ISO/IEC TR 9858:2026

Technical
Report
ISO/IEC TR 9858
First edition
Information technology — Use
2026-01
cases on advanced learning
analytics services using emerging
technologies
Technologies de l'information — Cas d'utilisation concernant
les services avancés d'analytique de l'apprentissage utilisant des
technologies émergeantes
Reference number
© ISO/IEC 2026
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
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Published in Switzerland
© ISO/IEC 2026 – All rights reserved
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Abbreviated Terms . 2
5 Advanced learning analytics service . 2
6 Emerging technologies for advanced learning analytics services . 3
7 Categorization of use cases on ALAS and their inter-relationships . 4
7.1 Categorization of use cases on ALAS .4
7.2 Interrelationships of the six groups .4
8 Group I learning environment or pedagogical support . 5
8.1 Learning environment .5
8.2 Pedagogical support .5
9 Group II data collection and data ecosystem. 6
9.1 Data collection .6
9.2 Data ecosystem .6
10 Group III assessment, reasoning and prediction. 6
10.1 Assessment .6
10.2 Reasoning .6
10.3 Prediction . .6
11 Group IV learning outcome and feedback . 6
11.1 General .6
11.2 Learning outcome .7
11.3 Feedback .7
12 Group V data visualization . 7
13 Group VI privacy protection and data ownership . 7
13.1 Privacy protection .7
13.2 Data ownership .8
14 Use case list . 8
Annex A (informative) Group I learning environment or teaching support . 9
Annex B (informative) Group II data collection and data ecosystem .15
Annex C (informative) Group III Assessment, Reasoning and Prediction .21
Annex D (informative) Group IV Learning Outcome and Feedback .24
Annex E (informative) Group V Data Visualization .28
Annex F (informative) Group VI Privacy Protection and Data Ownership .30
Bibliography .32

© ISO/IEC 2026 – All rights reserved
iii
Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are
members of ISO or IEC participate in the development of International Standards through technical
committees established by the respective organization to deal with particular fields of technical activity.
ISO and IEC technical committees collaborate in fields of mutual interest. Other international organizations,
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The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types
of document should be noted. This document was drafted in accordance with the editorial rules of the ISO/
IEC Directives, Part 2 (see www.iso.org/directives or www.iec.ch/members_experts/refdocs).
ISO and IEC draw attention to the possibility that the implementation of this document may involve the
use of (a) patent(s). ISO and IEC take no position concerning the evidence, validity or applicability of any
claimed patent rights in respect thereof. As of the date of publication of this document, ISO and IEC had not
received notice of (a) patent(s) which may be required to implement this document. However, implementers
are cautioned that this may not represent the latest information, which may be obtained from the patent
database available at www.iso.org/patents and https://patents.iec.ch. ISO and IEC shall not be held
responsible for identifying any or all such patent rights.
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT) see www.iso.org/iso/foreword.
html. In the IEC, see www.iec.ch/understanding-standards.
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 36, Information technology for learning, education and training.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html and www.iec.ch/national-
committees.
© ISO/IEC 2026 – All rights reserved
iv
Introduction
The ISO/IEC 20748 series on learning analytics provides reference and guidance for learning analytics
services.
[1]
ISO/IEC TR 20748-1:2016
[2]
ISO/IEC TR 20748-2:2017
[3]
ISO/IEC TS 20748-3:2020
[4]
ISO/IEC TS 20748-4:2019
Emerging technologies, such as cloud computing, big data technologies, AR/VR, Digital Twin, and AI are
being adopted and explored throughout learning, education, and training environments. Learning analytics
play a pivotal role in enhancing learning effectiveness, customizing learning content, as well as real-time
learning tool adaptations. Significant advantages of emerging technologies can be seen when they are used
in learning analytics services.
This document provides abundant use cases of advanced learning analytics services and presents the current
development status of learning analytics services. And it provides insights for potential standardization
fields related to learning analytics.
The study of advanced learning analytics services explored in this document focuses on the following
aspects,
a) to improve individual learning achievements;
b) to promote learning outcome-based decision-making, learning resource design, learning activity
selection and more;
c) to help teachers achieve improved instructional effectiveness;
d) to provide data-based learning evidence that can be used to support teaching planning;
e) to enable the integration of learner competencies and knowledge;
f) to incorporate standards and specifications for the purposes of data privacy protection and data
security;
g) to exemplify innovative technical implementation such as skill embedding, deep knowledge tracing and
more.
In order to encourage a more well-rounded and active discussion with stakeholders and to investigate
potential standardization items, a large number of private, public and non-profit institutions were invited to
share their use cases by using a use case template

