How to use Conceptual Maps to compose Learning Objects
Andreia Cristina G. Machion
1, Leliane N. de Barros
1,Karina V. Delgado
1Institute of Mathematics and Statistics - University of São Paulo
Key words : learning objects, conceptual maps, semantic web, ontologies
Abstract :
Learning Objects (LOs) are digital learning resources that are easy to retrieve and reuse. With the advance of the Semantic Web technology, it is possible to create and provide LOs with even more accessibility, reusability and interoperability through the use of logical based ontologies and reasoning engines. However, pedagogical aspects of LOs are generally neglected. This paper proposes the development of an original tool that uses conceptual maps to help teachers to select relevant LOs to build a didactical material for a course, based on the Semantic Web technology. In order to give some examples, we present a study case on the Electrostatic domain.
1
0BIntroduction
In general, when a teacher prepares a course for the first time, adopting a traditional or computer-based approach, she uses a number of resources, e.g., text books, lecture notes or Web sites. It is not a simple task to select materials from different origins and to combine them in a concise, coherent and easily comprehensible sequence, because it involves the discovery, analysis and selection of Web contents (or even books) relevant to the subject of matter and to the instructional context.
For example, when making a search on the Web for basic science instructional material (e.g.,
“electrostatic”), it is possible to find thousands sites for it. After a pre-selection, to choose the relevant ones, it is still possible to get hundreds of them. In addition, it is not difficult to recognize that most of those sites are quite similar or almost identical. Finally, with a particular selection of this material distributed on the Web (e.g., in educational repositories), the teacher still has to find the best sequence to present it, according with an instructional context.
Learning Object (LO) is the new proposed e-learning technology [1], where a set of software components (objects) can be reused and composed in several different learning contexts. By adopting a standard for LO description, it will be possible to develop powerful supporting tools that will certainly improve the efficiency of tasks such as search, evaluation and composition of instructional material.
There are not yet established standards and tools for discovery, analysis and composition of LOs that support teachers, instructional designers or students to use this technology in practice in a learning process. The so called Learning Object Metadata Standard - IEEE LOM [2], is one of the first attempts to provide such support. However, this standard has two major limitations in terms of allowing the development of supporting tools: (1) metadata is a syntactic description and (2) they focus on format not on pedagogical aspects. The first limitation does not allow a rich description of the LO content; while the second one does not give much information about what is the recommended way to use LOs to compose a course.
Conceptual maps are graphical representations of domain concepts and their relationships. In addition, these maps can show the domain hierarchical organization. This kind of representation has been shown to be very useful in the learning process, since it offers a general view of the domain.
The Semantic Web technology (SW) [3] is used to represent the available data on the Web in a common language so they can be processed by machines, not just read by humans. It offers such language using formal representations (ontologies) and tools to make logical inferences over these ontologies and their instances. The ontologies supply the necessary vocabulary to define domain concepts and their relationships (for instance, acquired from a conceptual map). Therefore, the SW facilitates the development of Web supporting tools, e.g., it can be used to represent a conceptual map and the LOs in the Web.
Although conceptual maps can be used to present a course content [13], it is not clear how they can be connected to Learning Objects. In this paper we show how to select LOs and compose them in a semi-automatic way, using conceptual maps to identify fragments of a domain knowledge that correspond to special Learning Objects, defined as Didactic Learning Objects. We show how to identify such special LOs and how, with the use of the SW technology, they allow for the automatic construction of coherent instructional sequences.
2 Learning Objects
A common definition for Learning Object is: any digital resource that can be reused to support learning in different instructional context [1]. Some research groups all over the world have been studying this technology to meet its goals: [4] [5] [6] [7], i.e., to develop standards, technical specifications and tools to promote learning objects interoperability and reuse. In general, it is possible to synthesize three fundamental functionalities for LOs:
reusability, accessibility and interoperability. However, as the LOs definition is wide, they can have different sizes, aggregation levels and number of objectives. Thus, to have a broad utilization for a LO, it must have the following characteristics [8]:
• Modular and transportable among applications and environments. In this way, the modules (each LO) may be combined in different sequences to compose didactic units, in different instructional contexts.
