Adaptation in Learning Analytics Dashboards: A Systematic Review

emi Barb

1 a

, Beno

ıt Encelle

1 b

and Karim Sehaba

2 c

Univ. Lyon, UCBL, CNRS, INSA Lyon, Centrale Lyon, Univ. Lyon 2, LIRIS, UMR5205, F-69622 Villeurbanne, France

Univ. Lyon, Univ. Lyon 2, CNRS, INSA Lyon, UCBL, Centrale Lyon, LIRIS, UMR5205, F-69676 Bron, France

Keywords:

Learning Analytics Dashboards, Systematic Review, Adaptation, Learning Indicators.

Abstract:

Although learning analytics dashboards (LAD) grow in numbers, they often fail to improve learner awareness

as they lack adaptation capabilities. This paper presents a systematic review following the PRISMA statement,

about the adaptation capabilities of LADs based on new deﬁnitions for LADs and learning indicators. A

detailed analysis of 23 articles selected among 426 articles retrieved from databases was conducted based on

a coding scheme, centered on adaptation and its dimensions, namely: to whom, what, to what, who, and how.

The main result of this study is that there is more evidence of adaptable LADs than adaptive LADs. As a result,

the road to adaptivity is worth exploring. The analysis of LAD’s common features led us to distinguish mainly

4 adaptable capabilities and 2 adaptive ones. Most of the adaptable capabilities consist of giving exploration

power to the user and providing him with data ﬁltering, zooming, or selection functionalities. In contrast,

users have limited options when it comes to selecting indicators, their visualizations, and organization on

the dashboard. Providing more ﬂexible LADs could enhance their usability and increase learner awareness.

Furthermore, the few adaptive features involve adaptations based on “if-then” rules and there are no reports of

advanced computing techniques such as machine learning that could empower LAD’s adaptation.

1 INTRODUCTION

Since the past years, the rise of hybrid learning activi-

ties and the higher disponibility of learning data have

reinforced interest in learning analytics (LA) which

is the measurement, collection, analysis, and report-

ing of data about learner and their contexts (Siemens,

2011), resulting from users’ interactions with educa-

tional and information technologies (Ga

sevi

c et al.,

2015). Learning analytics dashboards, deﬁned as

a “single display that aggregates different indicators

about learner (s), learning process(es) and/or learn-

ing context(s) into one or multiple visualizations”

(Schwendimann et al., 2016, p. 37) are the most com-

mon tools used to report the results of learning an-

alytics to many stakeholders such as teachers, learn-

ers or researchers. One of the major limitations of

LADs is that they fail to improve learner awareness as

they lack adaptation capabilities, resulting in less ac-

tionability (Jørnø and Gynther, 2018). This refers to

some challenges pointed out in (Verbert et al., 2020)

such as “one-size-does-not-ﬁt-all” and the need to

https://orcid.org/0009-0008-4484-8878

https://orcid.org/0000-0002-0734-6480

https://orcid.org/0000-0002-6541-1877

deal with data literacy. The adaptation capabilities we

refer to have been in particular speciﬁed in the ﬁeld of

Adaptive Hypermedia Systems (AHS) (Brusilovsky,

2001), with Oppermann (2017) emphasizing the dif-

ference between adaptable systems (adaptability) and

adaptive systems (adaptivity), saying that “Systems

that allow the user to change certain system parame-

ters, and thereby adapt the behavior of these systems,

are called adaptable. Systems that adapt to users au-

tomatically based on monitoring the users’ interaction

during runtime are called adaptive”.

Our main research goal is to explore the state of

the art regarding adaptation capabilities of LADs. We

identiﬁed two systematic reviews that are similar to

what we propose. The ﬁrst is from 2019 and re-

ports on the tailoring capabilities of information dash-

boards (V

azquez-Ingelmo et al., 2019), while the sec-

ond is from 2021 and is a replication of the previous

one, centered on performance dashboards (Kruglov

et al., 2021). The main similarity of these reviews

with our study is the shared focus on the adapta-

tion of the dashboards. However, this study focuses

on LADs and analyzes LADs adaptation dimensions

with a ﬁner granularity, while the other studies adopt

a more general analysis framework, not centered on

Barbé, R., Encelle, B. and Sehaba, K.

Adaptation in Learning Analytics Dashboards: A Systematic Review.

DOI: 10.5220/0012628600003693

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 75-86

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

learning. Moreover, in these studies a large range

of vocabulary is employed knowing that there is in

general a lack of agreement when speaking of adapta-

tion: terms used do not always refer to the same con-

cepts. For V

azquez-Ingelmo et al. (2019), ”A gen-

eral term is necessary, because using “customizable,”

“personalized” or “adaptive” indistinctly to refer to

these solutions, could lead to misconceptions around

these last terms, which, in the end, have different nu-

ances”. In our study, we precise the terms we refer to,

in connection with historical deﬁnitions grounded in

the ﬁeld of adaptive hypermedias.

