Applying Machine Learning in Science Education Research : When, How, and Why? / edited by Peter Wulff, Marcus Kubsch, Christina Krist.

Format:

Book

Contributor:

Wulff, Peter., Editor.

Kubsch, Marcus., Editor.

Krist, Christina., Editor.

Series:

Springer Texts in Education, 2366-7680

Language:

English

Subjects (All):

Science--Study and teaching.

Science.

Study skills.

Machine learning.

Science Education.

Study and Learning Skills.

Machine Learning.

Local Subjects:

Science Education.

Study and Learning Skills.

Machine Learning.

Physical Description:

1 online resource (XIII, 369 p. 57 illus., 38 illus. in color.)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

This open access textbook offers science education researchers a hands-on guide for learning, critically examining, and integrating machine learning (ML) methods into their science education research projects. These methods power many artificial intelligence (AI)-based technologies and are widely adopted in science education research. ML can expand the methodological toolkit of science education researchers and provide novel opportunities to gain insights on science-related learning and teaching processes, however, applying ML poses novel challenges and is not suitable for every research context. The volume first introduces the theoretical underpinnings of ML methods and their connections to methodological commitments in science education research. It then presents exemplar case studies of ML uses in both formal and informal science education settings. These case studies include open-source data, executable programming code, and explanations of the methodological criteria and commitments guiding ML use in each case. The textbook concludes with a discussion of opportunities and potential future directions for ML in science education. This textbook is a valuable resource for science education lecturers, researchers, under-graduate, graduate and postgraduate students seeking new ways to apply ML in their work.

Contents:

Introduction

Part I:Theoretical background

Basics of machine learning

Data in science education research

Applying supervised ML

Applying unsupervised ML

Sequencing unsupervised and supervised ML

Natural language processing and large language models

Human-machine interactions in machine learning modeling: The role of theory

Part II:Hands-on case studies.-Working with data getting started

Automation Supervised Machine Learning

Pattern Recognition – Unsupervised Machine Learning

Automation and explainability: Supervised machine learning with text data

Unsupervised ML with language data

Unsupervised ML with text data

Triangulating Computational and Qualitative Methods to Measure Scientific Uncertainty

Part III:Future directions

Risks and ethical considerations in the context of machine learning research in science education

Future directions

Conclusions.

ISBN:

9783031742279

3031742273