Redesigning Personal Learning Environments: Consolidation of

Empirical Findings and Conceptual Research Against the Background of

a National Educational Infrastructure

onke Erdmann

, Swathi Krishnaraja

, Benja Wiencke

and Ulrike Lucke

University of Potsdam, Department of Computational Science, An der Bahn 2, 14476 Potsdam, Germany

Keywords:

Personal Learning Environments, Digitalization, Digital Learning Spaces, Educational Infrastructure,

Educational Services, Self-Regulated Learning.

Abstract:

Personal Learning Environments (PLE) are often associated with learning spaces that offer learners the ability

to structure and self-regulate their learning processes. From a technical point of view, current learning spaces

are often fragmented and do not comprise educational content, services, and tools within a shared learning

space. In this paper, we present our ﬁndings from an empirical study conducted with (n=)32 samples of

students, to identify the present needs for designing a networked digital learning space. Furthermore, we

break down how we integrate and/or redesign existing educational services, technologies, and tools to align

with the current demands. We enhance the discourse on the added values of functionalities for a national digital

educational infrastructure by categorizing them according to the SAMR model. SAMR is an acronym for

Substitution, Augmentation, Modiﬁcation, and Redeﬁnition. This categorization enables a more differentiated

understanding of the components within a PLE and how they interact. Based on such an understanding,

characteristics for an efﬁcient e-learning infrastructure can be determined from the learner’s perspective.

1 INTRODUCING A NATIONAL

INFRASTRUCTURE FOR

EDUCATION

During their lifelong learning path, individuals fre-

quently encounter challenges associated with both

vertical and horizontal transitions. Vertical transitions

involve moving across different educational sectors,

such as transitioning from school to higher education.

During these transitions, individuals must repeatedly

recompile personal data and educational materials.

Horizontal transitions refer to movement within the

same educational sector, such as when students trans-

fer between universities or participate in international

education programs. Generally, both vertical and hor-

izontal transitions present challenges in the continuity

and recognition of educational progress in a digital

learning environment (Knoth. et al., 2022).

https://orcid.org/0009-0000-6715-5056

https://orcid.org/0000-0001-9079-6628

https://orcid.org/0009-0003-8048-5369

https://orcid.org/0000-0003-4049-8088

In 2021 the German Federal Ministry of Educa-

tion and Research (BMBF) initiated the development

of a National Infrastructure for Education, designed

to interconnect learners, educators, and educational

offerings (BMBF, 2021). This initiative aims to allow

learners to seamlessly construct their individual life-

long learning pathways, transitioning from one edu-

cational offering to another throughout their lifetime.

An open beta version is announced for 2025. The pro-

totype of this infrastructure and the subject of this ar-

ticle is developed in the BIRD Project. BIRD serves

as a technical reference and also provides associated

research results (Knoth. et al., 2022).

In a previous project, more than 170 user stories

were collected from learners and teachers. This sys-

tematic description was further condensed into con-

cepts of a Personal Learning Environment (PLE) (Kiy

et al., 2014), which constitutes a component of the

initial feasibility study and continues to represent the

core of the BIRD prototype (Bustorff et al., 2023).

Additional components were implemented, such as

the Learning Path Finder (LPF) to support users with

personalized recommendations based on their con-

cerns and shared data, showcasing educational of-

Erdmann, S., Krishnaraja, S., Wiencke, B. and Lucke, U.

Redesigning Personal Learning Environments: Consolidation of Empirical Findings and Conceptual Research Against the Background of a National Educational Infrastructure.

DOI: 10.5220/0013297200003932

In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 1, pages 92-101

ISBN: 978-989-758-746-7; ISSN: 2184-5026

ferings and diverse information for potential educa-

tional pathways (Ziemann et al., 2023). Moreover, the

Buddy Finder (BF) is currently being introduced by

BIRD to facilitate mutual exchange among users. It is

tailored to improve user interaction and collaboration,

fostering a community-oriented approach to learning

and knowledge exchange (Eilebrecht and Beskorova-

jnov, 2025). A fundamental premise in the concep-

tion of the prototype was to not replace established

offerings, but only to interconnect them and thus to

supplement them with new functions, tools or usage

options (Lucke, 2024). For this reason, we rely on the

SAMR model (Puentedura, 2006) as a sorting key for

dealing with educational services. In this article, we

compare the idea of expanding existing services both

from the empirical perspective of a user survey and

from a conceptual point of view and condense both

into a more nuanced understanding of PLEs.

The remainder of this article is structured as fol-

lows. In Section 2, we review current strategies for

personal, smart or adaptive learning environments.

Then we examine the ﬁndings from user surveys and

studies on digital learning spaces in higher education

in Section 3, focusing on student expectations and ex-

periences. This is further elaborated in Section 4 with

a description of the BIRD prototype as a realization

of a PLE. After a brief recapitulation of the architec-

ture, components and functions of the infrastructure,

particular attention is paid to the analysis according to

the SAMR model. Based on this differentiated under-

standing, a concept of a PLE within the national edu-

cational infrastructure is outlined in Section 5. Con-

clusively, the ﬁndings are summarized and an outlook

on future directions for research and development is

given.

