Concept Maps: An Automated Support for Monitoring the Learning Process

Davidson Cury Dept. of Computer Science

UFES

Rosane Aragon Dept. of Education

UFRGS

Credine Silva de Menezes Dept. of Education

UFRGS Vitoria, Brazil

dedecury@gmail.com Porto Alegre, Brazil credine@gmail.com

Porto Alegre, Brazil credine@gmail.com

Abstract-It is vital for a constructivist teacher to monitor the apprenticeship of each student in order to facilitate the definition of the next steps in the development of a discipline. This is a very time consuming and it requires a theoretical framework to

support the observations of the teacher. This monitoring would

benefit from certain automated tools to expedite parts of the process. Different pedagogical approaches say that the use of concept maps can help students in the processes of signification

of new contents or in the resignitication of those already learned. The epistemological position of Pia get states that the development of a student's logical-mathematical structures is related to the learning of concepts, forming in this way a conceptual system. In

the construction of concept maps we create, ultimately, what Piaget calls "significant implications" that evolve according to the semantic nature of the conceptual relations that we create. We propose here an environment for the automatic identification

of the significant implications, using a family of software agents guided by an ontology. The analysis of maps will be made based

on Piagetian theory. A prototype is under development and will be used to support the analysis of maps produced.

Keywords-concept map; ontology; intelligent agent; learning environment; constructivism.

I. I NTRODUCTIO N

It is essential for a constructivist teacher to accompany the learning process of each student in order to be able to define the necessary steps to be taken in the further development of a discipline. This task, however, is very complex and arduous, it is very time consuming and requires a theoretical framework to support the observations of the teacher. Traditional techniques used in the monitoring of learning (portfolio, performance test reports, interviews etc.) give it the characteristics of a classical assessment. Constructivist researchers and educators have demonstrated dissatisfaction with the efficiency and reliability of these methods, because the problems related to learning processes are generally diffuse, complex and often intractable by conventional techniques [11].

Alternative techniques to accompany learning have recently started to appear. Among them, the Concept Maps [12, 13] occupy a prominent position. Computational tools to facilitate the use of concept maps have been developed by research groups in order to meet the needs of interventions in the processes of learning. The results demonstrate the ease and

978-1-4673-5261-1/13/$31.00 ©2013 IEEE

precision the maps represent in tracking the changes that occur in a student's cognitive structure during learning.

Here we present a proposal for a tool with the goal of providing computational support to a theoretical investigation. [t is based on a Ph.D. thesis [I], which proposes a reading of concept maps incorporating Piaget's bias. We do not intend to discuss here all the theoretical aspects addressed in this thesis, but just focus on one of several that may redefine the importance of maps in the process of knowledge construction. This tool constitutes for us an important step in the development of technologies to assist the constructivist educators in their pedagogical tasks.

The tool discussed here will be one of a set of tools, some of which have already been developed [16,17,18]. They are a part of our project called Portal of Knowledge, which is based on Concept Maps under a constructivist approach. We want to emphasize the use of technological resources to support educational practices focused on meaningful learning. The scope of the Portal is to develop appropriate computational support for the construction of concept maps. This support will improve the efficiency, reliability and validity of the use of concept maps as a tool to accompany and evaluate students' progress, as well as to intervene in the processes of learning, based on cooperation and collaboration.

This article is organized as follows: Section 2 presents the conceptual maps and some ways to use them; Section 3 is a brief description of Piaget's significant implications, the motivating concepts of this work; Section 4 combines these implications with concept maps; Section 5 displays the specifications for a prototype of the proposed tool; and finally, Section 6 offers some conclusions.

II. THE CO NCEPT MAPS

Concept maps are graphical representations of relationships between concepts. They have been used in many different fields of knowledge. [n particular, they have attracted the interest of educators worldwide. Novak [6], the inventor of concept map, defines it as a tool for organizing and representing knowledge. The concept map, based on the meaningful learning theory of Ausubel [7] is a graphical representation of a set of concepts constructed in such a way that the relationships between them are evident. Concepts appear in boxes while the relations between concepts are

specified by means of phrases that connect the concepts. Concepts are defined by nouns while linking phrases must have a verb or a verbal composition. Two or more concepts connected by linking phrases creating a semantic unit, we call a proposition. Each proposition defines a truth, a fact, detachable and understandable by itself. The propositions are a particular feature of the concept maps as compared to other similar structures, such as mental maps or flowcharts. According to Novak, a concept map is a hierarchical tree structure, where the more general, or inclusive concepts appear at the top and the more specific ones in the lower parts of the tree. New knowledge is anchored to the old by semantic affinity.

There are different pedagogical approaches [3] where the use of concept maps can help students in the processes of signification of new contents or even in the re-signification of concepts already learned. This happens, because the maps allow students to locate and establish relations of composition, similarity, differentiation, and/or equivalence between what they are learning and the concepts already present in their cognitive structure. Therefore, various researches are being conducted and new tools developed to enable the use of concept maps for different pedagogical practices [4, 5].