• Free-standing about the concepts it covers.
• Built to satisfy only one didactic objective, once a LO that covers a whole course, for instance, a book, could not be used in different contexts.
• Coherent and unitary within a predetermined schema so that a limited number of meta- tags can capture the main idea or essence of the content.
• Accessible to broad audiences. This can be done by using technologies like metadata standard (e.g., LOM) so the search, retrieval and its content management can be done using filters to select just relevant elements to a determined context.
• Not embedded within formatting so that it can be repurposed within a different visual schema without losing the essential value or meaning of the text, data or images.
LOs may be developed to compose a specific course or to be available in didactic material repositories. An important requirement is that the teacher or instructional designer has to build small components (when compared to a whole course), facilitating reusability. As a consequence of the interest in LOs technology, both in its research and use, there are many LOs repositories on the Web [9] [10] [11]. These repositories correspond to database that store metadata instances and URL references to learning objects. Most of these repositories are free for access and utilization, allowing the inclusion of LOs. In addition, some of them offer
search machines that use a specific index system but not good enough to promote the interoperability among them.
Metadata for learning objects
The idea behind LOs metadata is to describe their characteristics in order to allow information sharing by interested communities, to allow the interoperability among different repositories, etc. Some LOs metadata standards include basic elements, e.g., title, author, publication year, and technical characteristics, as copyright and software packing protocols. One of the most used metadata standards for LOs is the Learning Object Metadata – LOM.
The IEEE specification LOM - Learning Object Metadata [3] - was developed to provide a taxonomic structure (i.e., a category hierarchy) to the metadata used to describe learning objects. This taxonomy groups metadata in nine categories: General, Lifecycle, Meta- Metadata, Technical, Educational, Rights, Relation, Annotation and Classification. One critic about the LOM standard is that it is focused on repositories management, LO’s technical classification and it does not give pedagogical support to the users. Next, we define some metadata from the three categories that are used in this work: General, Educational and Relation, and propose some extensions [23].
The LOM standard metadata keyword (Category 1 – General) is defined as: “A keyword or phrase describing the topic of a learning object” [6]. The topic covered by a LO may involves a set of concepts and their relationships. For example, the topic Charge Transfer from Physics involves the concepts Force, Charge, Attrition, etc. We believe that the important keywords that should be used to identify the LO are the didactic goals of a learning object, i.e., the concepts the student must learn from the LO. In this work, we propose to change the metadata keyword to describe “A set of keywords describing the didactic goals of a leaning object”.
Although LOM has some categories that describe LOs pedagogical aspects (Category 5 – Educational), these information are not enough to support teachers in the research for LOs selection. The Category 7 – Relation describes how one specific LO relates to another specific one, but it does not identify explicitly the necessary concepts linking the two LOs.
That is, LOM standard does not make references to previous knowledge needed by the student to effectively learn from a specific LO. Thus, in this work we propose to add the metadata previous knowledge assumption, to describe the concepts, and their relationships, assumed as precondition of the LO. This information can help to establish conceptual dependences between LOs.
Conceptual Maps
Conceptual Maps (CMs) are graphical representations, often used by educators in the teaching and learning process. They include concepts, usually enclosed in circles, and relationships between concepts indicated by a labeled line linking two circles [12]. Another characteristic of CMs is that the concepts can be represented in a hierarchical fashion with the most general at the top of the map and the more specific below. A CM can be built to represent the concepts of a discipline, part of it, or even a specific topic. There are many ways to build a conceptual map; the important idea is that a CM should be seen as one of the possible ways to represent a knowledge domain [13]. One of the uses of a CM is to guide the selection of didactic material or specific instructional activities [13]. In this work, we propose to use CMs to support the development of didactic material from LOs, i.e., the CM is used to assist the teacher in the decision making about the creation of content sequences, based on the reuse of learning objects.
