Towards Adaptive Dashboards for Learning Analytic

An Approach for Conceptual Design and Implementation

Dabbebi Ines

1, 2

, Iksal Sebastien

, Gilliot Jean-Marie

, May Madeth

and Garlatti Serge

UBL, University of Maine, LIUM Laboratory, Laval-LeMans, France

UBL, Telecom Bretagne, LABSTICC Laboratory, Brest, France

Keywords:

Learning Analytic Dashboard, Dashboard Generator, Dashboard Model, User’s Needs, Context.

Abstract:

Designing Learning Analytic (LA) dashboards can be a challenging and complex task when dealing with

abundant data generated from heterogeneous sources with various uses. On top of that, each dashboard is

designed in accordance with the user’s needs and their observational objectives. Therefore, understanding the

context of LA and its users is compulsory as it is part of the dashboard design approach.

Our research effort starts with an exploratory study of different contextual elements that could help us deﬁne

what an adaptive dashboard is and how it fulﬁlls the user’s needs. To do so, we have conducted a needs

assessment to characterize the user proﬁles, their activities, their visualization preferences and objectives when

using a dedicated dashboard. In this paper, we introduce a conceptual model, which will be used to generate a

variety of LA dashboards. Our main goal is to provide users with adaptive dashboards, generated accordingly

to their context of use while satisfying the users’ requirements. We also discussed the implementation process

of our ﬁrst prototype as well as further improvements.

1 INTRODUCTION

Human has been producing an overwhelming amount

of data and information on the Web (John et al.,

2016). In a learning situation, every time a learner

or a teacher interacts with the learning environment

such as taking an online class in a MOOC (Mas-

sive Open Online Course) or signing into their virtual

learning environment; he leaves behind him a signiﬁ-

cant amount of digital footprint or traces of his actions

(Greller and Drachsler, 2012). Through better use of

this data, teachers can be able to adapt their courses

and learners can use it to change their learning be-

haviors. As for the researchers, they are more inter-

ested in extracting new knowledge and in exploring

the phenomena of learning data exploitation (Verbert

et al., 2013). However, in the meantime, information

expand faster than our capacity to understand them

(Speier et al., 1999). Consequently, it becomes much

harder for users to observe, to control and to adjust

their learning process. For example, while MOOCs

helps teachers to reach thousands of students simul-

taneously (de Waard, 2015), it also creates a major

challenge for them to follow the interactions occurred

throughout a learning session effectively. To address

the problem of information overload, the adoption of

a learning analytic (LA) process is required. LA helps

the measurement, collection, analysis and reporting

of data about learners and their contexts, along with

a purpose of understanding and optimizing learning

and the environments in which it takes place (Siemens

et al., 2011). The development of this LA process rep-

resents a core objective of HUBBLE project (HUman

Observatory Based on analysis of e-Learning traces)

This project aims at creating a national observatory

with the scope of building and sharing extensive data

analysis processes based on traces generated from e-

learning environments. This process offers ways to

collect a critical mass of digital traces and to calcu-

late a signiﬁcant set of data called indicators (Iksal

and Choquet, 2007). Therefore, the visualization of

these indicators in the right way can help different

users such as decision makers (teachers, designers,

administrators or policy) to extract various facts about

a particular learning situation. They can then draw

meaningful conclusions about their different decision

contexts. Moreover, the visualization task can sup-

port other users such as researchers and analysts to

share, capitalize and reuse various tools and models,

and also dashboard models.

http://hubblelearn.imag.fr/

120

Ines, D., Sebastien, I., Jean-Marie, G., Madeth, M. and Serge, G.

Towards Adaptive Dashboards for Learning Analytic - An Approach for Conceptual Design and Implementation.

DOI: 10.5220/0006325601200131

In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 1, pages 120-131

ISBN: 978-989-758-239-4

Being part of HUBBLE project, the scope of our

research work covers the design of reusable dash-

board templates based on a user’s requirements, and

the generation of dynamic, contextual and adaptive

dashboards. In fact, the design of a dashboard model

should take into account various constraints related to

the user’s proﬁle and their characteristics, distinct de-

cision and learning contexts, etc. Our research also

has a strong connection with existing works where

a considerable number of heterogeneous indicators

produced by different analysis tools (KTBS (Zarka

et al., 2012), UnderTracks (Bouhineau et al., 2013),

SMOOPLE (Gilliot et al., 2013), UTL (Iksal and

Choquet, 2007)) will be dynamically and contextually

visualized.

In order to design an effective contextual dash-

board that provides a useful and dynamic way to

access to relevant indicators, a central assumption

in our work is that it is necessary to work on the

identiﬁcation of both visualization and decision con-

text’s elements. Hence, we conducted a needs assess-

ment study to better understand user’s requirements

in terms of data visualization, which leads us to pro-

pose a conceptual design process for a dynamic gen-

eration of contextual LA dashboards. The study we

have conducted helps us to shape our research ques-

tion that focuses on: (i) what is the user’s visualiza-

tion and decision contexts? What are the most rele-

vant indicators users need to make decisions? How to

present these indicators for maximizing the assistance

of the decision-making? How starting from context’s

description, an efﬁcient dashboard can be provided

to meet user’s needs? Our work aims to introduce

a conceptual model for a dashboard generator pro-

cess which will be used to generate a variety of LA

dashboards according to the different context of use.

