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Doctoral Research Statement

My research focuses on motivational design in online learning to motivate students from the designers’ perspective. For now, I have two directions with my research interests:

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Direction 1: Exploring motivational strategies in online higher education. I assume that motivation is an essential element in the online learning context, so instructional designers will consider motivation when designing online courses. Based on my assumptions, the research questions would be:

1. What is the most effective motivational design in online courses?

2. How do instructional designers apply motivational strategies in the course design?

3. How does motivational design affect students’ motivation in online learning?

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Direction 2: Exploring why instructional designers do not value motivation in the design process. My assumption for this direction is that instructional designers do not value motivation and have made decisions with their priorities when designing online courses. So the research questions would be:

1. What factors and what reasons contribute to instructional designers devaluing motivation in the course design? How do these factors differ from those designers who prioritize motivation?

2. What are those instructional designers’ priorities in designing online learning materials?

3. What impact of the instructional design designed by designers who do not value motivation on students’ online motivation?

Related Literature

Theories and models in motivation

        Broadly, motivation is the desire to start behaviors. In the educational context, students’ motivation describes that students invest attention and efforts in various pursuits, which is connected to their willingness to engage in the learning environments (Brophy, 2011).

        There are various theories and models of motivation, each with a different focus. Self-determination theory (SDT) was introduced and developed by Ryan and Deci (1985), who suggested that individuals will be motivated if the three elements of SDT, which are autonomy (feel agency), relatedness (feel connected), and competence (feel competent with tasks), are satisfied. They propose that there are three components to motivation: intrinsic motivation (doing something for enjoyment), extrinsic motivation (doing something for specific outcomes), and amotivation (lack of intensity to act).

        The expectancy-value theory (EVT) was first developed to understand kids’ mathematics performance and achievement (Eccles et al., 1983). Further, EVT has adopted that expectancy is individuals’ belief in how well they will do with tasks. The task value refers to the different reasons individuals are acting, including attainment (personal importance), intrinsic (enjoyment), utility (usefulness of the task), and cost (time and effort) (Eccles et al., 1983).

        The ARCS model emphasizes that learning materials should be designed to motivate students (Keller, 1987). The ARCS model has four elements: Attention, Relevance, Confidence, and Satisfaction. Attention represents that learning materials should engage students and attract their attention to learning. Relevance means instructions should be relevant to build the connection between what learners have learned in the classes and how they can use it in the real world. Confidence is that learners will perform better if they gain confidence through learning. For example, learning goals should be clearly stated in instructional materials, and the instruction should allow students to become increasingly independent and practice a skill. Furthermore, satisfaction means learners will be more focused if they feel satisfied with the materials. It requires that there should be unexpected rewards and positive outcomes and also to avoid negative influences through learning (Keller, 1987).

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Motivation for Online Learning

        The topic of motivation in online learning is essential in the field of Learning, Design, and Technology. Learners tend to face more challenges with motivation in online learning environments than in face-to-face education(Chiu et al., 2021). For example, learners may feel isolated from online learning courses (Hartnett, 2016), and they can quickly drop online classes and give up finishing learning tasks (Khalil & Ebner, 2014). Learners’ frustration with technology becomes an issue that will demotivate them from learning (Hara, 2000). Song et al. (2004) also reported that technical issues became challenges and affected students’ satisfaction in online learning environments. Overall, these studies suggest that learner motivation is essential to their success in online learning.

        With the development of online learning, some researchers noticed the connection between motivation and online learning, using different theories as the foundation or theoretical frameworks to explore the relationship between ideas and practice in educational contexts. Researchers have used the ARCS model in web-based lessons and online learning environments to explain how motivational design can increase the completion rate of online courses (Jokelova, 2013; Malik, 2014; Pittenger & Doering, 2010). The ARCS model has also been used to evaluate learners' motivational and cognitive processes with online learning (Huang, 2011). Researchers have also used SDT as the theoretical framework to illustrate how intrinsic and extrinsic motivation affect students’ online learning performance and outcomes, proving that the elements in SDT theory will support students learning so to give some teaching advice (Chiu, 2022; Hsu et al., 2019). The expectancy-value theory is also integrated into online learning, exploring how it could help students be motivated in online lessons, assignments, and online communities. (Eccles & Wigfield, 2001; Loh, 2019; Wu et al., 2021; Xu, 2022). Students will have intrinsic value with scaffolding tasks in learning, and  they also perceive that solid connections between the learning materials and their real life can help enhance value, expectancy, and interest in learning.

 

Instructional Designers’ Decision Making

        Despite the popularity of motivation theory in the field, there is an ongoing need for research in applying motivation theory in instructional design. Spitzer (1996) once noted how motivation was an overlooked aspect of instructional design. This trend appears to continue today. Mayer (2019) recently noted how our field still has much to learn about the role of motivation in online learning. The shift to online learning during the COVID pandemic has also spurred interest in developing online learning that motivates students (Chiu, 2021; Gustiani & Sriwijaya, 2020). Therefore, I am interested in studying how instructional designers decide when and how to use motivation theory in online learning design and what strategies they use. Instructional designers will apply instructional design models or theories into their practice, and researchers have researched the relationship between instructional design models and how instructional designers make decisions using those models in practice (Christensen & Osguthorpe, 2004; Wedman & Tessmer, 1993). Moreover, research has been conducted to help improve instructional designers’ performance in working. One study was conducted to understand that instructional designers could improve their design efficiency by using instructional design tools, understanding the roles of instructional designers and other members, and embracing the learning technology in their practice (Roytek, 2010). Instructional designers are also suggested to make assumptions using conjecture strategies to analyze the situation of cases before making decisions (Stefaniak et al., 2022).

However, there needs to be more research focusing on deciding if instructional designers value motivation in the design process and how they perceive the role of motivation in the design process, especially for online learning design.

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Research Design

A mixed-method design will fit my research. Mixed-method design will allow me to have several phases for my study to organize how I can get results. The qualitative design will inform me how instructional designers make decisions in the online learning design process. The quantitative data will allow me to gain results with a more significant sample, improving my research's validity. Moreover, the quantitative method will help me understand the relationship between factors that instructional designers consider in online learning design.

 

 

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Reference

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Christensen, T. K., & Osguthorpe, R. T. (2004). How Do Instructional-Design Practitioners Make Instructional-Strategy Decisions? Performance Improvement Quarterly, 17(3), 45–65. https://doi.org/10.1111/J.1937-8327.2004.TB00313.X

Chiu, T. K. F. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(S1), S14–S30. https://doi.org/10.1080/15391523.2021.1891998

Chiu, T. K. F., Lin, T.-J., & Lonka, Kirsti. (2021). Motivating Online Learning: The Challenges of COVID-19 and Beyond. The Asia-Pacific Education Researcher, 30, 187–190. https://doi.org/10.1007/s40299-021-00566-w

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