Learner Models: A Systematic Literature Research in Norms

and Standards

Felix Böck

, Dieter Landes

and Yvonne Sedelmaier

Department of Electrical Engineering and Informatics, Coburg University of Applied Sciences and Arts,

96450 Coburg, Germany

Keywords: Learner Model, Digital Twin, Learner Modeling, Systematic Literature Re view, Systematic Survey,

Systematic Review, Higher Education, Computer Science, Norms, Standards.

Abstract: Learners in higher education tend to become an increasingly heterogeneous group. Paying proper attention to

individual differences is a challenge that may be leveraged by ind ividualized automated recommendations of

learning elements. This presupposes some knowledge of the learners’ profil es which can be captured in so-

called learner models. Yet, so far, there is no comprehensive overview of existing standards and their

contribution related to learner models. This paper presents the resul ts of a systematic literature research

devoted to norms and standards in the area of learner models. As it turns out, 16 norms or standards have

some relationship to learner models, 3 of them present their versions of a learner model. None of the standards

and norms offers a comprehensive learner model, but in their entirety these models provide hints on reasonable

contents and structure of learner models.

1 INTRODUCTION

Learners in higher education tend to become an

increasingly heterogeneous group. Differences

between learners exist in terms of, e.g., individual

levels of knowledge and competences, varying

learning styles, or individual preferences for (digital)

media. Instructors face severe difficulties in paying

proper attention to these individual differences in

physical classes since individual coaching does not

scale to larger groups of learners.

A potential solution for this dilemma lies in

supplementary offerings of learning materials that are

targeted to the individual needs of a learner in a

specific situation. Yet, such supplementary offerings

face some challenges since learners often do not

know the best learning activity for them in a specific

situation. This implies that mechanisms are needed to

recommend meaningful next activities and materials.

Consequently, such recommendations presuppose

knowledge of properties of individual learners that

would determine the usefulness of learning material

in a specific situation. However, the required

https://orcid.org/0000-0001-7382-8333

https://orcid.org/0000-0002-0741-3540

https://orcid.org/0000-0003-1022-1467

knowledge about the learner is not available,

especially when initialising new (recommendation)

systems (called cold start problem), so that the

possibility of using learning analytics is very limited.

Information systems often have the so-called cold

start problem at the beginning (Bobadilla et al., 2012)

– due to a general lack of users within the system

(Schafer et al., 2007; Schein et al., 2002), the learner

is new to the system and no data is yet available (Park

& Chu, 2009; Park & Tuzhilin, 2008) or the learning

element has been newly created and has not yet been

used (Du Boucher-Ryan & Bridge, 2006; Rashid et

al., 2002) – i.e. there is no or too little data available

to be able to generate meaningful recommendations

from it. In addition to the behavioural data of the

individual, we believe that the structural

characteristics – such as age, previous educational

path, etc. – are also important in order to be able to

create a digital image of the learner that is as

comprehensive as possible (Bodily et al., 2018). This

type of knowledge is usually captured in a so-called

learner model or digital twin (Furini et al., 2022;

Hlioui et al., 2016).

Böck, F., Landes, D. and Sedelmaier, Y.

Learner Models: A Systematic Literature Research in Norms and Standards.

DOI: 10.5220/0012556100003693

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024) - Volume 2, pages 187-196

ISBN: 978-989-758-697-2; ISSN: 2184-5026

187

Learner models have been discussed for quite

some time, yet without consensus on which contents

should be captured. This paper presents the results of

a systematic analysis of standards and norms that deal

with aspects of learner models in e-learning systems.

With such an analysis, we want to identify

requirements for structure and contents of learner

models.

As it turns out, 16 standards and norms tend to be

relevant. Yet, there is no single source that specifies

the contents of a learner model in a comprehensive

fashion since some of the sources are too specific,

while others cover a variety of aspects in addition to

those related to learner models.

In the following, the paper will clarify important

terms and discuss related work, before section 3

details the employed research approach. Section 4

presents the results of the systematic literature

research. Section 5 concludes the paper and gives an

outlook to future work. A major effort will consist in

combining the results of the analysis of standards and

norms with results from a systematic literature

analysis of scientific papers on learner models that we

conduct in parallel to the work reported here.

Findings of the latter analysis will be presented in a

separate paper.