This paper is structured as follows: in the ﬁrst sec-

tion, we provide a brief background on the ﬁeld of

LA dashboards. We present in the second section, the

study about user’s needs and their different contexts.

The process of building an LA dashboard based on

the identiﬁcation of our user’s contexts is discussed

in section 3. A case study and our ﬁrst prototype are

presented respectively in sections 4 and 5. We draw a

conclusion and highlight future works in the last sec-

tion.

2 RELATED WORKS

Making use of a large amount of data such as pro-

duced artifacts (Arnold and Pistilli, 2012), social in-

teractions (May et al., 2011), resource usage (Verbert

et al., 2013), time spent (Arnold and Pistilli, 2012)

and exercise results (Mazza and Milani, 2004) in LA

process aims at providing a better understanding and

optimizing the learning experiences (Siemens et al.,

2011). To facilitate the process, especially in the

decision-making, data analysis tools such as data min-

ing and data visualization techniques are widely used

and integrated into learning analytic dashboards.

Data mining techniques can help users to de-

tect new patterns and new knowledge that are likely

to be relevant. While it can provide visualization

along with tools that support data interpretation, it

do not support decision-making (Wolff et al., 2013).

It should be noted that it is not how a data mining

process is meant to be used in LA as the data visual-

ization is another domain of applications. Plus, data

mining techniques are often too complicated for non-

experts such as some teachers and students to make

use of them and to interpret their meaning.

The other well-known visualization solution is

learning analytic dashboard (LAD). LADs have been

developed by a number of researchers. They aim at

consolidating and arranging a set of the most rele-

vant data related to user’s objectives on a single com-

puter screen in order to visualize and monitor them

at a glance (Few, 2013). In this section, we present a

group of LAD applications that have been deployed

over the past few years. Similar to our objectives,

these dashboard applications are designed to select

and visualize digital footprints in order to support

viewers to deal with the huge amount of data streams

generated from different sources. While various exist-

ing dashboards are targeted to provide users with gen-

eral information related to only one particular learn-

ing environments such as Travis (May et al., 2011)

and GISMO (Mazza and Milani, 2004), others go fur-

ther and work on giving the opportunities to users

to control data generated from multiple sources such

as REFLET CourseVis (Mazza and Dimitrova, 2007)

and Tatiana (Dyke et al., 2009). Concerning evalu-

ation, various research works highlighted the impor-

tance of the positive effect on different users given by

the use of the dashboard (Santos et al., 2013). A line

of existing works has been conducted with learning

analytics to help teachers in their activities, for exam-

ple, to track progress or to make their reports (Kelly

et al., 2013), (Xhakaj et al., 2016). Other works have

studied the effect of learning analytics on student re-

tention (Nunes et al., 2013), on their performance

(Arnold and Pistilli, 2012) or on improving students’

engagement (Bouvier et al., 2014). Another example

of learning analytic tool is the Open Learning Ana-

lytic platform (Siemens et al., 2011). This tool aim at

considering the need of four users’ categories at the

same time: learners, educators, administrators, and

Towards Adaptive Dashboards for Learning Analytic - An Approach for Conceptual Design and Implementation

121

researchers.

The problem is, in most cases, dashboards are

imposed on users, and provide them general infor-

mation without considering their abilities, prefer-

ences and personal observation objectives (Martinez-

Maldonado et al., 2016). In fact, the use of LADs

should satisfy users’ needs in several contexts. How-

ever, having multiple users with different character-

istics may imply different requirements for visualiza-

tion, where the same data can be presented at a dif-

ferent time and in a several ways for various reasons

(Olmos and Corrin, 2012). In other words, to in-

crease the dashboard efﬁciency, it is crucial to con-

sider the variety of users’ context during the design

process. Even though data visualization can be ap-

plied to a wide range of contexts, the creation of a

context-aware dashboard tends to be a complicated

task (Li et al., 2015). As, the context deﬁnition itself

is considered to be a challenge mainly because of its

differences from one case to another (Baldauf et al.,

2007). Several works such as the CAVE platform (Xi-

aoyan et al., 2012) tried to deﬁne their visualization

contexts in order to help their users to monitor visu-

alizations regarding different situations. Three con-

text categories are deﬁned: user’s problem, knowl-

edge and purpose contexts where the difﬁculty is to

ensure that visualization can not only match the prob-

lem, purpose and knowledge of user but also have to

be deployed in a way that makes sense.

