Using LSTM to Identify Help Needs in Primary School Scratch Students

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...

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Veröffentlicht in:Applied sciences Jg. 13; H. 23; S. 12869
Hauptverfasser: Imbernón Cuadrado, Luis Eduardo, Manjarrés Riesco, Ángeles, de la Paz López, Félix
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.12.2023
Schlagworte:
Analysis
Artificial intelligence
block-based programming language
Coronaviruses
COVID-19
Data entry
distance calculation
Distance learning
Education
Elementary school students
Ethical aspects
Ethics
ethics in AI
Feedback
LSTM model
Programming languages
Robots
Sex discrimination
Students
teaching with Scratch
Time series
Germany
Spain
ISSN:2076-3417, 2076-3417
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Zusammenfassung: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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app132312869