Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design

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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Marcus Kubsch [verfasserIn] Berrit Czinczel [verfasserIn] Jannik Lossjew [verfasserIn] Tobias Wyrwich [verfasserIn] David Bednorz [verfasserIn] Sascha Bernholt [verfasserIn] Daniela Fiedler [verfasserIn] Sebastian Strauß [verfasserIn] Ulrike Cress [verfasserIn] Hendrik Drachsler [verfasserIn] Knut Neumann [verfasserIn] Nikol Rummel [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	learning progression evidence-centered design (ECD) machine learning (ML) automated assessment learning sciences learning analytics (LA)

Übergeordnetes Werk:	In: Frontiers in Education - Frontiers Media S.A., 2017, 7(2022)
Übergeordnetes Werk:	volume:7 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/feduc.2022.981910

Katalog-ID:	DOAJ036456470

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spelling	10.3389/feduc.2022.981910 doi (DE-627)DOAJ036456470 (DE-599)DOAJ67d8bc55dad943d599a5ddf41a3732f7 DE-627 ger DE-627 rakwb eng L7-991 Marcus Kubsch verfasserin aut Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. learning progression evidence-centered design (ECD) machine learning (ML) automated assessment learning sciences learning analytics (LA) Education (General) Berrit Czinczel verfasserin aut Jannik Lossjew verfasserin aut Tobias Wyrwich verfasserin aut David Bednorz verfasserin aut Sascha Bernholt verfasserin aut Daniela Fiedler verfasserin aut Sebastian Strauß verfasserin aut Ulrike Cress verfasserin aut Hendrik Drachsler verfasserin aut Knut Neumann verfasserin aut Nikol Rummel verfasserin aut In Frontiers in Education Frontiers Media S.A., 2017 7(2022) (DE-627)878204881 (DE-600)2882397-7 2504284X nnns volume:7 year:2022 https://doi.org/10.3389/feduc.2022.981910 kostenfrei https://doaj.org/article/67d8bc55dad943d599a5ddf41a3732f7 kostenfrei https://www.frontiersin.org/articles/10.3389/feduc.2022.981910/full kostenfrei https://doaj.org/toc/2504-284X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 7 2022
allfields_unstemmed	10.3389/feduc.2022.981910 doi (DE-627)DOAJ036456470 (DE-599)DOAJ67d8bc55dad943d599a5ddf41a3732f7 DE-627 ger DE-627 rakwb eng L7-991 Marcus Kubsch verfasserin aut Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. learning progression evidence-centered design (ECD) machine learning (ML) automated assessment learning sciences learning analytics (LA) Education (General) Berrit Czinczel verfasserin aut Jannik Lossjew verfasserin aut Tobias Wyrwich verfasserin aut David Bednorz verfasserin aut Sascha Bernholt verfasserin aut Daniela Fiedler verfasserin aut Sebastian Strauß verfasserin aut Ulrike Cress verfasserin aut Hendrik Drachsler verfasserin aut Knut Neumann verfasserin aut Nikol Rummel verfasserin aut In Frontiers in Education Frontiers Media S.A., 2017 7(2022) (DE-627)878204881 (DE-600)2882397-7 2504284X nnns volume:7 year:2022 https://doi.org/10.3389/feduc.2022.981910 kostenfrei https://doaj.org/article/67d8bc55dad943d599a5ddf41a3732f7 kostenfrei https://www.frontiersin.org/articles/10.3389/feduc.2022.981910/full kostenfrei https://doaj.org/toc/2504-284X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 7 2022
allfieldsGer	10.3389/feduc.2022.981910 doi (DE-627)DOAJ036456470 (DE-599)DOAJ67d8bc55dad943d599a5ddf41a3732f7 DE-627 ger DE-627 rakwb eng L7-991 Marcus Kubsch verfasserin aut Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. learning progression evidence-centered design (ECD) machine learning (ML) automated assessment learning sciences learning analytics (LA) Education (General) Berrit Czinczel verfasserin aut Jannik Lossjew verfasserin aut Tobias Wyrwich verfasserin aut David Bednorz verfasserin aut Sascha Bernholt verfasserin aut Daniela Fiedler verfasserin aut Sebastian Strauß verfasserin aut Ulrike Cress verfasserin aut Hendrik Drachsler verfasserin aut Knut Neumann verfasserin aut Nikol Rummel verfasserin aut In Frontiers in Education Frontiers Media S.A., 2017 7(2022) (DE-627)878204881 (DE-600)2882397-7 2504284X nnns volume:7 year:2022 https://doi.org/10.3389/feduc.2022.981910 kostenfrei https://doaj.org/article/67d8bc55dad943d599a5ddf41a3732f7 kostenfrei https://www.frontiersin.org/articles/10.3389/feduc.2022.981910/full kostenfrei https://doaj.org/toc/2504-284X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 7 2022
allfieldsSound	10.3389/feduc.2022.981910 doi (DE-627)DOAJ036456470 (DE-599)DOAJ67d8bc55dad943d599a5ddf41a3732f7 DE-627 ger DE-627 rakwb eng L7-991 Marcus Kubsch verfasserin aut Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. learning progression evidence-centered design (ECD) machine learning (ML) automated assessment learning sciences learning analytics (LA) Education (General) Berrit Czinczel verfasserin aut Jannik Lossjew verfasserin aut Tobias Wyrwich verfasserin aut David Bednorz verfasserin aut Sascha Bernholt verfasserin aut Daniela Fiedler verfasserin aut Sebastian Strauß verfasserin aut Ulrike Cress verfasserin aut Hendrik Drachsler verfasserin aut Knut Neumann verfasserin aut Nikol Rummel verfasserin aut In Frontiers in Education Frontiers Media S.A., 2017 7(2022) (DE-627)878204881 (DE-600)2882397-7 2504284X nnns volume:7 year:2022 https://doi.org/10.3389/feduc.2022.981910 kostenfrei https://doaj.org/article/67d8bc55dad943d599a5ddf41a3732f7 kostenfrei https://www.frontiersin.org/articles/10.3389/feduc.2022.981910/full kostenfrei https://doaj.org/toc/2504-284X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 7 2022
language	English
source	In Frontiers in Education 7(2022) volume:7 year:2022
sourceStr	In Frontiers in Education 7(2022) volume:7 year:2022
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	learning progression evidence-centered design (ECD) machine learning (ML) automated assessment learning sciences learning analytics (LA) Education (General)
isfreeaccess_bool	true
container_title	Frontiers in Education
authorswithroles_txt_mv	Marcus Kubsch @@aut@@ Berrit Czinczel @@aut@@ Jannik Lossjew @@aut@@ Tobias Wyrwich @@aut@@ David Bednorz @@aut@@ Sascha Bernholt @@aut@@ Daniela Fiedler @@aut@@ Sebastian Strauß @@aut@@ Ulrike Cress @@aut@@ Hendrik Drachsler @@aut@@ Knut Neumann @@aut@@ Nikol Rummel @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	878204881
id	DOAJ036456470
language_de	englisch
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topic_title	L7-991 Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design learning progression evidence-centered design (ECD) machine learning (ML) automated assessment learning sciences learning analytics (LA)
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title	Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design
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title_sort	toward learning progression analytics — developing learning environments for the automated analysis of learning using evidence centered design
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title_auth	Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design
abstract	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.
abstractGer	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.
abstract_unstemmed	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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title_short	Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design
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