We tackle the aforementioned challenges and re-

trieve research that focuses on the adaptation of

LADs. First, we deﬁne the concepts of learning indi-

cator, LAD, and adaptation dimensions before precis-

ing our analysis framework. Then, using a systematic

review, we provide insights about the implementation

of adaptable and/or adaptive learning dashboards.

In line with what we found in similar works, we

map out such adaptation mechanisms with a level of

detail allowing us to get a proper understanding of

what has been made yet. As a result, this study ad-

dresses the following research questions:

• RQ1: What LADs since 2017 feature adaptability

or adaptivity mechanisms?

• RQ2: What adaptation mechanisms are imple-

mented into LAD, for what purposes?

The remainder of the article is organized as fol-

lows. Firstly, we provide an overview of existing def-

initions for the key concepts of the study before de-

riving our own, to precise the theoretical background

of the review. Secondly, we describe the method we

applied to lead a systematic literature review, follow-

ing the PRISMA statement (Page et al., 2021). We

then jointly present and discuss the ﬁndings for each

research question. Finally, we report the known limi-

tations of our study before concluding.

2 KEY STUDY CONCEPTS

DEFINITIONS AND RELATED

LITERATURE

2.1 Learning Indicators (LI)

The term ”indicator” is commonly used across vari-

ous scientiﬁc disciplines. Nonetheless, there is lim-

ited literature deﬁning this term in the ﬁeld of educa-

tion. According to Muslim et al. (2017), an indicator

can be deﬁned as “a speciﬁc calculator with corre-

sponding visualizations, tied to a speciﬁc question”.

Ahmad et al. (2022) provided a comprehensive def-

inition: “an indicator is the result of the analysis of

one or multiple metrics and gives a more comprehen-

sive picture of a particular (abstract) learner status, for

example, student engagement and so forth [...]”. For

Dimitracopoulou (2004):

The application of ‘data processing methods’

produces one or more ‘indicators’, that indi-

cate something’ related to the ‘quality’ of in-

dividual activity, the mode or the quality of

the collaboration, the quality of the collabora-

tive product, or the appropriateness of its pro-

duction process. These variables interpreted,

taking into account, the learning activity, the

proﬁle of the participants and the context of

interaction could support interaction partici-

pants on the level of awareness, or of the (self)

assessment.

According to these deﬁnitions, we suggest that a

learning indicator is a metric or latent variable that re-

sults from the analysis of data associated with a learn-

ing activity. To elaborate, we distinguish, in line with

the metric concept deﬁned by Ahmad et al. (2022),

low-level indicators which are observable and calcu-

lated from the raw data of a learning activity, from

higher-level indicators which are abstract variables

derived from raw data and/or lower-level indicators.

2.2 Learning Analytics Dashboard

(LAD)

Various ﬁelds integrate dashboards into their practices

(e.g. Business Intelligence, Logistics, Healthcare).

Few says that “A dashboard is a visual display of the

most important information needed to achieve one or

more objectives; consolidated and arranged on a sin-

gle screen so the information can be monitored at a

glance.” (Few, 2006). For Brouns et al. (2015), it’s

”An easy to read, often single page, real-time user in-

terface, showing a graphical presentation of the cur-

rent status (snapshot) and historical trends of an or-

ganization’s key performance indicators (KPIs) to en-

able instantaneous and informed decisions to be made

at a glance.”. In the ﬁeld of learning, LAD deﬁnitions

share similarities with the previous ones. A recent

deﬁnition is: “a learning dashboard is a single dis-

play that aggregates different indicators about learner

(s), learning process(es) and/or learning context(s)

into one or multiple visualizations.” (Schwendimann

et al., 2016). Yoo et al. (2015) consider a dashboard

as “a display which visualizes the results of educa-

tional data mining in a useful way”. Others see them

as “visualisations of learning traces” (Steiner et al.,

2014) and Podgorelec and Kuhar (2011) consider that

CSEDU 2024 - 16th International Conference on Computer Supported Education

“the Learning Analytics Dashboard (LAD) is an inter-

active, historical, personalized, and analytical moni-

toring display that reﬂects students’ learning patterns,

status, performance, and interactions. The outlook of

LAD includes visual elements such as charts, graphs,

indicators and alert mechanisms.”.

