A Novel Model for Web-Based Adaptive Educational Hypermedia Systems: SAHM (Supervised Adaptive Hypermedia Model) H. TOLGA KAHRAMAN,1 SEREF SAGIROGLU,2 I˙LHAMI COLAK3 1
Faculty of Technology, Department of Computer Education, Karadeniz Technical University, Trabzon, Turkey
2
Faculty of Engineering, Department of Computer Engineering, Gazi University, 06570 Maltepe, Ankara, Turkey
3
Faculty of Technology, Department of Electrical Education, Gazi University, 06500 Teknikokullar, Ankara, Turkey
Received 22 August 2009; accepted 14 March 2010 ABSTRACT: Existing adaptive educational hypermedia systems focus on preparing adaptive educational environments for students and educators meeting their needs. In an educational adaptive hypermedia (AH) application, important problems faced by application developers (educators) are to create and update the domain model accurately. This study presents a new reference model and its concept for web-based adaptive educational hypermedia called SAHM (supervised adaptive hypermedia model). The new model redefines the storage layer of existing AH reference models and helps to solve the problems encountered in AH applications. With the help of this proposed model, application developers might develop a domain model of AH applications easily, effectively, and successfully. It is expected that the new SAHM would provide a new direction in designing new and more adaptive systems and new applications. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 60–74, 2013; View this article online at wileyonlinelibrary.com; DOI 10.1002/cae.20451 Keywords: educational hypermedia; web-based information extraction; decision support model
INTRODUCTION In an adaptive educational hypermedia (AEH) application, the most important problem faced by specialists having less experiences and or knowledge about the technology or instructional content of AEH in creating the domain models (DMs) [1–4]. Success of AEH applications basically depends on two states [5–12]. The first state is to cover creating a successful adaptive educational environment for students. The second state is to offer better tools improving the capability level for application developers. The applications developed in AEH technology basically were composed of three models including DM, user model (UM), and adaptation model (AM) [2,3,9]. DM represents a model for course contents. UM represents knowledge Correspondence to H. T. Kahraman (
[email protected]). © 2010 Wiley Periodicals, Inc.
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level, learning style, preference, and development of a user within UM individually. An AM prepares adaptive course content for its users depending on DM, UM, and specific adaptation rules. Recent studies have focused on educational technology for determination of standards and common sides of the applications developed by adaptive educational hypermedia system (AEHS) technology [13–16]. The applications developed by AEHS technology do not become sufficiently widespread because of the difficulties faced for developing applications in AEHS [1,3,5,6]. For a smooth design, it is necessary to become an expert on the subject in order to design DM in existing AEH models. It is reported that application developers having no experience on the subject might face difficulties at DM design [1,6]. Developing a DM up to an AEH application requires the processes, which are [1–3][12] as follows: • defining objects and relations among the objects in DM, • determining teaching order of the objects intended for an instructional goal,
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• defining features of each object (difficulty level, time, etc.), • preparing materials belonging to the objects in different presentation formats and levels, and • preparing and defining of examinations for the domain objects suitable for different instructional goals and users at different knowledge levels.
As a result of these processes, a model that might help the application developers on the design of DM in AEH applications is always required. Reference models such as Dexter [17], AHAM [18], LAOS [6,8], and Munich [19] have been developed to define the basic components of hypermedia applications. These models are basic models and deal with features, functions of the basic components, their interactions with each other, and the way to be designed. The common features of these models are to define “UM,” “DM,” and “AM” in AEHS. So the structure of these existing models helps students to learn easily and effectively and application developers to offer an authoring tool, respectively [20]. However, the studies in the literature report that models and tools require to simplify the tasks of application developers in AEH systems [5,6]. The other studies in the literature support to develop authoring tools for content management system in the computer-aided education [1,4,6,8]. Several tools were introduced to AEHS such as AHA! [21] (an implementation of AHAM), MOT [6] (an implementation of LAOS), and SmexWeb [22] (an implementation of Munich). Developing DM and AM is essential in the implementations. However, success of application developer over DM development process is still one of the most important factors in achieving better performance for AEHS applications [5,6,23]. Up to application developers’ experiences
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in designing DM of AEH, some applications might fail or enable less performance due to ill-defined features and interrelations of the objects in DM. Besides, the materials, tests, and samples (learning resources in general) used for teaching the objects may not be found satisfactory or suitable. In such a case, the designed DM and its materials require to be corrected and improved. In this study, in order to overcome the problems faced in designing DMs, a supervisor adaptive hypermedia model (SAHM) was designed and introduced. The SAHM has a number of significant differences among the other existing models and represents the required data for testing and analyzing the properties of objects in DM. This article is organized as follows: shortcomings in reference models for AEHSs are summarized in the second section. Proposed method is introduced in the third section, in details having motivations, the conceptual definition of supervisor model (SM), structure of SM, achieving features in SM, determination of the relation degrees among the objects, determination of the suitability degree of materials, determination the suitability, and complexity degrees of the exams. Comparisons and conclusions are summarized in the fourth and fifth sections, respectively.