2 EXISTING APPROACHES FOR

PERSONAL LEARNING

ENVIRONMENTS

Though the notion of a PLE has existed for some

time, it has been thus far understood from a personal

or institutional perspective (Ebner and Taraghi, 2010;

Buchem et al., 2011; Hafer et al., 2014). Different

patterns for PLE implementations have been identi-

ﬁed (Kiy and Lucke, 2016), while all have in com-

mon to put the learner in control of his own learning

process for accomplishing the desired learning goals.

Before the advent of virtual solutions, analog and

later hybrid solutions already existed that included

physical notebooks, ﬁling systems, study groups, li-

braries, resource centers, et cetera, but the ﬂexibility

and broad range of connecting various digital content,

services, and tools via the internet was a major driver

of previous digital PLE developments.

Personal contexts are far more common for PLE

than institutional ones (Kiy and Lucke, 2016). Yet,

typical IT services especially in higher education in-

stitutions continuously evolved towards a consistent

ecosystem (Hafer et al., 2014), which could some-

times be considered a PLE like the Potsdam (Kiy

et al., 2014) or Graz (Ebner and Taraghi, 2010) so-

lutions. Both have a modular approach that combines

existing components such as widgets or (micro) ser-

vices. Further debate on PLE conceptions is domi-

nated by smart or adaptive approaches, fostering the

potential to adjust the offered content or tools to the

current needs of the users based on analyzing their

data traces in the system. This might be more promis-

ing as more data becomes available and more systems

are involved, which has brought forth visions of a

larger data ecosystem (BMWK, 2024; Degen et al.,

2025). At the same time strong concerns have been

raised both on pedagogical (Schiefner-Rohs et al.,

2024) and on ethical issues (Wiegerling et al., 2020)

of such data-driven approaches.

Cross-institutional approaches have so far re-

mained a vision (Kiy et al., 2014), not to mention na-

tional approaches. There have been attempts like the

US initiative ”in Bloom” for K-12 education, which

were shut down amid vociferous discussions. Data

security issues have come to the fore, however, dif-

ferent paces in the technology and education sectors

may also have been an obstacle (Bulger et al., 2017).

Similar situations have arisen on state level in Aus-

tralia (Tatnall and Davey, 2018). Furthermore, in

some cases teachers refused to migrate to a new plat-

form due to the lack of advantages compared to ex-

isting solutions they had in place (Jørgensen et al.,

2023), while in others initiatives have proven pro-

ductive because they fulﬁlled a need (Partners et al.,

2021). This emphasizes the need for an approach that

combines user requirements, existing services and the

potential beneﬁts of connecting them. The approach

should range from replacing, modifying and extend-

ing to supplementing.

3 EMPIRICAL FINDINGS ON

THE ADVANCEMENT OF

LEARNING SPACES

This section reports results of a study that investigated

wishes and experiences of students in digital learning

spaces in higher education (n=32 students). The aim

Redesigning Personal Learning Environments: Consolidation of Empirical Findings and Conceptual Research Against the Background of a

National Educational Infrastructure

of this study is to explore the needs of students in dig-

ital learning spaces and to identify potential directions

for building an educational infrastructure by redesign-

ing or remodeling existing personal learning spaces.

As this is an exploratory study aimed at gathering

preliminary insights on the needs of students rather

than drawing deﬁnitive conclusions, we assert that the

smaller sample size does not compromise the validity

of the study. Furthermore, we support our preliminary

ﬁndings with follow-up studies, which are beyond the

scope of this paper.

In this regard, this study investigates the following

research questions:

• What are the expectations of students for a digital

learning space?

• How do empirical ﬁndings contribute to the iden-

tiﬁcation of components and features for design-

ing a learning space?

Before we delve into the ﬁndings of designing

learning spaces, we attempt to familiarize the concept

of digital learning spaces and highlight their role in re-

deﬁning learning processes. Digital Learning Space,

per se, refers to a decentralized collection of edu-

cational solutions and digital resources that are con-

structed on existing technical structures, pedagogical

practices, and organizational infrastructures (Bygstad

et al., 2022).

In this research, we use the existing body of

knowledge to design, remodel, and reuse existing

learning spaces. In addition to that, we derive in-

puts from a sample of students through an empiri-

cal study that was conducted in order to support the

present needs for designing a networked digital learn-

ing space, i.e., the ﬂexible combination of different

educational tools in an adaptive infrastructure (Kiy

et al., 2014).

The collection of data follows a learner-centered

design approach - where we distributed a self-

designed survey (or questionnaire) among university

students with different disciplines and educational

backgrounds. A custom-designed questionnaire was

used to address the research objectives i.e. to as-

sess participants’ perceptions of digital learning en-

vironments, as no existing standardized instrument

addressed the speciﬁc variables of interest [network-

ing opportunities, organizational tools, collaborative

features, support mechanisms, personalized sugges-

tions, and customization options]. Since the study

involves understanding the experiences of students

within digital learning spaces, we targeted users of

Moodle.UP and Campus.UP. Moodle.UP is the cen-

tral learning management system provided by the uni-

versity to manage courses, course materials, examina-

tions, and registrations to students. Campus.UP is a

ﬂexible learning environment where services such as

Moodle, Cloud storage, Mail system and the Univer-

sity library system are all connected to the platform. It

also allows users to create digital workspaces to man-

age and organize their own learning processes.

