From the Editors …
Welcome to
the January 2012 issue of the Learning Technology newsletter on Adaptive and
Intelligent Systems for Collaborative Learning.
This issue
is edited by Guest Editor Prof. Demetriadis, and includes articles
from key experts and projects at a European level.
The issue
also includes a section with regular articles (i.e. articles that are not
related to the special theme).
We
sincerely hope that the issue will help in keeping you abreast of the current
research and developments in Adaptive and Intelligent Systems for Collaborative
Learning. We also would like to take the opportunity to invite you to
contribute your own work on technology enhanced learning (e.g. work in
progress, project reports, dissertation abstracts, case studies, and event
announcements) in this newsletter, if you are involved in research and/or
implementation of any aspect of advanced learning technologies. For more
details, please refer to the author guidelines at
http://www.ieeetclt.org/content/authors-guidelines.
Special
theme of the next issue: Social Networks and Social Computing in Technology-Enhanced
Learning
Deadline for submission of articles: March
15, 2012
Articles
that are not in the area of the special theme are most welcome as well and will
be published in the regular article section.
Articles
that are not in the area of the special theme are most welcome as well and will
be published in the regular article section.
Editors
Sabine Graf
Athabasca University, Canada
sabineg@athabascau.ca
Charalampos Karagiannidis
University of Thessaly, Greece
karagian@uth.gr
Preface from the
Guest Editor
This
special issue of the IEEE Learning Technology Newsletter focuses on adaptive
and intelligent systems for collaborative learning. The special issue includes
six articles from respective research teams, which advance our current
understanding of how to develop technological systems for collaborative
learning that are more aware of and responsive to group learner needs. Most of
these works have been funded either by the European Union (EU) or by national
research funding programs and this duly highlights the interest that the field
currently attracts and the opportunities that it offers.
The six
contributions can be classified in two groups, depending on their specific
focus. The first group includes two articles relevant to large scale projects:
(1) Dragon, Yang and Mavrikis present the Metafora project, focusing on science and math learning and
employing interaction analysis methods for providing feedback to learners; (2) Scheuer, Niebuhr, Dragon, McLaren and Pinkwart
demonstrate the LASAD project with emphasis on online argumentation, offering
tools for the flexible configuration of the collaborative workspaces while also
providing to group learners adaptive feedback and support.
The second
group includes the rest four articles where the authors report their latest research
advances and contributions in the field: (3) Pérez-Sanagustín
and Hernández-Leo present the 4SPPIces approach for modeling important
factors conditioning the design of adaptive/intelligent systems for
collaborative learning in blended settings; (4) Anaya and Boticario explore the
impact of two different data mining techniques in their effort to timely assess
students’ collaborative activity; (5) Valcárcel,
Rodríguez, and Moreno simulate the collaborative learning activity
through their agent-based Explora tool and provide
evidence on the efficiency of the collaborative approach as compared to the
individual learning strategy; (6) Mangione and Caballé introduce a new type of Learning Object,
called Collaborative Complex Learning Object, aiming to increase learner’s
engagement by providing learning experiences better suited to the preferences
of the Millennial generation.
I would
like to warmly thank all authors of articles in this special issue for their
top quality contributions, which offer to the reader a deeper understanding of
the current situation in this fast progressing research field.
Special
thanks go to the Editors of the Newsletter for providing the opportunity to
guest edit and present this special issue.
Stavros Demetriadis
Department of
Informatics
Aristotle University
of Thessaloniki
Greece
sdemetri@csd.auth.gr
Special
Theme Section: Adaptive and Intelligent Systems for Collaborative Learning
Metafora: Defining and Supporting “Learning
to Learn Together”
Introduction
The EU-funded Metafora
project (http://www.metafora-project.org) is developing a computer-supported
collaborative learning environment to enable 12 to 16-year-old students to
learn science and mathematics by undertaking relatively complex collaborative
challenges. The project combines both theory and
technology in an effort to better engage students in a process referred to as
“learning to learn together” (L2L2). During L2L2 process, learners must manage a variety
of social and organizational challenges in addition to actively engaging in the
learning activity. Throughout our efforts, we have identified
high-level, crucial aspects of the L2L2 process, and certain lower-level
behaviors that demonstrate competence in these key concepts. Accordingly, we
seek to offer both a pedagogical approach and an intelligent software framework
that can support these behaviors, and therefore the L2L2 process as a
whole.
The Metafora platform
The system itself is a web-based framework consisting
of loosely coupled, individual learning tools integrated with a unified
interface and communication channels that allow the tools to interact and share
information with one another. Through the basic functionality of the Metafora tools, we provide a space that allows for, and
inherently promotes, the identified behaviors deemed important to L2L2. The tools include:
●
Planning Tool (Figure 1), providing
a visual language and graphical space for students to describe and reflect upon
their group learning process.
●
Discussion Tool (Figure 2),
providing a graphical argumentation space where students can share and debate
ideas and issues arising from their work.
●
Exploratory environments, providing
simulations or microworlds where students can solve
problems and explore topics including mathematics, physics and environmental
concepts.
Through intelligent analysis of the students’ behavior
within these tools — accomplished through interaction analysis of indicators, both within
and across tools (Dragon, 2012) — we provide computer-based support to students whenever possible and
promote increased awareness of crucial L2L2 activities for both students and
teachers.
L2L2 in Metafora:
Representative Examples of Theory and Support
In the Metafora context,
once a group of learners is assigned to a shared challenge, they plan the key
stages of their group learning first (Figure 1, green cards), and then
specifically plan the activity processes involved in each stage (Figure 1, blue
cards). These processes include the use of other Metafora
tools, including the discussion and exploratory environments. From an
educational theory standpoint, the group learning process dynamically proceeds
and evolves in relation to the group’s shared mental models. Cooke, Salas, Kiekel, & Bell
(2004) introduced a useful distinction between two types of mental models,
referred to as taskwork models and teamwork models. Taskwork mental models are representations of how
particular cooperative or collaborative tasks should be accomplished. Teamwork
mental models are representations about knowledge, skills, abilities, and
availability of the team and its members. Both models play important roles in
L2L2, so we now present examples of how each type of model is represented and
supported within the Metafora environment.
In the Metafora context,
group learners share taskwork models directly by
creating plans in the planning tool. We have identified several behaviors
considered important to L2L2 in the plan-creation process; including the use of
divergence and convergence in plans to promote novel ideas and to distribute
leadership. Students can intuitively acknowledge divergence by branching from
individual nodes, and convergence by having multiple
activity results feeding into a single activity (see Figure 1). The intelligent
analysis system can directly support students by recognizing and providing
feedback when a plan has no divergence, or diverges but does not — in a later stage — converge.
Figure 1: A section of a student plan demonstrating
divergence (manifested by the different ‘build’ cards in parallel) and
convergence (all students meet to ‘report’ on their work).
Developing teamwork mental models is also important
during L2L2 activities. One specific application of the teamwork models is
help-seeking and giving. Previous research emphasizes this behavior as an
important element of self-regulated learning (Karabenick,
1988) without necessarily considering collaborative settings. Newman (1994)
defines a general model of help-seeking that highlights the importance of
several meta-cognitive skills related to help-seeking. Affective
characteristics also come into play particularly because help seeking is
regarded as a social transaction that takes place within an interpersonal
relationship (Newman, 2000).