5B
An example of a Conceptual Map for Electrostatic
There are several works on the elaboration and verification of CMs with students [13], [14], [15]. Among these works, we can cite Sonia Salem [15], whose effort was to build a complete CM for Electrostatic, called MCE, under the supervision of experimented Physics educators (Figure 1). This map, used as case study in our work, describes the Electrostatic theory as it is approached in Physics introductory courses at the Brazilian engineering schools. In this case, the emphasis is given to the concepts and not to the mathematical formalism [15]. This map can also be simplified considering a high school course.
Figure 1: Electrostatic Conceptual Map [15].
Although the CM showed in Figure 1 was built from main concepts [15], it is not clear which are them in the final map, i.e., it is not clear which relations represent the hierarchy between the main concepts and the secondary ones. Figure 2 shows some of these hierarchies that we have identified in the MCE. Even though they are not explicit in the original MCE [15], they can be trivially identified as follows. Figure 2a shows that electric charge ( ) is an essential concept to define electric field ( ), electric force ( ), electric potential ( ) and electric energy ( ) concepts, i.e., the student has to learn electric charge concept before the others. Figure 2b shows the properties of electric field ( ) as their sub-concepts. We can also identify hierarchical properties for electric force ( ), electric potential ( ) and electric energy ( ) concepts. Another MCE feature is the possibility of different readings, i.e., starting from electric charge ( ) as the initial main concept of the theory to be learned, it is always possible to choose any of the related concepts ( , , , ) (Figure 2a) as the next topic to be addressed,. The different readings of the map represent different pedagogical approaches to
teach Electrostatic concepts [15]. In this work, we use this idea to motivate the definitions of didactic sequences of LOs (Section 7).
Figure 2a: Electric charge as main concept. Figure 2b: Electric field and its properties.
6B
Semantic Web
The goal of the Semantic Web is to provide structure and standards to Web resources so that we can create softwares to execute automatic tasks in the Web [3]. Ontologies are one of the technologies used in the Semantic Web. They describe concepts of a domain and their relationship in a formal language, i.e., a machine readable language, e.g., the Web Ontology Language – OWL [16]. By using ontologies, and dedicated ontology reasoners, it is possible to build softwares to execute most of the processing required in retrieving and evaluating LOs. Most of the technologies involved in the Semantic Web are based on the Description Logics formalisms [17].
1.1 Ontology
An ontology is a formal, explicit and shared specification of a conceptualization [18], allowing a shared and common understanding of a domain that can be used by people and application systems. It includes definitions that can be used by people or machines about basic concepts of a domain and the relationship between them. Terms of an ontology represent important concepts or classes of domain objects. The relationships between terms include general hierarchies of classes. In the simplest case, an ontology describes a hierarchy of concepts inferred by subsumption relationships. In more sophisticated ontologies, suitable axioms are added in order to express other relationships between concepts and to constrain their intended interpretation. In the Semantic Web, ontologies are used to facilitate knowledge sharing and reuse, needed to construct effective Web applications. They can be used to represent the semantic of Web documents, for instance, structuring and defining the meaning of metadata terms that can be used to perform a more intelligent search [3], e.g., a search for relevant LOs.
Ontologies are described in languages based on Description Logics [17][21], to allow correct and complete inferences about classes, relationships and properties of a domain. With the popularization of the Semantic Web, a lot of effort has been given to define languages and tools for these automatic logical inferences (through specialized theorem provers).
1.2 Web Ontology Language -- OWL
OWL is an acronym for Web Ontology Language, a markup language for publishing and sharing data using ontologies on the Internet [16]. It provides three increasingly expressive sub languages designed to be used by specific communities of implementers and users.
• OWL Lite: supports those users primarily needing a classification hierarchy and simple constraints; it is complete and decidable.
• OWL DL: supports those users who want the maximum expressiveness while retaining computational completeness (all conclusions are guaranteed to be computed) and decidability (all computations will finish in finite time).
• OWL Full: meant for users who want maximum expressiveness and the syntactic freedom of RDF with no computational guarantees.
Each of these sub-languages is an extension of its simpler predecessor, which implies in an increasing computational complexity, from the OWL Lite to OWL Full. In this work we have developed three ontologies trying to keep the language as simpler as possible. A brief explanation on how we can use ontologies to reason about concepts and their instances is: (1) first we create instances for the ontology concepts or classes then; (2) we apply ontology reasoners [19] [20] to infer extra instances that can only be derived from the ontology axioms;
(3) finally, we can make queries about the instances of some concepts.