2 RESEARCH THREADS

2.1 Definition of Terms

There is no widely accepted definition of the term

“Learner Model”. Rather, discussions of this term are

often controversial. Still, a consistent definition of the

term is necessary as a basis for further work. As a first

step, the two components of the term “Learner

Model” will be considered separately before we

return to the term in its entirety.

A model describes some aspects of a real system

or subject relevant for the respective purpose as a

conscious abstraction of its real counterpart. The most

important characteristics of models are:

 Models do not describe the respective system

or subject completely, but from a certain point

of view while neglecting others;

 several models can exist for a system or subject

in parallel;

 models exist at different levels of abstraction,

ranging from a high-level view to a detailed

representation;

 all relevant properties of the original must be

adequately and completely mapped to

properties of the model (validation required).

In our field of research, a model serves as a digital

representation of students (learners), with the aim of

providing individual learning support in a digital

environment.

“Two things are crucial for an adaptive system to

work: the existence of a means to adapt the task and

the ability to detect the need for adaptation” (Johnson

& Taatgen, 2005, p. 430). This ability to adapt is

generally represented by so-called user models.

Interpretations of the term User Model (UM) differ

widely in the literature (Kay et al., 2022) and depend

on the intended use, scope, domain and the way to

collect information about the user (Hlioui et al.,

2016). In general, a “user model is a representation of

static and dynamic information about an individual

that is utilized throughout the whole interaction

process aiming to trigger a number of adaptation and

personalization effects. […] [This user model] entails

all the information which is considered important in

order to adapt and personalize the user interface

(content and navigation) and functionalities to the

unique characteristics of a user. […] Depending on

the domain and goals of the system, user models can

include different kinds of characteristics about the

users (e.g., interests, preferences, traits, etc.) or data

with respect to their overall context of use (e.g.,

environment, time, interaction device type, etc.)”

(Germanakos & Belk, 2016, p. 79).

Our research focusses on higher education where

the general user model takes a specific shape as

learner model. A Learner Model (LM) thus contains

the individual characteristics and interaction data of a

single learner, represents them explicitly in a

machine-internal representation, and includes many

different aspects, which ultimately depend on the

purpose of the application. The terms Student Model

or Digital Twin, which often appear in the literature,

are synonyms of Learner Model in our setting, as all

our learners are usually university students. The

process of creating such a learner model is called

learner modelling (Khenissi et al., 2015; Piao &

Breslin, 2018).

2.2 Research Question & Field

Even though learner models are relevant for different

disciplines, we explicitly focus here on computer

science (technical implementation) and pedagogy

(teaching methodology) and deliberately ignore other

important sub-disciplines, such as the humanities for

the time being. In doing so, we look at relevant norms

and standards from different points of view from the

perspective of computer science and pedagogy with

the aim of answering the question: “How can

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188

Learner Models look like and what are their

structure and components?”. The selection of the

models to be considered is guided by their (technical)

feasibility, even if they are currently only described

theoretically. Purely theoretical thought experiments

without practical relevance and implementation

possibilities will not be shortlisted.

2.3 Related Work

A widely recognized, well-founded learner model is

still lacking, even though learner models are

becoming increasingly important with the growing

number of e-learning systems and the possibilities of

computer-assisted learning. There is some work on

defining learner models within different projects

(Hlioui et al., 2016; Stiubiener et al., 2010) or for

different purposes (Bodily et al., 2018; Bull & Kay,

2007). A systematic review of scientific literature that

is conducted in parallel to the work reported here will

provide a comprehensive overview of existing learner

models. To the best of our knowledge, however, there

is no systematic analysis of learner models based on

standards yet.

3 RESEARCH DESIGN &

METHODS

With the increase in digital teaching and learning

opportunities, the desire for uniform standards in the

field of e-learning is growing. The same applies to

user models in digital ecosystems. To get an overview

of related work on the topic of learner models, a

systematic literature review (SLR) according to

(Brereton et al., 2007; Kitchenham & Charters, 2007)

is useful. The process of a systematic literature review

warrants a balanced and objective summary and

overview of the topic of the research question, which

is (theoretically) reproducible and transparent at any

time. Well-defined requirements form the basis for

the five-stage review process: (manual) search,

plausibility check, selection based on filter, check

references of each relevant item and total result. The

planning phase of the systematic literature review

consists of the following general steps.