© ISO/IEC 2026 – All rights reserved
v
Technical Report ISO/IEC TR 9858:2026(en)
Information technology — Use cases on advanced learning
analytics services using emerging technologies
1 Scope
This document provides a collection of use cases on advanced learning analytics services, which leverages
emerging technologies such as cloud computing, blockchain, virtual reality, internet of things, artificial
intelligence in K12 education, higher education, and vocational education and training (VET). This
document also establishes a categorization of different groups of learning analytics services and outlines
their interrelationships.
This document is applicable to the design and development of learning analytics activities.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org
3.1
learning analytics
LA
measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of
understanding and optimizing learning and the environments in which it occurs
[1]
[SOURCE: ISO/IEC TR 20748-1:2016 , 3.11]
3.2
learning analytics service
system that aggregates and analyses learner data that is collected when learners interact with a platform
and software
[4]
[SOURCE: ISO/IEC TS 20748-4:2019 , 3.7]
3.3
data analysis
systematic investigation of the data and their flow in a real or planned system
[5]
[SOURCE: ISO/IEC 2382:2015 ]
3.4
data collection
process for gathering information by different means
[6]
[SOURCE: ISO 19731:2017 , 3.14]

© ISO/IEC 2026 – All rights reserved
3.5
learning outcome
what a learner is expected to know, understand or be able to do at the end of a training programme, course
or module
[7]
[SOURCE: ISO/IEC TS 17027:2014 , 2.57]
3.6
privacy
right of individuals to control or influence what information related to them may be collected and stored
and by whom that information may be disclosed
[8]
[SOURCE: ISO/IEC TR 26927:2011 , 3.34]
3.7
data visualization
presentation of data or information in an insightful format such as a graph, chart, scatter plot, diagram,
infographics, data dashboards, interactive reports and even animations
3.8
dashboard
user interface based on predetermined reports, indicators and data fields, upon which the end user can apply
filters and graphical display methods to answer predetermined business questions and which is suited to
regular use with minimal training
[9]
[SOURCE: ISO/TS 29585:2010 , 3.3]
4 Abbreviated Terms
LAS Learning analytics service
ALAS Advanced learning analytics service
LET Learning, education and training
IoT Internet of things
AR/VR Augmented reality/virtual reality
AI Artificial intelligence
K-12 Kindergarten to grade 12 and secondary education or level 1-3 of UNESCO international
standard classification of education
TVET Technical and vocational education and training
ML Machine learning
5 Advanced learning analytics service
A learning analytics service (LAS) can be regarded as a system that aggregates and analyses learner data
generated through interactions with digital platforms and software.
In this document, an advanced learning analytics service (ALAS) refers to an enhanced LAS that leverages
cutting-edge technologies with innovations to deliver deeper insights and more sophisticated analytics
capabilities.
In this document ALASs are divided into 6 groups as follows,
a) learning environment or pedagogical support;