How to connect LOs with Conceptual Maps
For a teacher, not any CM fragment can be used to guide a lesson (i.e., a didactic unit that composes a course capable of satisfying a given didactic goal [22]). Therefore, a Conceptual Map can be divided into pieces, which we define as CM fragments that encompass the set of concepts and relationships necessary to accomplish a didactic goal.
Definition 1: Let G be a didactic goal and M a conceptual map composed by a set of concepts C and a set of relationships R. A didactic fragment is a CM fragment F = (M, C', R', G), where C’⊆C, R’⊆R, G ⊆ C’ ∪ R’ and C’ ∪ R’ are concepts and relationships that have been recommended by experienced educators to accomplish the didactic goal G.
Notice that the number of concepts and relationships |C’ ∪ R’| of a didactic fragment must be equal or greater than the number of concepts and relationships in G. Notice also that Definition 1 establishes that a didactic fragment is part of a conceptual map that has been extracted from recommended text books or school repositories. The subset C’ ∪ R’/ G can be seen as the set of concepts and relations previously known by the student and necessary for him to learn the didactic goals G, plus secondary concepts and relations that are not explicit in the didactic goal but they need to be also introduced in that fragment (or lesson) in order to accomplish the didactic goal. Notice still that it is possible to have two different didactic fragment for the same didactic goal, i.e., different ways to teach/learn the same concepts.
Definition 2: A didactic sequence (composition or course) is a partial or total order sequence of didactic fragments that covers a predefined course curriculum (or a big collection of concepts and relationships from a given CM).
We can also find didactic sequences, in recommended instructional materials, such as text books or educational Web sites. Finally, given definitions 1 and 2, we can now define a didactic learning object:
Definition 3: A didactic LO is a LO whose metadata keyword is a didactic goal GLO, for which exists at least one didactic fragment F = (M, C’, R’, G) such as G = GLO and the LO previous-knowledge-assumption metadata, named P, is in C’ ∪ R’, i.e., P ⊆ C’ ∪ R’.
The Definition 3 makes a hard constraint on the definition of the didactic goal of a LO: the instructional designer of a didactic LO must precisely define it in the metadata keyword (G=GLO). On the other hand, in the previous-knowledge-assumption metadata, the concepts and relations specified by the designer may be a subset of C’ ∪ R’ of a didactic fragment.
Definition 4: A didactic LO-sequence is a partial or total order sequence of a set <DL,F> of didactic LOs, that corresponds to a didactic sequence from Definition 2 and, given dl ∈ DL, the concepts specified by its previous-knowledge-assumption metadata are included in the keywords metadata of the previous didactic LOs in the sequence. That means, all the preconditions of the LOs are accomplished by the previous LOs in that sequence.
Notice that a didactic sequence of didactic fragments is only a skeleton of a didactic LO- sequence. Since there are many didactic sequences that can be built with a set of didactic fragments and many possible ways to construct a LO corresponding to the same didactic fragment (it depends on the assumptions made by the LO designer), not all of them can be part of a didactic LO-sequence together. The whole composition must obey the previous- knowledge-assumption metadata.
The SEQUOA tool
In this work we propose a tool, named SEQUOA (Seqüenciamento de Objetos de Aprendizagem), to support teachers in the difficult task of composing a course based on LOs.
This tool is based on a conceptual map of the subject of matter and includes:
• an ontology about LOs representing the LOM metadata;
• a set of domain ontologies, each one based on a different conceptual map;
• a set of didactic fragments and didactic sequences for each conceptual map, extracted from recommended material on the subject of matter;
• a set of learning objects, from LOs repositories or discovered on the Web;
• an inference mechanism to make inferences over the ontologies [20][21];
• a procedure to create LOs compositions, also called didactic sequences; and
• an interface to allow the user interaction.
The two main reasoning steps performed by SEQUOA are:
• to match LOs keyword metadata with the didactic goals of the fragments of the didactic sequences.