3.1 Relevant Research Sources

Apparently, there is no single point of contact for

norms and standards, but a wide range of different

providers, from government agencies to consortia of

various commercial companies. Seven popular

electronic database sources [RS1 – RS6] were

selected as the most relevant for the area of standards

in e-learning for adaptive teaching and learning. An

important criterion in the selection of data sources

was to obtain published works – standards, norms,

drafts or even technical reports – from the searched

area that fulfill basic quality criteria (e.g. peer-

reviewed) and are accessible to the public (to a certain

extent). If there are results that cannot be assigned to

any of the above-mentioned publishers, these are

summarized under various and shown separately in

the results. The aim was to identify and evaluate

existing standards in the area of learner models. For

this purpose, current standards, but also discontinued

ones and drafts were used for selection.

Table 1: Overview of Relevant Sources.

Ref.

Provider

RS1

World Standards Cooperation (WSC)

www.worldstandardscooperation.org

RS1a

International Organization for Standardization

(ISO)

www.iso.org

RS1b

International Electrotechnical Commission (IEC)

www.iec.ch

RS1c

International Telecommunication Union (ITU)

www.itu.int

RS2

IEEE Standards Association (IEEE SA)

standards.ieee.org

RS3

1EdTech Consortium

www.1edtech.org

RS4

World Wide Web Consortium (W3C)

www.w3.org

RS5

Internet Engineering Task Force (IETF)

www.ietf.org

RS6

Organization for the Advancement of Structured

Information Standards (OASIS)

www.oasis-open.org

This table of providers is supplemented with a

loose collection of other standardised specifications,

for example from ministries/governments or other

organisations that have written guidelines on this

topic. These are grouped under the term “various”.

The spectrum of standards publishers ranges from

international standards publishers to professional

associations and international industry consortia.

As an alliance, the World Standards Cooperation

(WSC) is a globally active association of the

voluntary, consensus-based system of

standardisation. Of the three member organisations,

the two international standardisation organisations

International Organization for Standardization (ISO)

and International Electrotechnical Commission

(IEC) are relevant, which jointly develop

Learner Models: A Systematic Literature Research in Norms and Standards

189

international standards in the fields of electrics and

electronics. Of the most important professional

organisations in this case, the Association for

Computing Machinery (ACM) and the Institute of

Electrical and Electronics Engineers (IEEE), the

latter develops global consensus-based standards

with the IEEE Standards Association (IEEE SA),

including in the area of learning technology and

information technology. Numerous international

industry partners are also joining forces to form larger

consortia in order to better represent their interests.

During the research phase, four consortia emerged as

an important opportunity. The 1EdTech Consortium

(formerly the IMS Global Learning Consortium) aims

to develop open standards for the e-learning sector. In

recent years, numerous IMS specifications have

become global de facto standards in the field of e-

learning, the issues surrounding networked "Internet

technologies", such as the WWW, are highly relevant.

This is why the two consortia World Wide Web

Consortium (W3C) and the Internet Engineering Task

Force (IETF) are an important starting point in the

search for relevant standards in this area. Less present

organisations, such as the Organisation for the

Advancement of Structured Information Standards

(OASIS), are also represented in this area and should

also be considered.

3.2 Search Strategy

In order to obtain all the desired results and thus to be

able to create as comprehensive an overview of the

domain as possible, it is important in the first step to

create a list of synonyms, abbreviations and

alternative spellings for the selected search terms.

Synonym dictionaries, spelling dictionaries and,

lastly, feedback from our experts were used for this

step. Table 2 shows the search terms used, including

various spellings and abbreviations. This list does not

claim to be complete, but should cover a large part of

the search terms. In the second step, the different

variations are meaningfully linked with each other

using Boolean operators.

The organisations that strive for standards were

searched with a slightly modified search strategy. For

this purpose, the search query above was simplified

and divided into several sequential search queries. If

a search was not possible or not feasible in a

meaningful way, all available standards were

manually sifted through including reading and

assessing the summary in interaction with the title.

Subsequently, the importance for the topics of

eLearning in connection with Learner Models was

checked manually via referenced standards. In

addition, all standards in the eLearning field were

searched by hand so that no important references

were left unnoticed.

Table 2: Search Query Components.

