Mental Workload Management as a Tool in e-Learning Scenarios

Andr

e Pimenta

, Sergio Gonc¸alves

, Davide Carneiro

, Florentino Fde-Riverola

, Jos

e Neves

and Paulo Novais

Departament of Informatics, University of Minho, Braga, Portugal

Informatics Department, University of Vigo, Ourense, Spain

Keywords:

Mental Workload, Mental Fatigue, Machine Learning, e-Learning, Fatigue Management, Human Perfor-

mance.

Abstract:

In our daily life, we often have a sense of being exhausted due to mental or physical work, together with a

feeling of performance degradation in the accomplishment of simple tasks. This is in part due to the fact that

the working capacity and the performance of an individual, either physical or mental, generally decrease as

the day progresses, although factors like motivation also play a signiﬁcant role. These negative effects are

especially signiﬁcant when carrying out long or demanding tasks, as often happens in an educational context.

In order to avoid these effects, initiatives to promote a good management of the time and effort invested in each

task are mandatory. Such initiatives, when effective, can have a wide range of positive effects, including on the

performance, productivity, attention and even mental health. Seeking to ﬁnd a viable and realistic approach to

address this problem, this paper presents a non-invasive and non-intrusive way to measure mental workload,

one of the aspects that affects mental fatigue the most. Speciﬁcally, we target scenarios of e-learning, in which

the professor may not be present to assess the student’s state. The aim is to create a tool that enables an actual

management of fatigue in such environments and thus allows for the implementation of more efﬁcient learning

processes, adapted to the abilities and state of each student.

1 INTRODUCTION

In our day-to-day we often feel a sense of tiredness

during mental or physical work, generally known as

fatigue. The term fatigue is used to describe a series

of manifestations that range from drowsiness or loss

of concentration to lack of physical strength or agility

(van der Linden et al., 2003). Thus, it is a very broad

and subjective term that may include symptoms such

as loss of performance (loss of attention, slowed reac-

tion and response times, impaired decision-making,

and poor performance on tasks that generally reﬂect

the good performance) as well as more subjective

ones such as sleepiness and tiredness (Williamson

et al., 2005; Perelli, 1980).

In seeking to formalize it, fatigue may be deﬁned

as a degree of failure of physical or mental factors as-

sociated with loss of physical or mental performance,

hindering the natural or spontaneous accomplishment

of a usual activity. Bartlett provides one of the clear-

est deﬁnitions of fatigue with respect to day-to-day

tasks (Bartlett, n.d.) :

Fatigue is a term used to cover all those determinable

changes in the expression of an activity which can be

traced to the continuing exercise of that activity un-

der its normal operational conditions, and which can

be shown to lead, either immediately or after delay,

to deterioration in the expression of that activity, or,

more simply, to results within the activity that are not

wanted.

I.D. Brown, on the other hand, conceptualized mental

fatigue as:

(...) the subjective experience of individuals who are

obliged (...) to continue working beyond the point

at which they are conﬁdent of performing their task

efﬁciently (...) [Fatigue is] the subjectively experi-

enced disinclination to continue performing the task

at hand. [The] main effect of fatigue [is] a progressive

withdrawal of attention from the task at hand. [This

withdrawal] may be sufﬁciently insidious that [oper-

ators] are unaware of their impaired state and hence

in no position to remedy it (Brown, 1994).

Beyond the well-known effects on mood or en-

ergy, fatigue is also the cause or partial cause of er-

rors and accidents. Often, this happens because fa-

tigued individuals are unaware of the degree of their

Pimenta A., Gonçalves S., Carneiro D., Fde-riverola F., Neves J. and Novais P..

Mental Workload Management as a Tool in e-Learning Scenarios.

DOI: 10.5220/0005237700250032

In Proceedings of the 5th International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS-2015), pages 25-32

ISBN: 978-989-758-084-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

impaired mental state (Miller, 2013). These errors

and accidents assume particular importance when we

consider the domain of high-risk jobs that involve op-

erating vehicles as well as the military, ﬁreﬁghters or

medical personal, just to name a few. Beyond these

immediate problems, fatigue can also lead to health

problems in the long term, such as chronic fatigue

syndrome or depression.

The negative effects of fatigue are thus clear.