• for each didactic sequence, eliminate LOs whose previous-knowledge-assumption metadata contains concepts that are not satisfied by the previous fragments in that sequence.
The teacher starts by selecting a CM of his preference. Then, she can visualize the whole CM to select all the concepts and relationships she wants to teach in her course (Figure 3).
Figure 3: A CM selected by the teacher. The green concepts were selected by the teacher as the didactic goal for which a didactic LO-sequence can be constructed.
Next, the teacher can visualize a set of didactic sequences (Figure 4) based on the selected CM. An interesting point is that there are several possible sequences to be selected by the teacher. So, the final decision about which one to use in an instructional context is made by teacher, according with her pedagogical preferences. Besides, each fragment may be instantiated with many LOs, each one with different characteristics such as difficulty level, type, etc.
2.1 Ontologies of the SEQUOA tool
In order to enable LOs discovering and selection, we constructed: an ontology for the Electrostatic domain from the MCE, named OntoMCE, and an ontology for the LOM metadata, named OntoLOM. Moreover, these ontologies were extended to define didactic fragments (from selected material on Electrostatic) in terms of formal axioms. The ontology OntoMCE defines the Electrostatic concepts as classes and their possible instances, e.g., synonyms terms or their translations into other languages. The Electrostatic class was divided into four subclasses: PrincipalConcepts, SecondaryConcepts, PrincipalRelationships and SecondaryRelationships. The principal and secondary partitions are based on the hierarchy of concepts defined in Figure 2. The OntoLOM ontology was built according with:
1. The LOM categories taxonomy were modeled as classes in a hierarchy of is-a classes.
The primer class LOM has two subclasses: LOMGeneral and LOMEducational. The class LOMGeneral has a subclass LOMIdentifier, which has as subclasses LOMEntry (with URL references as instances) and LOMTitle. The LOMEducational class has as subclasses LOMDifficulty, LOMResourceType and LOMContext, which have as instances the possible difficulty levels, types and application contexts from the standard LOM, i.e., they are pre-defined instances.
2. Other categories were modeled as properties that relate OntoLOM classes with OntoMCE classes, for example, the category LOM keyword has been modeled as a property LOMkeyword that relates a learning object (through its identifier LOMTitle)
with the domain ontology concept Electrostatic class. The new proposed previous- knowledge-assumption metadata was also modeled in the same way.
3. To establish the relationship between the OntoLOM classes, properties were created, for example, the property LOMdifficulty relates instances of the LOMTitle class (that identifies specific LOs on the Web) with instances of the LOMDifficulty class. The property LOMresourcetype relates instances of the LOMTitle class with instances of LOMResourceType class.
Figure 4: Possible didactic LO-sequences. The teacher can still select a LO for each fragment of a sequence.
It is important to note that some OntoLOM instances are pre-defined, according to the LOM standard recommendations for the corresponding metadata (e.g., the classes LOMDifficulty, LOMContext and LOMResourceType). The instances of LOMEntry and LOMTitle classes were obtained from the own LOs discovered on the Web. Those instances were created by analyzing 200 LOs about Electrostatic that we have found on the Web.
8B
Conclusions
In this paper we show how to use a conceptual map to select and compose LOs. In order to do so, we defined new concepts such as didactic learning objects and didactic learning objects sequences. We claim that the proposed blended interaction between the teacher and an automatic ontology reasoner (based on the Semantic Web technology), is a very nice solution to support teachers on the difficult task of selecting LOs from the Web. We have also implemented a prototype tool to support teachers to construct courses on Electrostatic.
References :
[1] WILEY, D. A. Connecting learning objects to instructional design theory: A definition, a metaphor, and a taxonomy. In: The instructional use of learning objects: Online Version.: David A. Wiley, 2000. http://reusability.org/read/, (December, 2005).
[2] IEEE. Draft Standard for Learning Object Metadata. 2002.
HU http://ltsc.ieee.org/wg12/index.html UH, (July, 2005).
[3] BERNERS-LEE, T.; HENDLER, J.; LASSILA, O. The Semantic Web - A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American, 2001. http://www.sciam.com/article.cfm?articleID=00048144-10D2-1C70-
84A9809EC588EF21&sc=I100322, (December, 2005).
