=Paper=
{{Paper
|id=Vol-1411/paper-02
|storemode=property
|title=Orchestrating Learning Across Spaces: Integrating Heterogeneous Technologies of the Existing Educational Practice
|pdfUrl=https://ceur-ws.org/Vol-1411/paper-02.pdf
|volume=Vol-1411
}}
==Orchestrating Learning Across Spaces: Integrating Heterogeneous Technologies of the Existing Educational Practice==
https://ceur-ws.org/Vol-1411/paper-02.pdf
Orchestrating learning across spaces: Integrating heterogeneous
technologies of the existing educational practice
Juan A. Muñoz-Cristóbal, Universidad de Valladolid (Spain), juanmunoz@gsic.uva.es
Juan I. Asensio-Pérez, Universidad de Valladolid (Spain), juaase@tel.uva.es
Alejandra Martínez-Monés, Universidad de Valladolid (Spain), amartine@infor.uva.es
Yannis Dimitriadis, Universidad de Valladolid (Spain), yannis@tel.uva.es
Abstract: Several approaches provide solutions for orchestrating across-spaces learning
situations involving heterogeneous technologies. However, the learning situations that such
systems support tend to be isolated from other activities and ICT tools of the teachers’ existing
practice. We present some lessons learned during our research in the field, which can be used
as guidelines to design systems for the orchestration of across-spaces settings that integrate
multiple technologies already in use by teachers.
Keywords: across-spaces, ubiquitous learning, augmented reality, virtual worlds, VLE
Introduction
Learning is not restricted to the physical space within the walls of a classroom. There are many other physical
and virtual spaces which have shown during years affordances for learning. For instance, Virtual Learning
Environments (VLEs) make students active participants and enable both distance and face-to-face learning
(Keller, 2005); 3D virtual worlds (3DVWs) enable the simulation of experiences not feasible in the real world
(Dede, 2009); and outdoor spaces enable contextual and experiential learning (Dyson, Litchfield, Lawrence,
Raban, & Leijdekkers, 2009). Some technologies can especially aid in connecting the different spaces, as is the
case of web technologies (access to virtual resources located in the Web), mobile devices (portability and
several context aware features) and Augmented Reality (link between physical and virtual spaces). The seamless
combination of the different learning spaces, also known as Ubiquitous Learning Environment (ULE; Li, Zheng,
Ogata, & Yano, 2004), has been marked by the research community as one of the key research challenges to
explore (Milrad et al., 2013). ULEs present several difficulties for teachers to orchestrate their learning
situations (Dillenbourg, Järvelä, & Fischer, 2009). Thus, multiple authors have proposed solutions aiming to
help teachers orchestrate scenarios that involve different physical and virtual spaces (see, e.g., Ibáñez, Maroto,
García Rueda, Leony, & Delgado Kloos, 2012; Sharples, 2013). However, the learning situations enabled by
such approaches tend to be isolated from other activities of the teachers current practice and from the ICT tools
they already use. This separation from the teachers’ everyday practice could affect negatively the teachers
orchestration load and the “classroom usability” (Prieto, Wen, Caballero, & Dillenbourg, 2014). Therefore, there
is a necessity of alternative approaches aiming to help teachers orchestrate their across-spaces learning
situations, while supporting the integration of different activities and tools that the teachers can be already using.
During the last four years, we have explored such line of research, proposing different constructs (POI
model and learning bucket notion) and systems (GLUEPS-AR and Bucket-Server) that enable the integration of
different kinds of existing technologies focused on different learning spaces (including those cases in which
such spaces are connected to the physical classroom). We have also evaluated such proposals through multiple
feature analysis as well as pilot and evaluation studies. In the following section we outline some lessons learned
regarding the integration and communication of heterogeneous learning technologies.
Lessons learned for integrating heterogeneous existing technologies
The following are some guidelines for designing systems enabling the integration of heterogeneous existing
technologies, that we can extract from the lessons learned in our research, aimed to help teachers in the
orchestration of across-spaces learning situations.