SHORTCOMINGS IN REFERENCE MODELS FOR AEHSS Dexter model was the first model to define the basic principles for the development of HyperText systems as given in Figure 1a [17]. Basically the model defines the hypertext systems as in
Figure 1 Comparison for reference models. (a) DEXTER model [13]; (b) AHAM reference model [14]; (c) Munich model [15]; (d) LAOS model [6]; and (e) proposed SAHM reference model.
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three layers. Run-time layer is responsible for viewing interaction of the users with system and submitting the materials that are specially prepared according to the user. Presentation specification sets up direct interaction between UM layers. It is responsible for preparing and submitting the materials to the run-time layer. Storage layer stores links that form the basic content in the hypertext network. It does not support the model in terms of any structure for the materials. Anchoring stores locations information of the materials. It is a bridge between the storage layer and the within component layer. Within component layer concerns with the contents and the structure of hypertext network. The focus of the model is the “storage layer.” This layer defines the structure of DM in hypertext applications [16]. In Figure 1b AHAM and in Figure 1c Munich reference models were developed in order to give adaptation property to hypermedia applications. Both models are extensions of the Dexter model and have also a three-layered structure. In the AHAM and Munich models DM, UM, and AM are defined in storage layer different from Dexter. The major difference between AHAM and Munich models is that “The Munich model takes an object-oriented software engineering point of view whereas AHAM takes more a database point of view” and Munich reference model is formalized using unified modeling language (UML) that provides to develop complex object-oriented applications and visualize representation of the techniques [19]. AHA! [21] was an hypermedia system of which AHAM was applied to. Application developers can develop DM and AM using the authoring tools of AHA! system, and they can also define various pedagogic rules [21]. SmexWeb was an AH system which is developed on the basis of Munich reference model [22]. In Figure 1d LAOS model was introduced to develop DM in a more flexible way and also to improve the AM by pedagogic rules. LAOS model was developed on the basis of AHAM reference model. Distinctly, “the models for goal and constraints and presentation model” have been defined in LAOS. LAOS separates information and presentation goalrelated connectivity. Goals are defined in the “Goal and Constraints Model.” This provides reuse of information in DM [6]. In MOT system, LAOS was tested. The application developers can develop DM, AM, and goal model belonging to AEH applications. As it was for LAOS, CAM model enabling the distribution of adaptation over multiple layers was developed on the basis of AHAM and LAOS models [5]. Defining the relationships among the concepts at arbitrary layers in DM of CAM, a more generic model is recommended. CAM model was applied to GRAPPLE Project [23]. The common property of the developed models is to define DM, AM, and UM in an AEH environment. The subject in progress is the definition of the domain objects in a more flexible way by supporting the various pedagogic strategies. Even if these models provide important contributions to the literature, they did not pay attention to some important issues such as how defined objects for achieving an instructional goal is not tested by AEH models if they are truly related to the educational goal. Similarly, mechanisms enabling the measurement of the effectiveness and correctness of the materials used for teaching of domain objects in AEH models were not properly developed in consideration with instructional goals and meeting the needs of the students. Suitability of the examinations which are used as a basic resource for the creation of UM in educational AEH applications should be evaluated by considering the instructional goals and the users with different knowledge levels [2,3,9,24,25]. However, some studies have been made by using data mining techniques on data saved in user log files in order to determine
whether the instructional objects of domain meet the needs of the users [26]. In another study [15], an advising system to help domain edit and increase its efficiency has been developed for traditional and adaptive education systems. The recommendations of expert teachers who developed applications for the course content are the main sources for the improvement of the course content. The expert teachers in this field examine the content by participating in the web application and make various recommendations. Depending on these recommendations the content and the levels of the materials can be edited by the application developer. This article introduces a new and advanced approach to help application developers and to overcome the problems pointed and provides more flexible environment for developing, reorganizing, and improving DM.