[4] ADL. Advanced Distributed Learning. 2005. http://www.adlnet.org, (December, 2005).
[5] ARIADNE. ARIADNE Foundation for the European Knowledge Pool. 2000. http://www.ariadne- eu.org, (December, 2005).
[6] IEEE-LTSC. Learning technology standards committee website. 2000. http://ieeeltsc.org, (December, 2005).
[7] IMS. IMS Global Learning Consortium, Inc. 1997. http://imsproject.org, (December, 2005).
[8] LONGMIRE, W. A Primer on Learning Objects. 2000.
http://www.learningcircuits.org/2000/mar2000/Longmire.htm, (November, 2005).
[9] ESCOT. Educational software components of tomorrow. 2000. www.escot.org, (January, 2006).
[10]MERLOT. Multimedia educational resource for learning and on-line teaching. 2000.
HU www.merlot.org UH, (January, 2006)
[11]DISTÂNCIA, S. de Educação a. RIVED - Rede Internacional Virtual de Educação. 2006.
http://rived.proinfo.mec.gov.br, (November, 2006).
[12]CAÑAS, A. J. et al. A Summary of Literature Pertaining to the Use of Concept Mapping Techniques and Technologies for Education and Performance Support, July 2003.
http://depts.washington.edu/biology/hhmi/conceptmaps/Canas%20et.al.%202003.pdf, (January, 2007).
[13] MOREIRA, M. A. Mapas Conceituais & Diagramas V. Porto Alegre: Editora Moraes, 2006.
[14]TAVARES, R. Aprendizagem significativa em um ambiente multimídia. In: V Encuentro Internacional sobre Aprendizajem Significativo. Madrid, Espanha, 2006.
[15] SALEM, S. Estruturas conceituais no ensino de física: uma aplicação à eletrostática. Master Thesis - Instituto de Física, Universidade de São Paulo, 1986.
[16] W3C. World Wide Web Consortium. 1994. http://www.w3.org/, (December, 2005).
[17]BAADER, F.; HORROCKS, I.; SATTLER, U. Description logics as ontology languages for the semantic web. In: HUTTER, D.; STEPHAN, W. (Ed.). Lecture Notes in Artificial Intelligence, Springer, 2003. http://citeseer.ist.psu.edu/baader03description.html, (January, 2006).
[18] GRUBER, T. R. Towards principles for the design of ontologies used for knowledge sharing. In:
GUARINO, N.; POLI, R. (Ed.). Formal Ontology in Conceptual Analysis and Knowledge Representation. Deventer, The Netherlands: Kluwer Academic Publishers, 1993.
[19]HAARLEV, V.; MOLLER, R. Racer system description. In: International Joint Conference on Automated Reasoning (IJCAR 2001), 2001.
[20]PARSIA, B.; SIRIN, E. Pellet: An OWL DL reasoner. In: International Semantic Web Conference (ISWC 2004). Japan, 2004.
[21]HORROCKS, I.; SATTLER, U. Ontology reasoning in the SHOQ(D) description logic. In:
Seventeenth International Joint Conference on Artificial Intelligence, 2001.
[22] ZABALA, A. A prática educativa, como ensinar. Trad. Ernani F. da F. Rosa – Porto Alegre: ArtMed, 1998.
[23]MACHION, A. C. G. Uso de ontologias e mapas conceituais na descoberta e análise de objetos de aprendizagem: um estudo de caso em eletrostática. 2007. 136p. Tese (Doutorado em Ciências). Instituto de Matemática e Estatística da Universidade de São Paulo.
Authors :
Andreia Cristina Grisolio Machion, PhD Student amachion@ime.usp.br
Leliane Nunes de Barros, Assistant Professor leliane@ime.usp.br
Karina Valdivia Delgado, PhD Student kvd@ime.usp.br
University of São Paulo, Institute of Mathematics and Statistics
Rua do Matão, 1010 - Bloco C - Sala 206. São Paulo, SP, Brasil - 05508-900