© ISO/IEC 2026 – All rights reserved
b) data collection and data ecosystem;
c) assessment, reasoning and prediction;
d) learning outcome and feedback;
e) data visualization;
f) privacy and data ownership.
6 Emerging technologies for advanced learning analytics services
6.1 The emerging technologies leveraged in advanced learning analytics services (ALAS) are diverse and
continually evolving. Currently, the major technologies include cloud computing, blockchain, AR/VR, IoT,
and AI, all of which are being widely adopted in ALASs.
Regarding Cloud Computing technology, it significantly enhances the capabilities of ALAS. By supporting
e-learning platforms and virtual classrooms, it enables remote learning and real-time teacher-student
interactions, generating rich data for ALAS. Cloud platforms also offer secure, reliable data storage and
backup solutions.
Through ALAS, cloud-based systems can analyze student data to deliver personalized learning experiences,
adapting content and suggestions to individual needs and learning styles. Additionally, educators can
leverage cloud-based applications to develop interactive lessons, quizzes, and simulations, thereby
improving student engagement and comprehension.
Regarding Blockchain technology, enables several key applications in ALASs, including:
a) secure credential and certification verification;
b) immutable learning records and credit management;
c) enhanced data security and privacy protection;
d) transparent sharing and trading of educational resources.
6.2 Regarding AR/VR technology, it has become essential to modern learning and training practices. The
AR/VR assisted learning and training processes can be recorded and used for advanced learning analytics
services (ALAS). Its key applications include:
a) Immersive Learning – Learners engage with complex concepts, historical events, or scientific phenomena
through interactive, hands-on experiences, deepening comprehension and retention.
b) Real-World Simulations – Safe, practical training environments replicate scenarios such as medical
procedures, engineering projects, or hazardous conditions, preparing learners for real-life challenges.
c) Skill-Based Training – High-precision practice in fields like healthcare, aviation, and engineering allows
learners to hone techniques in risk-free virtual settings.
6.3 Regarding the internet of things (IoT), it plays an increasingly vital role in advanced learning analytics
services (ALAS) through its robust data acquisition capabilities. Its key applications include:
a) Smart Classrooms: IoT-enabled devices like interactive whiteboards, tablets, and automated attendance
systems enhance daily teaching and learning processes.
b) Interactive Learning Tools: Smart lab equipment and AR-enabled kits facilitate hands-on, experiential
learning in STEM subjects through real-time data collection and analysis.
c) Automated Library Systems: IoT solutions streamline operations including book tracking, inventory
management, and self-service checkout, significantly improving operational efficiency.

© ISO/IEC 2026 – All rights reserved
d) Enhanced Remote Learning: IoT devices bridge the physical distance in education by enabling seamless
communication and resource sharing between students and teachers.
6.4 Regarding artificial intelligence (AI) technology, it is always evolving and its capacity is widely
explored in ALAS. Here are some key areas where AI is making a significant impact:
a) Data-Driven Decision Making
1) Automated assessment of assignments, quizzes, and exams with instant feedback.
2) Advanced analytics of educational data to evaluate student performance and curriculum
effectiveness.
3) Predictive modelling to identify at-risk students and inform intervention strategies.
b) Personalized Learning Systems
1) AI-powered analysis of individual learning patterns and behaviours.
2) Dynamic content adaptation based on real-time student progress.
3) Customized learning pathways to optimize each student's educational experience.
7 Categorization of use cases on ALAS and their inter-relationships
This document presents a classification system of ALASs, their interrelationships and dependencies. This
structured classification system serves as:
— the conceptual core organizing all documented use cases;
— the fundamental basis for systematic case collection and analysis.
7.1 Categorization of use cases on ALAS
By investigating the diverse ALAS implementations across the education sectors, and based on distinctive
characteristics of different types of ALASs, this document categorizes use cases into six distinct groups.
This categorization enables:
a) systematic organization of diverse learning analytics applications;
b) clear differentiation of service types by core functionality.
Table 1 — Categorization of use cases on ALASs
Group I Learning environment or pedagogical support
Group II Data collection and data ecosystem
Group III Assessment, reasoning and prediction
Group IV Learning outcome and feedback
Group V Data visualization
Group VI Privacy and data ownership
7.2 Interrelationships of the six groups
These six groups are interconnected, forming a comprehensive panorama of ALAS.
As shown in Figure 1, "group I learning environment or pedagogical support" is at L1, which provides the
foundational environment and tool support for learning service practices. "Group II data collection and data
ecosystem" at L2, collects data from the bottom level and provides necessary data for "group III assessment,
reasoning and prediction" which is at L3. Based on assessments, conclusions of learning outcome can be

