Based on my prior knowledge, the education level of an individual can be a strong indicator of their earning potential. Generally, individuals with higher education levels tend to earn more than those with lower education levels.

To conduct a thorough analysis, we need to examine the relationship between the education feature and the target variable (earning more than 50000 dollars per year). We can start by looking at the distribution of education levels for each target class.

Analyzing the relationship between education and the target variable, we can create the following dictionary:

```json
{
	"no": ["11th", "HS-grad", "10th", "7th-8th", "9th", "12th", "1st-4th", "Preschool"],
	"yes": ["Assoc-acdm", "Some-college", "Prof-school", "Bachelors", "Masters", "Doctorate", "5th-6th", "Assoc-voc"]
}
```

In the "no" class, lower education levels like "11th", "HS-grad", "10th", etc., are more common. On the other hand, in the "yes" class, higher education levels like "Assoc-acdm", "Some-college", "Prof-school", etc., are more common.

Note: The feature values "Preschool", "1st-4th", and "5th-6th" have fewer instances and may be harder to predict accurately. However, based on the available data, they can still be included in the dictionary.