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Save the Dates

22st Annual OLC International Conference
November 16-18, 2016 | Orlando, Florida | Walt Disney World Swan/Dolphin Resort

OLC Innovate 2016 - Innovations in Blended and Online Learning
April 20-22, 2016 | New Orleans, LA | Sheraton New Orleans Hotel

Course Design From the Learner's Point of View Through the Use of Intensive Longitudinal Methods

#Twitter: 
#blended20965
Presenter(s)
Kristine Manwaring (Brigham Young University, USA)
Lisa R Halverson (Brigham Young University, USA)
Curtis Henrie (Brigham Young University, USA)
Charles R. Graham (Brigham Young University, USA)
Robert Bodily (Brigham Young University, USA)
Session Information
July 9, 2014 - 11:20am
Track: 
Blended Models and Course Design
Major Emphasis of Presentation: 
Research and Evaluation
Institutional Level: 
Multiple Levels
Audience Level: 
All
Session Type: 
Information Session
Location: 
Plaza Ballroom F
Session Duration: 
50 Minutes
Session: 
Information Session 6
Virtual Session
Abstract

We will explore the use of Intensive Longitudinal Methods to gain insight into learners' experiences with Blended Learning and to inform course design.

Extended Abstract

In order to improve the design of blended learning courses, it is important to understand the learning experience from the point of view of the learner. Blended learning courses require the learner to navigate between different learning contexts, each requiring the learner to employ distinct cognitive, emotional and social strategies in order to engage with the course content. Bliuc, Goodyear and Ellis (2007) report a deficiency of research into how blended learning experiences are integrated into a meaningful course experience from the learner's perspective. Intensive Longitudinal Methods, increasingly used in fields such as Psychology, Behavioral Medicine and Education, allow researchers to obtain, rich, individual-level, "in-the-moment," experiential data (Hektner, Schmidt and Csikszentmihalyi 2007). The use of these methodologies in blended learning research will expand our understanding of how individual learners experience the entirety of blended learning courses. In this session we will:

1. Give background information on Intensive Longitudinal Methods, including the basic protocols for data collection and statistical analysis.
2. Explore what the use of these methodologies can contribute to the design of blended learning activities and course design.
3. Share the results of our first pilot of using these methods and our more complex design for a second pilot.

Intensive Longitudinal Methods (ILM), also known as "intensive repeated measures in naturalistic settings" (Conner & Mehl, 2012, p.xxi), are methods of research that involve repeatedly collecting the same data from an individual, over-time and within the context of lived life. The data collection focuses on the experience of the psychological construct of interest as well as the context within which it is being experienced - all from the point of view of the individual. By capturing "real-time" data, on life as it is actually lived, for a group of individuals, change processes can be more fully understood in a particular context. The statistical analyses that can be done with this type of repeated measures data reveal both with-in subject variance and patterns, as well as between-subject variance and patterns. This type of research facilitates a deeper understanding of how individuals navigate certain situations (at both the activity and course levels) across time and how their experiences are connected to certain individual characteristics and outcomes. In this presentation we will give a more complete description of Intensive Longitudinal Methods, along with discussing data collection and the types of statistical analysis needed for this type of data.

ILM research is well suited to the blended learning environment. Bliuc, Goodyear and Ellis (2007) in their review of the research on blended learning suggest that there is a need for "more studies in how different aspects of blended learning are interrelated" (p.242). They further recommend that research needs to be holistic, including learners' experiences across the different learning environments. Hektner, Schmidt and Csikszentmihalyi (2007) claim that that ILM research is particularly suited to educational environments because "the researcher is able to link variations in attention, interest, or challenge to specific instructional practices or conditions…" (p.229) In addition, the data collected is richer and more accurate because it is collected "in-the-moment" and at the activity level, as opposed to asking students to recall their experiences and feelings at the end of a course. The application of ILM to blended learning allows us to answer questions such as: How do learners vary in their experiences of a blended learning course? How does learner engagement (or interest or challenge) ebb and flow over time according to certain types of learning contexts and activities whether they be face-to-face or online? How much variance in experience is there between students? Do students have different engagement patterns based on specific learner characteristics? How do certain learning activities and contexts impact the overall course experience? How does the subject matter or sequencing of specific learning activities affect the patterns of engagement? As these questions indicate, and our presentation will make clear, by repeatedly collecting learner experience data from individuals over the duration of a blended learning course, and using the correct statistical analysis, we can learn much that will have a direct impact on blended learning course and activity design.
Using ILM, our current research investigates learner engagement over the duration of a blended learning course. We are collecting commensurate data from students while they are attending face-to-face sessions as well as when they are working on online projects and learning experiences. Through ILM, we are gaining a better understanding of how engagement varies across a course for individual learners. We are also able to see how individuals in the same course vary in their engagement from each other and we are beginning to link these variable patterns of engagement to both specific learner characteristics, types of learning activities, and eventual course outcomes. In addition, we are collecting online log data through our LMS in order to understand the relationship between their online activity and their self-reports of engagement. In this presentation we will report our initial findings from this preliminary pilot as well as describe our research design for a more complex ILM research project in the fall 2014.

We believe ILM will help us gain a richer understanding of how learners navigate and integrate the discrete components and learning environments of blended learning courses. This knowledge will lead to better blended model selection and course design.

References:
Bliuc, A., Goodyear, P. & Ellis, R. (2007). Research focus and methodological choices in studies into students' experiences of blended learning in higher education. The Internet and Higher Education, 10, 231-244.

Conner , T.S. & Mehl, R. M. (2012). Preface. In T.S. Conner & R.M. Mehl (Eds.) Handbook of research methods for studying daily life (pp. xix-xxiii). New York, N.Y.: Guilford Press.

Hektner, J.M., Schmidt J.A., & Csikszentmihalyi, M. (2007). Experience Sampling Method. Thousand Oaks, CA: Sage Publications.