© ISO/IEC 2026 – All rights reserved
generated and feedback can be provided at L4. All data-based analysis results generated at L2, L3 and L4 can
be displayed in an innovative way called "data visualization" at L5. Data visualization can be in the form of
digital batches, dashboards, e-portfolios or other forms.
"Group VI privacy protection and data ownership" serves as dimension 1 (D1) is one dimension that is
supposed to be considered from L1 to L5.
L5 Data visualization
L4 Learning outcome and feedback
L3 Assessment, reasoning and prediction
L2 Data collection and data ecosystem
L1 Learning environment or pedagogical support
Figure 1 — Interrelationships of the 6 groups of ALASs
8 Group I learning environment or pedagogical support
8.1 Learning environment
Learning environment where teaching and learning activities occur and pedagogical support that assist
teaching and learning activities are the basis for ALAS.
The learning environment here refers to the digital environment that support teaching and learning
processes. The construction of digital learning environment is based on various technologies. Digital
learning environment can facilitate the active knowledge construction and competency development for
learners. Digital learning environment is supposed to be learner-centered, more open and personalized.
As the technology has been evolved from the ICT stage to the current AI stage, the field of education has also
entered a new era that can be called "digital transformation era". The digital transformation in education
is based on data resources, utilizes modern information networks as its carrier, and integrates various
educational elements through digital technologies. This drives comprehensive reforms in teaching and
learning environments.
This transformation represents a paradigm shift in education, enabled by the strategic convergence of
digital technologies and pedagogical innovation.
8.2 Pedagogical support
Pedagogical Support here refers to all the digital tools and pedagogical resources that can be leveraged in
teaching and learning activities. There is an overlap between the instructional support and the learning
environment. In some circumstances, they share the same scope.

© ISO/IEC 2026 – All rights reserved
D1 Privacy protection and data ownership

Pedagogical support emphasizes the role and effects of digital technologies in teaching and learning, while
learning environment stresses the components that construct the environment.
9 Group II data collection and data ecosystem
9.1 Data collection
Data collection focuses on the data acquisition from the digital learning environment through the whole
teaching and learning processes. Data collection in education refers to the systematic gathering of
information related to students, teachers, schools, and educational processes. The data collected is used to
analyse, monitor, and improve educational outcomes, teaching practices, and institutional effectiveness.
9.2 Data ecosystem
Data ecosystem focuses on the data diversity and data values at all stages of the whole education life cycle.
Data ecosystem is a combination of infrastructure, analytics, and applications that are used to capture
and analyse educational data. The data ecosystem in education plays a crucial role in improving teaching,
learning, and administrative processes. It involves the collection, analysis, and application of data to enhance
decision-making and outcomes. Below are the key usages of a data ecosystem in education: personalized
learning, student performance tracking, curriculum improvement, institutional decision-making, predictive
analytics and more.
10 Group III assessment, reasoning and prediction
10.1 Assessment
Assessments serve as a way to track learner performance and learning outcome. Based on the assessments,
the reasons for poor or excellent learning outcomes can be inferred, and learning trends can be predicted.
Assessment is an activity through the use of examinations, shorter quizzes, recitations, or longer
assignments like research papers or science projects. Educators use various assessments to measure and
record students' academic achievement and skills. It is a process of investigation, gathering and synthesizing
evidence, summarizing the state or quality of teaching and learning.
10.2 Reasoning
Reasoning in education is an analysis process based on assessment results and other data collected
about learners, in order to determine what interventions are required for the learners in future learning.
Reasoning is a process to find out the causes and reasons for what has happened and how for the next steps.
10.3 Prediction
Prediction in education refers to the predicting activities through systematic and comprehensive analysis
of data collected, in order to forecast educational outcomes, trends, and behaviours. Prediction leverages
historical and real-time data to make predictions regarding teaching, learning and education management.
Some typical predictions include: educational development trend, professional or discipline development,
student performance and more.
11 Group IV learning outcome and feedback
11.1 General
Learning outcome reflects what learners have acquired or not. According to learning outcome and learning
process, feedback can be made and work as guidance for next learning plans.