1. To enrich the artifacts generated by existing tools with additional information regarding the space, thus
enabling the access to such artifacts from different spaces using existing technologies
Orchestrated Collaborative Classroom Workshop 2015, June 7, 2015, Gothenburg, Sweden.
Copyright held by the author(s).
In this regard we proposed two constructs: The Point of Interest (POI) model (Muñoz-Cristóbal et al., 2015),
and the notion of learning bucket (Muñoz-Cristóbal et al., 2013). The POI model aims at overcoming one of the
main challenges for integrating multiple technologies focused on different spaces: each technology tends to
implement a different data model for representing a virtual object positioned in a space. The POI model
encompasses a selection of the basic set of attributes included in the different data models, that we considered
enough (aiming at simplicity) to represent a learning artifact positioned in a space. As Figure 1 (top) illustrates,
common artifacts, such as those of the Web 2.0, can be converted into Positionable Learning Artifacts (PLAs)
by means of enriching (tagging) them with the POI model. PLAs can be accessed in different spaces, using
multiple existing technologies focused on each space (e.g., VLEs in web spaces, AR apps in physical spaces,
Virtual Globes [VGs; Rakshit & Ogneva-Himmelberger, 2008] in 3DVW spaces). Following a similar fashion
we proposed the learning bucket notion, which aims to help overcome another common limitation of multiple
across-spaces educational systems: typically, they do not provide a teacher-controlled degree of flexibility for
enabling students to manage their learning artifacts during the enactment. As Figure 1 (down) illustrates, a
learning bucket is a collection of PLAs, which has been enriched with the POI model and with configurable
constraints. Such constraints are a set of attributes that the teacher can configure at design time to restrict what
students are able to do with learning artifacts during the enactment (e.g., restricting the tools to use, the
positioning types, etc.). A bucket can be positioned in a space, and be used (together with the PLAs it contains)
by multiple technologies focused on different spaces. A bucket could also transform a system not initially
conceived for across-spaces (e.g., a VLE) in an across-spaces system, since using a bucket embedded in such
system, the teacher and the students could create and position learning artifacts (PLAs) in different spaces. Thus,
enriching the artifacts generated by existing tools (or a set of such artifacts) with additional information
regarding the space (vs. proposing new tools focused on specific spaces) enables the efficient access to such
artifacts from different spaces using existing technologies likely already available in the classroom.
Figure 1. Converting a virtual artifact into a PLA using the POI model (top), and a set of PLAs into a learning
bucket using the POI model and constraints (down).
2. To use a multi-to-multi architecture to integrate multiple existing technologies
Instead of adding new educational tools to the already complex technological ecologies of the educational
spaces, an alternative can be to leverage existing learning tools to support across-spaces learning situations. A
solution for integrating existing technologies of different types and allowing their interoperation is to use a
multi-to-multi architecture based on the well-known adapter pattern of software engineering. Figure 2 (left)
illustrates such architecture, based on the integration of the multiple systems by means of adapters. Different
approaches in this line have been proposed in our GSIC/EMIC research group during the last years for
integrating VLEs with third party tools (Alario-Hoyos et al., 2013) and for integrating learning design authoring
tools with web-based distributed learning environments composed of VLEs and third party tools (Prieto et al.,
2013). In the case of ULEs, we have proposed two systems based on the multi-to-multi architecture: GLUEPS-
AR, and the Bucket-Server. GLUEPS-AR (Muñoz-Cristóbal, et al., 2015), is a system that integrates multiple
learning design authoring tools, mobile AR clients (physical space), Virtual Globes (3DVW space), and VLEs
(web space). GLUEPS-AR also enables the access to PLAs of multiple types from the different spaces, thus
converting the set of isolated learning spaces in ULEs like the one shown in Figure 2 (right). Using GLUEPS-
AR, teachers can deploy learning designs created in any of the integrated authoring tools (not necessarily
conceived for across-spaces learning) into different ULEs composed of web, physical and 3DVW spaces. In
such ULEs, the PLAs can “flow” from one space to another (e.g., a Google Docs document can be accessed by
