=Paper=
{{Paper
|id=Vol-2712/paper6
|storemode=property
|title=Personalizing the connection between formal and informal learning in Smart Learning Environments
|pdfUrl=https://ceur-ws.org/Vol-2712/paper06.pdf
|volume=Vol-2712
|authors=Sergio Serrano-Iglesias,Eduardo Gómez-Sánchez,Miguel L. Bote-Lorenzo,Juan I. Asensio-Pérez,Adolfo Ruiz Calleja,Guillermo Vega-Gorgojo,Yannis Dimitriadis
|dblpUrl=https://dblp.org/rec/conf/ectel/Serrano-Iglesias19
}}
==Personalizing the connection between formal and informal learning in Smart Learning Environments==
https://ceur-ws.org/Vol-2712/paper06.pdf
Personalizing the connection between formal and
informal learning in Smart Learning
Environments
Sergio Serrano-Iglesias, Eduardo Gómez-Sánchez, Miguel L. Bote-Lorenzo,
Juan I. Asensio-Pérez, Adolfo Ruiz-Calleja, Guillermo Vega-Gorgojo, and
Yannis Dimitriadis
GSIC-EMIC Research Group, Universidad de Valladolid, Valladolid, Spain.
{sergio@gsic, edugom@tel, migbot@tel, juaase@tel, adolof@gsic, guiveg@tel,
yannis@tel}.uva.es
Abstract. Smart Learning Environments aim at automatically adapt-
ing the learning experience based on learner’s context. When this context
is not restricted to formal settings, SLEs are a promising solution for au-
tomatically connecting formal education with informal learning oppor-
tunities that emerge in different physical and virtual spaces. To achieve
this, SLEs can benefit from both the information from the formal learn-
ing design as well as the capability of sensing and analyzing the progress
of each learner. In previous research, we have devised an architecture
to interconnect the different technologies that form an SLE capable of
connecting formal and informal learning across-spaces. This paper goes
a step forward by exploring the information flow needed to model the
current context and state of the learner to eventually trigger informal
learning interventions.
Keywords: formal education, informal learning, personalization, Smart
Learning Environments
1 Introduction
Smart Learning Environments (SLEs) seek to automatically provide personalized
support to the students considering their individual needs and context [5, 7, 11]
across physical and virtual spaces. By means of technologies and systems such as
Virtual Learning Environments (VLEs) [1], mobile devices [13], wearables and
Internet of Things (IoT) devices [14], SLEs can interact and present students
with appropriate resources and activities, but also they can gather information
that help to construct their educational context. The prior knowledge of the
students, their learning style, the available resources and activities, their social
relations with other students or their location are some variables that help to
model their learning context [12].
When the learning context is not restricted to formal settings, SLEs are a
promising solution for connecting formal education with informal learning [3].
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
48 Serrano-Iglesias et al.
Prior attempts to bridge both types of learning were related to self-regulated
learning, where learners could establish communities of learning related with
their concerns [2, 4]. However, the provided support do not consider the con-
text of the learners to propose more meaningful experiences [9]. In this sense,
SLEs can benefit from information about how students learn in their daily life
and provide them with informal learning opportunities that emerge in different
physical and virtual spaces, related with the formal learning.
In [10], the authors proposed an architecture to interconnect different tech-
nologies that form an SLE capable of connecting formal and informal learning
across-spaces. However, to achieve such connection, SLEs have to align the ag-
gregated data from the different learning spaces with the learning objectives to
provide the appropriate support. This paper focuses on how SLEs can benefit
from the learning design to overcome this issue.
The rest of the paper is structured as follows. Section 2 exemplifies the sup-
port of SLEs in a sample scenario- Section 3 describes the information flow
happening in an SLE to support that scenario and how the learning design in-
fluences the different stages. Finally, section 4 present the main conclusions and
future work.
2 Illustrative scenario
For the sake of illustration, we present a scenario that reflects how an SLE can
achieve such connect. Stella teaches a Natural Science course in a high school.
She is devoting the following three weeks to teach about the fauna, flora and
landscape in the local region, focusing on one of these topics each week. With this
purpose, the teacher prepares different activities for her students. First, she gives
a short presentation in class of relevant aspects in one of the aforementioned
domains. Then, she proposes the learners to do a reading or watch a video on the
subject available in the VLE at home. The following day, in class she organizes
a debate among the students discussing about the revised material. Finally, she
asks her students to fill out a progress quiz through the VLE by the end of the
week. This sequence of formal learning activities is described in the learning
design.
In previous years, Stella observed that her students have some issues to reflect
the theoretical concepts in the real world, so she decides to support her lessons
with an SLE. As the SLE needs to notify students about possible activities,
she suggests her students to install a companion app in their personal mobile
devices, and eventually provide an informed consent regarding the use of the
data it collects for academic purposes.