Taking a closer look at each deﬁnition, it’s notice-

able that their authors do not place equal emphasis

on dashboard characteristics. Some deﬁnitions com-

plement each other, while others share traits such as

the central concept of visualization, the use of learn-

ing traces, usability, and the objective of supporting

awareness and decision-making. However, the con-

cept of a single screen and/or display is to our mind

ambiguous (e.g. is a window with several tabs con-

sidered as a single display or multiple display(s) ? Is

a window split on multiple monitors considered as a

single or multiple display(s) ?). To capitalize on these

previous deﬁnitions and to clarify our study terms, we

suggest a formal yet ﬂexible deﬁnition of a LAD. We

propose dropping the single display aspect and con-

sidering a LAD as an ordered aggregation of indica-

tors - usually represented using visualizations - which

support awareness, and decision-making in relation to

one or more objectives related to learning.

2.3 Insights About Adaptation and Its

Dimensions

In addition to adaptability and adaptivity, systems

adaptation also refers to the development of systems

tailored to meet speciﬁc user proﬁles. This study will

not cover adapted LADs, as there is already exten-

sive research utilizing participatory designs or user-

centered methods during the conception of LADs.

The focus of this study is primarily on adaptable

and adaptive dashboards. Park et al. (2023) de-

ﬁned an adaptive learning analytics dashboard as “a

speciﬁc learning environment where learners’ learn-

ing progress, performance, and adaptive recommen-

dations were provided on a real-time basis through

their LAD.”. In contrast, our study does not con-

sider adaptive environments in general, but rather the

adaptation capabilities of LADs themselves. Start-

ing from the deﬁnitions given in (Brusilovsky, 2001;

Brusilovsky and Mill

an, 2007; Oppermann, 2019), a

reformulation is proposed to make these deﬁnitions,

initially taken from AHS, applicable to LADs:

• Adaptable learning dashboard (adaptability): the

adaptation of the dashboard is triggered by the

user, following some of his interactions with the

LAD

• Adaptive learning dashboard (adaptivity): the

adaptation of the dashboard is automatically trig-

gered by the system itself, without any user inter-

vention

The adoption of the Five W’s (Who, What, Where,

When, Why) is a common practice in many ﬁelds to

overview a system. In this study, we analyze the adap-

tation dimensions we want to describe by adapting

and combining these questions:

• To whom (Who is the target of the adaptation ?):

in most cases, the user is either a learner, a teacher,

or a researcher.

• Who (Who initiates the adaptation ?): in adapt-

able cases, the user initiates the adaptation while

in adaptive cases, the system itself triggers the ad-

justments.

• To what (on what data and/or knowledge about

users are adaptations based ?): it’s only relevant

to monitor data involved in adaptive cases since it

is always a user input in adaptable cases.

• What (What is adapted in LAD): describing the

effects and changes occurring on the LADs.

• How (Which methods are involved in the adapta-

tion process ?): from a system perspective, what

are the steps in the decision-making process that

lead to adapting one thing over another?

3 METHODS

This section describes the steps and methods used to

conduct the review. A systematic review method-

ology was chosen due to its well-formalized frame-

work outlined by the PRISMA statement (Page et al.,

2021), and because this approach is becoming in-

creasingly popular, not only in the LA research ﬁeld.

It consists of identifying academic databases to search

into, formulating queries to effectively address our re-

search questions, adding constraints to ﬁlter the abun-

dant records collected, and then describing the pro-

cess of analyzing the reports included in the study.

3.1 Identiﬁcation of Databases

We ﬁrst identiﬁed suitable databases for our study us-

ing previous systematic reviews which are grounded

in the ﬁeld of LA such as (Matcha et al., 2019;

Bodily et al., 2018; Jivet et al., 2018; Papamitsiou

and Economides, 2014). Then, we ranked our in-

sights by popularity and compared the most fre-

quently used databases (IEEE Xplore, ACM Digital

Library, SpringerLink, ScienceDirect, ERIC) with the

ﬁndings of Gusenbauer and Haddaway (2020), re-

porting well-suited academic search systems for sys-

Adaptation in Learning Analytics Dashboards: A Systematic Review

tematic reviews. Lastly, we retained ACM Digi-

tal Library (ACM DL) as our principal data source

and complemented it with IEEE Xplore (IEEE) and

Web of Science (WoS). To complete records identi-

ﬁcation, we also searched speciﬁcally in conference

proceedings and journals related to the LA research

ﬁeld. We scanned LAK (Learning Analytics and

Knowledge) and L@S (Learning at Scale) proceed-

ings because such conferences are related to learning,

we also looked at UMAP (User Modeling, Adapta-

tion and Personalization) proceedings for its focus on

adaptation and personalization and we investigated in

the Journal of Learning Analytics.