© ISO/IEC 2026 – All rights reserved
Learning outcome demonstrates learners' knowledge acquisition and skill development. By analysing both
learning outcome and the learning process, educators can provide targeted feedback to students, develop
data-informed teaching plans, and personalize instructional approaches based on individual needs.
11.2 Learning outcome
Learning outcome is measurable achievements that learners gain when completing their studies. Learning
outcome contains knowledge acquisition, competencies and skills, as well as emotional attitudes and values.
Learning outcome can genuinely reflect teaching and learning effectiveness.
11.3 Feedback
Feedback on learning outcome, learning process and relevant statistical data of learners' performance,
is an essential for further learning and learner development. Feedback provides learners with how they
are performing and how can they make progress in the next. Feedback provides tips for learners to avoid
repeated failures and weaknesses.
There are different types of feedback, such as praise, punishment, rewards and suggestions. Corrective
feedback is more effective than single form of feedback.
12 Group V data visualization
Data visualization is the presentation of data or information in a more insightful format such as a graph,
chart, scatter plot, diagram, infographics, data dashboards, interactive reports and even animations.
Data visualization reveals trends from thousands or millions of data points, displays complex data
relationships, and makes it easier for the human brain to derive insights.
Many sectors are moving toward data-driven decision making and leveraging the power of information.
Colleges and universities are no exception and there are many strategies they employ to make sense of their
data. Data visualization is one such tool and it’s been found to improve student success, resource allocation,
persistence rates, and more.
One typical using scenario is about student development. It can be challenging to collect reliable student
data and then understand what to do with that information. Data visualization can help education managers,
teachers and students themselves gather and understand valuable information and student perspectives on
challenges and opportunities.
Another using scenario is about curriculum improvement. The best way to know whether a curriculum is
effective or not is through course success rates. Course success rates are defined by any student taking the
course and passing with an average or higher score. If it is found that a particular class has a high failure
rate, the possible source of the problem through these reports can be determined.
Data visualization is vastly applied in ALAS. This document provides more using scenarios in use cases.
13 Group VI privacy protection and data ownership
13.1 Privacy protection
Privacy encompasses multiple dimensions such as physical privacy, behavioural privacy, identity privacy,
reputation privacy, portrait privacy, personal income privacy, and personal experience privacy.
Data privacy is the principle that a person has control over their personal data, including the ability to decide
how organizations collect, store and use their data. Data privacy focuses on three directions: data privacy
protection laws or policies, data privacy protection technologies and data privacy protection behaviours.
Privacy protection refers to safeguarding personal information from unauthorized access, theft, or loss. It is
essential for ensuring trust, security, and conformance with relevant standards.