Search Term

Variations

Category

Attribute

Example values

for attributes

General Attributes

Metadata

Name of the Standard

Standard for

Awesomeness

Publication Date

01/01/2000

Last Update

05/30/2023

Standardisation

organisation/

Publisher

Standards

Association

Types of Standard

Technical

Specifications (TS)

Discipline

Computer Science

Status/

Revision process

Active Standard

Version

Superseded Standards

---

Linked Standards

---

Content Data

Central ideas / Target

- to standardise a

learner model that is

as generalised as

possible

- basis for the

specialisation of own

LMs

Specific

Attributes

---

4 SYSTEMATIC LITERATURE

REVIEW

4.1 Evaluation of Results

Figure 1 shows the individual (interim) results after

applying the methodology from chapter 3.

From a total of 869 (manually) searched possible

results, only 16 relevant results remain after the

process of systematic literature research. The

continuum of the scope of the standards and norms to

be analysed in detail ranges from around 30 pages of

technical reports to several hundred pages of

international standards. These relevant results are

compared and discussed in the following chapter.

Learner Models: A Systematic Literature Research in Norms and Standards

191

Figure 1: Overview of the (interim) results of SLR.

CSEDU 2024 - 16th International Conference on Computer Supported Education

192

4.2 Overview of Results

Before the remaining standards are compared with

each other, they are briefly presented individually

below.

ISO/ IEC 19479:2019 (ISO/IEC 19479, 2019)

Model for recording and exchanging attested learning

achievement information in a formal learning

environment to express the level, content and type of

qualification. In addition, it defines refinements to the

learner mobility achievement award (LMAI) model

for representing the digital diploma supplement,

which is defined in terms of a conceptual model and

a domain model.

ISO/ IEC 19788-9:2015 (ISO/IEC 19788-9, 2015)

Specification for metadata elements and their

attributes for the description of learning resources.

Providing a standards-based approach to the

identification and specification of the metadata

elements required to describe a learning resource.

ISO/ IEC TR 20748-1:2016 (ISO/IEC TR 20748-1,

2016)

Specifies a reference model that identifies the diverse

IT system requirements of learning analytics

interoperability. The reference model identifies

relevant terminology, user requirements (use cases),

workflow and a reference architecture for learning

analytics, assessments, accessibility preferences

and data flow and data exchange.

ISO/ IEC TS 29140:2021 (ISO/IEC 29140, 2021)

Provides a learner information model specific to

mobile learning to enable learning, education and

training environments to reflect the specific needs of

mobile participants. The use of a learner information

model for mobile technology in learning, education

and training (mobile learning) is also addressed.

W3C Working Group Note: Making Content

Usable for People with Cognitive and Learning

Disabilities (W3C Working Group, 2021)

Planning, creation and process of accessible

applications usable by people with cognitive and

learning disabilities.

IEEE P1484.2 (IEEE P1484.2, 1997)

Specify the syntax and semantics of a 'Learner

Model', which will characterize a learner and his or

her knowledge/abilities. These elements to be

represented in multiple levels of granularity, from a

coarse overview, down to the smallest conceivable

sub-element and allow also different views of the

Learner Model (learner, teacher, parent, school,

employer, etc.) and substantially address issues of

privacy and security. The Learner Model will provide

more personalized and effective instruction.

IEEE P9274.4.1 (IEEE P9274.4.1, 2022)

Describes the technical implementation of xAPI.

IEEE 9274.1.1-2023 (IEEE 9274.1.1, 2023)

Standardizes the data model format and

communication protocol for learning experience data

allowing vendors to build interoperable solutions and

to take advantage of many products that support the

xAPI.

IEEE P2997 (IEEE P2997, 2021)

Defines the Enterprise Learner Record (ELR) data

model for the various data objects. The ELR data

model preserves data ownership and integrity by

providing indications to where raw learner data is

stored and by providing the ability to track: - the

learner's path through different organizations - the

variety of learning experiences - demonstrated

competencies - conferred credentials - employment

history. Additionally, it defines the transfer methods

and application programming interface (API) for

communicating learner records between services that

adhere to the specification.

1EdTech Learner Information Package

Specification (1EdTech, 2001)

Addresses the interoperability of internet-based

Learner Information systems with other systems that

support the Internet learning environment.

1EdTech Student Learning Data Model (1EdTech,

2020)

Provides a complete view of a digital (1EdTech)

ecosystem interconnected with real-time data. This

includes the following sub-areas: User and

Organization, Enrollment and Attendance, Pathways to

Competency, Instructional Resources, Assignment and

Assessment, Learning Activities, Learner Record.