For a specific example, we consider a student
struggling with a task and seeking help from a peer. Metafora
encourages this type of help-seeking and help-giving behavior in order to
develop learners’ socio-metacognitive skills and particularly their ability to
identify the individual differences amongst group members, and balance their
individual help-seeking need with their group learning goals. In early pilot studies, students performed
this task within the platform by sharing their constructions in a discussion
space where they met with other students to attempt to resolve the issues
encountered (see Figure 2). Several requirements have emerged from these pilots
including the need to design methods of informing the student from whom help is
requested, and providing context information about the struggling student. In
addition, we seek to provide opportunities for students to better understand
their peers’ abilities and decide appropriately from whom to seek help when
they are struggling. Automated support can be used to recognize when students
are struggling and suggest appropriate peers for help-giving, alerting either
teacher or students to these possibilities (Dragon, 2012). .
Figure 2: One student providing help to another
student in reference to their work in a specific microworld.
Acknowledgments.
The European Union provided funding for this project
under the ICT theme of the 7th Framework Programme for R&D (FP7).
References
Dragon,
T., McLaren, B., Mavrikis, M., & Geraniou, E. (2012). Scaffolding Collaborative Learning Opportunities: Integrating
Microworld Use and Argumentation. Advances in User Modeling: Selected
papers from UMAP 2011 Workshops (pp. 18-30). Girona:
Springer-Verlag.
Cooke, N. J., Salas, E., Kiekel, P. A., & Bell, B. (2004). Advances in measuring team cognition. In E. Salas & S. M. Fiore
(Eds.), Team cognition: Understanding the factors that drive process and
performance (pp. 83–106). Washington,
DC: American Psychological
Association.
Karabenick, S. A. (1988). Strategic
Help Seeking Implications for Learning and Teaching. Lawrence
Erlbaum Associates, NJ, USA.
Newman,
R. S. (1994). Adaptive help seeking: a strategy of self-regulated learning. In Schunk, D. H. and Zimmerman, B. J., editors,
Self-regulation of learning and performance: Issues and educational
applications, pages 283–301. Hillsdale,
NJ: Erlbaum.
Newman, R. S. (2000). Social influences on the development of children's
adaptive help seeking: The role of parents, teachers, and peers. Developmental
Review, 20: 350–404.
Toby Dragon
Saarland University, Germany
dragon@celtech.de
Yang Yang
University of Exeter, UK
Y.Yang@exeter.ac.uk
Manolis Mavrikis
London Knowledge Lab, UK
m.mavrikis@lkl.ac.uk
In the past
two decades, approaches to support argumentation learning through graphical
representations have gained considerable attention, particularly in
collaborative settings (Scheuer et al., 2010). In
collaborative graphical argumentation, students create argument diagrams in a
shared workspace; boxes represent statements and links represent argumentative
or rhetorical relations between statements. The diagrams sometimes capture the
argumentative structure of texts given to students, sometimes outline the lines
of argumentation to help students prepare the writing of new texts, and
sometimes represent structured discussions between students. Many reasons have
been cited as to why graphical argument representations are beneficial for
learning, e.g., they make argument structures explicit, encourage reflection on
basic concepts of argumentation, reduce cognitive load, help systematically
explore a space of debate, facilitate the evaluation of arguments, serve as
resources and stimuli for discussions, and facilitate automated argument
analysis (e.g., Suthers, 2003; Andriessen,
2006; Scheuer et al., 2010).
Figure 1: LASAD
user interface: argument canvas (left) and sentence opener interface (right)
The LASAD
project (Learning to Argue – Generalized Support Across
Domains; http://cscwlab.in.tu-clausthal.de/lasad/) is motivated by the observation
that graphical argumentation systems typically are not easily adapted to new
requirements, since they tend to be tied to specific argumentation domains,
visualizations, or collaboration modes. The LASAD system (Loll et al., in
press), on the other hand, is a general, cross-domain framework that enables
users (i.e., developers, teachers and researchers) to configure workspaces
according to their specific requirements. Communication and task-related tools
can be added to the workspace such as a text chat, a sentence opener interface,
and a text display that allows linking of text passages to elements of the
argument diagram. Boxes and links can be
configured differently per application; labels, visual appearance, and
subcomponents (e.g., text fields, radio buttons and dropdown menus) can be
altered. A graphical administration and authoring system has been implemented
and integrated with LASAD, allowing users to easily define and administer
workspace setups, users and sessions. LASAD is purely web-based; a modern
web-browser and web access is all that is required to use the system. Figure 1
shows a screenshot of LASAD.
One of
LASAD’s key features is its ability to provide adaptive feedback and support to
students while they create argument diagrams. A configurable analysis service
has been developed, one that receives notifications about user actions, and
provides feedback in response. The analysis service uses production rules,
specifically the Jess library within Java, to evaluate an evolving argument
diagram. With author-specified configuration, the analysis service detects
patterns in argument diagrams such as cyclic arguments, boxes that are
connected through an incorrect link type, keywords in text fields, or important
text passages that have not been considered in the diagram yet. Patterns can
also include process characteristics such as actors and timestamps, e.g., to
limit the result set to recently created sub-graphs, sub-graphs entirely
created by one student or sub-graphs resulting from an interaction between
multiple users. This approach builds on previous research that has shown that
both structural and temporal characteristics can be important to define
meaningful patterns (McLaren et al., 2010, Pinkwart
et al. 2009). Feedback strategies are defined in XML files, including feedback
text, highlighting of graphical pattern, and pattern priorities. Figure 2 shows
a LASAD feedback message (in the window on top of the panel on the right) that
has been provided in response to a detected pattern (the box highlighted red).
Figure
2: LASAD feedback provision
We are
currently developing a graphical authoring tool to support administrators in
the definition of patterns and feedback, similar to the way they are already
supported in the definition of workspaces, boxes and links. The challenge is to
find the right balance between expressiveness and ease of use. We are planning
a first version that supports relatively simple patterns. In a second version,
we will consider an additional expert mode, in which iterative patterns are
also supported (e.g., sequences of undefined length). The general problem of
detecting patterns in graphs is known to be NP-complete (“subgraph isomorphism problem”). It is therefore also important to keep
runtime considerations in mind when specifying patterns. We are planning to
analyze such complexity issues both from a theoretical and empirical angle,
also considering specifics of the Rete pattern matching algorithm used in Jess,
to determine boundary conditions for admissible and non-admissible patterns.
The goal is to automate the complexity analysis to provide users with feedback
regarding expected pattern search times when they define new patterns.
The
flexibility of LASAD has been demonstrated through emulations of past systems
in different domains such as scientific argumentation (Belvedere; Suthers, 2003), legal argumentation (LARGO; Pinkwart et
al., 2009) and e-discussions about socio-scientific issues (ARGUNAUT; McLaren
et al., 2010). It has been used in a number of studies to investigate research
questions in computer-supported argumentation learning (e.g., Loll & Pinkwart, 2011). The system can be tested online for free (http://homer.in.tu-clausthal.de/lasad/).
Acknowledgements
The German
Research Foundation (DFG) provided support for this research under the grant
“LASAD – Learning to Argue: Generalized Support Across
Domains.”
References
Andriessen, J. (2006). Arguing
to Learn. In K. Sawyer (Ed.), Handbook of the
Learning Sciences (pp. 443–459). New York:
Cambridge University Press.
Loll,
F. & Pinkwart, N. (2011). Guiding
the Process of Argumentation: The Effects of Ontology and Collaboration.
In M. Spada, G. Stahl, N. Miyake, N. Law (Eds.), CSCL2011 Conference Proceedings Volume I (pp.