Out of 42 students who participated in the study

(Moodle.UP = 37; Campus.UP = 5), we received 32

completed questionnaires (Moodle.UP = 28; Cam-

pus.UP = 4). Therefore, we excluded 10 incomplete

questionnaires from our evaluation. The student pop-

ulation was spread across six faculties, and were ex-

perienced with digital learning for over 5 years (Moo-

dle.UP = 43%; Campus.UP = 60%).

The questionnaire was designed in such a way that

it would take as little time as possible to complete (SD

= 10.0 minutes) (Campus.UP = 36 questions; Moo-

dle.UP = 21 questions). We integrated questions that

aligned with our research scenario: (i) experiences

of students in digital learning spaces, (ii) experience

with Moodle.UP or Campus.UP platforms, and (iii)

expectations for improving digital learning spaces.

As mentioned in the introduction, our fundamen-

tal goal with this study was to explore established of-

ferings, by supplementing them with new functions,

tools, and usage options. Taking this into account, we

formulated our questions, with the aim of conceptual-

izing further functionalities for our components. The

following information is requested from the users:

1. Experience with digital learning space

• Usage frequency of collaborative tools,

• Familiarity with sharing, privacy protection, or-

ganizing learning processes,

• Experience with digital learning and content

management systems

2. Experience with digital learning and content man-

agement systems

• Help and support function,

• Desired additional features one likes to have,

• Additional improvements on existing tools,

• Further tools

3. Wishes for a digital learning space

• Interaction and Connectivity,

• Functionalities for a personal workspace,

• Privacy measures

4. General information

• Interaction and Connectivity,

• Functionalities for a personal workspace,

• Privacy measures

On average, students reported that they use digital

collaboration tools almost every day (2-3 times per

CSEDU 2025 - 17th International Conference on Computer Supported Education

week). Furthermore, the measures on the ability to

organize one’s own learning pathway, and to ﬁnd suit-

able digital tools were higher compared to previous

semesters, revealing that the situation of technology

use has changed with more students having digital lit-

eracy.

A previous study (Bond et al., 2018) reported that

students and teachers use a limited number of dig-

ital tools for predominantly assimilative tasks, with

learning management systems perceived as the most

useful tool. The current investigation has revealed

that students are open to trying out new digital learn-

ing spaces with improved functionalities – network-

ing opportunities with other learners, and an open en-

vironment to organize, conﬁgure, and receive support

for performing learning activities (Table 1).

Figure 1: A closer look at the agreements and disagree-

ments of students on ”how useful the mentioned functional-

ities” are for a digital learning space.

Figure 1 provides a visual representation, with

data along the positive axis indicating agreement with

the speciﬁed functionality, while data along the nega-

tive axis reﬂects a lack of preference towards the spec-

iﬁed functionality. Notably, the majority of students

show an indifference towards digital tools for collabo-

rative work, as well as for suggestions regarding edu-

cational offerings. Nevertheless, table 1 indicates sig-

niﬁcant variability in these mentioned aspects, imply-

ing that the questions formulated to assess these as-

pects may have been either unclear or insufﬁcient to

elicit a deﬁnitive response.

As a supplementary remark for this section, we

point out that the data presented here shows only one

facet of the project. Multiple studies and extensive

research works were conducted to derive the holistic

ecosystem. The scope of this research is to discuss the

technical components, their functionalities, and com-

pare the different elements against the SAMR model

categories.

Table 1: Descriptive statistics on student preferences for a

digital learning space. The Likert scale data indicates the

most frequent response: 1 = Totally disagree, 2 = Disagree,

3 = Agree, 4 = Totally agree. Rows marked with ”High

variability” show wide-ranging perspectives.

Statement N Moo-

dle

(Cam-

pus)

% Mean

(SD)

Likert

Mode

My digital learning

space (DLS) offers

me the opportunity

to network with other

learners.

17 (4) 45.95

(80)

2.86

(3.2)

An open environ-

ment allows me to

organize my learning

material.

20 (4) 54.05

(80)

3.03

(3.2)

DLS proactively sup-

ports me in structur-

ing my learning ac-

tivities.

13 (4) 35.14

(80)

2.57

(3.2)

I think it is good

when my DLS sup-

ports me in ﬁnding

learning material.

18 (3) 48.65

(60)

2.78

(3.4)

I can design my DLS

individually and con-

ﬁgure it according to

my private security

needs.

13 (3) 35.14

(60)

2.85

(3.4)

I cannot ﬁnd digital

tools for collabora-

tive work within my

DLS.

High Variability

My DLS proactively

supports me in struc-

turing my learning

activities.

High Variability

In my DLS, I re-

ceive suggestions for

educational offerings

that match my pro-

ﬁle.

High Variability

4 CROSS-INSTITUTIONAL

INTEGRATION OF

EDUCATIONAL SERVICES

In this section, we provide a short recap on the techni-

cal aspects of the national digital education space. It

implements the user needs for learning spaces as iden-

tiﬁed above, as well as some other functionality for

education, and can be understood as a PLE. This PLE

is analyzed by using the SAMR model (Puentedura,

2006) with a special focus on the deﬁned interfaces,

which provide the means for ﬂexible combination of

educational services.

