title: Using learning analytics for investigating learning processes in inclusive chemistry classrooms using the universal design for learning
title: Learning Analytics zur Lernprozessanalyse im inklusiven Chemieunterricht unter Nutzung des Universal Design for Learning
titleType: AlternativeTitle
Creators: name: Roski, Marvin
Publisher: name: Hannover : Institutionelles Repositorium der Leibniz Unversität Hannover
Year: 2024
Subjects: subject: Chemistry education
subject: learning analytics
subject: inclusive pedagogy
subject: Universal Design for Learning
subject: web-based learning
subject: contains research data
subject: Chemiedidaktik
subject: inklusive Pädagogik
subject: webbasiertes Lernen
subject: enthält Forschungsdaten
subject: Dewey Decimal Classification::500 | Naturwissenschaften

description: Learning analytics is a discipline that has developed rapidly in recent years and aims to gain insight into learning processes by applying artificial intelligence, among other techniques. Previous research has shown how learners use a digital learning platform designed according to the Universal Design for Learning (UDL) framework is unclear. The UDL framework aims to improve learning for all learners and thus follows a broad understanding of inclusion. It thus supports the idea of ”Education for All” of the United Nations. This research project investigates the integration of learning analytics into a learning platform for chem- istry education that promotes a broad understanding of inclusion. In addition, detailed data collection will be used to gain insights into general usage behavior and specifically into learning opportunities based on the UDL framework with the help of learning analytics. The research project is divided into three phases. In the first phase, the conditions are created to combine the three perspectives of chemistry education, inclusive pedagogy, and learning analytics. The Artificial Intelligence in Inclusive Science Education (AIISE) Frame- work was developed for this purpose. The AIISE Framework combines the characteristics and requirements of all three perspectives. The second phase focuses on developing the web-based learning platform I3Learn. The learning platform addresses the challenging topic of ion bonding for 9th and 10th grade learners in Lower Saxony. The Research Paper: Data Mining with WordPress describes the concept and implementation of the web-based learning platform I3Learn. Through extensive data collection, the interaction of 580 learners from 27 classes with I3Learn could be tracked based on 493,511 log file data. The third phase focuses on applying learning analytics to gain insights into the usage behavior of I3Learn. The Research paper: Dropout Prediction shows that the problem of a high dropout rate could be predicted with the help of artificial intel- ligence. The Research Paper: Cluster Analysis of Usage Behavior deals with the usage behavior of learning offers based on the UDL framework. Six fundamentally different usage behaviors were identified. The research project demonstrates the successful development of I3Learn. A web-based learning platform for chemistry lessons that promotes a broad understanding of inclusion and can collect detailed log file data. Based on this data, learning analytics was used to gain insights into usage behavior by predicting dropouts and examining different usage behaviors of learning opportunities based on the UDL framework. The research project is part of the interdisciplinary PhD project LernMINT, funded by the Ministry of Science and Culture of Lower Saxony (funding phase: 2019-2024).