296–303). Hong Kong: International Society
of the Learning Sciences (ISLS).
Loll, F., Pinkwart,
N., Scheuer, O., & McLaren, B.M. (in press). How Tough Should It Be? Simplifying the Development of Argumentation Systems using a
Configurable Platform. To appear
in: N. Pinkwart, N. & B. M. McLaren (Eds.), Educational Technologies for Teaching
Argumentation Skills. Bentham Science Publishers.
McLaren, B.M., Scheuer, O., & Miksatko, J. (2010). Supporting collaborative learning and e-Discussions using
artificial intelligence techniques. IJAIED 20(1), 1–46.
Pinkwart, N., Ashley,
K.D., Lynch, C., & Aleven, V. (2009). Evaluating an
Intelligent Tutoring System for Making Legal Arguments with Hypotheticals.
IJAIED 19(4), 401–424.
Scheuer, O., Loll, F., Pinkwart, N., &
McLaren, B.M. (2010). Computer-Supported
Argumentation: A Review of the State of the Art. IJCSCL 5(1), 43–102.
Suthers, D. (2003). Representational
Guidance for Collaborative Inquiry. In J. Andriessen,
M. Baker, D. Suthers (Eds.), Arguing
to Learn: Confronting Cognitions in Computer-Supported Collaborative Learning
Environments (pp. 27–46). Dordrecht:
Kluwer Academic.
Oliver Scheuer
Centre
for e-Learning Technology
Saarland University, Germany
o.scheuer@mx.uni-saarland.de
Sabine Niebuhr
Department of Informatics
Clausthal University of Technology, Germany
sabine.niebuhr@tu-clausthal.de
Toby Dragon
Centre
for e-Learning Technology
Saarland University, Germany
toby.dragon@celtech.de
Bruce M. McLaren
Centre
for e-Learning Technology, Saarland
University, Germany
Human-Computer
Interaction Institute, Carnegie Mellon
University, U.S.A.
bmclaren@cs.cmu.edu
Niels Pinkwart
Department of Informatics
Clausthal University of Technology, Germany
niels.pinkwart@tu-clausthal.de
Introduction
Several
Adaptive and Intelligent Collaborative Learning Systems (AICLS) have been
developed in the CSCL domain [3] to solve problems commonly related to
collaborative learning practices [2, 4]. However, current AICLS lack of support
for integrated structured activities flows conducted in several spaces beyond
the classroom, the Computer Supported Collaborative Blended Learning (CSCBL)
scripts [5]. Factors such as the spatial location where
activities are conducted or the interplay between these activities condition
collaboration and, consequently, the design of the AICLS to support CSCBL
scripts. We present 4SPPIces, a model that identifies 4 factors
conditioning the design of AICLS for blended settings and show how they are
combined to implement 4 illustrative AICLS.
4SPPIces factors
4SPPIces
defines 4 factors [5] (Fig. 1): (1) the Space,
which defines the planned environment where
learning activities are going to take place, (2) the Pedagogical method, that
defines a learning flow, (3) the Participants, which defines the
people involved in the activity and their characteristics and (4) the History,
which models those aspects from the other factors likely to be affected by the
unpredictable variations usually produced during the scripts enactment.
Figure 1 The 4SPPIces factors with their facets and inter-relations:
the Space, the Pedagogical method, the
Participants and the History.
Applications of the 4SPPIces factors in
illustrative AICLS
Four AICLS
considering combinations of the 4SPPIces factors have been implemented and
evaluated in user and case studies (Fig. 2).
Figure 2 Summary
of implemented and evaluated illustrative AICLS considering combinations of the
4SPPIces factors.
The
first system supports the adaptation of pre-defined group formation to the
variability of the context once the activity has started [7] (Fig. 2 (a)). This
AICLS is designed considering: (1) the PM factor, represented by a
collaborative learning flow pattern codified with IMS Learning Design; (2) the
P factor, controlling the lists of students expected before the class and those
actually attending each activity; and (3) the I factor, implemented as a constraint controlled module that
considering the characteristics of the PM and (the eventually changing) P
factor adapts and suggest the optimal group distribution on the fly.
The second AICLS
proposes supports teachers and students in a collaborative script that combines
an exploration of an urban space with classroom activities [5] (Fig. 2 (b)).
This AICLS considers the 4 factors and is implemented as a combination of
tools: the Moodle Learning Management System (LMS) for indoor activities and QuesTInSitu with mobile phones for outdoors activities. The
PM is divided into two phases: an exploration of the city and a presentation in
class. QuesTInSitu is used to create geo-located
questions organized in routes which are automatically triggered to the students
via mobile phones [8]. The P is the list of students registered in Moodle and QuesTInSitu. The S is the map of Barcelona city with the questions routes and
the classroom. The I is represented with a monitoring
functionality in QuesTInSitu for the teachers to
follow in runtime where the students are located during the exploration.
The third
AICLS supports a collaborative script to help students during their first days
at university [6] (Fig.2 (c)). The AICLS considers all 4SPPIces factors and is
implemented combining: (1) mobile devices with a Radio Frequency Identification
(RFID) tags reader, (2) Moodle and (3) GoogleDocs.
The PM is divided into three phases: (1) an exploration of the campus in which students access with mobile phones to the information hidden
on 46 RFID tags distributed around the campus, (2) an activity in expert groups
to prepare a presentation about a campus area and (3) an individual
questionnaire in Moodle. The P models the profile of the students, defined by
their expertise on a campus area. The S is the campus areas, the classroom and
home. The I models the log-files that collect the
actions of the participants (P) around the campus (S). Students become experts
on an area depending to their actions in the exploratory activity and are
grouped accordingly.
The fourth
implementation extends the third AICLS so as to provide complete automatic
support of the script [1] (Fig. 2 (d)). The PM is represented in IMS Learning Design and
enacted using the Generic Service Integration (GSI) system, to administer students data (e.g., mobile phone log files) and
automatically create groups by manipulating a Google on-line spreadsheet
integrated with the LMS.
Conclusions
This work offers
insights on how designers and researchers can address the design of AICLS for
supporting collaborative scripts blending spaces. We presented 4SPPIces as a
model that points out the critical factors to be
considered in these designs and four AICLS worked-examples. The results from
evaluating these AICLS indicate that they successfully support and facilitate
teachers’ and students’ tasks during the scripts enactment [5, 6, 7].
Acknowledgements
This work has been partially funded by the
Spanish Learn 3 (TIN2008-05163/TSI) and EEE projects (TIN2011-28308-C03-03).
References
[1] de-la-Fuente-Valentín, L.,
Pérez-Sanagustín, M., Santos, P., Henández-Leo, D., Pardo,
A., Blat, J. & Delgado-Kloos,
C. (2010) System orchestration support for a flow of blended collaborative
activities, 2nd International Conference on Intelligent Networking and
Collaborative Systems, Thessaloniki, Greece.
[2] Dillenbourg,
P. (2002) Over-scripting CSCL: The risks of blending collaborative learning
with instructional design, In Kirschner, P. A. (Ed.)
Three worlds of CSCL. Can we support CSCL?, pp. 61-91.
[3] Magnisalis, I., Demetriadis, S.
& Karakostas, A. (2011) Adaptive and Intelligent Systems for Collaborative
Learning Support: A Review of the Field, IEEE Transaction on Learning
Technologies, 4(1), pp 5-20.
[4] Ounnas, A., Davis, H. C. & Millard, D. E. (2009) A
Framework for Semantic Group Formation in Education, Educational Technology
& Society, 12 (4), pp. 43–55.