© ISO/IEC 2026 – All rights reserved
13.2 Data ownership
Data ownership refers to the accountability and responsibility for the data. It involves identifying who owns
the data, who is responsible for managing it, and who can access it. Data ownership serves as a critical pillar
of effective data governance by establishing clear accountability for data accuracy, data reliability, and data
security.
14 Use case list
The document incorporates 21 use cases. These cases have been systematically categorized, with a complete
list presented in the Table 2 below. Detailed descriptions of each use case are available in Annex A through
Annex F.
Table 2 — Use case list
Group Name Use Case Title
Group I UC 1-01 Semi-digital learning environment and complex assignments (See
Table A.1)
Learning environment or
pedagogical support UC 1-02 Provide the teacher with relevant learning resources (See Table A.2)
(See Annex A) UC 1-03 Teacher support on providing personalized teaching (See Table A.3)
UC 1-04 An adaptive learning environment and learning analytics dashboards to
resolve education divide (See Table A.4)
Group II UC 2-01 Pupils "trail of thought" from data to balanced perspective (See
Table B.1)
Data collection and data
ecosystem UC 2-02 Using autonomous tutoring system or intervention of instructors (See
Table B.2)
(See Annex B)
UC 2-03 Taking activity data into account when creating learning resources (See
Table B.3)
UC 2-04 Using activity data to improve learning resources (See Table B.4)
UC 2-05 Data governing and data analysis based on data middle platform (See
Table B.5)
Group III UC 3-01 Insight into computational reasoning (See Table C.1)
Assessment, reasoning and UC 3-02 Student learning style or behaviour support (See Table C.2)
prediction
UC 3-03 Retention analytics dashboard (See Table C.3)
(See Annex C)
UC 3-04 Application of predictive analytics to proactively identify and support
at-risk students to drive persistence (See Table C.4)
Group IV UC 4-01 Relevant resources suggestions (See Table D.1)
Learning outcome and UC 4-02 Learning analytics for curriculum improvement (See Table D.2)
feedback
UC 4-03 Comparisons and feedback in LA (See Table D.3)
(See Annex D)
UC 4-04 Student e-portfolio information model based on the blockchain technol-
ogy (See Table D.4)
Group V UC 5-01 Dashboards for academic administration (See Table E.1)
Data visualization UC 5-02 Student portrait solution (See Table E.2)
(See Annex E)
Group VI UC 6-01 Aggregated learning analytics with built-in privacy (See Table F.1)
Privacy protection and data UC 6-02 Design of trusted educational data system - data privacy management
ownership based on blockchain technology (See Table F.2)
(See Annex F)
© ISO/IEC 2026 – All rights reserved
Annex A
(informative)
Group I learning environment or teaching support
A.1 UC 1-01 Semi-digital learning environment and complex assignments
Table A.1 — UC 1-01 Semi-digital learning environment and complex assignments
Contributor Norway
Main stakeholders pupils, teachers, learning resource providers
Description How can learning tools/resources retrieve data from students in a class that is struc-
tured where a teacher introduces a semi-digital activity/exercise, the students then
work on the activity/exercise which is followed by a joint discussion about the activity
in the class.
A lot of learning happens through reflection and discussions, both individually and
in groups. How can learning analyse this in a good way? It is relevant to retrieve data
when a student is learning something:
a) Individual reflections/writing from a student.
b) Group/class discussions.
Additional perspectives which are interesting:
c) Group exercises: what group was the learner in, and how did the group perform as
a whole, and how did the learners contribute.
d) Assignment/planning of activities/exercises: what activities were assigned to the
learners, either to do in-session or as homework. And what is the completion status
of the assigned tasks.
e) Tracking of what competency goals or learning objectives the learner worked on.
Teaching / Learning — Problem solving.
Activities Supported /
— Discussion in groups and the class as a whole ("think-pair-share").
Pedagogical approaches
— The teacher guides the students and is interested in how they are thinking.
— Tasks/activities that are "low floor high ceiling".
— Formative assessment.
Technologies / Technical It is important to figure out how to obtain relevant data in a scenario where the teach-
Approaches and ing is not always digital. Some of the learning/teaching happens without digital tools,
Applications and the activities and discussions themselves can take place completely offline. But if
it is possible to include the students' work in an analysis, it can be registered in some
way. For example, by the teacher or student registering the work along the way or
afterwards, using text editing programs, a mobile app or another learning tool.
The summary of the analytics can be represented in a way which makes sense for and
supports the work of the teacher. The data can give the teacher information about what
the students have learned or not learned, which misconceptions they can have and also
show important aspects from the students’ work on the activity/task. In addition, it
can be appropriate to suggest further possible adaptations and activities. In this way,
the teacher can adapt his/her teaching to the class and individual students.
If the analytics is able to suggest adaptations or adjustments in the "teaching plan",
then the plan is supposed to be described in a machine-readable way, or be deduced
from the data that is captured or registered.