OASIS Specification for JSON Abstract Data

Notation (JADN) (OASIS JADN, 2021)

A formal description technique for expressing the

information needs of communicating applications,

and rules for generating data structures to satisfy

those needs.

Common Education Data Standards (Common

Education Data Standards, 2022)

Data standards and a shared vocabulary for

education data.

Dynamics 365 Education Accelerator (Microsoft

Education, 2023a)

A proprietary and commercial technical

realisation of an education data model component.

Learner Models: A Systematic Literature Research in Norms and Standards

193

Open Education Analytics (OEA) (Microsoft

Education, 2023b)

An open source-based reference architecture to

develop modern data intelligence capabilities.

Ed-Fi Unifying Data Model (UDM) (Ed-Fi, 2023)

An educational data tool suite (unifying data model,

data exchange framework, application framework,

and sample dashboard source code) that enables vital

academic information on K-12 students to be

consolidated from the different data systems of school

districts so that educators can start addressing the

individual needs of each student from day one, and

can measure progress and refine action plans

throughout the school year. Elements are aligned to

CEDS.

4.3 Discussion of Results

The remaining 16 publications were compared with the

aim of forming a basis for a learner model to be

developed. To this end, various aspects of the learner

model were examined in more detail. Basically, the

standards already differ in terms of the purpose and the

reason for their existence. These range from exchange

options for learner data (system requirements for

interoperability) (ISO/IEC 19479, 2019) (ISO/IEC TR

20748-1, 2016) and their implementation (IEEE

P9274.4.1, 2022), through the specification of learning

resources (ISO/IEC 19788-9, 2015), for example for

people with cognitive and learning disabilities (W3C

Working Group, 2021), to a complete comprehensive

digital ecosystem in which the learner is part of it

(1EdTech, 2020) (Ed-Fi, 2023). The target groups of

the various standards are also broadly diversified and

range from individual learners (ISO/IEC 29140, 2021)

(1EdTech, 2001) to entire groups involved in the

teaching and learning process, for example learners,

teachers, parents and also the administrative

educational institution (IEEE P2997, 2021) (Common

Education Data Standards, 2022). The type and origin

of the standard also differs from, for example, industry

standards (Microsoft Education, 2023a) (Microsoft

Education, 2023b) to active standards from consortia

(IEEE 9274.1.1, 2023) (OASIS JADN, 2021) or their

predevelopment: Drafts (IEEE P1484.2, 1997).

Finally, of the remaining standards, three can be

highlighted that are explicit learner models: (IEEE

P1484.2, 1997) (1EdTech, 2001) and (ISO/IEC

29140, 2021). (ISO/IEC 29140, 2021) describes a

mobile learner model and its specific attributes for

learning, such as device, connectivity, or location.

The (1EdTech, 2001) deals with the

interoperability of Internet-based information systems

for learners with other systems. For this purpose, the

learner is specified and stored in a learner information

server and made available for other applications. The

learner specification is based on a data model that

describes the characteristics of a learner that are

necessary for recording and managing the learner's

progress, goals, achievements and learning experience.

The draft (IEEE P1484.2, 1997) attempted to

specify the syntax and semantics of a learner model

that characterizes a learner and their knowledge/skills

and was first drafted in 1997 with the aim of

centralizing Public and Private Information for

Learners (therefore also known as PAPI Learner).

The learner information is divided into six categories:

• PAPI Learning Staff (demographic data)

• PAPI Learner Relations (relationships with

other learners)

• PAPI Learner Safety (enrollment information)

• PAPI Leaner Performance (future goals)

• PAPI Learner Preference (preferences)

• PAPI Leaner Portfolio (previous experience)

The early date of first publication is a possible

indicator that could point to this, but not a sufficient

condition for the conclusion that the topic of learner

models was already very important at the end of the

1990s. In the early 2000s, the draft was transferred

from IEEE SA (IEEE/LTSC) to ISO (ISO/IEC JTC1

SC36) in collaboration with IMS (now 1EdTech), but

soon disappeared from the scene. The research could

not uncover any obvious reason why work on the

draft was discontinued.

Research into possible norms and standards in the

area of learner models has shown that there are very

few approaches in this field and that these are either

specialized (ISO/IEC 29140, 2021) or very complex

and more than just learner models (1EdTech, 2001)

or have not yet been pursued further (IEEE P1484.2,

1997). However, these can form a very good basis for

developing your own learner models, such as

(Common Education Data Standards, 2022) or (Ed-

Fi, 2023) and can be extended according to one’s own

needs.