[5] Pérez-Sanagustín, M., Santos, P., Hernández-Leo, D. & Blat, J. (in press). 4SPPIces factors: a case study of
factors in a scripted collaborative-learning blended course across spatial
locations, International Journal of Computer Supported Collaborative Learning, doi:
10.1007/s11412-011-9139-3.
[6] Pérez-Sanagustín, M., Ramírez-Gonzalez,
G., Hernández-Leo, D., Muñoz-Organero,
M., Santos, P., Blat, J. & Delgado Kloos, C. (2010) Discovering the Campus Together: a mobile
and computer-based learning experience, Journal of Network and Computer
Applications, 35(1), pp. 176-188.
[7] Pérez-Sanagustín, M., Burgos J., Hernández-Leo D.
& Blat J. (2011) CLFP intrinsic
constraints-based group management of blended learning situations, In:
Daradoumis T.; Caballé S.; Juan A.; Xafa F.; (Ed.) Technology-Enhanced Systems and Adaptation
Methods for Collaborative Learning Support, Series “Studies in Computational
Intelligence”, 350, Springer: Verlag, pp. 115-133.
[8] Santos, P.,
Pérez-Sanagustín, M.,
Hernández-Leo, D. & Blat, J. (2011) QuesTInSitu: From tests
to routes for assessment in situ activities, Computers & Education, 57(4),
pp. 2517-2534.
Mar Pérez-Sanagustín
Telematics
Department, Universidad Carlos III de Madrid
Madrid, Spain
mmpsanag@it.uc3m.es
Davinia
Hernández-Leo
ICT Department, Universitat
Pompeu Fabra
Barcelona, Spain
davinia.hernandez@upf.edu
Introduction
Studies argued that collaboration
assessments improve the collaboration behavior and increase the student
motivation [1]. However, providing tools to collaborate does not ensure
collaboration. Frequent and regular analysis of students’ interactions is
needed to discover whether collaborative learning takes place [2].
Some researchers have shown
that machine learning (ML) techniques can be applied in e-learning environments
to obtain students’ assessments [3] in a regular and frequent way. Their
approach is based on applying data mining (DM) processes on collected data from
students’ interactions so that ML algorithms make predictions on students'
performance and collaboration [4, 5].
We have proposed two different approaches,
the Clustering approach [6] and the Metric approach [7], which proved that
quantitative collaboration analysis supports timely student collaboration assessments.
However, these approaches have not been used together in the same collaborative
activity (CA) within a given e-learning course. In this paper we introduce the
new CA, where both approaches will be compared.
Experiment
We have proposed a long-term CA divided into two
phases where the students of the Artificial Intelligence and Knowledge based Engineering
subject at UNED were invited to participate during the academic years 2006-07,
2007-08, 2008-09 and 2010-11 [6]. The CA was divided into two phases: the shorter, initial,
introductory and individual phase; the longer, where three-member teams worked
together and communicated through forums. We have investigated two data mining
(DM) approaches to assess collaboration and valuable outcomes from the modeling
viewpoint are described elsewhere [8]. Both approaches use student
information on collaboration and student interactions in forums as their data
source and each of them is based on different ML techniques.
First, the clustering approach is based on a ML clustering algorithm that
groups students according to their active interactions, which relate to the
students activity and activity caused by students. Here students are grouped
into three categories: high collaborative level, medium collaborative level or
low. Second, the metric approach is based on decision tree algorithms to
measure student collaboration from their interactions, which relate to
regularity of student activity and initiative and student acknowledgment from
their fellow-students. This approach provides a numerical student collaboration
metric value instead of the aforementioned categories.
In our new CA we are investigating over the longer
collaborative phase the effects on using both DM approaches to assess student
collaboration. We are supporting students with a metacognitive tool that
displays information about students’ personal and contextual information,
including collaboration assessment indicators on themselves and their team
mates. The purpose here is to provide students with suitable information so
that they are able to regulate their own collaboration process and improve
their collaborative learning.
Collaboration assessments are displayed using a
metacognitive tool, which is divided into two sections according to the two
aforementioned DM approaches. The first section shows collaboration assessment
indicators provided by the clustering approach, which includes: (1) average
level of team collaboration, (2) a link for every member to see their personal
collaboration level, and (3) a warning if there is undesirable teamwork (i.e.
high variance among team members’ collaboration levels or their average
collaboration level is low). The second section displays the same features but
provided by the metric approach. In particular it shows a warning when the
average collaboration value is low or the variance is high.
To check the usefulness of provided tools, CA students
are divided into four groups. The first group has access to the information displayed
from the two approaches. The second has access just to the first of the
aforementioned tool sections. The third group has access only to the second
section. The fourth group is the control group, i.e., oblivious to
collaboration assessments.
Once the CA is ended, students are provided with the
final evaluation questionnaire, where their collaborative work is assessed.
Later the students take the subject final exam. With all these data the
metacognitive tool and the assessments provided by the two different DM
approaches are evaluated and compared to each other. Expected outcomes are to
confirm which DM approach is more valuable in terms of students’ valorization
and learning impact.
References
[1] K. Swan, J. Shen, S.R. Hiltz,
Assessment And Collaboration In Online Learning.
Journal of Asynchronous Learning Networks, Vol. 10, No. 1.,
pp. 45, 2006.
[2] D.W. Johnson, R. Johnson, Cooperation and the use
of technology, In D. Jonassen (Ed.), Handbook of
research on educational communications and technology, 785-812, 2004.
[3] C. Romero, P.G. Espejo,
A. Zafra, J.R. Romero, S. Ventura, Web usage mining
for predicting final marks of students that use Moodle courses, Computer
Applications in Engineering Education, doi:
10.1002/cae.20456, 2011.
[4] E. Gaudioso, M. Montero,
L. Talavera, F. Hernandez-del-Olmo, Supporting
teachers in collaborative student modeling: A framework and an implementation,
Expert Systems with Applications, 36 (2009), 2260–2265.
[5] D. Perera, J. Kay, K. Yacef, I. Koprinska, Mining
learners’ traces from an online collaboration tool, Workshop Educational Data
Mining, Proceedings of the 13th International Conference of Artificial
Intelligence in Education. Marina del Rey, CA. USA.
July 2007, pp. 60-69.
[6] A.R. Anaya, J.G. Boticario, Application Of Machine
Learning Techniques To Analyze Student Interactions And Improve The
Collaboration Process, Special Issue of Expert Systems with Applications on
Computer Supported Cooperative Work in Design. Vo. 38 No. 2, Feb 2011.
[7] A.R. Anaya, J.G. Boticario, Ranking Learner
Collaboration according to their Interactions, The 1st Annual Engineering
Education Conference (EDUCON 2010), Madrid, Spain, IEEE Computer Society Press
2010, pp. 797-803.
[8]
A.R. Anaya, J.G. Boticario, Content-free Collaborative Learning Modeling Using
Data Mining, User Modeling and User-Adapted Interaction (UMUAI): Special Issue
on Data Mining for Personalized Educational Systems, Vol
21 (1-2), 2011, 181-216.
Antonio R. Anaya
Artificial
Intelligence Department
E.T.S.I.I., UNED
Madrid, Spain
arodriguez@dia.uned.es
Jesús G. Boticario
Artificial Intelligence Department
E.T.S.I.I., UNED
Madrid,
Spain
jgb@dia.uned.es
Using an Intelligent Agent based Platform
to simulate Learning Processes
Introduction
In the
latest years, e-learning platforms [1] provide users capabilities to
collaborate on-line, which improve the learning processes [2]. Collaborative
work among students is very important because it stimulates learning, increases
motivation, creativity and personal satisfaction [3].