Redesigning Personal Learning Environments: Consolidation of Empirical Findings and Conceptual Research Against the Background of a

National Educational Infrastructure

4.1 Architecture of the National Digital

Infrastructure for Education

The architecture of the BIRD prototype contains the

following main components (Knoth. et al., 2022):

• The frontend is realized as a web application

through which the connected educational services

are accessible in an integrated form. This portal

comprises various sections where users can man-

age their own educational data, discover relevant

educational offerings, and engage in exchange

with others.

• The middleware constitutes the core of the in-

frastructure. Operating in the background, it fa-

cilitates data exchange between various educa-

tional offerings through the developed interfaces

and data structures. Notably, this includes the

implementation of Single Sign-On (SSO) (allow-

ing access to different services with just one ac-

count) and Metadata Management (for managing

the properties of connected contents, tools, etc.).

• The personal wallet, typically hosted on a smart-

phone, stores certiﬁcates and learning artifacts ac-

quired during an individual’s educational journey.

Educational services can request these documents

via the middleware. Upon authorization by the

user, they can be directly transmitted to the re-

spective service. Importantly, there is no cen-

tral storage or transfer of personal data within the

middleware.

Other educational providers can connect their ser-

vices with the infrastructure via the deﬁned interfaces

to SSO, metadata and wallet. Depending on the depth

of integration, these services either retain their exist-

ing user interfaces and merely beneﬁt from the data

exchange across the infrastructure, or they are in-

tegrated more seamlessly as an infrastructural com-

ponent using the shared frontend. Thus, the added

value relies on the existence of third-party educational

repositories and other educational services, that shall

not be replaced, but connected and extended.

4.2 Required Components and

Functions

Utilizing the existing structure of prototype compo-

nents as a guiding framework, student wishes were

categorized against the BIRD components. Table 2

shows the main components, dimensions, and the cor-

responding functionalities derived from the empirical

study.

Table 2: Clustering wishes of students for a digital learning

space against existing BIRD components.

Component Ecosystem dimen-

sions

Functionalities

(I) Learning Path

Finder

Digital infrastructure Support in ﬁnding

suitable digital learn-

ing materials; Bun-

dled provision of dig-

ital services and of-

fers

(II) Buddy Finder Communication and

Collaboration

Provision of net-

working opportu-

nities; Provision

of digital tools for

collaborative work

(III) Data Wallet Data model Openness of data;

Customization of

data handling mech-

anisms

(IV) Personal

workspace (Ar-

beitsbereich)

Interaction and tech-

nical interoperability

Ability to organize

learning materials;

Ability to structure

learning activities

(VI) Integration of

existing services

Additional tools Collaborative

text/canvas; Personal

calendar; Etherpad;

Miro/Mind maps;

Surveys; Archive

management; In-

tegrated zoom;

Word-cloud gener-

ator; Presentations;

Exam administration

system

• The Learning Path Finder (LPF) primarily

presents students with personalized selections of

educational offers and information. With the iden-

tiﬁed student needs, the LPF should also aid in

discovering appropriate digital learning materials

and ensure a centralized provision of digital ser-

vices and offers (Ziemann et al., 2023). This

assimilation is crucial in promoting a seamless

learning experience (Zimmerman, 1994).

• The Buddy Finder (BF) is a new approach to

anonymously connecting peers (Eilebrecht and

Beskorovajnov, 2025). It can be considered cen-

tral to the Communication and Collaboration di-

mension. Based on the results, it emerges to be a

signiﬁcant feature to offer ample networking op-

portunities.

• The Data Wallet ensures openness and trans-

parency of data, and allows customization to data

handling mechanisms. The crucial dimension of

this component is the freedom to manage one’s

own data according to their preferences while en-

suring open access to necessary information.

• The Personal Workspace pertains to the Interac-

tion and Technical Interoperability dimension. It

provides users with the ability to organize learn-

ing materials and structure their learning activities

CSEDU 2025 - 17th International Conference on Computer Supported Education

effectively. With this component, the infrastruc-

ture achieves well-structured interactions and op-

erational compatibility, thereby simplifying and

enhancing the learning process.

• Furthermore, users mentioned additional tools

they would like to include into the ecosystem. The

ﬁndings are presented in the ’Integration of Ex-

isting Services’ component. Users wish to inte-

grate a variety of additional tools such as a col-

laborative text/canvas, personal calendar, collab-

orative editors, virtual whiteboards/Mind maps,

surveys, archive management, integrated video

conferences, word-cloud generator, presentations,

and an exam administration system. These tools

not only augment the learning experience but also

ensure an uncomplicated and comprehensive uti-

lization of the available resources.

A Learning Guidance was added subsequently, which

completes the E-learning arrangement. It is based on

the concept of self-regulated learning and thus repre-

sents a component that supports learners both in their

learning process and in navigating between the other

components (LPF, Buddy Finder, Data Wallet, Per-

sonal Workspace, etc.). A chatbot supports the learner

through the cycle of self-regulated learning (D’Mello

and Graesser, 2012) with hints, references and expla-

nations. The concept of the Learning Guidance is

based on theoretical ﬁndings and should be evaluated

in a further study.