For reasons of brevity, not all details can be

presented here. However, in order to ensure the

reproducibility of the SLR and the transparency of the

methodology, all information on the SLR is made

available to the public online (Böck, 2023).

5 CONCLUSION & FUTURE

WORK

Heterogeneity of learners in higher education is

continuously increasing due to, e.g., individual levels

CSEDU 2024 - 16th International Conference on Computer Supported Education

194

of knowledge and competences, varying learning

styles, or individual preferences for (digital) media.

Offering learning elements with an optimal fit to the

learner’s specific needs presupposes detailed

knowledge of the learner’s characteristics, captured in

a learner model. A systematic literature research on

existing norms and standards in the area of learner

models revealed 16 relevant publications, 3 of which

present their version of a learner model. Still, these

models are not comprehensive or overloaded with

additional content that does not contribute to

characterizing learners. Nevertheless, these models

provide some indications what should be included in

a learner model and how such a model might be

organized.

In parallel to the work reported here, we work on

a systematic literature research and analysis of

scientific publications on learner models. Next steps

will include matching the results of both analyses,

standards on the one hand and scientific literature on

the other, in order to derive requirements on

appropriate contents of such models.

ACKNOWLEDGEMENTS

This work is funded by the German Ministry of

Education and Research (Bundesministerium für

Bildung und Forschung) under grant 16DHBKI090

as part of the VoLL-KI project.

REFERENCES

1EdTech. (2001). Learner Information Package

Specification (Version 1.0.1). 1EdTech Consortium

(IMS). https://site.imsglobal.org/standards/sldm

1EdTech. (2020). Global Student Learning Data Model —

Interconnected Data Powers Better Teaching and

Learning. 1EdTech Consortium. https://site.imsglobal.

org/standards/sldm

Bobadilla, J., Ortega, F., Hernando, A., & Bernal, J. (2012).

A collaborative filtering approach to mitigate the new

user cold start problem. Knowledge-Based Systems, 26,

225–238.

Böck, F. (2023). All research-relevant attachments from

scientific publications for reproducibility and

transparency [dataset]. GitHub.com.

https://github.com/DevArchitectMaster/research-

appendix

Bodily, R., Kay, J., Aleven, V., Jivet, I., Davis, D., Xhakaj,

F., & Verbert, K. (2018). Open learner models and

learning analytics dashboards: A systematic review.

Proceedings of the 8th International Conference on

Learning Analytics and Knowledge, 41–50.

Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M.,

& Khalil, M. (2007). Lessons from applying the

systematic literature review process within the software

engineering domain. Journal of Systems and Software,

80(4), 571–583.

Bull, S., & Kay, J. (2007). Student Models that Invite the

Learner In: The SMILI:() Open Learner Modelling

Framework. International Journal of Artificial

Intelligence in Education, 17(2), 89–120.

Common Education Data Standards. (2022). CEDS Data

Model (Version 11) [Draft].

https://ceds.ed.gov/dataModel.aspx

Du Boucher-Ryan, P., & Bridge, D. (2006). Collaborative

Recommending using Formal Concept Analysis.

Knowledge-Based Systems, 19(5), 309–315.

Ed-Fi. (2023). Unifying Data Model (UDM) (Ed-Fi Data

Standard for Suite 3 v5.0-pre2). https://techdocs.ed-

fi.org/display/EFDS5/Ed-Fi+Data+Standard+v5

Furini, M., Gaggi, O., Mirri, S., Montagero, M., Pelle, E.,

Poggi, F., & Prandi, C. (2022). Digital Twins and

Artificial Intelligence as Pillars of Personalized

Learning Models. Communications of the ACM, 65(4),

98–104.

Germanakos, P., & Belk, M. (2016). User Modeling. In P.

Germanakos & M. Belk, Human-Centred Web

Adaptation and Personalization (pp. 79–102). Springer

International Publishing.

Hlioui, F., Alioui, N., & Gargouri, F. (2016). A survey on

learner models in adaptive E-learning systems. 2016

IEEE/ACS 13th International Conference of Computer

Systems and Applications (AICCSA), 1–7.

IEEE 9274.1.1. (2023). IEEE Standard for Learning

Technology—JavaScript Object Notation (JSON) Data

Model Format and Representational State Transfer

(RESTful) Web Service for Learner Experience Data

Tracking and Access. IEEE SA.