Concept maps put Piaget's vision of concept into a different paradigm from the one established by the cognitivist view of Ausubel and Novak. According to Piaget's Genetic Epistemology [8], the knowledge is acquired by mechanisms involving a process of assimilation: an external element is incorporated into a scheme of action or a concept of an individual. This process occurs when the individual starts an active coordination of his/her actions, while coordinating the observable characteristics of an object. The process of assimilation occurs when there is a mechanism of accommodation, which allows the individual to incorporate the particularities of the object. This process results in a transformation of the individual systems of signification. It updates the so-called "prior knowledge", a precondition for integrating - and not j ust "anchoring" - the new knowledge.

According to Dutra [I], we can follow the representation of the system of significations of a student in the dynamics of building a concept map. In this system we also recognize relating subsystems supporting each other for the construction of these significations.

It is essential to emphasize the central role of linking phrases in a concept map. When we compare concept map with Piagefs knowledge structure [14], we can conceive of the linking phrases as the structuring functions, because they are responsible for the laws of composition of the system represented by the map [1]. Jonassen [15] stresses the effort to choose a phrase that represents a relationship between two concepts, both due to the large number of possibilities as well as the need of placing such a relationship in the context in which the two concepts are presented.

As learning processes are the result of student-student and student-teacher partnerships, we understand that concept maps can also serve as an important guide for students, alerting them to more and more possibilities to qualitatively enhance

the cogmtlve concepts and relationships in the process of acquiring knowledge.

As we said in Section 1, our interest here is in a single aspect of Piaget's constructivism: the regulations in the conceptual systems arising from the logical operations between concepts. These issues are briefly discussed in the following section.

III. THE SIG NIFICA NT IMPLICATIO NS

We, as humans, construct knowledge through our senses at every moment of our existence. In fact, cognitive processes occur even independently from our will. Through our sensory system we identify a new object and, almost instinctively, want to define its properties. Upon these definitions, we can manipulate and apply the knowledge we acquired of the object and we can start to discover its interactions and relations with other objects of our interest.

A transitive implication sentence A � B � C, in Piaget approach, means that if at least one signification of C is contained in any of B's, therefore it is also contained in any of A's. Piaget [9] classified these as levels, and the set of them, as significant implications. A signification is all that can be said of an object, such as a description of its properties, as well as all we can observe on it. Furthermore, one implication is also all we can think of objects (sorting them, establish some kind of relationship etc.). For Piaget, all significations imply activities of the individual. These significations allow us to identify the degree of knowledge of a individual on a given domain. The following is a brief description of the implications.

• Local implication - It is local when the knowledge does not go beyond the object's observable properties in a given context. One local implication only characterizes an object. See examples in Figure 2.

• Systemic implication - It exceeds the limits of the observable. A systemic implication reveals object properties that are inferred or derived from some action on it. This implication displays relations of cause and effect without, however, revealing why it occurs. These relationships occur in general by the establishment of a relation of the object under study with some other object. Examples in Figure 3.

• Structural implication - It extends the previous implications because it contains the reasons, and explanations. Piaget speaks of endogenous understanding of the reasons and in the discovery of the relationships needed to occur the effect [9]. Thus, more than knowledge of causes and consequences the structural implications establish conditions which are essential to certain statements, distinguishing them from those that are just sufficient. The sum of the actions of several objects relating to the occurrence of another action establishes the existence of the implications of this new level. Examples in Figure 4. The concept map of Figure 1 illustrates the evolutive difference in the levels of the significant implications.

IV. THE IMPLICATIO NS A ND THE MAPS

It is important to mention that for the construction of concept maps we assume, that when we are choosing a relationship between two concepts we are doing, ultimately, a significant implication as suggested in [I]. This equivalence led us to adapt the characterization of the levels of the significant implications for analyzing the systems represented in concept maps. Because of this, we chose sets of verbs that best represented the implications.

For building a concept map representing each of the levels presented in the previous section we will show, as an example, concept maps produced by a master's student in computer science. The first map is shown in Figure 2.

The maps are a conceptual representation of his dissertation. Their conceptual nature approaches them to the requirements analysis of a system. Minor modifications to the sequence of maps displayed here were necessary in order for each map to represent one significant implication.

Figure 1 - The significant implications and their conceptual relations.

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One can note how complex the map is, even when showing very simple relationships. Having a class of about 30 students, a teacher would have the task of identifying the cognitive profile of each student. He/she would have to identify on the maps the three levels of implications. This would be very cumbersome and time consuming. And, unfortunately, the teacher can still have many other activities such as:

• Organizing students per group of knowledge. • Suggesting readings that are out of the ordinary for each

group. • Joining advanced groups with other less advanced. • Establishing an average of knowledge of the class by

comparing results at different times. • Creating new maps.