3.2 Search Terms and Requests

To answer our research questions, we designed a

query to question the search engines and gather

records in connection with LAD built around learner’s

data and any adaptation techniques. In a similar way

to the academic databases identiﬁcation, we selected

relevant keywords from previous reviews. Then, we

supplemented these with keywords we found through

our readings to either restrict or extend the results to

what interests us most. After iterative adjustments,

we conducted searches within each database in the

corpus using data from titles, abstracts, and keywords,

structured around a query comprising four parts:

• (A) (dashboard OR visual*): restrict to papers ex-

plicitly mentioning a dashboard or something re-

lated to visualization.

• (B) (”LA” OR “learning analytics” OR “learning

analytic” OR “educational data mining” OR “edu-

cational datamining”): restrict the application do-

main to learning analytics. We also included key-

words for data mining because the boundary with

the LA is thin.

• (C) (tailor* OR adapt* OR custom* OR intel-

ligent OR individual* OR personal* ): restrict

to the main topic of our study, by including the

words related to adaptation that are very often

used by authors.

• (D) (learner OR student): restrict results to works

based on learner data. We included “student” be-

cause it’s a term often used in the ﬁeld of educa-

tion.

The resulting query combines all expressions and is

adapted to the speciﬁcations of each search engine at

search time. Finally, for searching in conferences and

journals dedicated to learning, knowing that LA key-

words are not necessarily employed, we used a sim-

pler query by removing part (B) from Formula 1.

Query = (A) and (B) and (C) and (D) (1)

3.3 Delimiting the Search

As we intend to complete the map of the exist-

ing adaptable or adaptive LADs, we take 2017 as

a starting point, in line with the results achieved

by (V

azquez-Ingelmo et al., 2019). We ﬁltered the

records by date using search engines’ built-in func-

tionalities. In addition, we systematically excluded

records for which we were unable to obtain the re-

port (it’s a “document (paper or electronic), supply-

ing information about a particular study. It could

be a journal article, preprint, conference abstract,

study register entry, clinical study report, disserta-

tion, unpublished manuscript, government report, or

any other document providing relevant information”

(Page et al., 2021, p. 3)). A two-step ﬁltering process

was conducted on the resulting records, based on in-

clusion criteria (see Table 1). The ﬁrst step involved

excluding records after reading titles, abstracts, and

keywords. The second step involved reading the en-

tire texts of the remaining records.

Table 1: Inclusion criteria.

We added IC (inclusion criterion) 1-2-3 as cri-

teria to guarantee a minimum quality of the

publications to be included in our analysis

IC1 The paper must be written in English

IC2 The paper must have been peer-reviewed

IC3 The paper must be a complete study, ie. we

exclude everything which is a short paper

(less than 5 pages), a review, a poster, an ab-

stract, a work in progress, etc.)

We added IC4-5-6 to keep only records that

could answer our topics

IC4 The paper must describe a learning analytics

dashboard (it must relate to learners)

IC5 The paper must describe one or more adap-

tation mechanisms of the dashboard

IC6 The paper must show evidence of an evalua-

tion of the dashboard

We added IC7-8 to make sure we only study

unique solutions or the most mature ones

IC7 The paper must be the most mature solution

of a series of research (duplicates removal)

IC8 The paper must not present an existing dash-

board without proposing new features

The process up to the selection of the ﬁnal pool

of articles is illustrated in the PRISMA ﬂow diagram

(see Figure 1).

CSEDU 2024 - 16th International Conference on Computer Supported Education

Figure 1: PRISMA inspired ﬂow diagram.

3.4 Coding Scheme

The initial coding objective is to provide an overview

of each LAD by identifying its target (to whom:

learner, teacher, adviser, institutional. . . ) and of the

type of learning analytics presented by the indicators.

The indicators are classiﬁed using the terms presented

by Gartner, (2014) and the deﬁnitions in (Susnjak

et al., 2022; Sirje Virkus, 2023):

Descriptive Indicators: indicators showing data

about learner past actions. e.g. An indicator show-

ing how many modules a learner has completed yet

Diagnostic Indicators: indicators giving in-depth in-

sights about identiﬁed learner/learning state. e.g. The

dashboard shows that the learner didn’t pass his year

because of his lack of regularity

Predictive Indicators: indicators showing estimation

of future outcomes based on current and past data

analysis. e.g. An indicator showing the probability

a student will pass a test

Prescriptive Indicators: indicators aiming to rec-

ommend future actions. e.g. An indicator showing a

list of recommended learning material to work on to

improve a point of weakness

Note that we won’t describe the nature of the data

used to calculate the indicators, but the nature of the

indicators themselves: whereas descriptive data are

required to compute predictive data, there are no such

relations for the indicators, which means that a dash-

board can present predictive indicators only, without

any descriptive ones.

