we vary whether providing a learner with features about a person’s personality improves performance for predicting belief change, when compared with providing information about degree of entrenchment alone. We use different representations for personality and prior beliefs as features, the raw score from the Likert slider for belief change and the TIPI score, as well as normalizations of the raw scores according to the distributions per topic, and finally categorical binning of the transformed scores.

Feature Development and Selection
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\[sub:features\]

New features were created by computing the z-transformation score from the raw prior beliefs and personality traits scores. Applying Equation \[ztrans\] to the raw data creates a normal distribution where the new mean is 0 and the standard deviation is 1. For prior beliefs, $x_i$ is an individual prior belief for a particular topic, $\bar{x_i}$ is the mean, and $\sigma_i$ is the standard deviation for the particular topic.

$$\frac{x_i - \bar{x_i}}{\sigma_{x_i}}
\label{ztrans}$$

Categorical bins are derived from the transformed scores to describe the direction of the belief change by comparing prior and final recorded beliefs. The belief change is positive or negative depending upon where the Turkers rate themselves on the belief scale, moving more towards one side (1) 