To further validate the ﬁndings, qualitative inves-

tigations (n = 7) were carried out in the form of inter-

views on the BIRD prototype (Bustorff et al., 2023),

where different concepts were tested. These concepts

covered a range of functionalities that were discussed

above, and can be categorized as follows:

1. Recommendation for individual educational path

2. Information on individual educational pathways

3. Protected digital ﬁle folder

4. Exchange of ideas with other learners

5. Evaluation of one’s own educational path

6. Desired speciﬁcations

7. Handling of data

8. Login and usage behavior

In the next sections, we will sort the listed compo-

nents (as shown in Table 2) inline with the SAMR cat-

egories and discuss the evolution of personal learning

environments with respect to the national educational

infrastructure.

4.3 Positioning in the Media Ecosystem

Our method of classifying the types of values fol-

lows the SAMR model (Puentedura, 2006). It rep-

resents a framework used to evaluate how technol-

ogy is integrated into teaching and learning processes.

SAMR stands for Substitution, Augmentation, Mod-

iﬁcation, and Redeﬁnition. It suggests that as edu-

cators move through these levels, they are leverag-

ing technology to increasingly enhance and transform

learning experiences, moving from merely enhancing

traditional practices to enabling entirely new possibil-

ities for teaching and learning.

Categories that appear higher in the framework

correspond to greater innovation. The following ta-

ble (3) describes the functions of the PLE approach

that were identiﬁed during the design of the prototype

based on the three dimensions of functionality (Bus-

torff et al., 2023).

In the next section, we dive deeper into those cat-

egories and what they imply.

• Substitution: The national infrastructure for ed-

ucation is not intended to substitute already ex-

isting educational services. The meta-platform

should not have the functionalities to serve as

a Learning Management System (LMS). School

Clouds and LMS of Higher Education Institutes

(HEI) can be used seamlessly with our approach.

Another example is digital credentialing. Digital

credentials only serve as certiﬁed copies of their

analog ’originals’. All tools integrated in the in-

frastructure serve as extensions for existing for-

mal and informal e-learning scenarios and con-

texts. Only fully integrated editors could be seen

as substitutions, if they replace existing propri-

etary ofﬁce software suites.

• Augmentation: We utilize existing LMS and add

new functions, for example, options for transfer-

ring data and artifacts with the wallet and the

platform, SSO, and the use of external databases

(the data room). Upon integration into our data

ecosystem, they can be augmented with meta-

data for searches and repositories for learning ar-

tifacts. Web 2.0 brought new spaces and tools for

learners. Even spaces that are not designed for

learning, such as YouTube, are frequently used

by learners. In 2020, 83 percent of German stu-

dents between 12 and 19 used Youtube for learn-

ing purposes (mpfs, 2020). This bottom up ap-

proach in conquering new technologies for learn-

ing can be seen as a form of hacking. The same

can be observed with social media services, where

a lot of informal learning takes place. Recent pub-

lications refer to micro learning or improving lan-

Redesigning Personal Learning Environments: Consolidation of Empirical Findings and Conceptual Research Against the Background of a

National Educational Infrastructure

guage skills with Instagram or TikTok (Heilmann,

2024). Open-Source services of the Fediverse

(like Mastodon) could be added to give learners

more networking possibilities.

• Modiﬁcation: With the “Working Space” we of-

fer users a highly customizable virtual desktop,

that allows them to collaborate with others, and

use innovative tools. The “Working Space” is in-

dependent from local devices. It enhances reuse

and promotes the remix culture (OER movement)

by providing learners with access to necessary

tools for those new tasks. It can also help edit-

ing formats that are not supported in normal of-

ﬁce software suites like interactive content (H5P

for example) or courses of LMS, cMOOC’s or

OpenCourseware (not particularly prevalent in

German-speaking countries). With the network

that comes with the national educational infras-

tructure users can connect with the “owners” of

learning artifacts. That makes collaboration easier

and reduces parasocial interaction, which is com-

mon but has rarely been examined in many asyn-

chronous virtual learning contexts (Kumar et al.,

2021).

Table 3: Examples for functionalities sorted according to

the SAMR categories.

I. inform yourself II. collect and work

out

III. network

Redeﬁnition self-sovereign data

management (wallet)

wallet wallet

Modiﬁcation curated learning

paths, recommenda-

tions for artifacts and

courses

collaborative editors,

highly personalizable

virtual desktop

Buddy Finder

Augmentation a seamless search-

able data room,

connected Content

Management Sys-

tems (CMS)

OCR for certiﬁed

copies, “bring your

own cloud storage”,

“bring your own

LMS”

Social Media (not de-

signed for learning

purposes)

Substitution - fully integrated edi-

tors

The table shows that not all aspects of the SAMR

model can be considered equally in the PLE concept.

This is because the focus of the study is on the in-

teroperability of existing services, which means that

Substitution and Redeﬁnition are less in demand. The

PLE approach counters this problem by shifting the

perspective from the Graphical User Interface (GUI)

to the entire network of interactions and relationships.