This paper
addresses the evaluation of collaborative work using distributed simulation
software based on intelligent agents [4] that solve labyrinths. The labyrinth
is used as a metaphor of the work done by students during an academic course,
where each tile is a particular activity and the whole labyrinth represents the
complete learning sequence. The agents interact to solve the labyrinth in the
same way than students do to complete the course.
The
simulation has been done using a tool called “Explora”,
which pursues three main goals:
1.
To
built a Distributed Multiagent Platform that provides
support to study the learning process.
2.
To
analyze the obtained results and show that collaboration among students
improves the searching process within the labyrinth.
3.
To
obtain the optimum number of students that collaborate, taking into account the
kind of search, the labyrinth topology or its wideness.
Experiments Description
This paper
addresses the study of collaborative work among students using labyrinth as a
metaphor of the learning process, where:
§ Each intelligent agent (or explorer)
stands for an individual student. The behavior of these agents can change among
experiments: they can collaborate or not.
§ The labyrinth (or map) describes the
sequences of learning activities that must be done by the student in order to
pass the course (or, in the metaphor, find the exit).
§ The tile represents each of the
particular goals or activities the students must accomplish.
§ The crosses represent the different
decisions the student must take.
The
experiments consist on different tests, each of them with particular settings:
type, size and topology of the labyrinth, number of agents, etc.
The type of
labyrinth tries to show different ways of passing a course. Some subjects
introduce a sequential learning process while others permit parallel
activities, or different ways of achieving the same goals. Figure 2 shows the different types of
labyrinth; two particular kinds can be identified:
§ “Perfect” labyrinth, where there is
only a path between two tiles and there are not inaccessible tiles or loops.
This kind of map stands for sequential and fixed learning processes.
§ “Imperfect” labyrinth has loops with
a fixed probability, which can be changed. They try to represent more flexible
learning processes, which allow parallel activities and different learning
paths.
Figure 2: Labyrinth types, from
left to right: perfect, imperfect (prob.=0, prob.=5,
prob.=20, prob.=100)
Tool description
The experiments
have been made using a tool specifically created. This tool has been made using
intelligent agents as the basic concept for its design and implementation. The
final product obtained has been a distributed Web Application (Figure 3), which is available on-line [5].
Figure 3: Explora
web application
Results and Discussion
During the
experimentation 21.580 simulations, which cover all the combinations of
settings (kind of labyrinth, collaborative or non collaborative agent, number
of agents, etc.), have been made. They have been grouped in 14.300 tests that
share the same settings. For each simulation, the number of movements made by the
agents to find the exit has been counted.
Figure 4 shows that the movements (in
average) made by agents decreases when the number of explorers grows (in case
of collaborative explorers) while it remains unchanged when the explorers do
not collaborate. The basic line called minimum shows the minimal number of
movements for finding the exit with an optimal algorithm. As it can be seen,
this minimum is not reached in any case.
Figure 4: Average of the number of
movements done by agents, taking as basis the minimum number
Next table,
details the difference among collaborative and individual agents. This clearly
shows that the number of movements does not change in the case of individual
agents. Nevertheless, when using collaborative agents, the number of movements
is reduced a 44% using two agents, and 60% in case of three. The table also
shows that the increment is not linear, and the best results are using 3-4
agents. The improvements reduce drastically from 6 agents on.
|
Number
Explorers
|
Movements
|
Extra Movements
|
Improvement
|
Increased improvement
|
|
Minimum
|
Collab
|
Indiv
|
Collab
|
Indiv
|
Collab
|
Indiv
|
|
1
|
123
|
1751
|
1751
|
1328%
|
1328%
|
0,0%
|
0,0%
|
0%
|
|
2
|
118
|
1052
|
1785
|
791%
|
1412%
|
620,8%
|
44,0%
|
44,0%
|
|
3
|
127
|
797
|
1803
|
527%
|
1317%
|
790,3%
|
60,0%
|
16,0%
|
|
4
|
118
|
676
|
1796
|
473%
|
1423%
|
949,9%
|
66,8%
|
6,8%
|
|
5
|
124
|
601
|
1789
|
384%
|
1343%
|
958,6%
|
71,4%
|
4,6%
|
|
6
|
113
|
506
|
1790
|
350%
|
1490%
|
1140,6%
|
76,5%
|
5,2%
|
|
7
|
126
|
496
|
1827
|
292%
|
1345%
|
1053,1%
|
78,3%
|
1,8%
|
|
8
|
125
|
463
|
1827
|
269%
|
1356%
|
1087,3%
|
80,2%
|
1,9%
|
|
9
|
121
|
421
|
1750
|
249%
|
1351%
|
1101,9%
|
81,6%
|
1,4%
|
|
10
|
120
|
390
|
1750
|
225%
|
1360%
|
1134,6%
|
83,4%
|
1,9%
|
Other experiments have addressed the relevance of the typology of map
and the search algorithm in the number of movements, and have shown that there
is not a significant influence in the results.
Conclusions
The results
obtained conclude that collaboration among students leads to quicker learning
when compared to individual learning. Three or four students will be the best
number to learn in a group.
Results
also have shown that other aspects such as the type of learning or the
particular subject are not relevant for the collaborative issues.
In
addition, a new tool for collaborative learning is under construction. This
tool will allow defining behavior patterns that can be used as a way of guiding
the students to the achievement of particular goals.
References
[1] Peña, C. I., Marzo, J.
L., & de la Rosa, J. L. (2003). Intelligent agents in a teaching and
learning environment on the web. In ICALT2002.
Kazan, Russia: IEEE
[2] Akhtar, S., & Wyne, M. F. (2006). Distance learning education using the
web. In MATH’06: Proceedings of the 10th WSEAS international conference on
applied mathematics (pp. 565–571).
Stevens Point, Wisconsin, USA.
[3] Plantamura, P., Roselli, T., & Rossano, V. (2004). Can a CSCL environment promote effective interaction. In
ICALT’04: Proceedings of the IEEE international conference on advanced learning
technologies (ICALT’04) (pp. 675–677). Washington, DC, USA: IEEE Computer
Society.
[4] Russell , S., & Norvig, P. (2009). Artificial Intelligence: A
Modern Approach. Pearson.
[5] Explora tool: http://193.147.87.91/explora
David Ramos Valcárcel
Dept. Lenguajes y Sistemas Informáticos
ESEI-Universidad de Vigo, Vigo, España
david@uvigo.es
Alma Gómez Rodríguez
Dept. Lenguajes y Sistemas Informáticos
ESEI-Universidad de Vigo, Vigo, España
alma@uvigo.es
Juan Carlos González Moreno
Dept. Lenguajes y Sistemas Informáticos
ESEI-Universidad de Vigo, Vigo, España
jcgmoreno@uvigo.es
Introduction
Educators in the Digital Age must
understand the learner style of a new generation of teaching audience. The
majority of today’s students fall into the generational group called Millennials [1] (a.k.a. NextGen, GenY, C Generation, M Generation, and Echo Boomers), the
generation born 1979 through 2000. They are a new generation of impatient,
experiential learners, digital natives, multitaskers,
and social embedded who love the flat, networked world and expect nomadic
connectivity [2].
