Using LSTM to Identify Help Needs in Primary School Scratch Students

Abstract

In the last few years, there has been increasing interest in the use of block-based programming languages as well as in the ethical aspects of Artificial Intelligence (AI) in primary school education. In this article, we present our research on the automatic identification of the need for assistance among primary school children performing Scratch exercises. For data collection, user experiences have been designed to take into account ethical aspects, including gender bias. Finally, a first-in-class distance calculation method for block-based programming languages has been used in a Long Short-Term Memory (LSTM) model, with the aim of identifying when a primary school student needs help while he/she carries out Scratch exercises. This model has been trained twice: the first time taking into account the gender of the students, and the second time excluding it. The accuracy of the model that includes gender is 99.2%, while that of the model that excludes gender is 91.1%. We conclude that taking into account gender in training this model can lead to overfitting, due to the under-representation of girls among the students participating in the experiences, making the model less able to identify when a student needs help. We also conclude that avoiding gender bias is a major challenge in research on educational systems for learning computational thinking skills, and that it necessarily involves effective and motivating gender-sensitive instructional design.