When we calculated learning gains as average normalized gain, percent change, or raw change, we obtained the same result.
We ultimately chose to calculate learning gains using Cohen's d for a repeated measures design (Dunlap et al., 1996).
We then used a previously described expectation maximization algorithm to calculate learning curves for each mouse, as well as the learning trial if the mouse was identified as a learner (Smith et al., 2004).
It also offers a unique method of calculating the learning curve associated with ONSD measurement.
Despite the obvious limitations, this study offers a unique method of calculating the learning curve associated with ONSD utilizing a new definition of reliability by looking at within-subject variability and a unique statistical model to calculate the plateau in this variability.
That is why we -along with multiple studies on learning styles- calculate learning style quadrants to express the preferred learning style(s) of a person, instead of using the separate scores on both axes [ 10, 11, 19].
If you're actually doing anything meaningful in your role, you are likely taking calculated risks, learning from them, and then making future decisions based on what you've learned.
A grand mean of the five daily trials was calculated and learning was evaluated in the trained rats by a one-way ANOVA for repeated measures, with the mean error score for each day being the dependent variable.
A similar neuropsychological test in which an abstract design was presented was used to calculate design learning scores.
After 3 min, we determine the number of animals on either side to calculate an odor preference [−1; 1] as the number of animals at the AM side (#AM) minus the ones at the OCT side (#OCT), divided by the total (#TOTAL): (2) From alternately run, reciprocally trained groups we calculate a learning index [−1; 1]: (3) Thus, positive LIs indicate appetitive, negative values aversive memory.