With the diversity of existing visualization tech-

niques, it remains difﬁcult to represent an accurate

data with an adequate visualization technique. For in-

stance, performance results can deliver through sev-

eral techniques such as histograms, bubble, pies, ta-

ble, and so forth (Scheuer and Zinn, 2007). Ad-

ditionally, the diversity of data representations can

also be related to the visualization vision that changes

from one person to another and regarding their con-

texts (Silius et al., 2013). To better understand user

goals and needs, many research studies tried to ex-

plain and to deﬁne users needs through the use of sev-

eral techniques such as inquiry (Xhakaj et al., 2016),

interviews (Bakharia et al., 2016), questionnaires (Ali

et al., 2012), the evaluation of the dashboard design

(Park and Jo, 2015) or survey (Mazza and Dimitrova,

2007). Our proposal has a strong connection with

these works. However, our aims are not only based

on the study of the users visualization objectives,

and user’s characteristics, including their visualiza-

tion preferences, but also the study of their decision-

making context in order to pursue users to visualize

their desired data in a meaningful way that supports

them take action or a decision. We attempt to present

an expressive dashboard to be easily interpreted by

each user. As a matter of fact, a visual representation

can be meaningful to one user, but it may be incom-

prehensible to another. In the study, we deﬁned dif-

ferent learning context elements that led to the design

of a dashboard generator. Our goal with the latter is

to provide users with a dynamic dashboard along with

the data indicators and their visualization tools at the

right moment based on their context of use.

3 DASHBOARD GENERATOR

ARCHITECTURE

Answering users’ needs regarding LAD represents a

central part of our work. For this reason, we place

a special focus on identifying the users proﬁles and

their requests. In HUBBLE project, the study of the

massive amount of data produced by the interaction of

students and teachers with the existing learning tech-

nologies, interests not only students and educators

themselves as decision-makers, but also researchers,

analysts, designers, administrators, and policy mak-

ers and so forth. As we are dealing with a wide range

of contexts and we are aware of how important to

fully explore the user’s requirements (Stodder, 2013)

throughout the process of designing LAD, the study

of the variety of users, their different learning prob-

lems, and their intended purposes take a prominent

place among our research challenges.

Our work aims at designing a dashboard generator

process which enables a dynamic production of dif-

ferent effective dashboards in order to answer various

visualization needs expressed by each user in a wide

variety of contexts. Hence, each generated dashboard

should provide, on the one hand, a set of indicators

gathering that facilitates the links between the data

and the visualization objective. On the other hand,

it should provide adequate visualization components

to maximize the value of this relevant set of data to be

visualized at the right time.

The core of this generator process is to integrate

visualization components, user descriptions and dif-

ferent data/indicators where every set of indicators re-

lated to one visualization objective will be gathered

together in order to add an additional semantic in-

formation (as illustrated in Figure 1). These set of

components will act as a semantic framework for stor-

ing contextual information required for generating an

adaptive dashboard.

However, the problem within this information re-

lated to the user’s contexts is that they can change

rapidly (Dey, 2001), since users themselves may not

be able to easily maintain the same preferences or

objectives when, for example, observing a dynamic

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Figure 1: Dashboard generator illustration.

data evolving network. Hence, it can affect the choice

of indicators or visualization component selection.

Apparently, to bridge the gap between these differ-

ent dashboard components within a dynamic context,

a full description of the user’s contexts and desired

dashboards are needed. This description aims at facil-

itating the understanding of the links between existing

components and dynamic user’s needs.

Our work relies on user-centered (Abras et al.,

2004) and context identiﬁcation design. In the fol-

lowing section, we introduce a contextual study with

the aim of better understanding user’s requirements.

This need’s identiﬁcation will bring us to create the

main components for dashboard modeling, including

user, visualization, and data models.

3.1 Identifying Users Needs

We conducted a needs assessment study including

survey and interviews to investigate different users’

contexts. This needs assessment is used to gather

a different set of data about our probable end-users.

More speciﬁcally, it may help us to deﬁne what is

a user’s visualization and decision context? What

are the most relevant indicators users need to make

decisions? How to present these indicators to max-

imize the assistance of the decision-making pro-

cesses? How starting from context’s description efﬁ-

cient dashboards can be provided to meet user needs?

To answer these questions, we organized our study

into ﬁve sections presented in the table 1 where each

one describes a contextual factor: who, what, how,

when and then? The ﬁrst section ”WHO” aims at

gathering data to help the description of users char-

acteristics: their roles, their visualization knowledge

level, their activities, their preferences and what kinds

of decisions they want to make. This information

helps the dashboard generator to identify who is the

user, in which category they can be assigned. The sec-

ond section is dedicated to the identiﬁcation of ”what”

information each user needs to visualize to make de-

cisions. The third section is used to identify ”when”

does it make sense to visualize a particular data and

when it can be more useful in a given observational

scenario. The section thereafter focuses on what is

the best way to visualize data in a given situation for

a particular viewer. In other words, each user needs

to choose ”how” to view each data in order to facil-

itate its understanding and interpretation. Lastly, in

the case of context evolution or change in the situa-

tion, a user may detect, for example, some interesting

patterns which require to obtain more details or even

to get additional data visualization to ensure deep data

interpretation. For this reason, it seems necessary for

the user to identify also his needs and preferences in

such situation in order to improve the dashboards gen-

erator adaptation to their new requirements and this

new context. For example, how they want to be in-

formed? How can they interact with this evolution?

