National educational standards stress the importance of science and mathematics learning for today’s students. However, across disciplines, students frequently struggle to meet learning goals about core concepts like energy. Digital learning environments enhanced with artificial intelligence hold the promise to address this issue by providing individualized instruction and support for students at scale. Scaffolding and feedback, for example, are both most effective when tailored to students’ needs. Providing individualized instruction requires continuous assessment of students’ individual knowledge, abilities, and skills in a way that is meaningful for providing tailored support and planning further instruction. While continuously assessing individual students’ science and mathematics learning is challenging, intelligent tutoring systems show that it is feasible in principle. However, the learning environments in intelligent tutoring systems are typically not compatible with the vision of how effective K-12 science and mathematics learning looks like. This leads to the challenge of designing digital learning environments that allow for both – meaningful science and mathematics learning and the reliable and valid assessment of individual students’ learning. Today, digital devices such as tablets, laptops, or digital measurement systems increasingly enter science and mathematics classrooms. In consequence, students’ learning increasingly produces rich product and process data. Learning Analytics techniques can help to automatically analyze this data in order to obtain insights about individual students’ learning, drawing on general theories of learning and relative to established domain specific models of learning, i.e., learning progressions. We call this approach Learning Progression Analytics (LPA). In this manuscript, building of evidence-centered design (ECD), we develop a framework to guide the development of learning environments that provide meaningful learning activities and data for the automated analysis of individual students’ learning – the basis for LPA and scaling individualized instruction with artificial intelligence.

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The importance of learning technologies for mathematics education is increasing as new opportunities arise for mathematics education for all students, in school and at home. These so-called technology-enhanced learning environments (TELEs) incorporating technology with mathematical content are useful for developing mathematical knowledge and can simultaneously foster self-regulated learning (SRL) and motivational learning in mathematics. However, how do primary students’ differences in their SRL and motivation affect their rating of the quality of mathematical TELEs? To answer this research question, we asked third and fourth-grade primary students (n = 115) to evaluate both their SRL, including metacognition and motivation, and the quality characteristics of the ANTON application, a frequently and intensively used TELE in Germany. Using a person-centered research approach by conducting a cluster analysis, we identified three SRL profiles of primary students—motivated self-learners, non-motivated self-learners, and average motivated non-self-learners—who differ in their ratings of the quality characteristics of the TELE (output variables). Our results highlight that motivated self-learners and non-motivated self-learners vary significantly in their rating of the adequacy of the TELE to their mathematical learning and highly but not significantly concerning the TELE’s reward system. Moreover, differences existed between the motivated self-learners and the average motivated non-self-learners regarding their rating of the characteristic differentiation. Based on these findings, we assume that technical elements associated with adequacy, differentiation, and rewards of mathematical TELEs should be tailorable to the needs of individuals and groups of primary schoolchildren.

ZusammenfassungGesamtüberblick: Das sich aus dem theoretischen Teil ergebene Forschungsinteresse leitet zur Entwicklung eines Instruments zur sprachlichen Variation von Textaufgaben hin, das auf empirisch festgestellten Variationen basiert und spezifische Zielsetzungen verfolgt. Unter der Maßgabe der Entwicklung des Instruments ergeben sich ein auf die Zielsetzung ausgerichtetes Studiendesign und eine Auswahl an Methoden, um das Instrument zu konzeptualisieren. Unter Rückbeziehung des gewählten Verfahrens auf die in der Theorie gegebenen Ansätze, die die Basis für die empirische Studien bilden, ergeben sich Aspekte, die für die empirische Analyse von sprachlichen Variationen zu beachten sind.

ZusammenfassungGesamtüberblick: Die sprachliche Kommunikation ist geprägt durch die Vielfalt an Varianten, die existieren, um Gedanken und Beobachtungen sprachlich auszudrücken. Ein Instrument zur sprachlichen Variation von Textaufgaben im Mathematikunterricht sollte sich an den Veränderungen von Sprache in der Nutzung orientieren. Daher ist das Ziel dieses Kapitels die bedeutsamen Konzepte von Variationen von Sprache zu beschreiben.