The second coding goal is to describe the adap-

tation mechanisms present in the papers according

to 4 dimensions of adaptation: “Who”, “To what”,

“What”, “How”. As a result, we expanded our

coding scheme for describing each dimension with

the following codes:

Dimension 1 - Who ? Who initiated the adaptation

process ? It’s either the user or the system itself.

Dimension 2 - To what ? What data/knowledge about

users are used to perform the adaptation ?

User’s Conﬁguration: user actions to trigger a spe-

ciﬁc system behavior. e.g. The user selects a date

range or some data to “zoom in/out”. The user mod-

iﬁes the font size.

Prior Knowledge, Knowledge Growth: any data re-

lated to a learner model that can be used to report

on its progress in terms of knowledge or skills. e.g.

The history of what a learner has learned or mastered

since he enrolled in the online course.

Learning Paths: any data reporting the learning

paths or sequences of a learner within a learning ap-

plication e.g. The learner ﬁrst read the course mate-

rials and then tried the quizzes.

Performance: any data related to an evaluation

and/or an assessment task which indicates a learner’s

performance at a given time. e.g. The learner’s

grades for the semester.

Engagement, Motivation: any data or data aggrega-

tion which can be used to monitor the engagement

of a learner (activity logs, emotional or expressive

data. . . ). e.g. The learner’s engagement is in line with

his login frequency.

Metacognition: any data related to the learner’s

metacognitive state such as his self-regulated learning

level, the learning strategies or the methods he uses to

learn or solve problems. . . e.g. Learning objectives

formulated by the learner.

Collaboration: any data that highlights collabora-

tion or interactions between the stakeholders during

a learning activity/process. e.g. The number of times

a speciﬁc learner asked his teacher for help.

Socio-Demographic Data: any data related to the

socio-demographic proﬁle of a learner. e.g. The

learner is twenty years old and he is funded by a

scholarship.

Data Availability: when the data usually used to

compute some indicator is not available yet. It is sim-

ilar to a cold start. eg. The dashboard can’t compute

Adam performance metrics because he hasn’t passed

any test yet.

User’s Preferences: any data inferred from previous

user conﬁgurations and/or log analysis. These data

Adaptation in Learning Analytics Dashboards: A Systematic Review

are a product of past user-system interactions. e.g.

The system has learned from the logs that the user

has a preference for line charts

Dimension 3 - What ? Which elements of the LAD

are adapted ?

Indicators Set: the combination of indicators within

the LAD is changed. In particular, indicators can be

added, removed, or re-calculated to change the mean-

ing of the feedback conveyed by the dashboard. e.g.

A new indicator appears in the LAD

Indicators Importance: an indicator or a sub-set

of indicators can be highlighted. The degree of im-

portance could be modiﬁed using for instance visual

tricks or by changing indicator relative positions. e.g.

Indicator 1 is now the most important and its visual-

ization size increased.

Indicators Visualization: a visualization can be as-

sociated with one or more learning indicators. Some-

times, visualizations can change while the indicators

remain the same. e.g. The data are now presented

with a bar chart instead of a pie chart.

Indicators Timing: an indicator can be presented af-

ter a delay or at a given time, which can be conﬁgured.

e.g. The feedback was presented after each learning

session but is now presented only one session out of

two.

Indicators Source Data: when the data used to

compute the indicator is changed (ﬁltered, modi-

ﬁed. . . ), the indicator remains the same as long as the

data model is unchanged. e.g. The indicator is now

showing Adam’s grades instead of Collin’s grades.

Dimension 4 - How ?

How is the adaptation decided ?

Rules Engine: the adaptation is realized following

simple ‘if-then” rules. e.g. The alert box is shown

only if the learner underperformed his test.

Algorithms: the adaptation is realized following

more advanced programming concepts which are

more complex than ‘if-then” rules (e.g. loops). e.g.

Heavy calculation is needed before deciding if the

dashboard must include a particular indicator.

Machine Learning: to go further than “if-then” rules

and advanced programming concepts, the adaptation

is realized using machine learning. e.g. A prediction

machine learning algorithm is used to decide whether

or not it’s good to visualize a set of indicators with a

bar chart instead of a pie chart.

How is the adaptation performed ?

Activation/Deactivation: action to activate or deac-

tivate an indicator, to make a component appear or

not. . . e.g. A new indicator is now shown in the dash-

board.

Reﬁne Data: action often associated with the fact to

ﬁlter, select, or brush the data. The output value of

the indicator is actualized but the indicator remains

the same. e.g. The indicator showing Adam’s grades

for week 2 is now showing Adam’s grades for week 5.

This coding scheme is the result of several iterations

of analysis of samples of articles and associated dis-

cussions. Each iteration enabled us to reﬁne/improve

the previous version of the coding scheme until we

obtained a coding scheme that we consider stable and

complete. Finally, coding was carried out by a unique

coder to ensure homogeneity.