The following table 1 presents highlighted questions

raised in our needs assessment.

Table 1: Needs assessment sections and elements descrip-

tion.

WHO?

User proﬁle and

role: tutor, stu-

dents, etc.

This is about identifying the

user category in order to pre-

pare different dashboard ver-

sions, if it is possible.

Personal informa-

tion: name, age,

physical condition,

etc.

This refers to speciﬁc infor-

mation that can be used for

user classiﬁcation.

Preferences and

abilities

This question deﬁnes the

level of knowledge of the

user, and it helps to determine

his prior preferences.

Goals and interests

This is the main question

to understand what the user

wants to do with the dash-

board and to explain his

needs to have a personal or

speciﬁc dashboard.

WHAT?

Analysis scenario

It is helpful to provide the

user with a set of data gath-

ered during his assigned anal-

ysis scenario.

Desired indicators

Among the list of indicators

provided by a given anal-

ysis scenario, the user can

precisely determine what is

most relevant indicators to be

visualized in his dashboard

and they can explain their

choices.

Towards Adaptive Dashboards for Learning Analytic - An Approach for Conceptual Design and Implementation

123

Table 1: Needs assessment sections and elements descrip-

tion (cont.).

HOW?

Visual represen-

tation : Textual,

graphical repre-

sentation or using

audio effect, etc.

Each user may prefer or used

to a given data visualization

technique and thus, it is pos-

sible to maintain the same

data visualization preference

with the dashboard.

WHEN?

Temporal modal-

ity: synchronous,

asynchronous or a

hybrid visualiza-

tion,

It deﬁnes the possible time of

visualization.

Users interaction

and collaboration

We study the needs of the

integration of collaboration

tools to facilitate decision-

making via dashboards. This

information helps to iden-

tify ”when” the dashboard

should be generated to ensure

the communication between

a group of users and how in-

teract with dashboard during

visualization?

THEN?

How react during

context evolution?

Modify graphical

representation,

notify users, so on

and so forth.

This information helps the

dashboard generator to pro-

pose the relevant adaptive

strategy in order to guarantee

user satisfaction even with

change of context.

3.2 The Needs Assessment Analysis

We targeted several members working on 13 differ-

ent use cases involved in HUBBLE project through

either by contacting them through a survey or an in-

terview about their visualization needs. A use case, as

deﬁned in HUBBLE project, can be the study of data

gathered during a learning environment or a pedagog-

ical scenario that involve produced by a set of users

(teachers, students, researchers, and so on.). Each use

case includes a detailed documentation that would al-

low describing a sequence of events, activities, and

users involved in such platform.

Among 13 use cases, we received 10 feedbacks

which represent 10 different application of contexts.

As a result, we noticed a diversity of users’ proﬁles

that could be interested in data visualization, repre-

sented in Figure 2. While 4 use cases have targeted

the works and the requirements of researchers, 4 oth-

ers use cases interested in teachers-designers, and the

Figure 2: Breakdown of user’s proﬁle.

last two use cases are dedicated to teachers and to

project managers. Based on the initial results of our

needs assessment study, we identiﬁed a diversity of

user’s proﬁle and their different visions about the ef-

ﬁcient dashboard related to their situations and con-

texts. These primary results are detailed as follows:

Researcher Vision:

Three out of four use cases involving researchers

consider data visualization as a multi-user activity

which requires sharing the same dashboard to better

collaborate with other researchers. This collaboration

requires the integration of different tools of commu-

nication to facilitate users’ interaction such as chats

or forums, and so forth. These researchers expressed

the need to visualize data in order to:

1. Study the evolution and the progress of learners in

order to identify students behaviors with the aim

to allow extracting student series/ patterns of ac-

tions.

2. Determine learner typology.

We also understand that user like researcher is

able to interpret data with different and complex rep-

resentations. Regarding their high knowledge level

with visualization techniques, they can express eas-

ily what they expect to have in terms of graphical

representations. For this reason, they expressed their

need to have the ability not only to be a dashboard’s

viewer but also to manage their dashboards by hav-

ing the right and the possibility to personalize their

dashboards directly: the set of data, the choice of data

representation, dashboard template, time of visualiza-

tion, etc.

Teacher-designer Vision:

Regarding the study of the different answers col-

lected from the use cases designed for teachers-

designers, we observed that unlike the researchers’

demands, three use cases consider the use of dash-

boards as a single-user activity where sharing infor-

mation with other users from the same categories is

unnecessary to satisfy their visualization needs, espe-

cially when they have the need to:

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124

1. Evaluate proposed exercises and courses.

2. Determine the typology of their students.

Some pedagogical designers/teachers proposed to

integrate not only visual representations but also

sound effects like being informed with alert notiﬁca-

tions in order to detect unusual situation. Therefore,

it requires the development of a hybrid dashboard that

provides real-time visualization and alert notiﬁcations

during session and proposes a visual overview after

session.

