Co-Constructions of Learning and Technology. On the Change of "Learning Subjects" in the 20th Century

目前，关于“学习”的话题正经历着两个层面的激烈争论：一方面，学习的数字化利用电子媒体创造了新的学习机会和学习动力。另一方面，机器学习算法被训练为基于各种信息进行“学习”。两者都是基于学习的量化、科学化和技术化的过程，这些过程在20世纪彻底改变了传统人文主义的“学习主体”概念。Emmy Noether研究小组旨在历史地分析这些过程以及学习和技术的共同构建。对学习领域至关重要的三个相互关联的分析层次构成了这种方法的基础：“学科”、“环境”、“性别”。这种方法的目的是了解量化是如何用来构建可衡量的，以及性别的学习科目，谁，反过来，通过技术化进行优化和控制。这些历史分析的中心是实验、技术对象（教学/学习机器和程序）和技术环境（虚拟现实、模拟）的重要性，这些都被用于知识的生产和学习理论的发展。通过确定文化和认识论的前提和发展的共同建设的学习和技术，分析的目的是促进更深入地了解当前的发展，在数字学习和机器学习领域。

Currently, the topic of "learning" is undergoing a fierce debate on two levels: On the one hand, the digitalization of learning uses electronic media to create new opportunities as well as motivations for learning. On the other hand, machine learning algorithms are trained to "learn" based on a variety of information. Both are predicated upon processes of quantification, scientificalization, and technicalization of learning; these processes have thoroughly transformed traditional humanistic concepts of "learning subjects" during the course of the 20th century. The Emmy Noether Research Group aims to historically analyze these processes as well as the co-constructions of learning and technology. Three interrelated levels of analysis central to the field of learning form the basis for this approach: "subject", “environment”, "gender". This approach serves the purpose of understanding how quantification was used to construct measurable as well as gendered learning subjects, who, in turn, were being optimized and controlled through technicalization. At the center of these historical analyses is the importance of the role of experiments, technical objects (teaching/learning machines and programs), and technical environments (virtual reality, simulation), which are all being utilized in and for the production of knowledge and the development of learning theories. By identifying cultural and epistemological premises and developments of the co-constructions of learning and technology, the analyses are designed to contribute to a deeper understanding of current developments in the area of digital learning and machine learning.