4 RESULTS & DISCUSSIONS

In this part, we jointly present the analysis results and

discuss them regarding our research questions. Raw

data, resulting from our analysis, are shown in Table

2 where we conveniently removed empty columns for

visualization purposes. The systematic process we

followed led us to review 426 papers to determine

if they described adaptable or adaptive LADs. Af-

ter two screening steps and applying inclusion crite-

ria, we ended up with 23 papers integrating adapta-

tion mechanisms to analyze (see Figure 1). As there

is only one LAD per report read, we assume that say-

ing report, publication, LAD, or paper refers to the

same thing.

4.1 RQ1: What Dashboards Since 2017

Feature Adaptability or Adaptivity

Mechanisms?

As shown in Figure 2, we reported publications in-

cluding adaptable features every year since 2017, with

a total of 19 LADs. In contrast, publications embed-

ding adaptive features were only found in 2018 (n=1),

2019 (n=3), 2020 (n=2), and 2021 (n=1), with a total

of 7 LADs. As a result, we have found more evidence

of adaptability than evidence of adaptivity (19 against

7). We assume our sample size is too limited to dis-

cuss any trend. We report and discuss below the na-

ture of indicators involved in adaptable and adaptive

LADs respectively. Descriptive indicators are found

in all papers and are the most commonly used (n=19;

n=7), followed by diagnostic indicators (n=8; n=6),

then predictive and prescriptive indicators (n=1; n=2).

Table 3 shows for each adaptation category the per-

centage ratio of the number of LADs including de-

scriptive, diagnostic, predictive, and prescriptive in-

dicators respectively, over the number of LADs re-

CSEDU 2024 - 16th International Conference on Computer Supported Education

Table 2: Analysis ﬁndings.

Authors

End user

Indicators nature To what What How

Descriptive

Diagnostic

Predictive

Prescriptive

User’s conﬁguration

Knowledge

Paths

Performance

Engagement, motivation

Metacognition

Data availability

Preferences

Selection

Importance

Visualization

Source data

Rules

Activation

Reﬁne

• • •

(Hasnine et al., 2023) T • •

• • •

(Hou et al., 2022) L • • • •

(J. A. Ruip

erez-Valiente et al., 2021) T • • • • •

• • •

(Al-Doulat et al., 2020) A •

• • •

(Mohseni et al., 2022) T • •

• • •

• • • •

(Chaudy and Connolly, 2018) T, D, R • •

• • •

(Zamecnik et al., 2022) L • • • • •

(van der Stappen, 2018) I • • • • • • •

• • •

(D. P

erez-Berenguer et al., 2020) L, T • •

• • • • •

(S¸ahin and Yurdug

ul, 2019) L • • • • • • •

(Aslan et al., 2019) T • • • • • •

(Molenaar et al., 2020) L • • • • • • • •

(Jivet et al., 2021) L • • • •

• • • •

(de Quincey et al., 2019) L • • • •

• • •

(T. Rohloff et al., 2019) L • • • • • • •

• • •

(Chen et al., 2018) T •

• • •

(Scheffel et al., 2017) L • • • •

(Jonathan et al., 2017) L • • • •

(Smith, 2020) L • • • •

(Taibi. et al., 2018) L • • • •

(Ez-zaouia and Lavou

e, 2017) T • • • • •

(Torre et al., 2020) P • • • •

• • •

(Dickler, 2021) T • •

• • •

Light gray = Adaptive ; Dark gray = Adaptable

L=Learner; T=Teacher; A=Advisor; R=Researcher ; D=Developer; I=Institutional; P=Practitioner.

Adaptation in Learning Analytics Dashboards: A Systematic Review

Figure 2: Cumulative publications since 2017.

spectively. As a result, there are always descriptive

indicators in both adaptable and adaptive LADs and

the ratio is increasing between adaptable and adap-

tive LADs for other types of indicators. Based on this

analysis, we hypothesize that adaptive LADs embed

more complex indicator natures, such as diagnostic,

predictive, and prescriptive than adaptable LADs.

Table 3: Indicator’s nature ratio.

Desc.

Diag.

Pred.

Pres.

Adaptable

(n=19)

100 42 5.2 5.2

Adaptive

(n=7)

100 86 29 29

Last but not least, we mostly report dashboards for

learners (n=11) and teachers (n=8), one targeting both

learners and teachers and three others for advisers, in-

stitutional, and practitioners. There is no noticeable

trend or evolution of the targeted end users over time.

4.2 RQ2: What Adaptation

Mechanisms Are Implemented into

LAD, for What Purposes?

