Vollmeyer & Rheinberg 2006

Vollmeyer, R. & Pheinberg, F. (2006). Motivation effects on self-regulated learning with different tasks. Educational Psychology Review, 18, 239-253.

Problem
Vollmeyer and Rheinberg presented a cognitive-motivational process model that presents factors of initial motivation, collected mediators that influence initial motivation on performance, and found evidence to support different learning outcomes. Four factors influence initial motivation: probability of success, anxiety (fear of failure), interest, and challenge (importance or task value). In the first study, research consisted of a comparison between learning in a linear design setting and learning in a hypermedia environment. In the second study, researchers measured the performance using data from university students.

They hypothesized that initial motivation (interest, challenge, probability of success, and anxiety) would influence performance on four mediating variables, specifically Flow.

Vollmeyer and Rheinberg used several measurement tools. They constructed the Questionnaire on Current Motivation or QCM, designed to capture learners’ current motivation after being instructed to complete a task. The QCM produced Cronbach’s alphas that fell between 0.68 - 0.90 for probability of success, interest, anxiety, and challenge.

The researchers identified four mediators: time on task, learning objective, motivation, and flow.

Flow state balances challenge and skill and merges action and awareness. Flow provides unambiguous feedback and time transformation. Flow includes concentration and fluency of action. The Flow Short Scale (FKS) that considers all six characteristics of Flow. This instrument uses only ten items and takes about 40 seconds to complete. The FKS’s reliability is 0.90. The FKS can be combined with the Experience Sampling Method (ESM). Participants are paged in ESM studies, and are asked to stop what activity they are doing and answer scales.

Methods
Vollmeyer and Reheinberg used small real classrooms. Learners participated in two phases: a learning phase and an application phase. Participants changed an input variable and observed what happened to an output variable. Learners had to demonstrate knowledge about the system and answer the motivational questionnaire.

A path analysis was used to analyze the process through a structural equation model. The researchers combined three (interest, challenge, probability of success) of the four variables into a latent variable, eliminating anxiety because it does not share a relationship with other motivational factors.

The second study used university students. In a French class, students reported their initial motivation with all four factors before class, and reported their FKS during class. At the end, the students subjectively rated how much they learned. Students took an exam at the end of the semester.

Researchers collected students’ math grade and age from Psychology students. Students reported the strength of their intention to learn statistics. Near the end of the semester, researchers told to solve a statistics task. They administered the QCM and FKS. Number correct on the final exam served as a performance measure too.

Results
Learners with high initial motivation had a more systematic strategy, higher motivation, and consequently larger knowledge acquisition. Feedback was provided at regular intervals. The path from Round 3 to goal achievement was 0.63, and the path from motivation state to goal achievement was 0.22.

After learning of a topic, participants had rated interest, probability of success, challenge, and anxiety. Participants rated their motivation during their time with the hypermedia materials. Researchers found that the motivation in the hypermedia did not vary much.

In the French study, the path from Flow to final exam was 0.31, and in the statistics study, the path from Flow to final exam was 0.21.

Discussion
Students interested, challenged, and believing that they will be successful, come up with better strategies. They enjoy learning more too. Participants became annoyed with the interruptions when prompted to rate the QCM. Researchers noticed scores varied more on the initial motivation measure than on the during activity measure, indicating that the experience or feedback can influence motivation. knowledge and motivational state influence goal achievement.

Critique
This article seemed to jump around a bit. They spent a lot of time setting up the research, while simultaneously presenting their own, making it difficult to follow. Sections are clearly defined for constructs and ideas. The authors also assume that the reader has a good understanding of path analysis.

A few problems occurred in the hypermedia environment. Because participants did not follow a linear line of events, researchers had to find a way to create accurate comparisons with the other group. They tried using linking between sequential pages and time per page. But this did not provide an accurate picture of what participants read.

Theoretical and Practical Implications
The analysis combined all three variables into a latent variable. Analyzing variables separately would have been more interesting. Patterns of motivational factors need to be taken into account too. This study also suggests combining factors to make accurate comparisons. This information is useful for teachers because they can design lessons with timely feedback. Teachers also have a better knowledge of anticipating when motivation will fall.

Vollmeyer & Rheinberg 2006 download