By understanding the Millennial student and how they learn, the educator is more
successful in creating a learning centered environment. Such students prefer
inductive reasoning, desire frequent and quick interactions with content, and
display exceptional visual-literacy skills [3], all
essential when navigating the digital technology used today. Digital natives
[4] approach learning as a plug-and-play experience. They use the social
context for enjoyment, challenge, and learning together. Viewing interactivity
as a key component of technology-based learning activities, they expect those
types of activities in their college classrooms. Today’s students simply plunge
in and learn through peer reflection and active participation. Millennials want more learning in realistic contexts as
well as simulated environments and the use of more non-linear texts.
The literature [5] observes that Digital
Age students express a need for more varied forms of communication, and report
being fluency in (social) media use and easily bored with traditional learning
methods. In the same way, Millennials need
self-directed learning opportunities, interactive environments, multiple forms of peer feedback and assignment choices that
use different resources to create personally meaningful learning experiences. Millennials want more hands-on, inquiry-based approaches to
learning and are less willing simply to absorb what is put before them [6]. These
learners want to construct their knowledge and they want to immediately engage
in the process.
A New Approach for Digital Learners
The field of educational technology
considers the research of news models and the experimentations of news methods
as the major challenge for educational designers. How can educators reach the Millennial students and provide a productive and engaging learning
environment? Social and collaborative learning [7][8] is
one of the most successful forms used to get students to be responsible for
their own learning and maximize the knowledge from peers and social feedback.
This enables them to interact with course-mates, sharing their ideas and
supporting each other in the way they learn.
However, traditional social and collaborative learning approaches cannot
be applied in every e-learning experience because they require people’s presence
and/or collaboration is many times difficult to achieve. In addition, learning
systems often lack of challenging resources and tools to support socialization
and collaboration, making the learning experience unattractive, which
discourages progression. Although the learner expects to control the learning
experience, often is the learning experience that controls and limits the
learner. As a result, learning resources lack of collaboration,
authentic interactivity, social identity, and user empowerment and challenge,
thus having a negative effect on learner motivation and engagement.
Collaborative Complex Learning Objects
The above deficiencies and
limitations have been addressed by a new type of Learning Object (LO) [9] called
Collaborative Complex Learning Object (CC-LO) embedded into a Virtualized
Collaborative Session (VCS) [10]. A VCS is a registered collaboration session
augmented by alternative flows, additional content, assessment, emotional
state, etc., during an authoring phase (subsequent to the registration phase) to
enrich the learning experience provided by the VCS. For instance, assessment scenes are added in certain points
of a discussion where the learner is asked about the topic discussed so far,
and according to the given answer, the learner can jump to different points of
the discussion. The VCS can be interactive and animated (by movies or comic
strips) and learners can observe how knowledge is constructed, refined and
consolidated (see next figure).
Sequence
of snapshots of a CC-LO evolving over time after the virtualization of a live
collaborative session.
Four contributions of the text-based discussion are converted by the VCS system
into an animated storyboard supported by a text-to-voice engine
Overall, the VCS transforms a live
discussion forum into an animated storyboard and produces an event in which
CC-LOs are played and consumed by learners, sessions evolve (“animate”) over
time, and the ultimate end-user interactions with CC-LOs are handled. As a
result, the VCS becomes an attractive learning
resource so that learners become more motivated and engaged in the
collaborative activities. The VCS containing the CC-LOs is eventually packed
and stored as learning objects for further reuse (e.g., as learning video
materials accessible from the classrooms) so that individual learners can
leverage the benefits from live sessions of collaborative learning enriched
with high quotes of interaction, challenge and empowerment.
Conclusions
This approach considers the virtualization
of collaborative learning by reusing the knowledge elicited during live collaborations,
with the aim to improve the learner’s engagement, in terms of real interaction and
empowerment of the collaborative experience from attractive and challenging
learning resources. This provides a significant step forward in the development
of current social and collaborative systems for e-learning in the Digital Age.
Acknowledgements
This work has been supported by the
European Commission under the Collaborative Project ALICE ”Adaptive Learning
via Intuitive/Interactive, Collaborative and Emotional System”, VII Framework
Programme, Theme ICT-2009.4.2 (Technology-Enhanced Learning), Grant Agreement
n. 257639.
References
[1] Bartlett, M. (2005). Generation X? So old
school. The emphasis now is on the 'Millennials.'
Credit Union Journal.
[2] Sweeney, R. (2006), Millennial
Behaviors and Demographics. University Librarian, New Jersey Institute of
Technology.
[3] Black, Alison. 2010. Gen Y: Who They Are and How
They Learn. Educational Horizons, 88(2), 92-101.
[4] Prensky, M. (2006). Listen to the Natives.
Educational Leadership 63 (4): 8–13
[5] Oblinger, D. G., and J. L. Oblinger, eds. 2005. Educating the Net Generation. Washington, D.C.:
EDUCAUSE.
[6] Barnes, K., Marateo
R., Ferris, S. (2007). Teaching and learning with the net generation. Innovate
3(4).
[7] So, H. & Brush, T. (2008).
Student perceptions of collaborative learning, social presence and satisfaction
in a blended learning environment: Relationships and critical factors.
Computers & Education, 51(1), 318-336.
[8] Dillenbourg P. (1999). Collaborative
–learning: Cognitive and Computational Approaches. Advances in Learning and
Instruction Series. New York, NY, Elsevier Science Inc.
[9] Wiley, A. D. (2001). Connecting
learning objects to instructional design theory: A definition, a metaphor and a
taxonomy. In D. A. Wiley (Ed.), The instructional use
of learning objects. Assiciation for Educational
Communications and technology.
[10] Caballé, S., Mora, N., Dunwell I.,
Gañan, D. (2011). CC-LO: a New Type of
Learning Object for the Virtualization of Live Collaborative Sessions. In
proceedings of the Third IEEE International Conference on Intelligent
Networking and Collaborative Systems, pp. 818-823. IEEE Computer Society.
Giuseppina Rita Mangione
Modelli Matematici e Applicazioni (MoMA, SpA)
Baronissi, Italy
mangione@momanet.it
Santi Caballé
Open University of Catalonia, eLearn
Center
Barcelona, Spain
scaballe@uoc.edu
Regular Articles Section
Introduction
The technology acceptance model
(TAM) and the unified theory of acceptance and use of technology model (UTAUT) (Venkatesh, Morris,
Davis, & Davis, 2003) have often been used to evaluate
mobile learning adoption (See Table 1). TAM originates from the theory of
reasoned action (TRA). Perceived ease of use and perceived usefulness are two
key concepts that influence a user’s attitude toward using the system and that
attitude, together with perceived usefulness, determines use intention. TAM can
be regarded as representing a utilitarian and usability perspective. Some
opponents of TAM have argued that TAM is too general and lacks specificity and
explanatory utility. Because TAM and UTAUT have been extensively researched,
the question is whether they will continue to generate new knowledge especially
in mobile learning adoption. To this end, it is recommended that researchers
explore other theories to explain the adoption of mobile learning. This article
introduces the consumer values theory which is rooted strongly in consumer
behavior and marketing.
Consumer Values Model
Consumer
purchasing decisions are dependent on the perceived values embedded within the
product and service (Dodds &
Monroe, 1985).
Likewise, values embedded within mobile learning as perceived by learners
/consumers can influence adoption decisions. A framework for consumer values
has been developed by Sheth, Newman, & Gross (1991).
Their model is widely supported in a variety of fields associated with value.