PROPOSED MODEL: SUPERVISOR ADAPTIVE HYPERMEDIA MODEL (SAHM) The proposed model is given in Figure 1e. As can be seen in Figure 1e, this model has a three-layered structure including “session layer,” “storage layer,” and “material layer.” This model is an extended version of AHAM as given in Figure 1b. In Figure 1e, session layer offers an interface for developers to manage the application and to analyze the user’s DMs. Besides, it is responsible for displaying interactions among the users with the help of the system. It records session objects such as click count, visited time, and navigational features for each page. The session objects are defined to take into account the user’s information, which is required for the user modeling processes. In SAHM, users have uniquely session identities. Management/presentation specification defines relations between “session” and “storage” layers. It also defines behaviors of AEHS in points of students and administrators. Storage Layer consists of four models. These are DM, UM, SM, and AM. Anchoring stores locations information of the materials. It is a bridge between the storage layer and the material layer. Material layer stores materials belonging to content objects in the defined DM. The focus of SAHM is SM of which is defined in the storage layer of SAHM as given in Figure 1b. Therefore, SM is detailed in the Structure of Supervisor Model (SM) and Achieving Features in Supervisor Model Sections. SM interacts with UM and DM. The function of SM is to store the data that are going to help application developers about DM. For this reason, various data regarding the objects of DM are collected on-line in the web-based AEH application. The resources of these collected data are the questionnaires and the UMs. The gathered data can be then evaluated with the help of the rulebased inference mechanism and/or machine learning algorithms [2,3,25,27]. As a result of the evaluation, significant decisions can be made by the application developer for the designed DM. The decisions are about the defined objects in DM of the application developer’s AEH application. They are to determine: • the relation degrees of the objects related to any instructional goal, • the sufficiency and suitability of materials belonging to any object in point of instructional goals and providing the needs of the users with different knowledge levels, • the degree of complexity and suitability of the examinations belonging to any object in point of instructional goals and providing the needs of the users with different knowledge levels.
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It is possible to improve and adjust DM by delivering the evaluated data for the application developer. Therefore, SM is also supposed to help eliminate the obligation of being a specialist in knowledge DM for developing AEH application. Moreover, SAHM model presented in this article helps to develop applications in all levels from beginners to experts. In the following subsections, theoretical and conceptual definitions of SM are discussed. Finally, the structure of SM is given and the properties taken into considerations during the design process are expressed in details in the following subsections. The evaluation of the data stored in SM is the subject of another article. Therefore, the evaluation process of the data stored in SM is not detailed in this article.
Motivations The motivations for the SAHM model are explained as follows: (i) In DM design, requiring an expert in knowledge domain and AH technology is not necessary in AEH applications. As long as the application developers have enough assistance, they can then develop DM although having a minimum level of knowledge (with a particular foreknowledge) about the knowledge domain. (ii) An AEH application must gather data about the objects of DM in order to provide the needed assistance for the application developer. The collected data in AEH application about the objects of DM, their relationships with each other, the effectiveness and suitability level of the materials are used in learning. (iii) The resources of these data are UMs and the questionnaires. These data should be permanently stored in a model (in order to be processed by an intelligent system and to make inference about DM). (iv) An intelligent system can advice for these objects, their relationships, the effectiveness, and the suitability of their contents by processing the collected data about the objects of DM, in various rule-based inference mechanisms and/or machine-learning algorithms [25,27]. In next generation AEH applications, the development and improvement of DM might help to realize an intelligent application automatically.
An ill-defined or not well-organized DM by application developer at the design time can be improved at the run-time by means of data collected by the application and the processing of that data by an intelligent application. SM is such a model that various data concerning the objects of DM are permanently stored. An intelligent application can make useful evaluations about the structure and the objects of DM by processing the data stored or captured in SM and UM through machine-learning algorithms. Thus, non-specialists and inexperienced developers who do not have enough information about the knowledge domain and the technology of educational AH can be assisted and advised in order to develop DM.
