Preprocessing features of TADA-Ed

1. Preprocessing features of TADA-Ed Agathe Merceron Kalina Yacef

2. We aggregate the data using the attribute ‘login’ First we choose an attribute to aggregate the data. Let’s suppose we want to compare students

3. Here we choose to only consider the number of exercises in which each student made one or more mistakes • Qid: Number of types Number of types: count the number of distinct value per common attribute value. 2 2 3 • Chosen attributeto aggregate: login • Preprocessing performed: • Qid: Number of types

4. Mistake: Total Number Total Number : count the number of value per common attribute value 4 2 2 2 6 3 Now we want to only consider the total number of mistakes each student made • Chosen attributeto aggregate: login • Preprocessing performed: • Qid: Number of types • Mistake: Total Number

5. Rule : Nominal Nominal: create one attribute per distinct value. 1 0 1 1 1 0 1 1 0 1 1 1 2 2 2 2 3 3 We want to know with what concepts the student made mistakes • Chosen attributeto aggregate: login • Preprocessing performed: • Qid: Number of types • Mistake: Total Number • Rule : Nominal