4.2.1 Adaptive LADs

Most of the expected data types have been found,

except for collaboration data and socio-demographic

data. Additionally, during the adaptive process, two

out of seven LADs use two types of data instead

of one. The adaptive capabilities can be grouped

into two main categories, based on similarities shared

across “What” and “How” dimensions.

The ﬁrst category (n=5) concerns LADs that can

activate and/or deactivate certain indicators on their

own, thereby altering the resulting set of indicators.

Two publications include “all or nothing” indicators

as contextual notiﬁcations are displayed to the user.

For instance, in (Molenaar et al., 2020), indicators

are added to the screen to notify the learner that he

has completed the learning goals he set. In (Aslan

et al., 2019), a red bell appears on the correspond-

ing learner’s avatar when he is highly disengaged,

indicating that he is off the learning platform. In

(de Quincey et al., 2019) the authors insist on the

use of empty states to enhance the learning process:

some indicators are replaced with other learning ma-

terials, such as basic recommendations, when there is

not enough data to compute them. Replacement indi-

cators show personalized recommendations or various

descriptive data. The LAD of D. P

erez-Berenguer

et al. (2020) is adaptive due to the dynamic selection

of the indicators displayed on the screen. The deci-

sion process is based on rules that can be edited by

the teacher and the rule engine can be fed either by

performance or event sequences data of the learners.

Finally, the LAD in (S¸ahin and Yurdug

ul, 2019) can

complement the set of indicators by activating addi-

tional feedback to learners who have been classiﬁed

as poor performers by the system.

The second adaptivity category includes LADs

that automatically update their indicators when the

corresponding source data is changing (n=2). In

(Chaudy and Connolly, 2018), the LAD automati-

cally restricts the data accessible to a user based on

their role upon login. Teachers are limited to moni-

toring their students while administrators of the sys-

tem, such as developers or researchers, have access

to data from all the students. In (Dickler, 2021), the

LAD computes and triggers alerts from student data.

We consider this LAD to be adaptive because each

alert, whether it’s focused on a student in particular

or a group of students, pre-deﬁnes the data that the

teacher can consult after clicking on one of the noti-

ﬁcations. The teacher navigates through the data au-

tomatically selected by the LAD and doesn’t know in

advance what he will be able to monitor.

Table 2 data highlights that the use of rules pre-

dominates to activate or deactivate learning indica-

tors. It’s important to notice that no complex al-

gorithms or machine learning were reported in the

adaptation/decision process, leaving research on these

questions open. As previously reported, the two adap-

tivity capabilities only involve changes in the set of in-

dicators of the LAD or changes in the available source

data. Our analysis didn’t reveal any adaptation from

the system that focuses on changing the relative im-

portance (order) of indicators, visualizations that sup-

CSEDU 2024 - 16th International Conference on Computer Supported Education

port the reporting of data, or any delay that could be

considered before presenting one or more indicators

to a user.

4.2.2 Adaptable LADs

There is not much to discuss regarding the “To what”

dimension as all reported functionalities correspond

to a user input (User’s conﬁguration). In this section,

we organize and discuss the results according to the

“What” and “How” dimensions. Adaptable capabili-

ties are grouped into four categories based on shared

conﬁgurations.

Most of the LADs (n=15) are featured with the

functionality that consists in updating the dashboard

indicators when the user selects a new subset of data,

such as changing the date range (Hou et al., 2022;

Zamecnik et al., 2022; Scheffel et al., 2017; Mohseni

et al., 2022; Taibi. et al., 2018), subgroup or sub-

data to monitor (Hasnine et al., 2023; J. A. Ruip

erez-

Valiente et al., 2021; Torre et al., 2020; Chaudy and

Connolly, 2018; Chen et al., 2018; Al-Doulat et al.,

2020; Dickler, 2021; Jonathan et al., 2017; Taibi.

et al., 2018; Ez-zaouia and Lavou

e, 2017; Smith,

2020). This functionality mainly corresponds to ﬁlter,

select, and zoom actions. It is worth noting that the

codes “Reﬁne data” and “Indicator data” are strongly

correlated. Note that the nature of the indicators re-

mains the same, even if the values they return change.

This is because the indicators still answer the same

initial question.

It seems that some LADs (n=4) allow the user to

change/choose the indicators presented in the dash-

board through activation/deactivation. Among them,

two LADs enable the user to select their visualiza-

tions. Jivet et al. (2021) allowed learners to se-

lect up to six indicators from a choice of twelve to

be displayed on a radar chart. The learners could

change their choices at any time. Three distinct

functionalities were identiﬁed in the LAD developed

by de Quincey et al. (2019). The ﬁrst allows the

learner to choose between a professional visualiza-

tion with common charts and diagrams or a per-

soniﬁed theme that represents indicators with visual

metaphors: icons and images representing the con-

cepts are used instead of bar, line, or pie charts. The

second allows the learner to select the indicators he

prefers to monitor. The third consists of displaying

a contextual indicator (a tooltip) when the learner re-

quests it, providing additional explanations on how

the current score is calculated. The LAD presented

in (Hasnine et al., 2023) enables the user to access

a session summary on demand. It presents a more

general set of learning indicators than the real-time

indicators presented during the session. The analysis

dashboard, as presented in (Chen et al., 2018) allows

the teacher to progressively drill down into student

data, and some actions or speciﬁc inputs of the teacher

will result in the apparition of contextual indicators.