The model categorizes consumption values into five types: functional, conditional, social, emotional, and epistemic. Functional value refers to the
utilitarian or physical attributes of the product. Its influence on consumer
choice is established in traditional economic utility theory. Social utilities are values that enable
the individual to develop close associations with a community or group. Often,
this takes the form of a reference group, a community which the individual
wishes to join. Emotional values are
associated with the affect feelings stimulated through the consumption of a
product or service. Emotional values are not only embedded within the product
but also the atmosphere surrounding the product, or the context in which the
product is consumed. Epistemic value
is associated with customer curiosity or the need to learn, and is often seen
in the purchase of novelty products. Finally conditional values are those influenced by situational factors (Sheth, et al., 1991). Sheth’s
model has been applied in studies of consumption behavior in different
technology settings (Tang & Forster, 2007; Andrews, Kiel, Drennan, Boyle, & Weerawardena,
2007; Cheng, Wang, Lin, & Vivek,
2009; Pura, 2005). Therefore the theory of
consumption values can be quite robust and suitably deployed in technology
adoption.
A proposed Mobile Learning Adoption
Model
Based on
the above review and the theory of consumer values, a proposed model to
evaluate mobile learning adoption is depicted in Figure 1 and the hypotheses
are listed below:
H1: Functional values will positively
influence user behavioral intentions to adopt mobile learning
H2: Emotional values will positively influence
user behavioral intentions to adopt mobile learning
H3: Epistemic values will positively influence
user behavioral intentions to adopt mobile learning
H4: Conditional values will positively
influence user behavioral intentions to adopt mobile learning
H5: Social values will positively influence
user behavioral intentions to adopt mobile learning
Figure 5 Consumer Values Model
Conclusion
In the
field of education, learners are becoming more like consumers. Mobile learning
adoption based on a consumer values perspective has the potential to uncover
new findings not previously reported using traditional TAM and UTAUT. A quick
literature review from 2005 to 2011 has shown that our understanding of mobile
learning adoption remains limited. It is time that we handover from technology
to other perspectives.
References
Akour, H.
(2009). Determinants of mobile learning
acceptance: an empirical investigation in higher education. Retrieved from http://dc.library.okstate.edu/u?/dissert1,86727
Andrews,
L., Kiel, G., Drennan, J., Boyle, M. V., & Weerawardena,
J. (2007). Gendered perceptions of
experiential value in using web-based retail channels. European Journal of Marketing, 41(5/6),
640-658.
Chang, C.-K. (2010). Acceptability of an asynchronous learning forum on mobile
devices. Behaviour & Information Technology, 29,
23-33.
Cheng, J. M. S.,
Wang, E. S. T., Lin, J. Y. C., & Vivek, S. D.
(2009). Why do customers utilize the internet as a retailing platform?: A view from consumer perceived value. Asia Pacific Journal of Marketing and Logistics, 21(1), 144-160.
Dodds, W. B. &
Monroe, K. B. (1985). The effect of brand and
price information on subjective product evaluations. Advances in
Consumer Research, 12(1), 85-90.
Donaldson, R. L.
(2011). Student
acceptance of mobile learning. Retrieved from http://etd.lib.fsu.edu/theses/available/etd-05312011-074842/unrestricted/Donaldson_R_dissertation_2011.pdf
Huang,
J.-H., Lin, Y.-R., & Chuang, S.-T. (2007). Elucidating user behavior of mobile learning: A perspective
of the extended technology acceptance model.
Electronic Library, 25(5), 585-598.
Ismail, I., Idrus, R., & Johari, S. (2010). Acceptance on mobile learning via SMS: A
Rasch Model Analysis. International Journal of Interactive Mobile
Technologies, 4, 10-16.
Jairak, K., Praneetpolgrang, P., & Mekhabunchakij,
M. (2009). An
acceptance of mobile learning for higher education students in Thailand.
Paper presented at The Sixth International Conference of eLearning for
Knowledge-Based Society 17-18 December 2009, Thailand.
Liu, Y. (2008). An adoption model for mobile
learning. Paper presented at the IADIS International Conference
e-Commerce Amsterdam
2008.
Lowenthal, J. N.
(2010). Using mobile learning: Determinates impacting
behavioral intention. American
Journal of Distance Education 24(4), 195-206
Park,
S. Y., Nam,
M.-W., & Cha, S.-B. (2011). University students' behavioral
intention to use mobile learning: Evaluating the technology acceptance model. .
British Journal of Educational
Technology, doi: 10.1111/j.1467-8535.2011.01229.x.
Phuangthong, D., & Malisawan, D. (2005). A study of
behavioral intention for 3G mobile internet technology: preliminary research on
mobile learning. Proceedings
of the Second International Conference on eLearning for Knowledge-Based
Society, August 4–7, 2005, Bangkok,
Thailand.
Pura, M. (2005). Linking perceived value and loyalty in location-based mobile
services. Managing
Service Quality, 15(6), 509-538.
Sheth, J. N., Newman,
B. I., & Gross, B. L. (1991). Why we buy what
we buy: a theory of consumption values. Journal
of Business Research, 22(2), 159-170.
Tang,
Y., & Forster, P. (2007). Exploring the value
structure behind mobile auction adoption intention. AMCIS 2007 Proceedings. paper
499.
Venkatesh, V., Morris, M.
G., Davis, G. B., & Davis, F. D. (2003). User acceptance
of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Wang,
Y.-S., Wu, M.-C., & Wang, H.-Y. (2009). Investigating the determinants and age and gender
differences in the acceptance of mobile learning. British Journal of Educational Technology, 40, 92-118 doi: 110.1111/j.1467-8535.2007.00809.x.
Williams, P. W.
(2009). Assessing
mobile learning effectiveness and acceptance. Retrieved from http://tiny.cc/b1qdn
Tiong-Thye Goh
Victoria University of Wellington
New Zealand
Tiong.goh@vuw.ac.nz
Table 1 A review of mobile
learning adoption model
Conference Announcements
ICALT 2012 Rome features a workshop on game-based
learning.
It is broadly acknowledged
that digital games offer a high potential to foster and support learning.
The term “serious game” refers to games which primary purpose is other
than entertainment, and most serious games have a purpose for learning and
training.
Most research studies analyze
the relationship between games (characteristics/genres), learning objectives,
and target groups from various perspectives. Such studies investigate, for
instance, which games are suited best for applying the learning objectives
while simultaneously considering the game context and target population.
This workshop will address, in
particular, how digital games can contribute to contemporary knowledge society
requirements towards the effective acquisition of more transferable skills
(i.e. those abilities that support learning in task performance across multiple
disciplines and subject areas, thus enhancing sustainable learning). Examples
of transferable skills: collaboration, critical thinking, creative thinking,
problem solving, reasoning abilities, learning to learn or decision making.
This workshop will explore new opportunities offered by (digital) serious games
in meeting these new demands.
Two complementary perspectives
are considered in this workshop:
- How can
games foster formal and informal learning and
- How can their design, development and
deployment contributes towards this learning purpose.
The first perspective refers
to the fact that learning processes cannot be understood by merely looking at
the specific characteristics of the ICT-based tools used to promote learning,
but that one also needs to consider the complete
context in which games are deployed (including goals, tools,
tasks, and culture). Educational researchers become increasingly aware of this
integrated perspective. In fact, it is needed to address the interplay between
the game technology and the educational practice: that is, the activities that
can be accomplished thanks to technology mediation for achieving the agreed
learning goals.