Teacher and Pedagogical Team Visions:

Both users have described similar goals and inter-

ests. Their main purpose is to:

1. Identify parameters representing the popularity

and the healthiness of courses.

2. Determine parameters indicating the reason for

both failure and learner success.

3. Understand the situation and interact with stu-

dents.

For example, in the case of MOOCs, with the remark-

able number of registered students, analysts paid at-

tention to the use of simple visualization representa-

tion in order to facilitate the interpretation of this mas-

sive amount of data, such as using signiﬁcant colors

to indicate success as green color and red one repre-

senting fail.

Hence, we can consider that their most impor-

tant challenge here is to reduce the number of failure

students. For this reason, it seems necessary to of-

fer teachers and the pedagogical team a hybrid dash-

board. Thus, it may allow them to be reactive and

aware of the situation of their students, to be informed

about the student’s problems and to detect the student

risk to be a failure as soon as possible with the aim

to help them very quickly. One of their requests is to

provide sound effects and alerts and to have also the

ability to share some results, if it is necessary, with

other users (other teachers or project managers).

Based on the ﬁrst results of our needs assessment

study, namely what and how users need to visualize

and to organize the data visualization to get ”the best”

dashboard for a particular scenario, we represent a

proposed model of the different learning context el-

ements related to the dashboard generator’s process.

This proposed model is described in detail in the fol-

lowing section.

Figure 3: The structure of a generated dashboard.

4 TOWARDS THE GENERATION

OF CONTEXTUAL LEARNING

DASHBOARDS

Based on our needs assessment analysis of how each

end-user perceives his dashboards, we identiﬁed a set

of dashboard descriptions. The dashboard could be

deﬁned as a visual display that enables visualizing the

most important indicators produced during the analy-

sis process. In our case, a structure of the dashboard,

presented in Figure3, not only have included a set of

indicators consolidated on a single computer screen

(Few, 2013), but also to regroup and link a set of indi-

cator together based on their relationship. In addition,

it has to incorporate different visualization function-

alities which may help to manipulate and to highlight

the most relevant information. These functionalities

can be the use of alert notiﬁcations, positioning on

the page, and careful use of colors and fonts, etc.

4.1 Dashboard Organizational Model

As introduced earlier in the paper (refer to Figure1),

the proposed dashboard generator process, takes into

account, every time, the different contextual descrip-

tion provided by each particular user.

Based on this description, we depict in Figure3 a

sample of dashboard structure as it will be proposed

to our users.

This dashboard template (Figure4) incorporates

Towards Adaptive Dashboards for Learning Analytic - An Approach for Conceptual Design and Implementation

125

three basic components. The ﬁrst component is dash-

board headers. Headers provide dashboard identity or

logos and incorporate the set of functionalities that it

can make the dashboard more useful. It can facilitate

the collaboration between a group of users by adding,

for instance, sharing tools or chat. Other requested vi-

sualization functionalities may be used to re-organize

the dashboard structure. The dashboard can also in-

corporate other functionalities such as alert warning

notiﬁcations with the aim of providing a hint about

unusual events and to highlight the most interesting

changes in the dashboard.

The second component represents the choice of

the navigation methods and the levels of the content

organization. It may allow the representation of the

set of presented indicators either on one page or using

a menu or tabs with the aim to facilitate the naviga-

tion on the dashboard and also it will serve to pool

indicators with the same objective together in order to

maximize efﬁciency.

The third component represents the core of the

dashboard or the content area which focuses on in-

dicators. It consists of two main elements. The ﬁrst

element -deﬁned as summary indicators in Figure3-

consists of the representation of textual indicators that

ensure the display of a textual description about the

most relevant indicators. The second element con-

cerns the visualization of a set of organized and re-

grouped indicators. This part takes into account the

importance of choice of visualization forms and the

choice of which indicators will be grouped together

in a way that facilitates understanding and shows the

relationship between them.

Figure 4: Main components of the dashboard structure.

Organizing dashboard’s contents in a meaning-

ful way is considered as a challenging issue where

indicators are not designed to be placed anywhere

on the dashboard and every item related to another

one should be positioned close to each other (Few,

2013). For this reason, we deﬁned relationships be-

tween dashboard content in order to set the appropri-

ate way to look at user’s objectives. This component

is represented by the model in Figure5, and consists

of two main relationships:

1. To group main/desired indicators -deﬁned by the

user- associated with other selected and proposed

indicators that may serve to satisfy the same ob-

jectives (refer to the ﬁrst and the second indicator

in Figure3). The simple way to relate associated

indicators can add more accessibility to them.

2. Moreover, this component represents a set of dif-

ferent structures for the same information with a

different level of detail such as the additional In-

dicators in Figure3.

Figure 5: The relationship between indicators.

So, the dashboard generator has to identify the re-

lationship between these indicators and to represent

them appropriately for every end-user. In order to

achieve that, three main information are required. The

ﬁrst one is related to the user’s characteristics. It in-

cludes models that deﬁne their visualization prefer-

ences. The second information corresponds to data

representation layer or visualization model, including

the dashboard’s functionalities. The last component

is related to modeling data including the learning sce-

nario and the activities’ models.

