Learner modelling is a process consisting of collecting information explicitly from users and inferring some data from the learner activity. This information is basic for recommending resources as well as to predict performance. 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 subjects to be integrated into those models, such as IMS-LIP, IMS-RDCEO, IMSAFA. 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.
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, socioeconomical data, among others) and the available standards to cope with (e.g., IMSRDCEO, 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
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 elearning 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
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, competences 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 collaboration 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, psychological, 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] [
To provide the required standards-based modelling support at TEC, we are
following 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 [
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 elearning platform.
Syllabus IMS-MD
GDI
MoGA (Competences) IMS-RDCEO td-UM
IMS-LD IMS-RDCEO
IMIMSS--LAIPFA IMS-LIP IMS-AFA
Learning Path Recommender
IMS-LOM
AHROA (LO Recommender)
Collaborative
Logical Framework IMS-QTI
GAAP (Assessment tools)
From that integrated approach and after studying available information from
literature, 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 [
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 [
Learning activities editor application (GAAP) implements learning styles
test based on Felder&Soloman theory [
Several test to determine personality and character, leadership, entrepreneur ship, communications competences. These tests are defined by the Psychology 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
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 features 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 remain stable, some even could vary daily. 3
The use of international specifications, such as the IMS family, is aimed to support integration and interoperability. The LMS used in TEC is based on dotLRN, which supports 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 attributes of the UM. IMS-LIP categories: Academic record is being adapted to support historical information. 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
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 {reading-writing, understanding, speaking, math, attention, depression, anxiety, difficulties with peers, family problems, difficulties with teachers}. These learning needs are captured 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
representation 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 [
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
recommend. The tests used to capture information are being used by TEC since 2002 and
they are bases in [
Finally, this research is aimed to fill the gaps beyond current usage of UM in adaptive 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 [
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. References 1.