Building and Assessing Intelligent Tutoring ... - Semantic Scholar

Building and Assessing Intelligent Tutoring Systems with an e-Learning 2.0 Authoring System M.L. Barrón-Estrada1, Ramón Zatarain-Cabada1, Rosalío Zatarain-Cabada1, Hector Barbosa-León2, and Carlos A. Reyes-García3 1

Instituto Tecnológico de Culiacán, Juan de Dios Bátiz s/n, Col. Guadalupe, Culiacán Sinaloa, 80220, México 2 Instituto Tecnológico de Colima, Calle Av. Tecnológico #1, Villa Alvarez, Colima, 28976, México 3 Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE) Luis Enrique Erro No. 1, Sta. Ma. Tonanzintla, Puebla, 72840, México {rzatarain,lbarron,rcabada}@itculiacan.edu.mx, [email protected], [email protected]

Abstract. Knowledge Societies also named Social Learning Networks (SLN) allow interaction and collaboration between individuals (instructors and students), who share their connections under a scheme of learning communities around common learning interest. In this paper, we present Zamná, a Knowledge Society implemented as an adaptive learning social network. A community of Instructors and Learners can create, display, share and assess communities, intelligent tutoring systems or adaptive courses in a collaborative environment. The communities and courses are tailored to the student's learning style according to the learning style model of Felder-Silverman. The identification of community’s and student's learning style is performed using self-organizing maps. The main contribution of this paper lies at the integration of Artificial Intelligence with SLN. Keywords: Adaptive mobile learning, Social learning networks, Authoring tools, Learning Styles.

1 Introduction Social networks sites (SNS) have become a social revolution within web communities, especially young users. They are specifically used by user communities for exchanging and sharing various components such as videos, news, pictures and more. Within these applications, we find that in the area of education SNS have also become a success. Social Learning Networks (SLN) allow interaction and collaboration between individuals (instructors and students), who share their connections under a scheme of communities around common learning interest. The main benefit of using this new approach in education is collecting the information and ideas to create the store of the courses from the whole community of users (including students and instructors). Another benefit is taking advantage of technologies (wikis, blogs and social networking) that young learners are using in their spare time. A. Kuri-Morales and G. Simari (Eds.): IBERAMIA 2010, LNAI 6433, pp. 1–9, 2010. © Springer-Verlag Berlin Heidelberg 2010

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M.L. Barrón-Estrada et al.

LearnHub, Wiziq, and LectureShare are examples of SLN or e-Learning 2.0 applications (e-Learning software which applies Web 2.0 technologies like social networks, blogging, or wikis) [1]. These SLN provide an online education site for instructors and learners of all kinds. These users can create communities, share courses and lessons, have discussions, make quizzes, etc. However, learning material (courses, lessons, quizzes or tests) authored and used by the users, does not provide direct customized or intelligent instructions to the learners. In this paper, we propose a new method to create adaptive and intelligent learning material in a context of a SLN. The method implements and uses a Kohonen Neural Network, trained with a set of courses with different learning styles and later used in the learning network or in mobile devices. The paper’s organization is as follows: In Section 2 we give a comparison to related work. In section 3 we present the main structure of the social network Zamná. Results and Discussion are shown in Section 4. Conclusions and future work are discussed in Section 5.

2 Related Work The boom of social networks within the Web has led to the emergence of social networking sites aimed at the education field. Ivanova in [2] presents an analysis of seven systems of e-learning 2.0: EctoLearning, Edu 2.0, 2.0 eLearningCommunity, LearnHub, LectureShare, Nfomedia, and Tutorom(Eduslide). Some of them can be commonly identified as social learning networks according to their characteristics. EctoLearning hosts libraries of knowledge that can be explored, imported, traded, qualified, modified or shared. In Edu 2.0 we can simulate the operation of an educational institution through the functionality provided by the system. The use of the system starts by recording a school through the website, thus generating an online portal of the institution where teachers, students and parents can have access. The site allows teachers to create online classes and students to join them. Within each class the teacher creates lessons that can make use of images, video and audio. It also has the ability to assess and monitor the tasks assigned to students. WiZiQ is completely free online software, which allows users to interact through text, audio and video with other participants in a shared virtual space. The space contains a whiteboard with powerful drawing tools, where users move between different boards and download PowerPoint presentations to show one or more users. All these systems have multiple functionalities to enable an author to create, deploy, share and evaluate different types of learning objects. However, learning objects deployed to teach and evaluate all students are the same. This means that there is no recognition of individuality in the way students learn and are evaluated. Related work for adapting learning styles have been implemented using different approaches like Bayesian networks, decision trees, or Hidden Markov Models [3, 4, 5]. In those works, learning styles are calculated based only on the ILSQ questionnaire and none of them are authoring tools.