After the first week, Pedro has attended all in-class activities (as reported
by Stella in the assistance report), watched the mandatory video but he did not
checked any of the available readings (as reported by the VLE). As well, he did
not score very high in the progress quiz (as reported again by the VLE). The
SLE consults the learning design to seek the data sources linked to the different
activities and gather the information about the performance of the students. As
Personalizing the connection between formal and informal learning in SLEs 49
a result, from the information above, the SLE detected that Pedro’s knowledge
in local fauna is low. Pedro decided to install the companion app and, one day,
he happens to walk in his town near a natural park, as reported by the app.
The SLE, with the information coming from the app, detects that it is a suitable
context to support Pedro, so it proposes Pedro an informal activity to identify the
different tree species in the park, considering the topic of the learning situation.
3 Information flow during the enactment
In order to support the scenario described above, the SLE has to manage different
information related with the actions of the students during the learning situation.
The information flow that takes places in SLEs can adhere to the sense-analyze-
react model [10]:
– Sense: the SLE gather data about the students’ actions and interactions
during the enactment of the learning situation, along with information about
the learning space where students’ are participating.
– Analyze: with the above information, the SLE models the students’ context
and their progression through the learning situation. These models evolve as
the learning situation continues.
– React: with an understanding of the learning status of the students, the SLE
can intervene and interact with them by providing appropriate resources and
activities. These reactions are not defined beforehand by the teacher, due to
the multiple conditions that should be considered to trigger them. Instead,
the teacher can define guidelines that control the reaction of the SLE.
The inclusion of the learning design in the information flow can benefit SLEs
to better orchestrate the aforementioned actions. Through specifications such as
IMS-LD [6], learning designs can be structured and computationally understood
by systems and applications, allowing, for example, their deployment in multiple
learning spaces [8]. In a similar approach, SLEs can considered the information
provided in the learning design through the information flow, presented in 1.
This flow begins with the deployment of the teacher’s learning design among
the learning spaces where the specified activities and resources take place. The
information contained in the learning design (such as activity timing, topics,
goals and related learning spaces) will be used during the next phases of the
information flow.
Once the learning design has been deployed, the SLE can proceed with the
collection of data related with the student participation in the learning sit-
uation. Each learning space offers measuring tools to track how students are
participating in the different activities or how they interact with the available
resource (e.g., timestamped actions in the VLE such as retrieving a document
or answering a quiz, or presence and location in the physical space). The SLE
gathers all the appropriate information from the pertaining learning spaces, ac-
cording to the learning design, and prepares it for its analysis.
50 Serrano-Iglesias et al.
Fig. 1. Information flow in the Smart Learning Environment during the enactment of
learning situations
In the analysis phase, the SLE extracts the indicators that complete the
actionable information the SLE has about the student. This information can be
classified in two sets: (1) the student’s model, that contains information derived
from the progress and performance through the different activities (e.g. degree of
knowledge of a certain topic); and (2) the student’s context, more related to the
current conditions of the learner (e.g. his location and whether he is currently
connected to the SLE). Both of these sets are constantly evolving as the learning
situation takes place. During this phase, the SLE relies on the learning design to
determine the appropriate analysis to perform, as well as to match the student’s
model with the goals and topics considered in the design.
With the student’s model in continuous evolution, the SLE can automati-
cally react and intervene and provide the personalized support to the students.
To do so, the SLE evaluates previously configured reaction rules and activity
templates, leading to a personalized, informal support. The reaction rules trig-
ger by evaluating information from the student model that concerns the topics
and goals of the situation. On the other hand, the activity templates contain
a variety of possible reactions, from simply informing the teacher (so that she
can decide how to react) to suggesting simple or more complex activities (e.g. a
personalized reading or quiz, to take pictures and comment a physical resource,
or to identify the tree species as in the scenario). It should be noted that rules
and templates are defined before in a general fashion, but are tailored on the fly
to the corresponding topics, goals and context. All this reaction process takes
place automatically without the teacher’s intervention.
Personalizing the connection between formal and informal learning in SLEs 51
4 Conclusions and future work
SLEs present potential opportunities to enhance students’ learning experiences
by connecting formal and informal learning. To this end, these environments
should properly align their understanding of the students with the learning goals
of the formal education. This paper presents how the inclusion of the learning
design in the information flow of an SLE can help in the construction of the model
of the student and in the provision of appropriate resources. Nevertheless, there
is pending work in the automatic provision by SLEs of appropriate resources
according to the learning design. In future work, the authors will collaborate
with stakeholder for the definition of the reaction templates and the provision
of resources to present to the students in different learning spaces and context.
Acknowledgments
This research is partially funded by the European Regional Development Fund
and the National Research Agency of the Spanish Ministry of Science, Inno-
vations and Universities under project grants TIN2017-85179-C3-2-R, by the
European Regional Development Fund and the Regional Council of Education
of Castile and Leon under project grant VA257P18, and by the European Com-
mission under project grant 588438-EPP-1-2017-1-EL-EPPKA2-KA. The first
author is supported by the European Social Fund and the Regional Council of
Education of Castile and Leon.
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