The second perspective refers
to the methods, techniques and tools that are applied in the design and the
development of pedagogically sound games. In particular, this perspective aims
to focus on the development and application of methods and tools that can
support effective user assessment in game based learning. Breakthroughs in this
area can be made by advancing the effectiveness and efficiency of issues
including, but not limited to:
- User feedback mechanisms.
- User
data gathering and management.
- Sensor
data fusion and integration.
- Data analysis methods.
- Easy-to-use user interfaces.
We regard the interplay of
these two perspectives (i.e., the use and design of games for education)
crucial for the future of game based learning and this workshop therefore
intends to stimulate a fruitful dialogue between them.
Authors are invited to submit
original research work that contributes to new developments in the area of game
based learning for 21st century transferable skills including devices,
hardware/software tools, design and development methodologies, educational
applications, evaluation and assessment studies or case studies of exemplary
use.
Important Dates
Deadline: proposals deadlines will be in
accordance with the ICALT deadlines.
- Deadline for workshopcontributions: February 15, 2012
- Notification
of acceptance of Workshops' papers: March 5, 2012
- Authors' Registration Deadline: March 15, 2012
- Camera-Ready paper (up to 2 pages): April
1, 2012
How to
submit papers
In order to submit a paper to
the workshop you should send your contribution by email to Francesco Belloti (sg.icalt@gmail.com) with copy (CC) to icalt2012@e-ucm.es.
We will confirm you if your paper has been received correctly.
More information
http://seriousgames-icalt2012.e-ucm.es/
Advances
in Human Computer Interaction Special Issue
Call
for Papers
Serious
games (SG) and technology enhanced learning (TEL) tools are becoming ever more
important for education and training. However, their effective application
demands appropriate metrics, tools, and techniques for measuring elements such
as learning outcomes, engagement or gameplay performance. Devices like stereo
cameras, eye trackers, galvanic skin response sensors, and neural impulse
actuators (amongst others), now available at
reasonable prices, not only support innovative interactions, but they also
present opportunities to new user monitoring and evaluation.
Due
to the complexity of human nature and individual differences, objective and
systematic assessment of human behavior and performance remains highly
difficult. In addition, data analysis and evaluation methods for
technology-assisted learning and assessment are still under-developed because
of different perspectives in evaluation. Development of systems and tools able
to support provision of effective feedback is a major requirement for a new
generation of SGs and TEL tools. Breakthroughs in this area can be made by
advancing issues including, but not limited to: a) an efficient and easy-to-use
user interface, b) effective data management, c) sensor data fusion and integration,
d) data analyses methods, and e) user feedback mechanism.
Authors
are invited to submit original research articles as well as review articles
that describe new devices, hardware/software tools, methodologies, systems,
applications and evaluation studies about user assessment in SGs and TEL – with
a special perspective on usability and usefulness for learning. Potential
topics include, but are not limited to:
·
Automatic/interactive assessment of user
performance
·
In-game assessment mechanics
·
Time and precision effects
·
Metrics for measuring fun and/or
learning outcomes
·
User satisfaction and fun evaluation
·
User modeling and profiling
·
User adaptivity and personalization
·
Score rules and mechanisms
·
Automated recommendation mechanisms
·
Feedback to the users
·
Advanced user interaction
·
Advanced user sensors and transducer
systems for assessment
·
Sensor data fusion
Before
submission, authors should carefully read over the journal’s Author Guidelines,
which are located at http://www.hindawi.com/journals/ahci/guidelines/.
Every article requires a 600$ processing charge. Prospective authors should
submit an electronic copy of their complete manuscript through the journal
Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable:
|
Manuscript
Due
|
April 13,
2012
|
|
First
Round of Reviews
|
July 6,
2012
|
|
Publication
Date
|
August 31,
2012
|
Lead Guest
Editor
·
Francesco Bellotti,
Department of Electronics and Biophysical Engineering, University of Genoa, Italy;
franz@elios.unige.it
Guest
Editors
·
Bill Kapralos, Faculty of Business and Information
Technology, University of Ontario Institute of Technology, Oshawa, Canada; bill.kapralos@uoit.ca
·
Kiju Lee,
Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH,
USA; kiju.lee@case.edu
- Pablo Moreno-Ger, Faculty of Computer Science,
Universidad Complutense
de Madrid, Spain;
pablom@fdi.ucm.es
8th
Pan-Hellenic Conference with International Participation
28-30 September 2012, University of Thessaly,
Volos, Greece
http://hcicte2012.uth.gr
The 8th
Pan-Hellenic Conference with International Participation "ICT in
Education" (HCICTE 2012) is the biannual scientific conference of the
Hellenic Association of ICT in Education (HAICTE), aiming to address the main
issues of concern within ICT in Education and e-Learning.
HCICTE 2012
covers technological, pedagogical, organizational, instructional as well as
policy aspects of ICT in Education and e-Learning. Special emphasis is given to
applied research relevant to educational practice guided by the educational
realities in schools, colleges, universities and informal learning
organizations.
HCICTE 2012
aims to serve as a forum for academicians and researchers from around the world
to present their current work. The Conference especially welcomes articles
coming from the Greek Diaspora, as well as the Mediterranean countries.
The main topics
of interest include, but are not limited to:
|
· ICT-based Learning
· Computer Supported Collaborative
Learning
· Learning, eLearning and Pedagogy
· Learning Technologies
· ICT and Instructional Design
· E-Content - Development and
Delivery
· 21st century Education
- Educational policy and ICT
· Education for sustainable
development, Sustainable
School and ICT
· ICT and Teachers' Professional
Development
· Sociology of Education and ICT
· ICT-enhanced Science Education
· ICT-enhanced Language Learning
|
· Educational Gaming
· Virtual Learning Environments
· Web 2.0 applications in Education
· Social Networks for Learning and
Knowledge Sharing
· Wireless, Mobile and Ubiquitous
Technologies for Learning
· E-learning in Higher and Tertiary
Education
· E-learning and lifelong Learning
· ICT and lifelong Learning
· Distance Learning – Models,
Systems and Architectures
· Digital Literacy and Digital
Competence
· E-Assessment - Theories and
Methodologies
|
Authors are invited to submit original papers on research results or
novel applications of ICT in education and e-learning. The Conference will be
composed of several types of contributions:
- Keynote Talks: These will be invited contributions from
well-known scholars and scientists in the field. An abstract will be
included in the conference proceedings.
- Full Papers: These include mainly accomplished
original research results and may have 8 pages at maximum.
- Short Papers: These are mostly composed of work in
progress reports, fresh developments, individual projects or on-going work
of PhD students. They have 4 pages at maximum.
- Workshops: A Workshop is a collection of papers on
a theme that has been coordinated and led by the workshop's leader(s). The
aim the workshops is to bring together various research and development
groups, to serve as a forum for establishing new collaborations, to
attract both research results and work in progress, and to define main
enablers and future challenges. The workshop leader should provide an
abstract briefly describing the theme and its significance for the field
of ICT in education, and a submission file with around 200 word abstracts
of each paper. The workshop contribution papers may publish as full papers
and have 8 pages at the maximum.
- Panels: Discussions on selected
topics will be held. A proposal of maximum 250 words is expected.
Important Dates
- Submission
deadline: March 1, 2012
- Author's
Notification: May 31, 2012
- Final
Camera-Ready Submission: July 15, 2012
- Author's
Registration: Until July 15, 2012
- Early
Registration: Until August 15, 2012
- Late
Registration: After August 15, 2012
- Conference: September 28-30, 2012 Volos, Greece
Contact
