Examining the Effectiveness of Achievement Goal-Based Personalized Motivational Feedback in Online Learning

Huanhuan Wang, Purdue University

Abstract

Current online learning approaches are sometimes criticized for a “one- size- fits -all” approach, low levels of interactivity, and insufficient feedback, which may result in low levels of learning satisfaction and high dropout rates. To mitigate these shortcomings, this study proposed a set of rules to design personalized motivational feedback based on students’ personal achievement goals. The researcher expected this specially designed personalized feedback to be able to improve student motivation and learning outcomes.To examine the effectiveness of such feedback, an explanatory mixed-methods study was implemented, which included two consecutive phases. The first phase was a quasi-experimental study. A 2018 online master’s degree program course offered by a large R-1 University in the U.S. served as the study context. Twenty-eight students were selected as the test group where personalized motivational feedback based on the proposed rules was delivered along with regular instructor feedback. Another forty students were selected as the control group who only received regular instructor feedback. Students’ motivation and perceived satisfaction were measured by using pre and post surveys. Students’ learning performance was measured by using the collected assignment scores after the semester ended. The second phase was a set of post interviews, in which 13 students from the two groups were asked about their perceptions of the impact of the feedback they received and how they used feedback in their learning process during the study.In the first study phase, ANCOVA F test results indicated the post-test scores of learner motivation and perceived satisfaction in the test group were significantly higher than those of the control group. The mean value of the cumulative assignment scores in the test group was somewhat higher than that of the control group, but this difference was not statistically significant based on the results of Wilcoxon Two-Sample test and ANCOVA F test. In the second study phase, the post-interviews showed that students in the test group expressed more consistently and strongly that they had an overall positive perception of the feedback received in the course. The participants from the test group further explained the underlying mechanism of this personalized motivational feedback was that it affected students’ learning positively by helping them set and regulate learning goals, activate self-regulation mechanisms, and adjust their learning behaviors.Based on the results and the features of the study design, the researcher concluded that the personalized feedback designed by following the set of rules proposed in this study has the potential to improve learner motivation in the online learning context. While its effect on learning outcomes was not significant, the researcher speculated that learning outcomes might have been affected by more complex factors, such as ceiling effects and predominant class structures.The researcher suggested online instructors and instructional designers consider students’ achievement goals when conducting learner analysis and creating learner profiles. She also suggested developers of next-generation LMSs include achievement goals in the learner model and include such rules in a personalization mechanism. One primary limitation of this study was that a ceiling effect on learning performance emerged leading to insufficient variation for the researcher to detect a statistically significant difference in learning performance. Therefore, the researcher suggests future researchers in this area replicate this approach by using automated feedback delivery tools and consider employing personalized feedback in different types of classes and using specific instructional approaches, such as problem-based learning and competency-based learning. Future research should also consider achievement goal’s mediating factors, such as students’ self-regulation skills, in learner analysis.

Degree

Ph.D.

Advisors

Lehman, Purdue University.

Subject Area

Higher education|Instructional Design|Cognitive psychology|Design|Education|Educational administration|Educational technology|Management|Pedagogy|Psychology

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