In the following, we present a tool that can help both the teacher and the student.

v. THE TOOL

Based on the characteristics of the situations previously described, the architecture of the tool would consist of some databases, one knowledge base, and a software agent that uses these deposits. The agent is also responsible for interfacing with the users. An ontology of a domain chosen by the teacher guides the formation of concepts. The databases contain maps with their implications properly marked, and groups with their suggested readings. The knowledge base contains the significant implications with their features.

A. A preliminary requirements specification We present here an initial specification only to guide the

construction of the prototype. The prototype, in turn, will lead to a better specification. Our central problem in this activity consists of identifying patterns in existing concept maps capable of providing the necessary conditions for matching the knowledge base with the elements of the maps. We also want to emphasize that the patterns presented here are strictly temporary and, therefore, subject to much future research.

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A concept map can be described as a well-known data structure called graph. A graph consists of vertices and edges labeled by strings. The vertices correspond to the concepts while edges denote the relationship between a pair of concepts.

The main idea presented here is to develop a tool to navigate the graph edges. The navigation is performed by a software agent that is guided with the aid of two bases. One contains a domain ontology for identifying the nature of the vertices. Another base contains the knowledge about the significant implications for the identification of edges.

For the pattern matching edge-implication we will use two different ways. For local and systemic implications we will use only the verb contained in the sentence designating the edge. Local implications will be identified by the occurrence of the verbs "be" or any other indicating possession or

composition, such as "have", "compose" etc. Systemic implications will be identified by any transitive verb.

The structural implications, on the other hand, will be identified by any subset consisting of edges and vertices whose combination results in explaining a cause-effect relationship (Section 3). Here the structural implications will be identified by the occurrence of verbs which require a preposition. Once the implication is identified, the agent will paint it with a certain color: yellow for local, green for the systemic, and blue for the structural.

The tool will be available both for students and teachers. Students may access it at any time to see how good the quality of their maps is. For example, if just yellow, showing only local implications, that suggests the student proceed to the next level. Once saved and finally sent to the teacher, the map will be temporarily unavailable for the student. Figure 5 illustrates the architecture resulting from the specification. However, it is in its preliminary version where the relationships between its elements are not yet well constituted.

B. The Functioning of the Tool In the following, we describe all the activities and steps

involved in the functioning of the architecture proposed in this paper.

1) Students deposit their maps of the domain previously created by the teacher.

2) The software agent searches the maps in the corresponding base. For a map, each link is analyzed according to the following actions: a) Stop words are eliminated, since they are negligible for the retrieval of information, b) the agent checks if the verb of the connecting sentence matches some of the verbs of the base of significant implications.

3) The concepts are identified in the ontology by a recognizer. This recognizer is a dictionary automatically populated with concepts of the ontology. Words which are not concepts of the ontology are stemmed.

4) The map is then colored by the agent and deposited in the map and implication database. Figure 6 shows the expected result of the action of the tool.

Whenever the state of the maps with implications database changes, a message is sent to the teacher. He/She accesses the readings database and gives to each map the corresponding reading. The readings with their respective working groups are then stored in a groups with readings database. Messages are sent to the groups.

C. The Prototype We created a prototypical version of our future tool only

partially to illustrate the functionality we desire. The prototype takes as input a set of propositions extracted from concept maps constructed by our students. Figure 6 shows part of the code of the prototype with results. In the future, these propositions will be obtained through software agents. The output shows the implications to which propositions belong. The prototype was developed in Java. The experiment was conducted in a master degree class in computer science

researching the question "What's an Environment for ClickA<;ao, a chatterbot?".

The results obtained are just the same as the ones shown in Figure 7. However, the tool is presently in its first version and

not yet able to identify the whole set of the significant implications. The fmal tool will be one of the results of a master thesis in computer science.

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I. PRELIMINARY CONCLUSIONS

The main idea is the use of concept maps as support to record students' understanding of a particular domain. The project proposes a form of automatic identification of the significant implications defined by Piaget in a map. This identification is made by using a family of software agents guided by an ontology of the relevant domain.

We are using concept maps for carrying several disciplines in computer science, where the analysis of maps based on Piagetian theory is being considered. Some related results are presented in this article. A prototype of the proposed environment is under development and will be used to support the analysis of the maps produced. This project is still in its first year, so the results are still preliminary. However, some experimental results have demonstrated its viability.

[t is very well known that concept maps are good graphic representations for monitoring the learning process. Besides, they are a useful tool for knowledge construction. We dare to add that binding concept maps with piagetian implications creates a favorable context to learning. That becomes even more evident when the maps are automatically painted with the colors of the implications. This is valid not only for the teacher but also for the student. Like in a game, the implications establish the need of changing level, i.e., the need of going deeper into the domain. Immediately after seeing the map colored by the tool the teacher can see at which level the student is. Teaching becomes much easier than before, for the benefit of education.

So far, only a few computational tools have been developed to support constructivist teaching. We believe that the one presented here, which is based on Piaget's learning theory, is an important step in facilitating the effective monitoring of the learning process.

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AKNOWLEDGMENT

We thank Marta T. Amorim, a former student of Master of Computing, for

helping in the programming of the prototype in Java.