Moreover, they are also broad in the sense that affect

many of our cognitive abilities. Learning is one of

the functions that becomes impaired when under fa-

tigue. Hence the importance of addressing this issue,

especially in a time in which the teacher and student

are growing apart due to the increasing use of elec-

tronic tools for learning. Indeed, due to the separation

imposed by technology, it results more and more dif-

ﬁcult for teachers to be sensible to the state of their

students, impairing their ability to adapt both the con-

tents and the teaching strategy accordingly.

This work details a tool for fatigue management

in e-learning scenarios, with or without the teacher’s

presence, through the assessment of mental work-

load quantiﬁed in terms of the interaction patterns of

the user with the computer. Through the use of be-

havioural biometrics, speciﬁcally Keystroke Dynam-

ics and Mouse Dynamics, we analyse the type of task

performed by each user, the time spent performing it,

as well as the mental workload of the task. With this

information we train classiﬁers that are able to distin-

guish situations in which users show signs of fatigue

or high mental workload.

This approach can be considered both non-

invasive and non-intrusive, since it is based solely on

the observation of the use of the mouse and keyboard,

which allow for an assessment of the user’s perfor-

mance. This approach opens the door to the develop-

ment of fatigue management initiatives in the context

of e-learning, allowing teachers to not only have a bet-

ter notion about their student’s state but also to more

efﬁciently adapt and above all personalize teaching

strategies.

1.1 The Need for Monitoring in the

Context of e-Learning

Electronic instruction, more commonly known as e-

learning, is increasingly used as a method of teach-

ing. E-learning differs from classroom-based train-

ing in several ways. Thus, the transition from a tradi-

tional course to a course supported by e-learning can

be complex and difﬁcult. There is the need for a good

course planning and an increased effort in monitoring

and controlling all participants in all the different mo-

ments of the course while at the same time focusing

on getting feedback that may allow to better steer the

course (Hamburg et al., 2008).

Without the obligatory physical presence of a

teacher, the process of e-learning is exposed to some

deﬁciencies that may result in poor student learning.

Speciﬁcally, the teacher is not able to observe the stu-

dents in search of signs evidencing problems such as

doubts, frustration, stress or fatigue, preventing teach-

ers from taking action in such scenarios. The set-

ting up of appropriate monitoring mechanisms in the

context of e-learning is therefore very important in

achieving an efﬁcient learning process.

As shown in the literature, for an efﬁcient mon-

itoring of the student to take place, it is crucial that

the e-learning system allows for a personalized study

strategy and is able to show the needs and strengths

of each student (Cantoni et al., 2004). Thus it be-

comes possible to track the progress of students, as

well as improving their learning, by providing better

personalized learning methods. The identiﬁcation of

learning problems and the cause of those problems is

another advantage that can be achieved through the e-

learning context, and via monitoring systems, such as

the tool proposed in this paper.

1.2 Including Subjective Measures of

Workload

In this paper we look at the monitoring and managing

of mental workload as a way to improve the quality

of information of the e-learning environment, espe-

cially to improve the teacher’s decision making abili-

ties. One of the important parts of this work is a previ-

ously developed approach, deemed non-invasive and

non-intrusive, for the analysis of the students’ inter-

action patterns.

Indeed, it was established in preliminary work that

one’s patterns of interaction with the computer, mea-

sured in terms of the use of the keyboard and mouse,

change when under fatigue as well as in periods of in-

creased mental workload or even stress. Moreover, it

were also found behavioral differences in performing

different kinds of tasks, allowing to analyze patterns

of attention in the students who participated in the

experimental studies (Pimenta et al., 2015; Pimenta

et al., 2014).

However, the work developed so far has the short-

coming of not considering mental workload, which is

an important aspect when it comes to determining the

actual level of fatigue. It is also important, for ex-

ample, to distinguish between scenarios of boredom

or excess of work (which, in a ﬁrst instance, are both

characterized by slowed performance).

PECCS2015-5thInternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems

Figure 1: Conceptualisation of the set of factors that inﬂuence human performance and fatigue. (Balkin and Wesensten, 2011).

This aspect is now included in this paper, thus

representing a step forward in the development of

more accurate fatigue assessment approaches (Figure

1) that encompass the type of task, the time on task,

and the mental workload of the task. Indeed, work-

load levels can help isolate the causes affecting per-

formance at a given time, improving fatigue manage-

ment initiatives. To this end, and besides the metrics

derived from the use of the mouse and keyboard, sub-

jective measures of mental workload are also used.