Conceptual Definition of Supervisor Model An important issue of AEH applications is to design DM. The objects of DM, various features of these objects, and the relations among these objects are represented in DM [3,5,12,24]. The accuracy and effectiveness of a DM depend on the expertise of the application developer and the learning resources. In other words, DM of AEH applications should be developed by developers who are specialized in that area [5]. This situation is an obligation
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implied by existing AEH models. However, an AEH application developer may not be an expert and/or experienced enough in designing DM of AEH. Namely, the application developer may not have well-defined features and interrelations of the objects in the designed DM [6]. Besides, the materials, tests, and samples (learning resources in general) used for teaching the objects may not be satisfactory or suitable. In such a case, the designed DM and its materials require to be corrected and improved. DM is defined on the basis of object-oriented schema in SAHM. Depending on the object-oriented definition, the objects with same features are derived from the same classes. Classes define the features and the functions (methods) of the objects that have the same functions and features in DM. In SAHM model, the objects of DM can be achieved from three different classes including goals, topics, and concepts. These three classes are derived from the element base class. The element class is the base class of defining the common features and methods of goal, topic, and concept classes. When a different class rather than these three classes is required in the domain, a new class from element base class must then be derived and the different features and functions must be defined inside that class. Thus, re-use of the code and the extendibility of the application in SAHM are provided. The structure of DM in SAHM is independent from the application domain. DM is developed by application developers. First of all, the application developers define the goal objects in SAHM. Other goal and/or topic objects related to these objects are then defined if it is appropriate. In addition to these, the topic and concept objects of DM are also defined by the application developer. The topic objects are the subjects that must be represented in DM in order to prepare the users for the goals. Likewise, the concepts related to these subjects are defined by the application developers. To make corrections and improvements in DM designed systems that will evaluate the accuracy and effectiveness of DM should be developed. This process is similar to the creation of UMs in AEH applications. An UM is assigned to each user in the user modeling process of AEH application. Various data concerning the user are gathered depending on the interaction of the user with the application [2,28]. These gathered data are for determining the user’s knowledge level, preferences, and learning style about the objects of the domain [9,11,27]. The collected data are permanently stored for using in future sessions. UMs are updated by processing the collected data by an inference mechanism and/or machine-learning algorithms [9,10,24]. Therefore, users and DM are the resources in the process of creation and updating of UMs [25]. Similar to the process of the constructions and updating of UMs, DM can also be constructed and updated in AEH applications. The construction of DM is fulfilled at the design time by the application developer. As for SAHM, the application developer can benefit from the advices and information offered by an intelligent application for updating (correcting and/or improving) DM. At that stage, the intelligent application needs the data about the objects of DM in order to perform its task. These data are stored in SM and UM of SAHM model. The data stored are concerned with: the features of each object in the domain, its relations with the other objects, and the effectiveness and the suitability of the materials used for the teaching of that object. These data can be obtained from the users by receiving feedbacks (with questionnaires) and tracking the user actions and data stored in the users’ models. The evaluation is achieved by an intelligent approach, and a decision can be made about the object, the relations
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Figure 2
Structure of the supervisor model [16].
of the object, and the learning resources used for the teaching of the object (materials, examinations, etc.). During the evaluation process, the evaluation rules which the intelligent application will depend on are determined by the experts or educators. An intelligent application helps to make improvements and regulations on the domain by presenting the evaluation results to the educators. The content of the data basically stored in SM and the structure of the model are discussed in the Structure of Supervisor Model (SM) Section.