These different components are represented in

Figure 4, in order to provide the user with a set of

appropriate data and kind of visualization. In the fol-

lowing paragraphs, we describe these different com-

ponents.

4.2 Dashboard User Model

The user model is a fundamental component of an

adaptive system. Our generator has to adapt the dash-

board in different contexts automatically, so, the de-

sign of the user model is crucial. It must take into

account the analysis of the results we gather in our

needs assessment grid. This model has to represent a

broad range of assumptions about different users. A

user model should consider these two major needs:

First, it has to represent the user’s multiple visualiza-

tion objectives. Next, it has to be sufﬁciently adapt-

able to integrate the evolution of a user’s needs. Our

model is initialized with a stereotype, and in a future

stage, it will be modiﬁed according to the user activity

on the dashboard.

In this section, we describe the user model’s el-

ements necessary to establish our objectives. This

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126

model considers the user’s level of visualization

knowledge, preferences, interests, behaviors and

other facts. The different sections of our user model

are the following:

Personal Information: It helps to classify users ac-

cording to their personal characteristics such as their

roles, their visualization objectives. This section has

a twofold purpose. On the one hand, it is necessary

to the dashboard identiﬁcation (use case, user id) that

will lead dashboard generator to identify the user sta-

tus. Knowing the use case assigned to our end-user, it

may help us to limit the data selection and then help us

to provide only interesting data related to user needs.

When users log on, they will be driven to their dash-

board according to their preferences. In fact, the dash-

board generator will present an overview dashboard

containing all the latest visualized indicators in order

to keep them informed about the last events.

On the other hand, personal information section

aims at classifying viewers by their roles, their visual-

ization objectives or even by their skills and abilities.

The purpose of user classiﬁcation is to help in facil-

itating the display of the right information for each

user. Moreover, we will be able to infer other user

preferences based on similar proﬁles in order to en-

hance the accuracy of the provided data.

• User id: It is used to identify the user and to con-

nect him to their session.

• User role: It aims at categorizing users by their

roles, such as tutor and student.

• User type: It indicates whether the user is con-

nected as an individual or is associated with an-

other group of users. In case, the user is related

to a particular group of users; dashboard gener-

ator will enable the use of collaboration tools to

ensure the communication within the group.

• User status: It identiﬁes whether the user is

active/connected or not. It appears to be a relevant

information which helps to ensure the best way of

presenting data: the dynamism of data represen-

tation. For example, while a newly active user is

interested in visualizing real-time indicators, the

other user may prefer to have a summative repre-

sentation and an overview of the interesting infor-

mation due in a given period.

• Use case: Every use case is attached to an analysis

scenario and their list of data. This information is

important for data selection where each user will

be provided with a set of indicator related to his

speciﬁc use case to ensure data accuracy.

• Visualization objective: It describes the user’s

goals and interests: what he needs to explore, ex-

plain or decide. It describes the visualization ac-

tivity that users care about. It helps also to rec-

ognize what users are trying to achieve with the

dashboard. Knowing users objectives are consid-

ered more than just comprehension; it also in-

volves deﬁning his context. For example, users’

goals and interests can be the identiﬁcation of

a particular phenomenon or the evaluation of a

group of activities, signaling problems, transfer

knowledge.

User’s Knowledge: This part describes the visual-

ization experience of the user and their abilities. It is

initialized with his previous experience. Users can be

a beginner, a competent, a proﬁcient, an expert level:

For example, a beginner level user often needs step-

by-step support for how to manipulate visualizations

while an expert-level user may require more support

for personalizing visualization to complete complex

tasks.

User’s Visual Preferences: A user preference in-

cludes the dashboard template, the desired data, users

preferred graphics and another in which it describes

users adequate dashboard. Users’ preferences can be

assigned to not only to a particular user but also to

an entire group of users sharing similar users’ pro-

ﬁles. This section is necessary for the deﬁnition of

an adaptive strategy in which the automatic creation

of information graphics tailored to users’ preferences

and tasks.

4.3 Dashboard Visualization Model

The main objective of data visualization is to let the

viewer get a relevant analysis and understand what is

happening. Visual representation does not only repre-

sent a signiﬁcant amount of data coherently but also

help to extract new knowledge and/or to let the viewer

be a decision maker within this particular context.

However, with the variety of existing visual graph-

ics and a large amount of data, the choice of the ade-

quate data representation may be critical. The choice

of a visual form should be easily understood by users

and should represent information in a meaningful way

to ensure the user’s needs by providing familiar vi-

sual components to the user and respect their level of

visualization knowledge. Presenting a wrong visual

graphics or providing a sophisticated type of visual-

ization to a beginner viewer, for example, may lead to

wrong data interpretation.

In an attempt to easily provide the best-suited data

representation, we devoted the following part to de-

scribe and to index the characteristics of our visual-

ization model or a right visual graphic selection:

• Form of visualization: It helps to determine the

presentation types: It can be a table, chart, text or

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127

image.

• Number of dimensions: It speciﬁes the amount of

information that one single visual form can repre-

sent.