IEEE P1484.2. (1997). Standard for Information

Technology—Learning Systems—Learner Model

(Draft 8). IEEE SA.

IEEE P2997. (2021). Standard for Enterprise Learner

Record. IEEE SA.

IEEE P9274.4.1. (2022). Recommended Practice for the

Implementation of the Experience Application

Programming Interface (API) 1.0.3. IEEE SA.

ISO/IEC 19479. (2019). Information technology for

learning, education, and training—Learner mobility

achievement information (LMAI). ISO/IEC.

ISO/IEC 19788-9. (2015). Information technology—

Learning, education and training—Metadata for

learning resources—Part 9: Data elements for persons.

ISO/IEC.

ISO/IEC 29140. (2021). Information technology for

learning, education and training—Nomadicity and

mobile technologies (Version 1). ISO/IEC.

ISO/IEC TR 20748-1. (2016). Information technology for

learning, education and training — Learning analytics

interoperability — Part 1: Reference model. ISO/IEC.

Johnson, A., & Taatgen, N. (2005). User Modeling. In R.

W. Proctor & K.-P. L. Vu (Eds.), Handbook of human

Learner Models: A Systematic Literature Research in Norms and Standards

195

factors in Web design (pp. 424–439). Lawrence

Erlbaum Associates.

Kay, J., Bartimote, K., Kitto, K., Kummerfeld, B., Liu, D.,

& Reimann, P. (2022). Enhancing learning by Open

Learner Model (OLM) driven data design. Computers

and Education: Artificial Intelligence, 3, 100069.

Khenissi, M. A., Essalmi, F., Jemni, M., & Kinshuk.

(2015). Learner Modeling Using Educational Games: A

Review of the Literature. Smart Learning

Environments, 2(1), 6.

Kitchenham, B. A., & Charters, S. (2007). Guidelines for

performing Systematic Literature Reviews in Software

Engineering (EBSE 2007-001). Keele University.

Microsoft Education. (2023a). Dynamics 365 Education

Accelerator (v5.0.3.1). https://learn.microsoft.com/en-

us/dynamics365/industry/accelerators/edu-data-model

Microsoft Education. (2023b). Open Education Analytics

(OEA) (v0.1). https://openeducationanalytics.org

OASIS JADN. (2021). Specification for JSON Abstract

Data Notation (JADN) Version 1.0 (Committee

Specification 01). Organization for the Advancement of

Structured Information Standards. OASIS.

Park, S.-T., & Chu, W. (2009). Pairwise preference

regression for cold-start recommendation. Proceedings

of the Third ACM Conference on Recommender

Systems, 21–28.

Park, S.-T., Pennock, D., Madani, O., Good, N., &

DeCoste, D. (2006). Naïve filterbots for robust cold-

start recommendations. Proceedings of the 12th ACM

SIGKDD International Conference on Knowledge

Discovery and Data Mining, 699–705.

Park, Y.-J., & Tuzhilin, A. (2008). The long tail of

recommender systems and how to leverage it.

Proceedings of the 2008 ACM Conference on

Recommender Systems, 11–18.

Piao, G., & Breslin, J. G. (2018). Inferring user interests in

microblogging social networks: A survey. User

Modeling and User-Adapted Interaction, 28(3), 277–

329.

Rashid, A. M., Albert, I., Cosley, D., Lam, S. K., McNee,

S. M., Konstan, J. A., & Riedl, J. (2002). Getting to

know you: Learning new user preferences in

recommender systems. Proceedings of the 7th

International Conference on Intelligent User

Interfaces, 127–134.

Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S.

(2007). Collaborative Filtering Recommender Systems.

In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The

Adaptive Web (Vol. 4321, pp. 291–324). Springer

Berlin Heidelberg.

Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M.

(2002). Methods and metrics for cold-start

recommendations. Proceedings of the 25th Annual

International ACM SIGIR Conference on Research and

Development in Information Retrieval, 253–260.

Stiubiener, I., Vicente, W., & Rosatelli, M. (2010). An

Approach in Personalisation and Privacy in E-Learning

Systems. In M. Buzzi (Ed.), E-learning. InTech.

W3C Working Group. (2021). Making Content Usable for

People with Cognitive and Learning Disabilities.

World Wide Web Consortium (W3C).

CSEDU 2024 - 16th International Conference on Computer Supported Education

196