The following section shows the PLE concept itself

and how it responds to the challenges highlighted by

the SAMR model.

5 EVOLVED CONCEPTION OF

PERSONAL LEARNING

ENVIRONMENTS

The impact on the cross-institutional connection in a

federal Personal Learning Environment is best visible

for users in the component “Working Space”.

Figure 2: Personal Learning Environment in an national ed-

ucational infrastructure.

The persistence layer contains learning artifacts

that are mostly not assigned to a bigger context.

Those are classiﬁed by structured metadata and col-

lected in the next bigger space, the Data Room. The

Data Room is linked to the PLE to exchange infor-

mation (metadata) and artifacts. The PLE represents

the service layer. Artifacts can be either created in the

”Working Space” and sent to the Data Room or can

be claimed from the Data Room for reuse or remix-

ing. The PLE consists of several connected services.

The “Working Space” can be seen as the heart of the

infrastructure, where participation of learners is en-

hanced. The PLE uses different integration mech-

anisms and levels to connect to and integrate mi-

croservices. Signiﬁcant microservices in e-learning

contexts are LMS, OpenCourseWare, cMOOCs, edi-

tors, and data storages among others. The higher the

level of integration, the deeper the implementation in

the infrastructure solution. These integration levels

(descending from three to one) can determine if mi-

croservices are either substituted, modiﬁed, or aug-

mented. The only redeﬁning service is the data wallet

approach. Users are given full control over their own

data and data transfers. Therefore, the data wallet is a

new solution built for a completely new task.

• level 3: Tools that are part of the GUI. This could

either be Substitution or Redeﬁnition if there is

no existing service for this task. Editors, for ex-

ample, could substitute already existing solutions

CSEDU 2025 - 17th International Conference on Computer Supported Education

in other environments (ofﬁce suites), but we en-

hance them with collaboration features and addi-

tional data sovereignty. Redeﬁning can be editors

built for tasks where currently no solutions are

offered. Some smaller projects, also funded by

the Federal Ministry of Education and Research

in Germany work on such innovative prototypes.

Some of them may not work as “standalone” solu-

tions, so they need to be integrated within a portal

or LMS solution.

• level 2: Portlet integration with ”foreign” GUI.

This could either be Modiﬁcation or Augmenta-

tion depending on the added functionalities. Mi-

croservices in portlets could be “wrapped” LMS

(see (Kiy and Lucke, 2016)), editors, CMS and

others. We provide APIs to enhance their func-

tionalities and connect them to the Data Room.

Depending on how many “interfaces” are made

available, it changes or adds to the connected ser-

vice.

• level 1: hyperlinked and level 0: not mentioned

but connected to the middleware. Hyperlinks are

not truly integrated, therefore level 0 and level 1

are very similar in this discussion. Existing mi-

croservices are supplemented by a connection to

the middleware and visualised on the platform via

hyperlinks. This is the most basic form of integra-

tion from a user’s perspective.

One layer remains that has to be taken into account:

the subject or user layer. If we take the educational

understanding of (Marotzki, 1990) and others, edu-

cation means the transformation of world and self-

relations. Transformation of world relations, in this

case technical, is covered in the parts above. As for

subjective transformation, the PLE approach can op-

erate in two ways. As mentioned earlier with the data

wallet approach, where the user is given full control

over their data, learners are able to restore their data

sovereignty which is often neglected by proprietary

software solutions. Otherwise we provide easy and

seamless access to learning artifacts and tools and

support them with recommendation systems. Users

can freely compile and arrange needed microservices

in their personalized learning environment.

The next big step would be to integrate a media

education concept that helps users with data literacy,

by reducing technical barriers and those deriving from

missing skills or knowledge. As can already be seen

in the context of data literacy, adaptive skills are be-

coming increasingly important on the part of learners.

In the context of lifelong learning, adaptive compe-

tences are becoming important in both vertical and

horizontal educational transitions. Learners are fo-

cusing on regulating their own learning and adapt-

ing to changing circumstances (G

otz and Nett, 2017).

Self-regulated learning (SRL) enables learners to in-

dependently manage and organize their own learning

through reﬂection and assessment. At the same time,

it enables teachers and parents to react individually

to the learner’s learning status and thus to support or

challenge them accordingly. Skills in the area of SRL

must be supported and trained at an early age in order

to lay the foundation for independent lifelong learn-

ing (Schuster et al., 2020).

This action control, which originates from the in-

dividual, can be supported in the digital space, as it is

possible to switch from linear sequences to an individ-

ualized construction of the learning process: from a

rather rigid, linear sequence, for example when work-

ing with texts, to a sequence with different individual

customization options, for example in school lessons

or in Web based trainings (WBT), to a construction

of the sequence by the learner themselves. The digi-

tal space enables the merging of different content and

tools in an individual workspace via cross-references

atzold, 2004) (Walber, 2005).

6 CURRENT STATUS AND

REMAINING WORK

There is some truth in the perspective that current

LMS platforms tend to adopt a “one-size-ﬁts-all” ap-

proach (Ben Rebah et al., 2023) and often do not

adequately support informal learning. Most systems

align with behavioral learning theories, which can

limit the ability of learners to co-create and partici-

pate meaningfully. To foster greater participation, it is

essential to shift the focus from the institution (school

or university) to the learner.