Obtaining mental workload levels during task per-

formance may be a challenging procedure. Moreover,

the workload level experienced by an individual can

affect task performance twofold: either through ex-

cessive or reduced mental workload. To this end,

subjective measures are often used, some of them de-

tailed in (Reid et al., 1982).

The two instruments most often used in research

were developed in parallel in the 1980s, one at the

NASA-Ames Research Center in California and the

other within the U.S. Air Force human factors re-

search group at Wright-Patterson AFB, Ohio.

The NASA Task Load Index (NASA-TLX) is a

multidimensional assessment tool (Hart and Stave-

land, 1988). The main seven-point scale is: Overall

Performance: How successful were you in perform-

ing the task? How satisﬁed were you with your per-

formance? The TLX has ﬁve seven-point subscales

that help identify difﬁcult task characteristics. The

subscales are:

• Mental Demand: How much mental and percep-

tual activity was required? Was the task easy or

demanding, simple or complex?

• Physical Demand: How much physical activity

was required? Was the task easy or demanding,

slack or strenuous?

• Temporal Demand: How much time pressure did

you feel due to the pace at which the tasks or task

elements occurred? Was the pace slow or rapid?

• Frustration Level: How irritated, stressed, and an-

noyed versus content, relaxed, and complacent

did you feel during the task?

• Effort: How hard did you have to work (mentally

and physically) to accomplish your level of per-

formance?

Although these measures require a manual data

entry, they are extremely useful to validate the

interaction-based performance. It must thus be made

clear that we do not intend for a ﬁnal version of a

fatigue management tool to include such indicators.

Nonetheless, at the moment we look at such indica-

tors as a viable way to assess the validity of the de-

veloped approach, when both are used in parallel, as

described further ahead in this paper.

2 FATIGUE MANAGEMENT AS A

TOOL FOR IMPROVING

LEARNING PERFORMANCE

As stated before, this paper presents a tool for mon-

itoring fatigue in e-learning scenarios using non-

intrusive and non-invasive techniques. To this end it

proposes an analysis and classiﬁcation of the interac-

tion patterns of users with the computer while using

the mouse and keyboard. It is thus based on the stu-

dents behavioural changes regarding the computer.

Similar approaches have been used in the past to

estimate performance, albeit with more limited or dif-

ferent ﬁnal aim. Cart et al. (Card et al., 1980) pre-

sented, in 1980, on of the earliest works on the topic,

MentalWorkloadManagementasaTooline-LearningScenarios

aimed at the development of better interaction mech-

anisms with computers.

The proposed approach distinguishes from exist-

ing work twofold: (1) the application area and (2) the

features considered, as detailed further ahead in this

section. The tool collects data bout the user’s inter-

action with the computer and stores it in the form of

a log. This log contains each particular interaction

event, their timestamp and other important informa-

tion such as coordinates or key code, when applica-

ble.

The following events are considered:

• MOV, timestamp, posX, posY - an event describ-

ing the movement of the mouse, in a given time,

to coordinates (posX, posY) in the screen;

• MOUSE DOWN, timestamp, [Left—Right],

posX, posY - this event describes the ﬁrst half of

a click (when the mouse button is pressed down),

in a given time. It also describes which of the

buttons was pressed (left or right) and the position

of the mouse in that instant;

• MOUSE UP, timestamp, [Left—Right], posX,

posY - an event similar to the previous one but

describing the second part of the click, when the

mouse button is released;

• MOUSE WHEEL, timestamp, dif - this event de-

scribes a mouse wheel scroll of amount dif, in a

given time;

• KEY DOWN, timestamp, key - identiﬁes a given

key from the keyboard being pressed down, at a

given time;

• KEY UP, timestamp, key - describes the release

of a given key from the keyboard, in a given time;

From these events, that fully describe the interac-

tion of the user with the mouse and keyboard, we ex-

tract a set of features, based on notions of behavioural

biometrics:

KEY DOWN TIME - the timespan between two

consecutive KEY DOWN and KEY UP events, i.e.,

for how long was a given key pressed.

TIME BETWEEN KEYS - the timespan between two

consecutive KEY UP and KEY DOWN events, i.e.,

how long did the individual took to press another key.

VELOCITY - The distance travelled by the mouse

(in pixels) over the time (in milliseconds). The

velocity is computed for each interval deﬁned by

two consecutive MOUSE

UP and MOUSE DOWN

events. Let us assume two consecutive MOUSE UP

and MOUSE DOWN events, mup and mdo, re-

spectively in the coordinates (x1,y1) and (x2,y2),

that took place respectively in the instants time

and time

. Let us also assume two vectors posx

and posy, of size n, holding the coordinates of the

consecutive MOUSE MOV events between mup and

mdo. The velocity between the two clicks is given

by r dist/(time

− time

), in which r dist represents

the distance travelled by the mouse and is given by

equation 1.

r dist =

n−1

∑

i=0

(posx

i+1

− posx

)

+ (posy

i+1

− posy

)

(1)

ACCELERATION - The velocity of the mouse (in

pixels/milliseconds) over the time (in milliseconds).

A value of acceleration is computed for each in-

terval deﬁned by two consecutive MOUSE UP and

MOUSE DOWN events, using the intervals and data

computed for the Velocity.

TIME BETWEEN CLICKS - the timespan between

two consecutive MOUSE UP and MOUSE DOWN

events, i.e., how long did it took the individual to

perform another click.

DOUBLE CLICK DURATION - the timespan between

two consecutive MOUSE UP events, whenever this

timespan is inferior to 200 milliseconds. Wider

timespans are not considered double clicks.

AVERAGE EXCESS OF DISTANCE - this feature mea-

sures the average excess of distance that the mouse

travelled between each two consecutive MOUSE UP

and MOUSE DOWN events. Let us assume two

consecutive MOUSE UP and MOUSE DOWN

events, mup and mdo, respectively in the coor-

dinates (x1,y1) and (x2, y2). To compute this

feature, ﬁrst it is measured the distance in straight

line between the coordinates of mup and mdo as

s dist =

(x2 − x1)

+ (y2 − y1)

. Then, it is mea-

sured the distance actually travelled by the mouse by

summing the distance between each two consecutive

MOUSE MV events. Let us assume two vectors posx

and posy, of size n, holding the coordinates of the

consecutive MOUSE MV events between mup and

mdo. The distance actually travelled by the mouse,

real dist is given by equation 1. The average excess

of distance between the two consecutive clicks is thus

given by r dist/s dist.

AVERAGE DISTANCE OF THE MOUSE TO THE

STRAIGHT LINE - in a few words, this feature

measures the average distance of the mouse to the

straight line deﬁned between two consecutive clicks.

Let us assume two consecutive MOUSE UP and

PECCS2015-5thInternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems

MOUSE DOWN events, mup and mdo, respectively

in the coordinates (x1,y1) and (x2,y2). Let us also

assume two vectors posx and posy, of size n, holding

the coordinates of the consecutive MOUSE MOV

events between mup and mdo. The sum of the

distances between each position and the straight

line deﬁned by the points (x1,y1) and (x2,y2) is

given by 2, in which ptLineDist returns the distance

between the speciﬁed point and the closest point on

the inﬁnitely-extended line deﬁned by (x1,y1) and

(x2,y2). The average distance of the mouse to the

straight line deﬁned by two consecutive clicks is this

given by s dists/n.

s dists =

n−1

∑

i=0

ptLineDist(posx

, posy

) (2)

DISTANCE OF THE MOUSE TO THE STRAIGHT LINE

- this feature is similar to the previous one in the sense

that it will compute the s dists between two consecu-

tive MOUSE UP and MOUSE DOWN events, mup

and mdo, according to equation 2. However, it returns

this sum rather than the average value during the path.

SIGNED SUM OF ANGLES - with this feature the

aim is to determine if the movement of the mouse

tends to ”turn” more to the right or to the left.

Let us assume three consecutive MOUSE MOVE

events, mov1,mov2 and mov3, respectively in the

coordinates (x1,y1), (x2,y2) and (x3,y3). The angle

α between the ﬁrst line (deﬁned by (x1,y1) and

(x2,y2)) and the second line (deﬁned by (x2,y2) and

(x3,y3)) is given by degree(x1,y1,x2, y2,x3,y3) =

tan(y3 − y2,x3 − x2) − tan(y2 − y1,x2 − x1). Let

us now assume two consecutive MOUSE UP and

MOUSE DOWN events, mup and mdo. Let us also

assume two vectors posx and posy, of size n, holding

the coordinates of the consecutive MOUSE MOV

events between mup and mdo. The signed sum of

angles between these two clicks is given by equation

s angle =

n−2

∑

i=0

degree(posx

, posy

, posx

i+1

posy

i+1

, posx

i+2

, posy

i+2

)

(3)

ABSOLUTE SUM OF ANGLES - this feature is very

similar to the previous one. However, it seeks to ﬁnd

only how much the mouse ”turned”, independently

of the direction to which it turned. In that sense, the

only difference is the use of the absolute of the value

returned by function degree(x1,y1,x2,y2, x3,y3), as

depicted in equation 4.

s angle =

n−2

∑

i=0

| degree(posx

, posy

, posx

i+1

posy

i+1

, posx

i+2

, posy

i+2

) |

(4)

DISTANCE BETWEEN CLICKS - represents the to-

tal distance travelled by the mouse between two

consecutive clicks, i.e., between each two con-

secutive MOUSE UP and MOUSE DOWN events.

Let us assume two consecutive MOUSE UP and

MOUSE DOWN events, mup and mdo, respectively

in the coordinates (x1,y1) and (x2,y2). Let us also

assume two vectors posx and posy, of size n, hold-

ing the coordinates of the consecutive MOUSE MOV

events between mup and mdo. The total distance trav-

elled by the mouse is given by equation 1.

After the collection of the data, it is processed and

converted into a set of behavioural biometric features,

able to classify the behaviours of the user in terms of

fatigue and level of attention to the task, described in

detail in (Pimenta et al., 2015). This approach to as-

sess performance has been developed, validated and

used previously. However, after its development, we

concluded that its most signiﬁcant drawback is that it

looks at performance of interaction alone. Fatigue is a

complex phenomenon and performance measures by

themselves may not be enough for an accurate mea-

surement. In particular, and as stated before, mental

workload is a very important aspect when it comes to

characterizing fatigue.

We are thus now extending this approach with the

acquisition of contextual information about the user,

including the type of task being performed, the time

spent on each different task, as well as the mental

workload felt while performing the task (Figure 1).

This means that this tool will now also be able to anal-

yse the level of attention of a user to each task (e.g.

distinguish between time spent on tasks related to e-

learning activities against time spent on other tasks).

In the overall, the new tool results in a more complete

approach by including these important contextual fac-

tors.

Figure 2 depicts the process through which the

system operates, where it is possible to observe the

different classiﬁcations of information in order to al-

low, in the end, the management of fatigue. Initially

the system captures the mouse and keyboard inputs.

These data are further processed, stored and then used

to calculate the values of the behavioural biometrics.

In the learning phase the system shows a question-

naire in order to evaluate the subjective feeling of

fatigue of the user, as well as the mental workload.

When the system has a large enough dataset that al-

lows to make classiﬁcations with precision, it will

classify the inputs received into different mental fa-

tigue and mental workload levels in real-time. At this

MentalWorkloadManagementasaTooline-LearningScenarios

Figure 2: The ﬂow of data in the fatigue management tool for e-learning scenarios.

point, the system can start to be used by the people

involved, especially the teacher who can better adapt

and personalize his teaching strategies.

3 CASE STUDY

In order to assess the validity of the approach de-

scribed in the previous section a case study was im-

plemented with the aim of collecting data over a pe-

riod of time that encompassed different sessions of

e-learning, and thus test if it is possible to monitor fa-

tigue through the use of behavioural biometrics and

mental workload.

For this purpose, twenty four students volunteered

(19 men, 5 women), all students of the course of Phys-

ical Sciences at the University of Minho. Their age

ranged between 18 and 30. Participants were pro-

vided with the application for logging the events of

the mouse and keyboard during the duration of the

class. This application started automatically in the

background, when the Operating System started, re-

quiring no speciﬁc action from the part of the stu-

dents. The previously mentioned list of features was

extracted from the use of the mouse and keyboard for

the whole period.

3.1 Methodology

The methodology followed to implement the study

was devised to be as minimally intrusive as the ap-

proach it aims to support. Participants were provided

with an application for logging the previously men-

tioned events of the mouse and keyboard. This ap-

plication, which maintained the conﬁdentiality of the

keys used, needed only to be installed in the partic-

ipant’s computer and would run in the background,

starting automatically with the Operating System.

The only explicit interaction needed from the part of

the user was the input of very basic information on

the ﬁrst run, including the identiﬁcation and age.

The course takes place physically in a classroom

and comprises a teacher who is responsible for teach-

ing a programming language (in this case MatLab)

to a class of students. Each class has a duration of

three hours, which always follows the same ”proto-

col”: some theoretical concepts are introduced at the

beginning of the class and the rest of the session is

spent practising and solving exercises using the com-

puter and a speciﬁc IDE. During each session the sys-

tem, while in a learning phase, presents the user with

a questionnaire (Figure 3) based on the NASA TLX

for measuring mental workload.

Thus, in each session all inputs resulting from the

interaction of the user with the computer using the

mouse and keyboard are collected, together with the

subjective values of cognitive load, acquired from the

NASA TLX.

3.2 Results

Using the data collected in the classroom over the

two weeks, a classiﬁcation model was trained based

on the K-Nearest Neighbour (KNN) algorithm. It is

a method of classiﬁcation based on closest training

samples in the feature space.

A model was built based on a dataset with 74 in-

stances, each instance being constituted by the aver-

age values of all features during periods of one our.

Each instance is also assigned a label, which repre-

sents the response of students to the questionnaire for

measuring workload, provided while using the com-

PECCS2015-5thInternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems

Figure 3: NASA TLX questionnaire used to collect infor-

mation of mental workload.

puter during classes, thus in parallel with the collec-

tion process of the aforementioned data.

Figure 4: Results of different models trained with different

kernels and number of neighbours (K).

Several tests with different numbers of neigh-

bours (K) and with different heuristics to the dis-

tance between neighbouring (rectangular, triangu-

lar, epanachicov, gaussian, rank, optimal) were per-

formed. With a maximum of 50 neighbours, the so-

lution having a lower mean squared error (MSE), was

found with K = 30 and using the rectangular kernel,

as shown in Figure 4.

The trained model was then used to predict the

mental workload in data collected in the second pe-

riod, in a total of 78 instances resulting from the in-

teraction of users with the computer. According to

the classiﬁcation carried out, 64 out of 78 (83%) of

the instances were in accordance with the subjective

opinion of the user about the mental workload of the

task that was to perform, i.e., were correctly classi-

ﬁed. It is also important to note that the remaining 14

instances (17%) were classiﬁed as adjacent values.

Table 1: Results of the validation of the classiﬁcation

model (KNN). 83% of the instances were correctly classi-

ﬁed (green cells). The 17% misclassiﬁed instances where

nonetheless classiﬁed as neighboring values (red cells).

PREDICTED

1 2 3 4 5 6 7

1 13 3 0 0 0 0 0

2 2 11 1 0 0 0 0

3 0 1 12 2 0 0 0

4 0 0 2 15 0 0 0

5 0 0 0 0 7 1 0

6 0 0 0 0 1 6 0

X 7 0 0 0 0 0 0 1

4 CONCLUSIONS

This paper describes a prototype of a tool for man-

aging fatigue. Its main innovative aspect is that, for

the ﬁrst time, it considers the mental workload of a

user while performing a task as an important compo-

nent of fatigue assessment. The main objective is to

detect patterns of behavior at different levels of men-

tal workload. Measurement of levels of mental work-

load are obtained through the NASA TLX instrument,

which is based on a subjective self-evaluation. These

subjective measures, paired with measures of perfor-

mance and context of the task being performed by

a user, allow to train a classiﬁer as the one depicted

which achieved fairly good results. In the described

case study, the tool was used in several classes during

the period of two weeks, which allowed not only to

test it in a real scenario .

The results achieved from the implementation of

the case study show that it is indeed possible to anal-

yse and quantify mental workload through the use of

the mouse and keyboard, and this allows not only to

measure cognitive load but also to improve the pro-

cess of monitoring mental fatigue.

Although at the moment we aim to support the

teacher’s decision making process, the long-term goal

of this work is to develop environments that are

MentalWorkloadManagementasaTooline-LearningScenarios

autonomous and take actions concerning their self-

management. These actions will be guided by sev-

eral objectives, one of them being to manage cogni-

tive load, minimize fatigue and increase performance

and well-being of an individual or group of individu-

als through an appropriate selection of tasks and task

durations.

ACKNOWLEDGEMENTS

This work is part-funded by ERDF - European Re-

gional Development Fund through the COMPETE

Programme (operational programme for competi-

tiveness) and by National Funds through the FCT

( Portuguese Foundation for Science and Tech-

nology) within project FCOMP-01-0124-FEDER-

028980 (PTDC/EEI-SII/1386/2012) and project PEst-

OE/EEI/UI0752/2014.

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PECCS2015-5thInternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems