=Paper=
{{Paper
|id=Vol-1388/PALE2015-paper4
|storemode=property
|title=Modeling Learner Information wihting an Integrated Model on Standard-based Representations
|pdfUrl=https://ceur-ws.org/Vol-1388/PALE2015-paper4.pdf
|volume=Vol-1388
|dblpUrl=https://dblp.org/rec/conf/um/Chacon-RivasSB15
}}
==Modeling Learner Information wihting an Integrated Model on Standard-based Representations==
https://ceur-ws.org/Vol-1388/PALE2015-paper4.pdf
Modeling Learner information within an Integrated Model on
standard-based representations
Mario Chacón-Rivas1,*, Olga C. Santos2, Jesus G. Boticario2
1
TEC Digital, Instituto Tecnológico de Costa Rica, Cartago, Costa Rica
machacon@itcr.ac.cr
2
aDeNu Research Group, Artificial Intelligence Departament, Computer Science School,
UNED C/ Juan del Rosal, 16. Madrid 28040. Spain
{ocsantos,jgb}@dia.uned.es
Abstract. Learner modelling is a process consisting of collecting information
explicitly from users and inferring some data from the learner activity. This in-
formation is basic for recommending resources as well as to predict perform-
ance. There are open issues when it comes to integrate in standards-based user
models that information, which covers learning styles, competences, affective
states, interaction needs, context information and other learner´s characteristics.
In particular, there are standards that can be used to cover several of the sub-
jects to be integrated into those models, such as IMS-LIP, IMS-RDCEO, IMS-
AFA. This paper presents a work on implementing a user model that aims at
providing a holistic UM perspective, which is able to hold and collects all relev -
ant information, thus supporting its real-life usage. This is expected to facilitate
interoperability and sustainability while we are progressing on filling the gaps,
where representation and management is required.
Keywords: User modelling, IMS standards, Interoperability of user models,
Lifelong Learning User Modelling
1 Introduction
User Models (UM) have been considered as a representation of information on indi -
vidual users, which is essential for building applications of adaptive systems, intelli -
gent interfaces, intelligent information retrieval and expert systems, among others
[1]. Also UM are being used for over the last two decades on implementing personal
learning environments, adaptive learning environments and intelligent tutoring sys-
tems [2]. Information about UM is usually categorized in terms of personal, affective
and cognitive information [2]–[4].
Nowadays there is an increasing interest in taking advantage of new interaction
data which cater from learner affection thus requiring integrating into UM affective
state indicators [4] [5] [6]. These indicators provide valuable pedagogical pointers,
which affect the cognitive process. Actually, learners´ affective modelling is impact -
ing positively on adaptive systems, recommender applications and personalized learn -
ing environments [6].
In order to cope with both existing UM information and providing a real life stand -
ards-based application this paper introduces existing challenges in terms of the in -
formation to be integrated into the model (ie., competences, learning styles, socio-
economical data, among others) and the available standards to cope with (e.g., IMS-
RDCEO, IMS-LIP, IMS-AFA). The rest of the paper consists of section 2, where UM
components and the identification of variability levels are presented, section 3, which
summarizes the IMS family international specifications to be integrated into the UM,
and last but not least, section 4 where some lines of work in progress are introduced.
2 Identifying UM Components and Information Levels
UM components have been specified in terms of categories or data to be captured to
model the learner. Those components could be specified explicitly asking information
to the learner or could be specified inferring from the learner interaction with the e-
learning platform.
Independently the way to capture the information of the learner, as commented by
Brusilovsky and Millán in [2], the interest of information to be modelled in learning
environments must allow to identify the user as an individual, thus supporting a feed-
back process which can be managed by providing recommendations oriented to the
meet learners´ needs.
In the context of this research, we identify the UM components and classify them
in terms of variability. The variability term is oriented to classify the information de-
pending on the frequency of change, because it will influence any process of recom -
mendation. The UM attributes identified are generated by a methodological process
that integrates several sources of information and stakeholders. During the methodo -
logy application, the identification of stakeholders designs preliminary security roles.
2.1 UM Components
Several authors defined the UM components oriented to personal information, know-
ledge and interest. In [5] Bull and Kay enumerate as cognitive, affective and social at -
tributes. Baldiris et. al. [7] present the user model in terms of learning styles, compet-
ences and access device preferences, also it includes knowledge level based on six
levels of knowledge defined by Bloom´s taxonomy. This proposal also includes a col-
laboration level based on indicators obtained from learners´interaction in the learning
management system.
Based on aforementioned and related literature we are currently defining the UM
components information in terms on the following information categories: personal,
provenance, academic record, socio-economical, accessibility or special needs, psy-
chological, learning styles, competences, knowledge level and collaborative level.
Those components can be modelled and organized in terms of standards, such as
the IMS family specifications. The use of these international specifications is aimed
to support collaboration and systems interoperability and have the advantage of being
specifications that are already integrated into dotLRN [7] [8] [9]. IMS-LIP is a collec-
tion of information about the learner, which supports data exchange between applica -
tions, agents, server and other services concerned about the learners` characteristics
[10] IMS-RDCEO is a concise and flexible structure to represent competencies, fur -
thermore this specification is extensible to any competence model [7] [11].
To provide the required standards-based modelling support at TEC, we are follow-
ing these previous approaches while extending them and filling information gaps
when needed. Based on IMS-LIP categories, the element identification, is loaded
with personal, provenance, academic, socio-economical information. The element
Competency is loaded with competences information. The TEC competences model
is based on CEAB model [13] and this competencies information is represented using
IMS-RDCEO. Accessibility is represented using IMS-AFA. Additional details on
IMS family specifications are provided in the section 3.
The UM in our context, called td-um means TEC Digital-User Model. td-um is an
integrated model because it is gathering together learner information from applica -
tions, databases and some indicators collected from learner interaction with the e-
learning platform.
Syllabus
Learning Path
IMS-MD Recommender
GDI IMS-LD
IMS-LOM
MoGA
IMS-RDCEO AHROA
(Competences)
(LO Recommender)
IMS-RDCEO
-LIP
IMS -AFA
IM S
td-UM IMS Collaborative
IMS -LIP Logical
-AF Framework
A
IMS-QTI
GAAP
(Assessment tools)
Figure 1: td-UM Integrated model
From that integrated approach and after studying available information from liter-
ature, we have detected some gaps in the information to be modelled, these are the
following:
• Learner knowledge level: it is been modelled based on specific background
of knowledge from each discipline studied by the learner. In case of comput -
ing students, the knowledge level is modelled using knowledge areas presen -
ted by ACM in [14]. The gap to be resolved is based on several bodies of
knowledge from different disciplines. The other disciplines to be modelled
in TEC are: Industrial engineering, Electronic engineering, Materials engin -
eering, Electromecanic engineering, Construction engineering, Agricultural
engineering, Industrial Maintenance engineering, Occupational Safety and
Environmental Hygiene Engineering. This other disciplines have a different
body of knowledge, therefore the structures used to model should be suffi-
ciently flexible.
• Academic record attributes: these cover information reflecting the progress
in program courses in terms of final grades or qualifications. It is frequently
confused with the knowledge level. This is mainly required to preserve his-
torical information.
• Competences attribute: it contains a set of competences that requires reflect -
ing the level of domain of each competence, and evidences used to assess
each competence, among others. The work in progress is designing the
model which we are adapting to cope with TEC competence model.
• Variability of information: an important issue is to track the progress in com-
petence domain, as well in academic records. This progress tracking repres-
ent some level of variability of information that could impact the recom -
mendation and adaptivity of platform.
• Categories and attributes privacy levels: the privacy level of some attributes
or for the whole category are not clear in the specifications. For example,
in socio-academic attribute, the sub-attributes: level of sociability, esteem,
motivation, coping strategies contains private information accessible only for
department of Psychology and the learner. In this work is needed to define
and to model the privacy level by category and attribute based on privacy
modelling [15]. The model is contemplating the user roles definition and the
integration with dotLRN.
Currently at TEC Digital we have implemented several applications that are using
partial learner models, Figure 1 shows the integration architecture. The immediate
work is being focused on adapting td-UM, to be used as source of integrated learners´
information, which will be able to support recommendations and assessments. Those
applications are:
• Adaptive Learning Paths, which use the learning design of a course and the
students´ performance information to recommend learning resources. The
recommendation is based on bayesian networks [16].
• Hybrid Agent Recommender of Learning Objects (AHROA), based on the
learning design and syllabus of the course, recommends learning objects to
learners. The recommendation is prepared using TF-IDF 1 to work the terms
relevancy, also uses cosine similarity. The UM will provide information
about learner needs to AHROA in order to identify the impact on the quality
of recommendations.
• Collaborative Logical Framework, (CLF) implemented by aDeNu [17], it
uses collaborative indicators to assess the learners collaboration. An import-
ant activity on the CLF is the identification of each group moderator during
1
More details in http://nlp.stanford.edu/IR-book/html/htmledition/tf-idf-weighting-1.html
the consensus stage, this activity is currently based on the learner interaction
in the platform. The UM will impact the CLF integrating specific attributes
concerning to the leadership and entrepreneurship. The TEC Digital adapted
CLF to improve the indicators information [18] and to analyse the impact of
the UM in CLF assessment.
• Learning activities editor application (GAAP) implements learning styles
test based on Felder&Soloman theory [19].
• Several test to determine personality and character, leadership, entrepreneur -
ship, communications competences. These tests are defined by the Psycho-
logy department and by the team responsible to design the competence
model. Some tests are in processes to be patented by TEC. The variability
and tracking progress of these competences are very important to be con -
sidered in the CLF, GAAP and AHROA.
2.2 UM Variability Information Levels
In order to take advantage of the information being modelled in real-life situations we
are particularly interested in taking into consideration the “variability” factor. The
levels of information variability reflect the frequency of variability or changes on the
values of UM components. Authors as Sosnovsky and Dicheva in [4], defined this
variability as long term and short term variability. In our research we are defining
these variability levels as low, medium and high as explained bellow:
Low Variability: Some UM components hardly ever vary during the learning
period and are used either for managing personal information (e.g., name, birthdate,
provenance, native language) or academic processes, such as birth date, which can be
used to calculate the learner age, the native language and other language domain fea-
tures that may have an impact on the learning process.
Medium Variability: UM components seldom vary on a daily basis but the change
more frequently than those being described as low, including periods of relatively
stable values; usually are characteristics that are modified during the learner progress
in the curricula. It could be presented with the competences category. Improvements
on competences are been registered and assessment once at year. Some examples of
competencies are communication skills, team work, problem analysis, knowledge
base of engineering, ethics and equality, among others. The impact on medium-level
variability changes in the learning process is very important for learners because the
progress on some of those components affected by them has a direct and immediate
influence in the learning performance.
High Variability: Those components vary almost continuously and hardly ever re-
main stable, some even could vary daily.
3 Representation based in standards
The use of international specifications, such as the IMS family, is aimed to support in-
tegration and interoperability. The LMS used in TEC is based on dotLRN, which sup-
ports IMS-LD and IMS-QTI. The model td-um is based on IMS specifications adapt-
ing to the specific needs mentioned above as gaps in the models.
Table 1 shows the categories and standards we are using along with the main at-
tributes of the UM.
Table 1: Standards and information category in td-UM
Standard Category Attributes2
personal name, birthdate, address, phone number, email address, native
language, affective, socio-academic needs{ study conditions,
study habits, metacognitive study strategies, development study
strategies, organizational study strategies, level of sociability,
esteem, motivation, coping strategies}
provenance place of provenance (based to recognize the social develop-
ment index)
IMS-LIP
socio-economical scholarship, loan financing
academic record based on progress record on each course
learning style based on Felder and Solomon learning style test
knowledge level based on the body of knowledge of specific disciplines
collaborative based on indicators tracked from the interaction with the plat-
level form.
IMS-AFA accessibility visual adaptation, hearing adaptation, cognitive adaptation,
learning needs {reading-writing, understanding, speaking, math,
attention, depression, anxiety, difficulties with peers, family
problems, difficulties with teachers}
IMS-RDCEO competences knowledge bases of engineering, problem analysis,
investigation, design, use of engineering tools,
communication skills, professionalism,
impact of engineering on environment and society,
ethics and equity, economics and project management,
lifelong learning, resource utilization
IMS-LIP categories: Academic record is being adapted to support historical informa-
tion. Knowledge level is being adapted to cover the body of knowledge of several dis-
ciplines. Also the knowledge level should model the knowledge area or topic with a
level reflecting the expertise in the given domain. This level is going to be described
in terms of the Bloom´s Taxonomy, following previous approaches [7].
