This paper introduces a set of resources that provide web learning environments with student modeling services. SAMUEL is a user modeling server for registering, updating and maintaining student knowledge data from different sources that use their own ontologies. In order to make inferences about student knowledge, it becomes necessary to establish equivalences between concepts of different domains. For this purpose we have developed SONIA, a tool to edit and integrate models which allows semi-automatic ontology mapping. The set of resources is completed with INGRID, an independent generic open learner model for interacting with external student models.
In the last decade, several adaptive and intelligent web-based learning environments (WILE) have been developed. Most of these systems are the result of research efforts focused on a particular pedagogical task, learning domain or teaching strategy, like for example ELM-ART [1] or AlgeBrain Tutor [2] which deal with LISP and Algebra domains respectively. Although less common, there are also educational web-based tools for generic domains such as SIETTE [3] or DCG [4]. Given the availability of all these tools, educators and course designers may be interested in integrating some of them in their own courses. Since these and other systems are high-quality software based on solid theoretical foundations, they may be of great value for the development of other educational systems. However, modularity is limited in most cases, which makes reusability almost impossible unless the system is used as a whole.
When thinking about developing tools that allow reusability of existing components, desirable features for such tools are: domain-independence (tools can be used for any subject domain); extensibility (in the sense that any component can be integrated), and component interoperability (components can communicate and interoperate despite differences of implementation language, execution environment 1 This work has been partially supported by the ARTEMISA project number TIN 07-67515 of the MINISTERIO DE CIENCIA E INNOVACIÓN, Spain. or model abstraction). Interoperability can be approached from two different angles: as a distributed software problem, and as a semantic conceptual issue.
Nowadays, web-based learning environments are evolving by adapting their architectures to new Web 2.0 technologies. The new approaches rely on the development of distributed architectures based on the integration and reuse of learning activities. Consequently WILEs become hybrid web applications (mashup).
Furthermore, the web teaching/learning model evolves towards an auto-learning scenario in which students complete their training using resources located all over the web. In any case, we deal with students who are using different systems. If these systems are intelligent (i. e. manage their own student model), each one of them stores part of the information about the student’s knowledge, that is, the student model is distributed over the network. In this context, it would be useful that a system “asks someone for references” when it has to work with a new student, in order to provide adapted instruction.
This problem can be approached by using user modeling servers (UMS) [5;6;7].
According to Kobsa [
Our research group have been working during the last years in MEDEA [
SAMUEL (Spanish acronym for User Modeling Accumulative Server for ELearning), is a User Model which allows storing student knowledge evidences obtained from different learning resources. SAMUEL is an independent component that can be requested by any web LR that needs to obtain data about a certain student.
SAMUEL stores heterogeneous information from different systems that deal with different domain ontologies. In order to perform evidence integration it is necessary that systems agree on the semantic of the domain terms, so they can exchange data for the equivalent domain concepts to update their own models.
Some attempts can be found in the literature for user model integration. In [
The set of student modeling services is completed with INGRID (Spanish acronym for Domain Independent Graphic Interface). This system allows students to consult (via Web) user models of resources with which they work and interact.
In the next section a description of different usage scenarios of MEDEA is discussed. In the rest of the paper, each tool is described in detail: SAMUEL (user modeling server) in section 3; SONIA (ontology mapping tool) in section 4, and INGRID (Open Learner Model) in section 5. Finally some conclusions of this work are presented. 2
In this section we describe new MEDEA architecture through different usage scenarios.
MEDEA provides all the components needed to create and execute an Intelligent Learning Environment: support for Domain Model definition, User Model (data storage and diagnosis processes), Instructional Planner and User Interface. All of them can be used together to act as a WILE while some of them can be used independently by any Educational Software.
The figure 1 shows the scenario 1, where all MEDEA components are used (MEDEA planner isn´t introduced in this paper). Teachers create the domain model and decide which learning resources are adequate to each concept (dotted line). Then a student interacts with the WILE through a web browser as described below. Web browser 3
2 Domain Model 5 4
INGRID External Learning Resources 5 5 Scenario 1 1. Student asks MEDEA planner for advice. 2. MEDEA planner consults domain model and student model (SAMUEL).
SAMUEL uses SONIA to obtain all the data related to MEDEA domain model concepts. Each domain topic is related to one or more external LR that can be used to learn it. MEDEA planner suggests next topic to be learnt and some tasks (LR) to be done by the student.. 3. Student works with a LR. 4. LR updates student model invoking SAMUEL web services. 5. Teachers and/or students can consult the student model using INGRID (Graphic Interface).