Personal Learning Environments can be an impor-

tant contributor to this transformation, if they com-

bine quality-assured content from formal education

to counter the risks of disinformation and the re-

production of “detrimental” practices with degrees

of freedom from informal settings. However, this

also necessitates a ﬂexible technical infrastructure

to integrate these diverse elements effectively. For

example, it symbolizes the “chicken-and-egg prob-

lem” where both elements rely on each other to ex-

ist or function, thereby creating a circular cause-and-

effect dilemma. This is where an interconnectiv-

ity infrastructure comes into play. Some problems

that other approaches had in the past (see Section

2) have already been tackled in the national edu-

cational infrastructure. By building the infrastruc-

ture at the same time as exploring, connecting (meta-

data/semantic standards) and ﬁlling (OER Movement,

Redesigning Personal Learning Environments: Consolidation of Empirical Findings and Conceptual Research Against the Background of a

National Educational Infrastructure

nationwide “lernen:digital” project) the Data Room,

it eliminates the need for users to function as “pio-

neers”. Furthermore, data security and sovereignty

issues are prioritized by following the Data Wallet ap-

proach, addressing data security and sovereignty is-

sues.

In the next iterative step, the matrix presented in

Section 5 should be applied to locate all the microser-

vices within the current agenda of the national educa-

tional infrastructure. Otherwise, the level of integra-

tion is not the only point at which our PLE approach

can transform educational services and empower the

learner themselves. So this topic could be considered

at other levels, for example governmental aspects. In

the interoperability framework provided by (Ben Re-

bah et al., 2023), four topics have been identiﬁed in

this regard: legal, semantic, organizational and tech-

nical interoperability.

For a further media educational analysis of the

PLE the dispositif (very short: network of intermedial

and interdiscursive elements) model by Focault (Fo-

cault, 2000) could serve as an analysis template. Cur-

rently there are two coexisting and sometimes com-

peting dispositifs: (i) the media and (ii) the educa-

tional dispositif. With the holistic approach of the

national educational infrastructure there is a need for

an e-learning dispositif, which consists of equal parts

of educational and media aspects, taking into account

all conditions of success for subjects for their lifelong

learning tasks.

One of the main goals is not only to provide a tech-

nical solution but to guide users through all stages of

lifelong learning. Seamless utilization and access in

horizontal and vertical educational transitions, for ex-

ample curated learning pathways, recommendations

for learning artifacts and also a matching tool for ﬁnd-

ing learning buddies. This should be ﬂanked by a me-

dia education concept that helps interested users to

learn more about data sovereignty and data security

in order to improve their media skills.

On the other hand, the media education concept

should show ways, in which learners can be supported

as best as possible in their learning process. The dif-

ferent needs of the users should be taken into con-

sideration. An initial proposal for this support is the

Learning Guidance, which should be tested and ex-

panded in further studies. In this way, new devel-

opments in the ﬁeld of PLE can be comprehensively

adapted to the realities of users’ lives.

ACKNOWLEDGEMENTS

This work was partially funded by the European

Union - NextGenerationEU through the German Fed-

eral Ministry of Education and Research (BMBF) as

part of the Digital Education initiative under grant

number 16NB001. We are deeply grateful to our part-

ners in the project for the valuable exchange.

REFERENCES

Ben Rebah, H., Barthes, D., and Carnus, M. (2023). Per-

sonal learning environment: instrument system for

learning beyond the boundaries of the university.

Learning Environments Research, 26:843–871.

BMBF (2021). Bmbf: Richtlinie zur f

orderung von pro-

totypen f

ur eine bildungssektor

ubergreifende, trans-

disziplin

are meta-plattform f

ur kollaborativen, kom-

petenten und digital gest

utzten zugang zu innovativen

lehr-/lernformaten und unterst

utzenden lerntechnolo-

gien: ”initiative nationale bildungsplattform”.

BMWK (2024). Bmwk: Das gaia-x

Okosystem.

Bond, M., Mar

ın, V., Dolch, C., Bedenlier, S., and Zawacki-

Richter, O. (2018). Digital transformation in german

higher education: student and teacher perceptions and

usage of digital media. International Journal of Edu-

cational Technology in Higher Education, 15(1):1–20.

Buchem, I., Attwell, G., and Torres, R. (2011). Understand-

ing personal learning environments: Literature review

and synthesis through the activity theory lens. pages

1–33.

Bulger, M., McCormick, P., and Pitcan, M. (2017). The

legacy of inbloom. Data & Society Research Institute.

Bustorff, A., Tille, R., Wisniewski, S., Blum, F., and Lucke,

U. (2023). Nutzungsanforderungen, p

adagogische

Uberlegungen und grobstruktur einer digitalen vernet-

zungsinfrastruktur f

ur die bildung. E-Learning and

Education.

Bygstad, B., Øvrelid, E., Ludvigsen, S., and Daehlen, M.

(2022). From dual digitalization to digital learning

space: Exploring the digital transformation of higher

education. Comput. Educ., 182:104463.

