Readers know that I do my best to follow new research and explore — and experiment — with ways to apply their findings to the classroom (or, in some cases where I believe the process used is ethically questionable and of dubious value,criticize them loudly).

In fact, I publish an annual compilation of what I believe to be the best research each year (see My Best Posts On New Research Studies In 2012 — So Far).

Last year, I also published a post titled “Is This The Most Important Research Study Of The Year? Maybe” It reviewed a meta-analysis finding that direct instruction was less effective than “enhanced discovery learning.” I still believe that it is a very important study.

Though you’ll find a lot of good research in my already-published 2012 summary, I think research that was just released today might be this year’s most important one. It’s titled Predicting Long-Term Growth in Students’ Mathematics Achievement: The Unique Contributions of Motivation and Cognitive Strategies. Access to the full report costs $35, and I purchased it so I could write my own analysis. You can also see a report in Science Daily summarizing its results here — Motivation, Study Habits — Not IQ — Determine Growth in Math Achievement.

The researchers followed 3,500 German students over a period of five years to identify which learning/teaching/IQ factors might contribute to immediate academic achievement in math and if they were any different to those that might lead to academic growth and improvement over the long-term.

And, boy, did they find some differences….

A quick summary is that, though extrinsic motivation and “surface learning” (such as memorization) might result in short-term gains in assessments, they actually hurt long-term (five-year) academic growth. The development of student intrinsic motivation, “deep learning strategies” (requiring “elaboration” and connections to other knowledge — I think that might correspond to the idea of “transfer”), and students feeling that they had more of a sense of control (though this last quality had a less consistent effect — it seemed to depend on grade level) of their learning were the main ingredients necessary for increased academic growth:

perceived control, extrinsic motivation, and surface learning strategies did not predict growth of math achievement, intrinsic motivation and deep learning strategies were significantly positive predictors of the total amount of growth (c16 = 4.51, p < .05; c18 = 4.64, p < .05). Again, intelligence had null relations with the amount of growth.

Here’s how they summarized the results:

One of the features of the current investigation is that we controlled for intelligence when examining the predictive relations of motivation and cognitive strategies. This is by itself of considerable importance, as discussed at the outset. In addition, the inclusion of intelligence as a predictor produced interesting findings: Long-term growth in math achievement was predicted by motivational and strategy factors, but not by students’ intelligence (after controlling for demographic variables). This stands in marked contrast to the commonly observed finding that intelligence explains a much larger proportion of the variance in current achievement scores, as compared to motivational and strategy variables (e.g., Spinath, Spinath, Harlaar, & Plomin, 2006). We should be aware that this study focused on the development of achievement in one academic domain only. Nonetheless, our findings clearly underscore the importance of paying attention to adolescents’ motivation and learning strategies when wanting to understand the development of their academic achievement. Thus, an intriguing message from this study is that the critical determinant of growth in achievement is not how smart you are, but how motivated you are and how you study. (Emphasis mine)

You can bet that next week I’m fitting this research into the copy edits of my sequel to Helping Students Motivate Themselves. Though it only focuses on math, it doesn’t seem to me to be that great of a stretch to be able to apply it to learning in general.

Certainly, it reinforces a lot of previous research. And it’s especially useful that it specifically focuses on academic achievement.