Besides being used together, each resource can constitute an independent component which may be used in combination with an LR. MEDEA has therefore evolved into a mashup. The figure 2 shows another possible scenario where some MEDEA components are used by external resources.
PLANNER Domain Model
SAMUEL
The figure 3 describes how INGRID can be used by a LR that has no way to show graphically its student data.
SAMUEL is a user modeling server that allows storing of student knowledge evidences about different domain concepts obtained from different learning resources (Figure 4). Each LR can manage its own domain model, so each topic (domain concept) mark is stored independently together with the student ID, the domain and the source LR Therefore it is necessary, in order to make inferences about student knowledge, to establish equivalences between concepts of the same domain that belong to a different ontology. For this purpose we have developed an Intelligent Ontology Server for E-Learning (see next section).
SONIA (Ontology Server) Maps terms from an LR domain to another
Domain i
Topic 1 Topic 2 Topic 3
SAMUEL (UM Server) Student1 LR1 Domain1 Topic1 Mark Student1 LR2 Domain1 Topic1 Mark … Studenti LRj Domaink Topicl Mark
Web Services LR
LR Web Learner Resources For example, if we consider two WILEs which have a domain related to economics, we could map the terms as in Table 1. parameters: the set of evidences used to estimate the student’s knowledge, the evidence sources and the method used to obtain the estimation. n evidences case the n value represents the n parameter and the mi value is the mark of the i-th last evidence registered. The wi values are the mark weights. One example could be wi=1/i, where more weight is given to the most recent evidences since they represent more accurately the student’s current knowledge. 4
As it has already been discussed, in order to make inferences about student knowledge using data collected from
different learning resources, it becomes necessary to establish equivalences between concepts of different domains. We have approached this problem
with the development of a web ontology server called SONIA. It is an AJAX application that allows editing concept semantic networks (first level ontologies) and establishing relations among them semi-automatically (Figure 6).
Stemming algorithms (for English and Spanish languages) are applied to ontology terms and then a set of string similarity metrics (Hamming distance, N-gram, Levenshtein distance and Maximum common substring) are used to calculate the probability of two terms from different ontologies referring the same concept. In addition to these metrics, a synonyms dictionary has been added.
The Figure 5 shows an example where terms of two ontologies are compared. Each arrow’s color represents a different metric. The number above the arrow indicates the similarity degree between the terms. These correspondences can be updated (inserted and/or deleted) manually.
These mappings are stored and, when SAMUEL is requested information about a concept (Figure 4), it searches for all the terms related in order to collect data which refer to the same concept and have been provided by different WILEs.
Open learner models (OLM) are accessible and open models that extend the
traditional WILE models to turn them into a visible and interactive part of the system
[
There are two types of OLM: those integrated in a tutor system and the independent ones (IOLM), whose purpose is to help students to identify and to solve learning problems by themselves, without tutor system help, that is, to encourage metacognitive skills.
Still in the field of resource integration, we have developed INGRID [
The strengths of the system are that it is generic and it can represent data from any WILE. For this purpose, INGRID has a JSP which receives as input (xml format) a list of concepts and the marks obtained by the students, as well as the semantic network of the domain model (concepts and relationships) and is capable of representing it. So far it has been successfully tested with SIETTE and user model server SAMUEL. Both tools use it as a plug-in to graph the data from their students. The general objective of our research work is the development of different domain independent and interoperable components that can be used in the construction of a web based intelligent learning management systems.
The user interaction with different learning resources can provide valuable information that helps other systems to provide the student with a more accurate instruction. In this sense we work in the integration of heterogeneous sources of user information. In order to achieve this, our group is working on the development of independent and generic tools that provide student modeling services. A functional approach has been presented in this paper. It includes a set of tools that can be used in any learning environment to share user data.
In this first stage of the work, all the tools needed to provide user modeling
services have been implemented and all of them are working successfully in a real
environment. As part of a formative evaluation process, these tools have been
integrated with the test system SIETTE [
First, we are aware of the importance of the semantic issue in WILE interoperability. It is a bottleneck in intelligent learning resources integration. A first approach to this problem is SONIA. So far, we have applied ontology mapping techniques based on lexical components. The next step is to use techniques based on ontology structure as graph matching and studying relations semantic. In the next stage of this work we plan to conduct research into the application of machine learning or statistical techniques for ontology mapping.
Furthermore, we have implemented a set of services to accumulate user evidences. We plan to add new services which apply formal diagnosis methods to make inferences about student.
Finally, we think that teachers and students are an important source of evidences, therefore we will not only allow INGRID to consult the server data but also to update them taking into account teachers and students’ contributions.