Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design. / Kubsch, Marcus; Czinczel, Berrit; Lossjew, Jannik; Wyrwich, Tobias; Bednorz, David; Bernholt, Sascha; Fiedler, Daniela; Strauß, Sebastian; Cress, Ulrike; Drachsler, Hendrik; Neumann, Knut; Rummel, Nikol.
I: Frontiers in Education, Bind 7, 981910, 22.08.2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design
AU - Kubsch, Marcus
AU - Czinczel, Berrit
AU - Lossjew, Jannik
AU - Wyrwich, Tobias
AU - Bednorz, David
AU - Bernholt, Sascha
AU - Fiedler, Daniela
AU - Strauß, Sebastian
AU - Cress, Ulrike
AU - Drachsler, Hendrik
AU - Neumann, Knut
AU - Rummel, Nikol
N1 - Funding Information: This work was supported by the Leibniz Foundation. Publisher Copyright: Copyright © 2022 Kubsch, Czinczel, Lossjew, Wyrwich, Bednorz, Bernholt, Fiedler, Strauß, Cress, Drachsler, Neumann and Rummel.
PY - 2022/8/22
Y1 - 2022/8/22
N2 - 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.
AB - 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.
KW - automated assessment
KW - evidence-centered design (ECD)
KW - learning analytics (LA)
KW - learning progression
KW - learning sciences
KW - machine learning (ML)
KW - mathematics education
KW - science education
UR - http://www.scopus.com/inward/record.url?scp=85137829844&partnerID=8YFLogxK
U2 - 10.3389/feduc.2022.981910
DO - 10.3389/feduc.2022.981910
M3 - Journal article
AN - SCOPUS:85137829844
VL - 7
JO - Frontiers in Education
JF - Frontiers in Education
SN - 2504-284X
M1 - 981910
ER -
ID: 375591345