including blended and online research, to determine key desired outcomes in practice and research in the field.
- Identify and engaging with national experts, including researchers and practitioners, in the field to identify pertinent research questions and variables of interest for enhancing the understanding of the desired outcomes.
- Review germane research and current national efforts to ensure alignment with the development of research model and the framework of inquiry, including identifying any gaps and future areas of research needed.
- Create research designs, including formulating measures, instrumentation, and coding to conduct cross-institutional research within the framework of inquiry.
- Develop a research model for online learning appropriate for interdisciplinary research and diverse methodologies to be brought to fruition in the development and use of research toolkits by researchers and practitioners across the country.
The National DETA Research Model for Online Learning
Prior to the DETA national summit, held at the 2015 EDUCAUSE Learning Initiative (ELI) meeting, the DETA Research Center reviewed pertinent literature and documents in developing the desired outcomes (see http://uwm.edu/deta/desired-outcomes/). These desired outcomes were published on the DETA community site and feedback was solicited from the national experts who participated in the summit. The desired outcomes guiding the activities at the DETA national summit are also appended.
Participants at the DETA national summit (see http://uwm.edu/deta/summit/) were asked to participate in two key sets of activities related to developing and prioritizing research questions and the process of creating a framework of inquiry to guide current and future research by identifying key variables for research model.
The research questions and associated votes were statistically analyzed for prioritization. The top research questions were identified by highlighting those that were one standard deviation at or above the mean. The top research questions can be viewed at: http://uwm.edu/deta/top-research-questions/. Additionally, the variables were examined to identify conceptual alignment with existing literature and to sort based on level of inquiry, which resulted in the framework of inquiry (see Figure 1, General Framework of Inquiry). The detailed version of the framework of inquiry, including variables, can be viewed here.
Figure 1, General Framework of Inquiry
Situated within the framework of inquiry, several research designs were created, including formulating measures, developing instrumentation, and coding to conduct cross-institutional research within the framework of inquiry. These research designs included experimental and survey study designs to address the top research questions. Experimental designs included interventions identified for testing that burgeoned from discussions at the DETA national summit. Survey studies and instrumentation (applicable to both survey and experimental studies) were developed from existing research at UWM and a review of the literature, including utilized instrumentation. Survey studies included questions to gather qualitative data for analysis to address research questions of exploratory nature. Both the survey and experimental research designs are complemented by data mining of student information systems to provide learner characteristics (low-income, minority, first generation, and disabled) and outcome data (grade, completion).
Taking a structured approach to model development, a research model for online learning appropriate for interdisciplinary research and diverse methodologies was derived from a grounded and theoretical approach (see Figure 2, Developing Research Model of Online Learning). The model is considered grounded because it is a reflection of the research questions, framework of inquiry, including variables, and research designs developed as part of the grant activities. The model is considered theoretical since social and learning theories inform the development.
Figure 2, Developing Research Model of Online Learning
There are four primary components that compose the research model for online learning. The four components include (1) inputs and outputs, (2) process, (3) context, and (4) interventions. The inputs and outputs include both agency and structural level inputs. Agency level inputs include students (learners) and instructors. Structural level inputs include the characteristics of the course, instruction, and the program that provide structure, rules, and resources to agents to facilitate online learning process. The second component is the process, which includes in-class and out-of-class interactions that are online learning. The third component is that of the context. The context for the research of this grant is institutions of postsecondary higher education. Although much learning may happen in informal settings, it is not a focus of this model. The final component of the model is intervention. Interventions create variable conditions intended to result in a predetermined outcome, usually to increase student success.
There are three facets of the model that describe the relationship between and among the components of the model. First, the model is cyclical in nature in that learning is conducted in cycles with each end playing the role of input and output through an interactive process representing a continuous lifecycle of online learning. Second, the model is transactional. This means that online learning is a simultaneous engagement of students and instructors in the learning process. Students and instructors are linked reciprocally. Third, the model can be structurational. Courses, instructional, and program characteristics are outcomes from human action (instructors and staff) in design, development, and modification. Also, these facilitate and constrain student interactions in online learning. Furthermore, institutional properties influence individuals in their online learning interaction through instructional and professional norms, design standards, and available resources. Likewise, the interactions in online learning will influence institutional properties through reinforcing or transforming structures.