• The quantity of data: It describes some items that

could be displayed for each dimension. Only a

few or many?

• Data structures: It indicates the appropriates

structure of data and format of the information to

be represented. This information is necessary to

link the desired data with the best visual form.

• Purpose of visualization: Another way to offer the

suited display from the angle of what user tasks a

visualization aims to support. Visualization can

help viewers to compare data sets easily or may

present composition, data distribution or to high-

light the relationship between data. For exam-

ple, tables are useful to show the differences or

similarities between values while a hierarchical

data representation shows relationships between

groups.

• Visual granularity: It allows changing the aggre-

gation of the data to display the desired level of

detail.

• Meta data: It is optional information that contains

details. It serves to index visual representations

like if the associated chart is a multidimensional,

temporal, hierarchical graph.

• Similar visualization: It serves to recommend and

to associate other visual graphics that share simi-

lar characteristics (number of dimensions, the for-

mat of the information) and can be used in the

same context or some contexts which fulﬁll sim-

ilar objectives. This can help the user to have a

different interpretation of the same data.

4.4 Analysis Scenario Model

The key objective of scenario description is to ex-

press an additional contextual information like data

relationships that allows the dashboard generator to

understand, ﬁnd the right indicators easily and to be

able to combine it with other information as needed

in a meaningful way. In each speciﬁc scenario, all

data and indicators are related to satisfying a particu-

lar analysis objective. Each speciﬁc scenario involves

different activities including a set of data. For in-

stance, to identify student engagement, we need to

access to each student activities like the number of

possible connections established by each student or

the amount of exchanging messages between forum’s

members.

In this section, we deﬁne different scenario prop-

erties:

Analysis Scenario Description:

• Learning Scenario Objectives: Each scenario is

associated with visualization goals and interests.

Every objective of visualization can be achieved

by including one or multiple tasks. For example,

to classify of students over time, dashboard gen-

erator need to access to every student’s activities

like student response rates, connection ratio, etc.

• Use case: It deﬁnes the user’s pedagogical con-

text, their ﬁeld of education or training, used in-

formation technology platforms and their users. It

helps to associate a list of data for each user.

Activity Description: Each activity is described by a

set of data like its users, the activity status to indicate

whether the activity is ﬁnished, started or ongoing.

• Task-related purpose: It deﬁnes the speciﬁc goal

of activity within a particular scenario.

• Activity status: The activity status indicates the

level of processing activity (planned, started or

ﬁnished), where a planned activity is yet to begin,

a started activity is continuing or ongoing activity

and a ﬁnished activity is complete. This ﬁeld is

bound with the date and time stamp that provides

information about the right moment to visualize

the set of indicators/data related to the activity.

• User of an activity: e.g. group of students

Dashboard Indicator Description: Every scenario

includes a list of indicators related to different activi-

ties. The data/indicators should be organized and de-

scribed in such a way that it can be selected easily by

the dashboard generator for a speciﬁc context. The

part illustrates the data structure and characteristics:

• Type of data: Data can be quantitative if it is in

numerical form, whether it is continuous and dis-

crete or whether it is qualitative. For instance, line

Graphs can be used to display the development

of quantitative values over a period of time rather

than pie charts and bar charts that can both be ef-

fective methods of portraying qualitative data.

• Number of dimensions: It indicates the amount of

information that one indicator can provide. For

example, an indicator can represent user devel-

opment over time. This indicator provides two

information: Time and users work’s evolution.

Both types of data and number of dimensions aim

at bridging the gap between data and visualiza-

tion model by ﬁnding best match data (the type

of data) and visual elements and properties (data

structure) to pick the right data visual presentation

easily.

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128

• The quantity of data: When the user has a signif-

icant amount of data to visualize, the dashboard

generator can propose data visualization at a dif-

ferent level of abstraction in order to be able to

facilitate the data interpretation.

• Meta data: It associates other similar indicators

that are dedicated to the same context, scenario,

use cases, users and may have related purpose.

In order to fulﬁll the requirements identiﬁed due to

our needs assessment survey and interviews, we pro-

posed and designed a dashboard generator process,

which is further presented through the presentation

and the implementation of a prototype (section 5).

5 ILLUSTRATIVE CASE STUDY

In HUBBLE project, we had to work on various

datasets; each dataset is associated with a use case.

A use case is composed of a data and individuals de-

scription such as the teacher, researchers, analysts,

who were involved in a real learning session. In or-

der to build our ﬁrst prototype, we did focus and used

data from the MOOCAZ case study; it is a MOOC

which concerns the creation of a MOOC from A to

Z. We received data and observational needs from re-

searchers of the French ENS Cachan. We had two-

course sessions with approximately 5000 students in

each one. Each course session has a start date, set by

the pedagogical teams, and an end date. The learn-

ers of the same session follow the MOOC with pro-

posed deadlines. These learners had to work on sev-

eral proposed chapters that may include videos, quiz,

and assessments. During these MOOC sessions, re-

searchers have identiﬁed a set of users stereotypes.