Building and Assessing Intelligent Tutoring Systems

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3 The SLN Structure Figure 1 illustrates the structural design of Zamná. A user enters the site (Zamná) using any browser. Then, the user can create courses, communities and lessons, upload documents, make contacts (friends, etc.). The user can build adaptive courses (intelligent tutoring systems) for students who learn under different learning styles according to the Felder-Silverman Learning Styles Model [6]. We can also observe in figure 1 that a course created with Zamná can be exported to a mobile device or browsed on the site. A community or knowledge society contains, among other intelligent tutors, lessons and educational materials used by this community. For example, a knowledge society of Compilers contains all the learning materials that users of this society upload, create, share and study. The courses and communities contain sections of assessments, which are also carried out according to the best student's learning style.

Fig. 1. Zamná Structural Design

With respect of the society evaluation, the goal is to increase their knowledge in a particular area of study by positioning them in a new and higher level. The current method to gather information from a community is through the publication of individual tests with adaptive characteristics for each student. This instrument is an objective and formative test to give feedback to the individual and evaluate the

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group´s outcome. We use the user´s learning style identified in Zamná to define the first adaptation process. We use also adaptation techniques and methods to construct adaptive tests by grouping items according the associated learning material. To define the adaptive test we developed multiple versions of a single question which present alternative multimedia material (i.e. audio, video or text) according to the users’ learning style identified previously in Zamná. Also we categorize those items according a complexity level to perform a second adaptation process. Once the student accomplished a learning activity in Zamná he/she performs a formative test to measure the acquired knowledge and contribute to the group´s outcomes. Figure 2 shows different sections of the Zamná Environment. In clockwise order we observe 4 different interfaces for creating a community, uploading a course, showing a quiz, and browsing a course.

Fig. 2. Zamná Environment

3.1 The Intelligent Module The Intelligent Module consists of a predictive engine used to identify dynamically the student´s learning style whenever a tutoring system is running. At the beginning an interpreter selects content (learning objects) based upon the student´s learning style obtained from the student profile. The learning style of a student can be modified according to evaluations applied to the student. The process of constructing a new tutoring system (an adaptive or intelligent system) consists of three main steps. During Step 1 a tree structure of the adaptive or intelligent tutoring system is designed by the authors of the learning material. On the tree structure, the designer


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also inserts quizzes. Quizzes are an important element to provide adaptation capabilities to the produced tutors. In step 2 the tree structure is filled with domain contents (a knowledge base), and some other learning resources. At the beginning of the creation the author creates the tutoring system by inserting different learning objects like text fragments, images, audio/video clips, and by defining learning styles, prerequisites, tags and quizzes. At a later time, learning resources are added to the tutoring system by learners, who recommend resources they find commonly on the web. Those resources can be found also in a special resource repository. After the author creates the tutoring system, she/he can save it and export it to be browsed or displayed in the Learning site or in mobile devices (step 3). The saved/exported file will enclose three elements: an XML file corresponding to the learning resources or contents, a predictive engine for navigation purposes, and a Kohonen Neural Network [7] for learning style classification. The predictive engine dynamically identifies the student´s learning style whenever he/she is running the tutoring system. At the beginning, an interpreter selects content (learning objects) based upon the student´s learning style, obtained from the student profile. The learning style can be modified according to evaluations applied to the student. The engine makes use of an Artificial Neural Network, a Self-Organizing Map or SOM, for selecting student´s learning styles. We decided to use a SOM because it is an unsupervised network, and this allows us to avoid the support of experts in education to build a desired output for each combination of learning styles. The input layer of the neural network has 7 neurons. The Kohonen layer in the SOM has 1600 neurons, organized in a lattice with hexagonal cells with dimensions of 40x40 neurons. The signals are part of the training data space, and they are vectors composed of three elements: two vectors and a scalar. The first vector is the student's learning style obtained from a test: The Questionnaire Learning Styles Inventory of Felder-Solomon [8] applied to a group of high school students. The second vector is the learning style with which we designed the learning material studied by the group of students. The last element (a scalar) is the performance of the student who has a learning style obtained from the Felder-Solomon test and is studying a course designed with one of the possible learning styles. We created learning material for three different subjects or courses: photography, eolic energy and introduction to computers. For each of these courses we created 8 different versions. Each version was modeled using a different learning style. We implemented 3 dimensions of the Felder-Silverman model, with a result of 8 different learning styles (visual-verbal, sensitive-intuitive and sequential-global dimensions). Once the neural network is successfully trained, it may be used to identify learning styles of students. 3.2 Editing the Knowledge Base for a Tutoring System We apply the Knowledge Space Theory [9]. This theory gives a sound foundation for representing the knowledge domain for adaptive (intelligent) learning. An adaptive assessment based upon knowledge spaces and student prerequisites (and employing a Kohonen neural network for identifying learning styles), will derive a particular or personalized path of learning objects. For each topic a set of prerequisites and quizzes are set. Figure 3 shows the knowledge domain of the topic parsing or syntactic analysis for a compiler construction course. A Dashed line represents a personalized path.