The following LADs (n=2) seem to feature adap-

tation capabilities, which involve recomputing the in-

dicators to ﬁt a new subset of data, along with other

effects on the set of indicators or their relative impor-

tance. In (Zamecnik et al., 2022), the learner has

ﬁltering functionalities, but the indicator set is also

likely to change since the learner can remove certain

types of information, which alters the nature of the

indicators. In (van der Stappen, 2018) the authors

propose a customizable LAD. It’s mentioned that the

users can personalize the order in which visualiza-

tions are displayed.

Finally, there are two dashboards (n=2) that al-

low the user to conﬁgure the indicators he wishes

based on the edition of rules. The design proposed by

van der Stappen (2018) includes adaptation mecha-

nisms, such as the capability for the user to customize

the indicators on the LAD using SQL queries and se-

lect their associated visualizations. Similarly, teach-

ers can set adaptation rules using a speciﬁc domain

language as presented in (D. P

erez-Berenguer et al.,

2020) to determine which indicators will be present

at execution time, depending on the input learning

traces.

Neither algorithms nor machine learning power

the adaptability mechanism after the user triggers the

adaptation. It’s important to note that we are refer-

ring to the decision step of the adaptation and not to

the steps used to compute the learning indicators. Ad-

ditionally, as reported for adaptivity mechanisms, no

features allow the user to conﬁgure a delay that the

LAD should respect before presenting one or more

indicators.

5 LIMITATIONS

The study has some limitations. Firstly, we examined

the proceedings of specialized conferences, namely

LAK, L@S, and UMAP but we were unable to do so

for the Educational Data Mining conference (EDM)

due to restrictions upon the EDM proceedings search

engine. Secondly, our study could beneﬁt from the in-

tegration of more data sources, notably LADs identi-

ﬁed by other means than academic databases. Thirdly,

it could be difﬁcult to deal with the analysis step as

adaptation mechanisms were not always clearly de-

scribed by the authors. Concerned papers have been

discussed by experts until a consensus was reached.

To improve the analysis step, it could be beneﬁcial

Adaptation in Learning Analytics Dashboards: A Systematic Review

to involve multiple coders and calculate an inter-rater

reliability metric.

6 CONCLUSIONS

A systematic review was conducted to investigate the

adaptation capabilities of LADs in terms of adaptabil-

ity and adaptivity. An analysis framework was de-

signed to reduce the diversity of the vocabulary used

when reporting on adaptation capabilities. Our ﬁnd-

ings highlighted less work on adaptive dashboards

than on adaptable dashboards since 2017. Our de-

tailed coding scheme helped us to identify categories

of adaptation capabilities and gave us more insights

about the learner data used for adaptivity. There

is currently little focus on the adaptation function-

alities of LADs, and there is likely insufﬁcient evi-

dence to support their beneﬁts at this time. Going fur-

ther, it could be proﬁtable to investigate the effects of

such adaptations on user’s perception, behavior, and

learning in future research. Additionally, the analysis

has pointed out paths that could be explored further.

These include the utilization of advanced computing

techniques in the adaptation process, adaptive capa-

bilities that could be based on more, or new types of

data, and functionalities that show up differently on

the dashboard, such as the adaptation of the indica-

tor’s relative importance or time-delayed presentation

of indicators according to learners’ needs.

To nuance this review, we have identiﬁed sev-

eral papers that were not included in the analysis

but which are representative of some of the work on

LADs. One body of research focuses on the develop-

ment of intelligent learning indicators (e.g. (Khos-

ravi et al., 2021)) which could be associated by tran-

sition with work on intelligent LAD. Another body

of research, such as (Dabbebi et al., 2017; V

azquez-

Ingelmo et al., 2020), focuses on the development of

“meta dashboards” instead of a particular intelligent

LAD.

ACKNOWLEDGEMENTS

This research is funded by TOKATAB SAS, the

French Agency of Research and Technology [ANRT,

grant number 2022/0171] and the LABEX ASLAN,

Universit

e de Lyon, within the framework of the

French program ”Investissements d’Avenir” managed

by the ”Agence Nationale de la Recherche” (ANR).

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