Degen, K., Lutzens, R., Beschorner, P., and Lucke, U.

(2025). Public education data at the crossroads of

public and private value creation: orchestration ten-

sions and stakeholder visions in germany’s emerging

national digital education ecosystem. Electronic Mar-

kets.

D’Mello, S. and Graesser, A. C. (2012). Dynamics of moti-

vation in learning: Effects of automated feedback and

self-regulation on motivation and learning. Computers

in Human Behavior, 28(5):1730–1740.

Ebner, M. and Taraghi, B. (2010). Personal learning envi-

ronment for higher education – a ﬁrst prototype. pages

1158–1166.

Eilebrecht, S. and Beskorovajnov, W. (2025). A for-

mal treatment of homomorphic encryption based out-

CSEDU 2025 - 17th International Conference on Computer Supported Education

100

sourced computation in the universal composability

framework. under review.

Focault, M. (2000). Dispositive der Macht:

Uber Sexu-

alit

at, Wissen und Wahrheit. Merve Verlag.

otz, M. and Nett, U. (2017). Personal learning environ-

ments (ples) - a conceptual framework. Journal of

Educational Technology Development and Exchange,

10(1):1–23.

Hafer, J., Kiy, A., and Lucke, U. (2014). Moodle & co. auf

dem weg zur personal learning environment. eleed,

10(1).

Heilmann, L. (2024). Politisches lernen auf tiktok. aushand-

lung von diversit

at in digitalen r

aumen. Hessische

atter f

ur Volksbildung, 74(2):43–54.

Jørgensen, R. F., Valtysson, B., and Pagh, J. (2023). Work-

ing with aula: How teachers navigate privacy uncer-

tainties. The Information Society, 39:225–235.

Kiy, A. and Lucke, U. (2016). Technical approaches for per-

sonal learning environments: Identifying archetypes

from a literature review. 2016 IEEE 16th Interna-

tional Conference on Advanced Learning Technolo-

gies (ICALT), pages 473–477.

Kiy, A., Lucke, U., and Zoerner, D. (2014). An adap-

tive personal learning environment architecture. Int.

Conf. on Architecture of Computer Systems (ARCS) /

E. Maehle et al. (Eds.) Architecture of Computing Sys-

tems – ARCS 2014., pages 60–71.

Knoth., A., Blum., F., Soldo., E., and Lucke., U. (2022).

Structural challenges in the educational system meet a

federated it-infrastructure for education: Insights into

a real lab. pages 369–375.

Kumar, A., Singh, S., Kumar, P., and Singh, R. (2021). A

novel approach for sentiment analysis of online re-

views using deep learning techniques. PLOS ONE,

16(12):e0299014.

Lucke, U. (2024). Digital education ecosystems: Visions

and decision needs. Weizenbaum Journal of the Digi-

tal Society, 4(4).

Marotzki, W. (1990). Theorie und Praxis der Fernunter-

richtslehre. Fernunterrichtslehre.

mpfs, M. F. S. (2020). Jugend, information, medien, jim-

studie 2020.

Partners, R., Services, J. E., and merSETA (2021). A guide

on using scenarios to align skills supply & demand

through interoperable data platforms.

atzold, H. (2004). Lernberatung und Erwachsenen-

bildung. Schneider-Verlag Hohengehren, Balt-

mannsweiler.

Puentedura, R. (2006). Transformation, technology, and ed-

ucation.

Schiefner-Rohs, M., Hofhues, S., and Breiter, A. (2024).

Dataﬁzierung (in) der Bildung - Kritische Perspek-

tiven auf digitale Vermessung in p

adagogischen Kon-

texten. transcript Verlag.

Schuster, C., Stebner, F., Leutner, D., and Wirth, J.

(2020). Transfer of metacognitive skills in self-

regulated learning: an experimental training study.

Metacognition and Learning, 15:455–477.

Tatnall, A. and Davey, B. (2018). Birth, life and death of

the victorian education ultranet. Education and Infor-

mation Technologies, 23(4):1585–1605.

Walber, M. (2005). Konstruktivismus als legitimation f

selbstgesteuertes lernen mit neuen medien. Report,

28(1):102–109.

Wiegerling, K., Nerurkar, M., and Wadephul, C. (2020).

Dataﬁzierung und Big Data Ethische, anthropolo-

gische und wissenschaftstheoretische Perspektiven:

Ethische, anthropologische und wissenschaftstheo-

retische Perspektiven. Springer, Wiesbaden.

Ziemann, F., Nguyen, D., Blum, F., and Lucke, U. (2023).

Personalized recommendations for individual learning

pathways: Supporting ukrainian refugee students in

continuing their university education. In Proceedings

of European University, volume 95, pages 170–180.

Zimmerman, B. (1994). Dimensions of academic self-

regulation: A conceptual framework for education. In

Schunk, D.H., Z. B., editor, Self-Regulation of learn-

ing and performance. Issues and Educational Appli-

cations, pages (p.3–24). Routledge, New York.

Redesigning Personal Learning Environments: Consolidation of Empirical Findings and Conceptual Research Against the Background of a

National Educational Infrastructure

101