different groups of students from a VLE and afterwards from an AR-enabled physical space).The Bucket-Server
is another system with a multi-to-multi architecture, which implements the notion of learning bucket, and
enables the integration of buckets in third party applications (e.g., orchestrating systems [such as GLUEPS-AR],
VLEs, AR apps and VGs). The buckets can contain artifacts of multiple types, since the Bucket-Server can be
also integrated, using adapters, with several artifact providers. We have integrated the learning bucket with
GLUEPS-AR, thus enhancing the default flexibility offered by GLUEPS-AR, which was somewhat rigid due to
its learning design basis (in which everything is typically predefined at design time). In addition, we have
integrated the Bucket-Server with multiple VLEs, mobile AR clients and VGs, enabling also ULEs of the type
shown in Figure 2 (right). Thus, learning buckets can be created from a VLE and be embedded in a VLE course,
allowing students to create (under the constraints configured by the teacher) PLAs accessible later on from other
spaces (e.g., physical ones using any of the integrated AR apps). It is interesting to highlight that the use of web-
based or mobile artifacts/tools make this kind of architecture easier to implement by developers and easier to use
across spaces by teachers and students.
Figure 2. Multi-to-multi architecture (left) and Ubiquitous learning Environment (right).
3. To provide alternatives for different user profiles in order to comply with teachers and institutions’
constraints
Different teachers and institutions may have different constraints, beliefs, and profiles. The proposal of
architectures allowing them to choose among a range of technologies of different types could not be sufficient,
since the underlying approach might not fit well with some teachers and institution. This is especially important
in orchestration technologies, which tend to add a layer of complexity to the already complex educational
ecologies of technological resources (Sharples, 2013). Therefore, proposals adaptable to different teachers’
profiles would improve their possibilities of adoption. During the research, following an interpretive research
perspective and a responsive evaluation model, we detected that GLUEPS-AR fitted well with some teachers’
profiles (e.g., those planning and designing everything in advance, highly methodical and tending to reuse their
designs). However, GLUEPS-AR showed to be not so appropriate for other teachers’ profiles (e.g., those taking
several last-minute design decisions, with a certain degree of improvisation, overloaded, and with a high level of
innovation in their designs from one year to another). Thus, for the latter, the direct integration of the Bucket-
Server with their usual VLE fitted better than the use of two additional systems (an authoring tool and
GLUEPS-AR), even though learning buckets are less suitable that GLUEPS-AR for supporting complex
pedagogical settings (e.g., those based on collaborative learning techniques). Thus, as Figure 3 illustrates, we
proposed two alternative solutions for enabling teachers to create and orchestrate across-spaces learning
situations conducted in ULEs: using learning design authoring tools (GLUEPS-AR), or creating directly the
learning situations in their usual VLE (learning buckets).
Figure 3. Options for deployment of across-spaces learning situations into Ubiquitous Learning Environments.
Conclusions
We have presented some guidelines for the integration and orchestration of heterogeneous existing technologies,
that we have obtained from the lessons learned during four years of research in the field of across-spaces
learning orchestration. However, it is worth highlighting that a distributed architecture based on adapters has
also different inherent problems, which we plan to research in the future. Some of them are: the scalability (e.g.,
towards massive approaches, such as MOOCs); the delays (due to the multiple elements involved in the
interactions between systems), the direct communication between applications; the maintenance (since changes
in the contracts or APIs imply development in the adapters); and the scope of supported systems (only those
with APIs can be integrated). All in all, the results using the guidelines described in this document have been
positive, enabling multiple rich educational scenarios with a loosely-coupling of technologies and a simple
“flow” of learning artifacts between spaces.
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Acknowledgments
This research has been partially funded by the Spanish Projects TIN2011-28308-C03-02, VA277U14 and the
European Project 531262-LLP-2012-ES-KA3-KA3MP. The authors thank the rest of the GSIC/EMIC research
team for their ideas and support.