After conducting its analysis, a visualization of learn-

ers categories and their work’s evolution during the

entire MOOC sessions are needed.

6 PROOF OF CONCEPT OF

CONTEXTUAL DASHBOARD

Our dashboard generator is deﬁned as a context-

sensitive framework that adapts continuously to many

viewers’ contexts with the aim at satisfying their de-

scribed needs where these different components rep-

resent together the dashboard context. This frame-

work as illustrated in Figure 1 depends on three fun-

damental components that are, user, data, and visual-

ization models according to the dashboard structure

model discussed in the previous sections.

Figure 6: A sample of generated dashboard page:

MOOCAZ use case.

To validate our work in a real world setting, we

implemented a prototypical tool, an example of a gen-

erated dashboard is presented on Figure 6. The pro-

totype helps the viewer to answer their contextual re-

quirements through providing an adaptable visualiza-

tion solution that satisﬁes their desired visualization

representations of a speciﬁc set of indicators. For a

ﬁrst time, the adaptation of the generated dashboard

will be done at any time but only on the demand of

the users where they can manually conﬁgure the entire

dashboard. This will be our ﬁrst step toward the de-

sign of adaptive dashboards. As illustrated in Figure

7, the dashboard generator starts by identifying the

concerned viewer: his role, his objectives, and his use.

These information aim at distinguishing the appropri-

ate analysis scenario including a set of indicators. In

the HUBBLE project, indicators are collected, trans-

formed and calculated separately by various analysis

platforms: UTL, KTBS, SMOOPLE, UnderTracks.

When some indicator’s descriptions meet users’

described objectives, they will be selected by the

dashboard generator that will prepare a set of associ-

ated indicators that serves for the same analysis sce-

nario or user activities (as it is explained in Figure 5)

as means of enriching the dashboard’s content.

Figure 7: Dashboard generation process.

In the third step or form of visualization selection

step, the dashboard generator will take into account

the description of the selected indicators such as a

number of dimensions, the type of data. This step

helps to sort a group of visual representations -already

pre-selected based on user preference- then, to select

the adequate visual form that matches with the indi-

cator description.

Towards Adaptive Dashboards for Learning Analytic - An Approach for Conceptual Design and Implementation

129

Finally, by having the set of appropriate content

depending on users’ visualization preferences, the

dashboard generator will propose the integration of

this content with a set of desired functionalities to the

viewer regarding his dashboard description.

As a result of this process implementation, we

generate a dashboard’s instance, presented in Figure 6

to answer the visualization objective requested by an

analyst in our case study MOOCAZ. The user main

objective was to study the learners’ behavior through-

out the MOOC. Three types of pedagogical resources

are available to the learners and four possible actions

they can do: to watch a video, to download a video, to

answer a quiz, to work on an exercise. These different

actions are used to determine the learner states. The

dashboard generator has provided to the end-user the

possibility to visualize the evolution of the learners

through a time display in order to enable the study of

user behavior week after week. Hence, it may allow

learners classiﬁcation through a comparative visual-

ization form.

To facilitate the indicator interpretation, we re-

grouped the set of proposed visualization forms an-

swering the same objective together according to

dashboard structure deﬁned in Figure 3.

7 CONCLUSIONS AND FUTURE

WORKS

The main objective of our work is to provide an ad-

equate learning analytic dashboard by meeting each

user’s requirements. The design of such LA dash-

boards requires a deep understanding of users’ con-

texts such as his preferences, his main objectives, and

activities and his dashboard’s vision. In this paper,

we conducted a needs assessment study with 8 use

cases out of 13 involved in the HUBBLE project with

the aim of generating a solution to meet his identiﬁed

needs. This study led us to construct a dashboard gen-

erator which it based on a set of models that enable the

process of learning contextual dashboard design. By

describing each of user, data, and visualization com-

ponent models, the dashboard generator will set the

link between these different components to allow data

visualization according to different contexts.

We applied the proposed dashboard generator pro-

cess for designing and deploying dashboard for HUB-

BLE project’s use cases where the results and the ex-

periment demonstrate the feasibility of this process.

However, users often have just a slight idea about

their preferences, and they are not able to express

them precisely. In this case, our aim in the future is

that users will not be asked directly for their prefer-

ences, but this information will be derived from their

behavior while interacting with the dashboard. It re-

quires from the dashboard generator to take into ac-

count and to adapt dynamically to this user’s context

evolution. Therefore, user’s visualization preference

and the change of context will be captured through

the study of their user’s interaction history. For ex-

ample, through the combination of visual activities or

the repetitive choice of data representation. Where

our tool should tracks and memorizes a sequence of

actions and interaction with the dashboard that may

describe the choice of data, visual representation, the

combination of data, the time of visualization. Having

a history provides the dashboard generator with new

user visual preferences that help eventually to modify

the dashboard’s behavior dynamically.

ACKNOWLEDGEMENTS

This research activity is funded by the French Na-

tional Research Agency (ANR). We specially thank

all participants of the needs assessment study, and

people involved in MOOCAZ, the project used as our

illustration.

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