Personal category is been adapted to model socio-academic needs{ study conditions,
study habits, metacognitive study strategies, development study strategies, organiza-
2
This column enumerates only principal attributes.
tional study strategies, level of sociability, esteem, motivation, coping strategies} all
captured using a test of 40 questions.
IMS-AFA category of Accessibility is being adapted to model learning needs {read-
ing-writing, understanding, speaking, math, attention, depression, anxiety, difficulties
with peers, family problems, difficulties with teachers}. These learning needs are cap-
tured using a test of 66 questions.
IMS-RDCEO category of competences {Knowledge base of engineering, Problem
analysis, Investigation, Design, Use of engineering tools, Individual and team work,
Communication skills, Professionalism, Impact of engineering on environment and
society, Ethics and equity, Economics and project management, Lifelong learning,
Resources utilization}. Adaptation to be provided require to support the representa-
tion of the domain level of each competence, the evidences used to assess each com -
petence and the authority. Another modelling issue is to support the integration and
matching of the competence model with those required for an international accredita -
tion process. Accreditation processes are oriented to model the program of courses or
careers in universities, while learner models reflects personal and individual informa -
tion. The competence model for international accreditation is based on statistical
samples [20].
4 Works in progress towards an integrated learner model
Once the aforementioned issues are designed, structured and integrated to cope with
the information to be represented, the next decision will be the specific way to repres -
ent information on each competences, learning styles and affective indicators. The in -
teroperability of those indicators will require an ontological representation that allows
to deal with the information to be used in each foreseeable situation.
As an example of the decision to be done, we are planning to apply a test of tem -
perament to identify (1) extroverted – introverted temperaments, (2) ways to capture
information: by intuition-by senses, (3) ways of making decisions: by thought- by
feeling, (4)ways to organize time: judicious-mandatory. This test has 70 questions,
the interpretation of the results will give a value for each element to identify. A
learner could have a value for the way to capture information of 6-4 (ie, 6: by thought,
4: by feeling). The internal decision about the representation of those values could af -
fect the adaptive process, if the UM stores the 6 value or the pair 6-4. A recommender
system could take several considerations concerning the type of resources to recom-
mend. The tests used to capture information are being used by TEC since 2002 and
they are bases in [21].
Finally, this research is aimed to fill the gaps beyond current usage of UM in adapt-
ive learning systems thus making it really extensible, sustainable and applicable in
any situation.
We are currently progressing on the first stage of this research, which covers: (1)
understanding the dimensions of UM and the results of experimental research in
learning scenarios, (2) focusing on reviewing the state of the art in UM and its com -
ponents, (3) identifying a methodological approach to gather the UM attributes in a
real learning environment, (4) identifying possible gaps that may come up when in -
tegrating UM into real dimensions of learners characteristics that have impact on the
learning process.
This contributions of our research are focused in (1) defining a methodology to
identify attributes of UM in real learning environment that support personalised and
inclusive e-learning scenarios, (2) identify the UM attributes that really impact in re -
commendation processes using AHROA and CLF, (3) validating if the standards are
enough to cope modelling real learning environments supporting relevant recom -
mendations.
The work in progress is done in the context of a PhD thesis research with aDeNu
group. This is implemented in the Instituto Tecnológico de Costa Rica (TEC). The
implementation is based on dotLRN platform, instantiated by TEC Digital [22]. This
research is aimed to provide a model containing learner information to be used in ad -
aptive and recommendation processes, based on interaction indicators computed from
large scale setting which corresponds to official courses run in TEC.
5 Acknowledgements
Authors would like to thank the Spanish Ministry of Economy and Competence
(MINECO) for funding BIG-AFF project (TIN2014-59641-C2-2-P), where this re -
search is partially supported. Authors would also like to thank the Department of
“Orientación y Psicología” (DOP) at TEC, specially to Alejandra Alfaro.
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