Learning assessment in the age of big data: Learning analytics in higher education

Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online pla...

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Published in:Cogent education Vol. 10; no. 1
Main Author: Caspari-Sadeghi, Sima
Format: Journal Article
Language:English
Published: Abingdon Cogent 31.12.2023
Taylor & Francis Ltd
Taylor & Francis Group
Subjects:
Academic Achievement
Algorithms
Big Data
Blended Learning
Decision Making
Educational Assessment
Educational research
Educational Resources
Educational Technology
Electronic Learning
Enrollment
Graduation Rate
Handheld Devices
Higher education
Influence of Technology
Intelligence
Learning analytics
Learning Management Systems
Learning Theories
Management Systems
MOOCs
Network Analysis
Online Courses
Open Educational Resources
Resource Allocation
Science Instruction
technology-enhanced assessment
ISSN:2331-186X, 2331-186X
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Abstract Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication applications. Meanwhile, it is acknowledged that education lacks computational infrastructure and human capacity to fully exploit the potential of big data. This paper explores the use of Learning Analytics (LA) in higher education for measurement purposes. Four main LA functions in the assessment are outlined: (a) monitoring and analysis, (b) automated feedback, (c) prediction, prevention, and intervention, and (d) new forms of assessment. The paper concludes by discussing the challenges of adopting and upscaling LA as well as the implications for instructors in higher education.
AbstractList Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication applications. Meanwhile, it is acknowledged that education lacks computational infrastructure and human capacity to fully exploit the potential of big data. This paper explores the use of Learning Analytics (LA) in higher education for measurement purposes. Four main LA functions in the assessment are outlined: (a) monitoring and analysis, (b) automated feedback, (c) prediction, prevention, and intervention, and (d) new forms of assessment. The paper concludes by discussing the challenges of adopting and upscaling LA as well as the implications for instructors in higher education.
AbstractData-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication applications. Meanwhile, it is acknowledged that education lacks computational infrastructure and human capacity to fully exploit the potential of big data. This paper explores the use of Learning Analytics (LA) in higher education for measurement purposes. Four main LA functions in the assessment are outlined: (a) monitoring and analysis, (b) automated feedback, (c) prediction, prevention, and intervention, and (d) new forms of assessment. The paper concludes by discussing the challenges of adopting and upscaling LA as well as the implications for instructors in higher education.
Author Caspari-Sadeghi, Sima
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  organization: University of Passau
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2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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  doi: 10.1186/s41239-020-00193-3
– ident: e_1_3_4_44_1
  doi: 10.1186/s41039-017-0062-8
– volume-title: Die digitale Bildungsrevolution. Der radikale Wandel des Lernens und wie wir ihn gestalten können
  year: 2017
  ident: e_1_3_4_18_1
– ident: e_1_3_4_8_1
  doi: 10.3389/feduc.2022.840947
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Snippet Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and...
AbstractData-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business,...
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SubjectTerms Academic Achievement
Algorithms
Big Data
Blended Learning
Decision Making
Educational Assessment
Educational research
Educational Resources
Educational Technology
Electronic Learning
Enrollment
Graduation Rate
Handheld Devices
Higher education
Influence of Technology
Intelligence
Learning analytics
Learning Management Systems
Learning Theories
Management Systems
MOOCs
Network Analysis
Online Courses
Open Educational Resources
Resource Allocation
Science Instruction
technology-enhanced assessment
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Title Learning assessment in the age of big data: Learning analytics in higher education
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