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Fig. 3. Knowledge Domain for the parsing topic

3.3 Assessment Module for Students and Knowledge Societies (Communities) In figure 3, we can observe two evaluation elements included in the course of compilers. The results of these assessments applied to each student will help the neural network to dynamically assign the best student's learning style. Assessments are also made according to learning style. So that the student can study, learn and be evaluated according to a learning style. Moreover in addition to the individual questionnaires in Zamná, the tool also allows you to design and develop community-adaptive tests or assessments [10] to enable members of a society or community to measure their knowledge in a particular topic, encouraging self-learning and growth throughout a community identified as a "knowledge society." The community assessment is currently implemented with only the scale of input (visual/verbal). Each test to the community is based on only two different learning styles. The test result allows us to decide whether the community learns more by studying verbal or visual learning material. We use also the SOM neural network to change the learning style of the whole community. Next, we present the algorithm to identify the student´s learning style.


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I. The SOM receive D = [e r] as data input, where e is the learning style of the learning material presented by the student in Zamná, and r is the current score of the student which has a predefined learning style with the FelderSolomon test. In the case of a community assessment e only takes the scale of input (visual/verbal). II. A search for the BMU (best matching unit) that contains a weight vector W = [E r e] where E is the Felder-Solomon student´s learning style in the space of the Kohonen network is made, to find the neuron that best matches the input vector. III. The network returns the new student´s or community´s learning style E. IV. The algorithm repeats steps I, II, and III whenever a test to a student or to the community is performed, and it is done when evaluation has ended.

Fig. 4. Example of a Community (System Programming)

4 Results and Discussions The tool has been tested developing communities, courses, etc. Figure 4 shows the community "systems programming" (programación de sistemas) which includes members (students and teachers), intelligent tutors, resources used (software, documents, data files, etc.). Zamná has been used to build intelligent tutors on issues such as basic mathematics, the Maya language, compiler construction, etc. One of the features of the website of the tool is that when viewing an intelligent tutor, you can also display a log of the learning styles since the student has taken the

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Fig. 5. An Intelligent System (left) and the Learning Style Log (right) of a Student taking the Course

Felder-Solomon test, passing through the different styles the neural network calculates. Figure 5 illustrate part of a Tutoring System (left) and the graphic for the learning styles the system calculates (right).

5 Conclusions and Future Work The design of the graphical user interface (presentation layer) was developed using Adobe Flex technology. The implementation of the classes (business layer) used in the interface was developed with the PHP programming language. The data transmission between the two layers is through standard XML markup language. The web server is Apache and the Database Management System is MySQL. We can reach the SLN Zamná in http://www.posgradoitc.edu.mx/zamná/. We have plans to add new components to the editor, for example we want to add the dimension of processing (active / reflective), which requires components with


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focus on individual work (active) or group (reflective). For example we want to add forums, blogs, chats, etc. With them we will strengthen the reflective learning. Acknowledgments. The work described in this paper is fully supported by a grant from the DGEST (Dirección General de Educación Superior Tecnológica) in México.

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