In face-to-face courses, student motivation is associated with academic learning outcomes. What happens when learning moves online? This presentation reports results from a meta-analysis examining the relationship between student motivation and learning outcomes in both online and blended courses. Findings indicate a modest relationship that varies based on course modality.
In face-to-face (F2F) courses, there is strong evidence supporting a relationship between college students’ motivation and their academic success. In one impactful review (Richardson et al., 2012), modest to strong correlations were found between students’ GPAs and a range of motivational constructs including academic self-efficacy, grade goal, and performance self-efficacy. These results have implications for F2F course design, as well as for student academic supports.
Within online and blended learning, however, only a subset of motivational constructs related to student success have been thoroughly reviewed, such as self-efficacy in online learning (Alqurashi, 2016), online math (Hodges, 2008), and computer-based learning (Moos & Azevedo, 2008). These systematic and narrative reviews cover a narrow range of motivational measures and have broad definitions of learning that often involves self-report measures. To date, there is no comprehensive review of the relationship between student motivation and objective learning performance measures in both online and blended learning environments.
To address this gap in the literature, we conducted a meta-analysis to answer the following three research questions:
RQ1: What is the relationship between students’ overall motivation and learning performance in online and blended learning environments?
RQ2: How does this relationship vary based on the type of motivational construct that is measured (e.g., self-efficacy, locus of control, goal orientation)?
RQ3: Does this relationship differ between online and blended learning environments?
In this educational session, the presenter will share our method for conducting this meta-analysis and summarize findings from our three research questions. The presenter will then discuss with attendees implications of this research for different stakeholder groups in online education: student success teams, faculty, instructional designers, and online program administrators (e.g., department chairs, deans).
Learning objectives:
By attending this session, attendees will able to:
1. Describe key steps in conducting a meta-analytic review
2. Discuss the relationship between motivation and learning performance in online students
3. Consider ways in which the relationship between motivation and learning performance might be affected by measurement, learning environment, and other external factors (e.g., students’ individual circumstances)
In what follows, we briefly describe the methods and results of our study, as well as an interaction plan to engage attendees.
Study method:
Literature Search Procedure & Inclusion Criteria
The search process included searches of all recommended education databases, including Education Source, ERIC, Wiley Online Library, Proquest, Psychinfo, Psychological and Behavioral Sciences, Science Direct, and Scopus. We looked for motivational constructs and blended or online learning from 1990-2023 using the OLC definition (Mayadas & Miller, 2014) as coding criteria specifically for blended learning. We excluded COVID-19 studies because the impact of the pandemic on online teaching and learning could have influenced study results.
Literature Search Results
Our initial search yielded 7,718 articles. After exclusions, 47 articles with 82 outcomes were found to meet all inclusion criteria. Many studies were excluded because they mentioned but failed to measure motivation, used a non-learning or performance outcome, or failed to meet the criteria for online or blended learning.
Procedures
Effect sizes were largely Pearson’s r (n = 56), point-biserial (n = 11), combined outcomes (n = 4), imputed from beta coefficients (n = 3) and other correlations (n = 6); none were standardized. We used Fischer’s Z transformed values before engaging in all analyses and kept transformed values for reporting of results.
Results:
RQ1: The relationship between overall motivation and learning performance in online and blended courses
There was a considerable range of correlations from a standardized value of -0.66 to 0.78 with an overall effect that was relatively small (r = .12). To be clear about how small this relationship is, across all findings, student motivation is able to explain about 1% of the variability in learning outcomes. However, given the significant amount of variability in effect sizes, further analysis by motivational construct was necessary.
RQ2: Variations based on motivational constructs
As expected, there was a range in effects sizes when grouped by motivational construct. The largest effect sizes were in measures of intrinsic and extrinsic motivation (r = .21). However, these constructs were measured in only a small number of studies (n = 9 ). This was true of other motivational constructs, such as internal (r = .09, n = 5) and external locus of control (r = -.15, n = 5). The majority focused instead on learning self-efficacy ( r = .14, n = 26) and technology self-efficacy (r = .07, n = 17). Nevertheless, there was clearly a much larger range of motivational constructs in the existing literature than reported in prior reviews.
RQ3: Difference in course modality
The overall relationship between student motivation and learning performance did vary by course modality. This relationship was strongest in blended learning courses (r = .17) compared to online courses (r = .09).
Impact and Interaction plan:
Overall findings support a relationship between motivation and learning performance in blended and online higher education courses. However, effect sizes are small, variable, and contingent upon course modality. These findings raise further questions about the role of motivation in students’ learning journeys, particularly in courses that are designed to be online only. Additional questions are raised about the extent to which student support systems should target motivation, or if there are other factors that might impact the relationship between motivation and learning performance in online students.
To engage attendees, the presenter will facilitate three think pair share exercises, one at the start of the presentation, and two at the end of the presentation. These interactions are planned to activate participants’ experience, apply their understanding, and reflect on the findings from this study to their research, teaching, and administration. At the start of the presentation, attendees will be asked to discuss in pairs the importance of motivation with respect to student engagement and learning outcomes. Team ideas will then be shared in a brief, full group discussion. At the end of the presentation, attendees will be asked in pairs to discuss what these findings might mean, and how they can apply these findings to work at their institutions (e.g., Why do you think there are differences based on course modality? What other factors might impact student motivation?). Pairs will then share their thoughts in full group discussion.
References:
Alqurashi, E. (2016). Self-Efficacy In Online Learning Environments: A Literature Review. Contemporary Issues in Education Research (CIER), 9(1), Article 1. https://doi.org/10.19030/cier.v9i1.9549
Hodges, C. B. (2008). Self-efficacy, Motivational Email, and Achievement in an Asynchronous Math Course. The Journal of Computers in Mathematics and Science Teaching, 27(3), 265–285.
Moos, D. C., & Azevedo, R. (2008). Monitoring, planning, and self-efficacy during learning with hypermedia: The impact of conceptual scaffolds. Computers in Human Behavior, 24(4), 1686–1706. https://doi.org/10.1016/j.chb.2007.07.001
Mayadas, F., & Miller, G.E. (2014, September 14). Updated e-learning definitions. Online Learning Consortium. https://onlinelearningconsortium.org/updated-e-learning-definitions/
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838