© ISO/IEC 2026 – All rights reserved
TTabablele A A.11 ((ccoonnttiinnueuedd))
[1]
Industry Standards / ISO/IEC TR 20748-1:2016
[2]
Specifications
ISO/IEC TR 20748-2:2017
[3]
ISO/IEC TS 20748-3:2020
[4]
ISO/IEC TS 20748-4:2019
[10]
1EdTech
[11]
IEEE 9274.1.1-2023
Potential areas where How to do learning analytics of non-digital activities, including capturing data about
standardization is needed such activities and presenting information about them at an individual learner level
and an aggregated group level, and in relation to other activities in the learning envi-
ronment.
The data captured about such non-digital activities are likely much more high-level
and qualitative especially if based on manual registration. Such data cannot be as
detailed as for digital learning resources where every mouse click and other detailed
action can result in quantitative learning activity data being generated.
Additional notes / One possible way to explore this use case further is to come up with some example sce-
considerations narios and model how the data registered or captured from those scenarios can look
like (for example in xAPI). What verbs are needed, and are there any general concepts
which would be the objects (or in the context) of such statements?
A.2 UC 1-02 Provide the teacher with relevant learning resources
Table A.2 — UC 1-02 Provide the teacher with relevant learning resources
Contributor Norway
Main stakeholders learning resource suppliers, teachers
Description Given a supplier of learning material, how can the supplier provide the teacher with
relevant learning resources for the teacher's learning scenario based on activity data
Teaching / learning a) Assessment results data supplied to learner to support mastery-based learning,
Activities supported / cooperative learning, collaborative learning, team-based learning or other
Pedagogical approaches approaches;
b) Data provided to instructor from scaffolded assessments to support identification
of required learning resources for a problem-based learning approach;
c) Learning outcome data used at an institutional level to analyse and evaluate
whether program goals are being met when using a competency-based approach.
Technologies / technical Techniques: AI/ML, user modelling, citation tracking, statistical, mathematical, compu-
approaches and tational techniques, etc.
applications
Applications: personalization, prediction, trend analysis, etc.
[1]
Industry standards / ISO/IEC TR 20748-1:2016
specifications [2]
ISO/IEC TR 20748-2:2017
[3]
ISO/IEC TS 20748-3:2020
[4]
ISO/IEC TS 20748-4:2019
[10]
1EdTech
Potential areas where N/A
standardization is needed
Additional notes / N/A
considerations
© ISO/IEC 2026 – All rights reserved
A.3 UC 1-03 Teacher support on providing personalized teaching
Table A.3 — UC 1-03 Teacher support on providing personalized teaching
Contributor Finland
Main stakeholders students, teachers, learning content providers
Description Provide the teacher with information on each student learning style and capability at
that moment. Based on this, the teacher can tune suitable learning content to those
learners that benefit that best. The teacher configures the content from a course con-
tent blocks that contain parallel modules for different learning styles.
Teaching / learning a) Assessment results data supplied to learner to support mastery-based learning,
Activities supported / cooperative learning, collaborative learning, team-based learning or other
Pedagogical approaches approaches;
b) Data provided to instructor from scaffolded assessments to support identification
of required learning resources for a problem-based learning approach;
c) Learning outcome data used at an institutional level to analyse and evaluate
whether program goals are being met when using a competency-based approach.
Technologies / Technical Techniques: AI/ML, user modelling, citation tracking, statistical, mathematical, compu-
Approaches and tational techniques, etc.
Applications
Applications: personalization, prediction, trend analysis, etc.
[1]
Industry Standards / ISO/IEC TR 20748-1:2016
[2]
Specifications
ISO/IEC TR 20748-2:2017
[3]
ISO/IEC TS 20748-3:2020
[4]
ISO/IEC TS 20748-4:2019
[10]
1EdTech
Potential areas where Tag a curriculum with content identifiers. These identifies are then linked to courses.
standardization is needed
Additional notes / N/A
considerations
A.4 UC 1-04 Adaptive learning analytics service to resolve education divide
Table A.4 — UC 1-04 Adaptive learning analytics service to resolve education divide
Contributor South Korea
Main stakeholders Pre-K and K1 to K-9 Students who use self-directed learning service based on AI tutor-
ing
Parents who are full-time workers and hard to care their children due to limited time
Teachers who need to focus on at-risk population in their class
Governments who need to take care of the socially underprivileged families

© ISO/IEC 2026 – All rights reserved
TTabablele A A.44 ((ccoonnttiinnueuedd))
Description From May 2020, some of the governments in Korea have started providing mobile con-
tent platform and learning analytics service in terms of dashboard reporting system
by selecting socially disadvantaged families in need of care. Within the first 3 months,
there were very positive impacts on learners who made progress in self-directed
learning habits and in academic achievements.
a) Learning environment
Contents which are consist of subject such as "Korean, English, Math, Social Studyand
Science" as well as extracurricular such as "Creativity, Humanism, Coding, Physical
Computing, AI, Reading, Soft Skills and others", are provided in subscription type
through the learning-dedicated mobile device. Learning plans in terms of learning
pathway are customized for each learner, and intervention guides for individual vul-
nerable knowledge and content preferred by learners are provided through a sugges-
tion page using content platforms and AI models.
Human tutors provide one-on-one management, but due to time constraints, there is a
limit to in-depth management, so an active AI tutor with voice interfaces is supporting
the role of learning guides and tutoring as a digital companion.
b) Data for learning analytics
[10]
The content platform of this use case adopted 1EdTech Caliper A
...