The proposed model describes a series of inputs that can have a relationship with online learning, which is a throughput or process, inside and outside the classroom within the contexts of institutions. For DETA research the institutional context is postsecondary institutions of higher education. The cyclical elements of the model are evident in the inputs, including the characteristics of students, instructors, course as well as instruction, and programs, may influence the online learning process, which, in return, will influence future inputs of online learning process in a cyclical fashion. For instance, a course is designed by an instructor in such a way that it leads to increased rates of completion, which eventually can alter the program profile and potentially future course designs. Therefore, the inputs will influence the online learning process, which will in return influence the inputs through a feedback loop process. For example, students may become more confident and have a greater growth or mindset for achievement in future courses, instructors may learn from what works in the classroom and improve future instructional methods and course designs, and programs may have greater success. Not only is there a lifecycle of online learning, but an important interplay between the success of students in a course and the continued development of courses and programs by instructors and staff within the institution.
There are individual agents in the model, including students and instructors, that have characteristics of which have a relationship with online learning. First, these students and instructors are agents within the context of institutions but have influences from beyond the institution, too. The cognition and experiences (from within and outside of the institution) of students and instructors will potentially affect online learning interactions within and outside a class. Second, there are also course, instructional, and program characteristics. The design of these, in particular, will have a relationship with and potentially enhancing or hindering the process of online learning. These five inputs will have relationships with the online learning process.
Interventions can be employed at any level of these input variables in order to enhance the probability that the online learning process will be positively influenced. Interventions can be at the agent level to develop students or instructors, or at the course, instructional, or program levels to potentially improve the interactions of students and instructors to enhance online learning. At the learner level, an intervention may be a workshop about taking an online course. At the instructor level, an intervention may be a faculty development program for teaching online. At the course and instructional level, an intervention may be focused on how content is designed to meet the course learning outcomes to enhance the student-content interaction. At the program level, an intervention may be the receipt of tutoring support during the course. Interventions at the agent or structural levels are intended to increase student success by enhancing online learning.
The model represents an array of research designs, including experimental, quasi experimental, survey, and qualitative appropriate for DETA research. Input variables, such as student or course characteristics, can be mined through institutional technology systems, such as student information systems, or can be reported on surveys. This information can be used for all research designs. Experimental or quasi experimental studies would focus on comparisons of the control and experimental condition based on the intervention applied usually through the comparison of student assessments. Survey studies can examine the ability to predict student outcome variables based on the student self-report of instructional and program/institutional characteristics including reports of behaviors taking place or perceptions of in-class and out-of-class. Finally, qualitative data can be collected through surveys and other methods to better understand or develop measurement for an array of constructs (e.g., student motivation, ecosystem components).
This research model and the associated toolkits serves to guide research conducted across institutions and disciplines, including both experimental and survey studies. The DETA Research Center will disseminate a call for proposals in the grant’s second year, October 2015, to identify partners across the country who are interested in using the research toolkits to gather data to better understand the key factors in distance education courses and programs that are impacting student success. Once research has been conducted, an evaluation of the model and toolkits will be conducted to improve the quality of the grant products for dissemination in the final year of the grant.
Corman, S. R., & Poole, M. S. (2000) Perspectives on organizational communication: Finding common ground. Guilford Press.
Dziuban, C. D., & Picciano, A. G. (2015). What the future might hold for online and blended learning research. In Dziuban, C. D., Picciano, A. G., Graham, C. R., & Moskal, P. D. (Eds). Conducting research in online and blended learning environments.
Moore, M. G. (Ed.). (2013). Handbook of distance education. Routledge.
Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Cengage Learning.
Orlikowski, W. J. (1992). The duality of technology: Rethinking the concept of technology in organizations. Organization science, 3(3), 398-427.
Saba, F. (2013). Building the future: A theoretical perspective. In Moore, M. G. (Ed.). Handbook of distance education. Routledge.
Analysis of Summit Data: