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her way of stating this is: Does there exist a path
starting at the initial state, such that ”the battery
state” is eventually over along that path? We tra-
duce this property in temporal logic formula: ”E <>
Smartphone.BatteryOver and duration < 240” and
this property is satisfied. A second possible situation
of deadlock could happen when student starts lesson
in the Lower zone. To check this property, we are
again, based on reachability property: Does there ex-
ist a path starting at the initial state, such that stu-
dent couldn’t finish the learning scenario success-
fully?”. We traduce this property in temporal logic
formula: ”E <>Smartphone.LessonLower and du-
ration > 240” and this property is verified. This ex-
periment shows that 37% percent of students could
be blocked at this stage. They couldn’t progress in
the lesson because the state of energy is low. Then,
we conclude that dependability is not assured and we
had to adapt mobile application to the learning envi-
ronment’s context (if the student is located in Lower
quality zone, the mobile application had to reduce the
energy’s consumption by loading only the necessary
widgets. Also, we propose to notify student whet-
her the quality of signal doesn’t allow to start learning
session. Then, he should to move on.
6 CONCLUSION
ReStart-Me is a predictive analytical framework for
mobile and pervasive learning scenarios. It aims at
supporting pedagogical designers to assess reliability
of their scenarios at an early stage through automata
modelling, tracks simulations and properties verifica-
tion. The proposed analysis process and supporting
tools provide an inexpensive mean of errors and fault
detection before starting implementation (thus retur-
ning on the learning scenario design phase to add mis-
sing elements and restraining development cost). Our
contributions are three-folds as explained below. Fir-
stly, we propose a framework to formally model and
evaluate the system design and the environment in-
puts. Important key dimensions of mobile and perva-
sive computing systems such as contextual activities,
actors and space interaction are discussed. Modelling
patterns for these features are provided and illustrated
with examples. Secondly, we identify critical proper-
ties of mobile and pervasive computing systems and
provide their specification patterns in corresponding
logics. According to the stakeholders (designers, en-
gineers and users of these systems), reliability requi-
rements are essential to pervasive computing systems.
In our work, we classify the important requirements
into reachability properties. Furthermore, formal spe-
cification patterns of these properties are proposed.
We verify formal properties against the system model
by using Uppaal model checker. Hence, design incon-
sistencies can be detected at the early design stage.
Thirdly, to demonstrate our approach, we present a
case study of applying the evaluation framework to
mobile and pervasive learning environment. Finally,
we will attempt in future works to deepen our pro-
posal on learning scenario re-engineering process in
such a way that a comparative report concerning dif-
ferent iterations of analysis can be generated automa-
tically.
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