Table 1 Copied and Newly Defined Features of Objects in SM Basic features of element base class for goal, topic, and concept classes Features copied from DM to SM For each object in DM: object identity, related objects in the same class, related objects in the different classes, types of supported presentations, identities of learning resources Features newly defined in SM For each object in SM: relation degrees with other objects in the same class, relation degrees with objects in the different classes, degree of suitability of materials belonging to object, degree of complexity of exams belonging to object
Structure of Supervisor Model (SM) The structure of SM is illustrated in Figure 2. The structure of SM is similar to the structures of the DM and UM. Most of the AEH applications use the copy of DM as a resource for the construction of UM based on the overlay model approach [2,18–22][24,25]. Overlay model approach is based on the features and the relations of each domain object to be represented within SM. As can be seen in Figure 2, the structure of SM is very close to DM and UM. In this study, a copy of DM is used for constructing SM. The features and relations belonging to goal, topic, and concept objects of the domain are represented in SM. The resources of the data be stored in SM are the questionnaires filled up by the users, their preferences, knowledge status about the domain objects, their success in exams, and other actions. Therefore, SM should communicate with UM just like the “teaching (adaptation) model.” These two basic needs explain the reason that SM is defined in storage layer and its relations with other modules in the same layer. Table 1 shows the steps for basic features of objects stored in SM. Firstly, the features defined by the application developer in DM are displayed. The features are received together with the first values by copying from DM while creating SM. Secondly, the new features defined by concerning the objects within SM are shown. These features represent the relevance level of the related objects of DM, the suitability of materials used for the
learning of objects, and suitability and complexity degrees of the exams concerning the objects. First values of these features are assigned to the default values. The classes (goal, topic, and concept classes) in SM are derived from the element base class. Thus, the element base class defines the basic features of the three classes. There are more tables for each class in SM’ database layer. Generally, an application maintains these tables for each class of objects. The features for these objects are represented in the tables.
Achieving Features in Supervisor Model This section briefly presents how the data are obtained and stored in SM. According to the classes they belong to the basic features of objects stored in SM are given in Table 2. The first column in Table 2 demonstrates the common features and methods of element base class for goal, topic, and concept classes. Sources for obtaining the required data to evaluate the individual and the relational features of the objects are given in the second column in Table 2. The data used for determining the object and the relationships of the object with the other objects in SM are basically obtained from two resources. The first one is the questionnaires asked for the users to get feedback from users what object they
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Table 2 Features, Methods, Sources Required for Determining the Values of Attributes Belonging to the Objects Within SM The common features and methods of element base class for goal, topic, and concept classes The methods of element base class
Data obtained from users via questionnaires
Data obtained from UMs
Relation degrees with other objects in the same class
Calculate the relation degrees among the objects in the same class
What is the degree of relevance of this object with your goal? Is it necessary in fact?
Relation degrees with objects in the different classes
Calculate the relation degrees among the objects in the different classes
Suitability of materials used for learning the objects
Calculate the suitability of materials belonging to objects for users who have different knowledge levels
Are the contents of the materials satisfactory? Are the presentation types of the materials sufficient?
Complexity and suitability of exams belong to the objects
Calculate the complexity and suitability of exams belong to objects for users who have different knowledge levels
Are the difficulty levels of the exams suitable? Are the exams sufficient?
Knowledge levels of users about the current object Knowledge levels of users about the related objects Degrees of study time of users for the current object Degree of study time of users for the related objects The degrees of study time of users for the materials according to the presentation types Success of users in exams belong to the current object and the related objects
The features of element base class Relational features of objects
Individual features of objects
Sources for obtaining the required data to evaluate the individual and the relational features of the objects
studied. Table 2 shows the data that will be provided from the users via the questionnaire. The second one is the other data used as a resource for evaluation of the features obtained from UMs. The data obtained from UMs are given in Table 2. The basic features that will be tested for an object in SM are “relation degree,” “the effectiveness and suitability of the materials,” and “the exams in terms of users with different knowledge levels and instructional goals.” The possible values of these features under different conditions are discussed in the following sections. First of all we introduced which data should be represented in DM, UM, and SM to determine the value each of features and then we illustrated determination values of the features in SM in the following subsections. Determination of the Relation Degrees Among the Objects. At the design time, the relations among the objects are created by the application developer. These relations are pre-conditions and propagations among the objects in DM. Process of determining the relation degrees is to state whether the objects, that are the application developer associated with an object, are really related to each other or, if so, what is the relation degree. The degrees of relation were basically defined in SM at three levels including “unrelated,” “little related,” and “related.” At this stage, the data to determine objects’ relation degrees must be gathered. Table 2 shows the data that will be collected via the questionnaires and UMs. By processing these data the relation degrees among objects can be determined. Suppose that the relation degree between any object o and any object in the associated object set will be determined. Firstly, the related object set must be obtained from DM and the feedbacks (results of questionnaires) and the knowledge status of users about the set must be then obtained from UM. Representations of the
required data stored in DM, UM, and SM are given in Figure 3 and expressed as follows. Representing O as a set of objects, for each o ∈ O can be achieved objects’ set Xo relating to an object (o ) in DM network. The related object set is denoted as {Xo }o ∈ O and stored in DM as in Figure 3a: o = Xo . To determine the relation degree between the object o and any object in the set Xo the stored data in UM are used. The representation of stored data in UM is given in Figure 3b–d. Degree of relationship is a representation of relations among the objects. For each object it is represented as object id, related object id, degree relationship in SM and UM. User identity is used to acquire the user’s info such as knowledge state and feedbacks about the current object, the object’s related objects, etc. Suppose that R is a set of relation degrees about the set O and for each r ∈ R we have a users’ answer set (feedbacks for relation degree set) Xr about the set Xo in UM. Xr is obtained by the system using the questionnaire. A user can select the relationship degrees among objects in the set Xo and the object o for each o ∈ O by answering the questions in the questionnaire. The answers can be one of the three options as “unrelated,” “little related,” and “related.” The answers of users are stored in the questionnaire table of SM as in Figure 3b: user identifier o , Xo , Xr . Suppose P is a set of user knowledge levels about the set O and for each p ∈ P we have a user knowledge level set Xp about the set Xo in UM as in Figure 3c. The knowledge level of a user can be categorized at three different levels as “beginner,” “intermediate,” or “advanced.” The value of p is one of these levels and determined by the system (specifically by UM component) in the AEHS [2,3,25]. Suppose p is a knowledge level of a user about the object o . The representation of a user’s knowledge level about the set Xo and the object o’ in the tables of UM is user identifier p , Xo , Xp .
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Figure 3 (a) Object o and the related object set Xo in DM, (b) users’ feedbacks and (c,d) knowledge status about the set Xo in UM, (e) relation degrees among the o and the related object set Xo in SM.
Suppose T is a set of degrees of study time of user about the set O in DM and for each t ∈ T we have a user study time degree set Xt about the set Xo in UM. The study time degrees of users can be categorized as four different levels as “advanced,” “average,” “insufficient,” or “poor” as shown in Figure 3d. The value of t is one of these levels and it is determined by the system (by user modeling component) in the AEHS [2,3,25]. Suppose t is a user’s study time degree about the object o . The representation of a user’s study time degree about the set Xo and the object o in the tables of UM is user identifier t , Xo , Xt . Calculating Relation Degrees of Objects The calculation process is an interpretation of the obtained data from UM by AEHS. Typically the system collects data about the users’ activities, assesses the data, and then decides the value for each object of the relationship feature. Finally, it stores the value in SM as given in Figure 3e. Suppose n is a number of users considered by the system in the process of the decision-making about the relation degrees. Let y be the relation degree between the object o and any object (o) in the set Xo . Suppose number of objects as m and j the index number of any object in the set Xy[j] . Index number is used to access any object in the set Xy . The data of a user about the jth object are represented as Xy[n, j] . The relation degree (y ) between o and o can be calculated from the data obtained by UM as y (unrelated/littlerelated/related) ≡
=
m n j=1 i=1
m
Xo[j]
j=1
Xr[j,i] ,
n m j=1 i=1
Xp[j,i] ,
n m j=1 i=1
Xt[j,i]
Determination of the Suitability Degrees and Difficulty Ratings of Materials. The application developer prepares the learning sources (materials) belonging to the objects in the knowledge domain and installs them into the material layer of AEH system. These materials are prepared in different presentation forms. At that stage, additionally various questions, tests, and exams are prepared about each domain object. These prepared materials should be tested for whether they meet the users’ needs from the educational point of view or not. As so, various data should be gathered by the application from the users by means of the questionnaires and from UMs. Table 2 shows the data that will be collected from the users by means of the questionnaires and from UMs by the application. In order to determine suitability of any material content belonging to any object, firstly, the related material set must be obtained from DM and the knowledge status of users about the set must be obtained from UM. Representation of the stored data in DM, UM, and Supervisor Model is illustrated in Figure 4 and in the following. Degree of suitability of any material content belonging to any object is represented as object id, material id, degree suitability in SM and UM. Degree of suitability of any material content has three different values. These are “inexpedient,” “little expedient,” and “expedient.” Suppose M is a set of materials and for each m ∈ M we have a material set Xm that belong to an object (o ) in DM as in Figure 4a. The material belonging to o is represented as o = Xm . Suppose A is a set of feedbacks about the set Xm and for each a ∈ A we have users’ answer set (suitability degree set) Xa that is obtained by the system using the questionnaire as in Figure 4b. A user can select the material’s suitability degree for each object in the set Xm by answering to the questionnaire. The answer can be one of three options such as “inexpedient,” “little expedient,” and
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Figure 4 (a) The material set Xm belonging to the object o , (b,c) users’ feedbacks and (d,e) knowledge status about the set Xm , and (f) degree of suitability of materials’ contents.
“expedient.” The users’ feedbacks about the set Xm in the tables of SM are represented as user identifier o , Xm , Xa . Suppose D is a set of feedbacks about the Xm set and for each d ∈ D we have users’ answer set Xd (difficulty rating set) that is obtained by the system using the questionnaire as in Figure 4c. A user can select the material difficulty rates for each m ∈ M by answering the questions in the questionnaire. The answer can be one of the three options such as “lower level,” “middle level,” and “upper level.” The user’ feedback about the set Xm and in the tables of UM is represented as user identifier o , Xm , Xd . Suppose E is a set of users’ performance set in the exams and for each e ∈ E we have a users’ performance set Xe about the Xm set in UM as in Figure 4d. The performance degree of a user can be categorized at four different levels as “advanced,” “average,” “insufficient,” or “poor.” The value of e is one of these levels and is determined by the system (specifically by the user modeling component) in AEHS. A user modeling component is developed to classify users according to their knowledge levels. The classification is based on users’ logged data (visited pages, study time, etc.) and performances in the exams [2,3,25]. The users’ performance degree about the set Xm and the object o in the tables of UM is represented as user identifier o , Xm , Xe . Suppose T is a set of users’ study time degrees to materials’ pages in the domain and for each t ∈ T we have users’ study time degree set Xt about the set Xm in UM as in Figure 4e. The study time degree of a user can be categorized at four different levels as “advanced,” “average,” “insufficient,” or “poor.” The value of t is one of these levels and it is determined by the system (specifically by the user modeling component) in AEHS [25]. The users’ study time degrees about the set Xm and in the tables of UM are represented as user identifier o , Xm , Xt . Calculating Suitability Degrees and Difficulty Ratings of Materials The materials related with an object o can be obtained from DM. The suitability degree and difficulty rating of materials about an object o can be calculated using stored data in tables of
UMs and can be stored in SM as given in Figure 4f. The calculation process is an interpretation of the obtained data by AEHS. Suppose n is a number of users gave a feedback to the system through the questionnaire about the set Xm that belong to an object (o ) in DM. ai is the ith user answers about the set Xm and represents suitability degree of materials for the ith user. Similarly di is ith user answer about the set Xm and represents difficulty rating of materials for ith user. The suitability degree (s ) between the set Xm and o can be calculated from the data obtained by UM as s (expedient/littleexpedient/inexpedient) =
n i=1
Xa[i] ,
n i=1
Xd[i] ,
n i=1
Xe[i] ,
n
Xt[i]
i=1
The difficulty rate (d ) between the set Xm and o can be calculated from the suitability degree. Determination of the Complexity Degrees of the Exams. In educational purpose AEHSs, the main instrument for detecting the users’ knowledge levels about the objects of DM is the exams. An educational application should have suitable questions to the users who have different knowledge levels. Therefore, at the design time, the application developer prepares various exams with different difficulty and complexity levels about the objects. Determination of complexity of the exams in terms of the users and instructional goals is a significant issue. The data stored within UM provide the determination of complexity degree from the points of the users and the educational purposes. For this purpose, Table 2 shows the data that will be obtained from the questionnaires and UMs. The application developer is supported by the needed assistance for this issue by processing these data • Degree of complexity of exams belonging to any object is represented as object id, exam id, degree complexity in SM
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Figure 5 (a) Set of questions belonging to the object o in DM, (b) performance degree and degree of study time and knowledge level of a user, (c) results of questionnaires about the set Xq in UM, and (d) the degree of complexity of a question set in SM.
and UM. Degree of complexity has three different values. These are “simple,” “complex,” and “very complex.” • Suppose Q is a set of questions and for each q ∈ Q we have a question set Xq that belong to an object (o ) o = Xq in DM as in Figure 5a. • Suppose B is a set that represents performances of all users about the set Q in the exams and for each b ∈ B we have the performance set Xb of users about the set Xq in UM as in Figure 5b. The performance degree of a user can be categorized at four different levels as “advanced,” “average,” “insufficient,” or “poor.” The value of b is one of these levels and it is determined by the AEHS. • Suppose K is a set that represents knowledge levels of all users about the objects in DM and for each k ∈ K we have users’ knowledge levels Xk about the object o in UM as shown in Figure 5b. The knowledge levels of users can be categorized at four different levels as “advanced,” “average,” “insufficient,” or “poor.” The value of k is one of these levels and it is determined by the system in the AEHS.
The users’ performance degrees in the exams, study time degrees to pages, and knowledge levels about the object o in the tables of UM are represented as user identifier o , Xq , Xb , Xt , Xk . • Suppose F is a set users’ of answers about the Q set and for each f ∈ F we have users’ answer set Xf about the Xq set as given in Figure 5c. Users gradate the questions belonging to an object and give their feedbacks to the system. They can select the complexity degrees of the questions by answering to the questionnaire. The answers of the users for complexity degree can be one of the three options as “simple,” “complex,” and “very complex.” The set Xf is obtained by the system through questionnaire. The users’ feedbacks about the set Xq in the tables of SM are represented as user identifier o , Xq , Xf .
Calculating the Complexity Degrees of the Exams The questions belonging to an object o can be obtained from DM. The complexity degree of questions about an object o can be calculated
from the stored data in tables of UMs and can be stored in SM as given in Figure 5d. The calculation process is an interpretation of the obtained data by the AEHS. The complexity degree (u ) between the set Xq and o can be classified as “simple,” “complex,” and “very complex” considering the degrees of performances, the degrees of study times, the levels of knowledge, and the results of questionnaires of users u (simple/complex/verycomplex) = o , Xq , Xb , Xt , Xk , Xf
Realization of SM Figure 6 shows the collection and processing stages of the data stored within UM and SM. Figure 6 shows that data needed on the first stage for SM are obtained from UM and questionnaires. This stage is implemented by tracking and saving the user activities on session layer of SAHM. It is necessary to eliminate the useless data, which may give rise to inaccurate inferences before evaluating the collected data by means of an intelligent system. For this reason, in the second stage, useful data are obtained by applying filtering rule on the collected data. In the third stage, the feature values of the instructional objects within SM can be determined by the evaluation of the useful data through an intelligent system. In the last stage, the obtained data are saved within SM and presented to the application developer. Therefore, the features of instructional objects defined by the application developer can be tested through evaluating the feedbacks from students together with the data obtained from UMs. An example of an instruction page template for the session layer of an AEH application properly developed for SAHM is given in Figure 7. All the activities of the students are tracked and saved within the session layer. On this layer, the students select the instructional goal objects from “Select a Goal Object” box that they specifically want to be prepared for. Obtaining subjects, concepts,
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Figure 6
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The collection and processing stages of the data stored within the SM.
and exams related to the selected goal object from DM, the system places them into the area of “instructional objects related to the selected goal.” After the students click on the subject–concept links in this area, the suitable materials for the knowledge levels of the students belonging to the previously clicked instructional objects are viewed in “Materials–Exams” section and the survey questions are viewed in “Questionnaires” section. The answer given by the student to the survey questions in Figure 7 belongs to the instructional object which he/she is working on at that time. Thus, an interactive AEH platform is formed through which the students can express their thoughts about the instructional objects that they prepared for. Data Collection. Two main data sources are used in SM. The first source is UM. In AEH applications, data such as the study times of students for the subjects and concepts related to the instructional goal for which the student is currently preparing and the answers of the student for the questions are saved into the database. After evaluating these data by the user-modeling component of the system, the knowledge level of student concerning the instructional objects is determined [25]. Thus, the application can obtain the users’ knowledge states from the UMs. The data that will be obtained from the UMs are the user’s knowledge level concerning
the goal object, the object’s knowledge level concerning the related objects, the study time degrees for these objects, and the success in the exams. Secondary data sources are the questionnaires. In order the student to be prepared for an instructional goal (Fig. 7 shows the preparation for Goal 1), he/she studies the instructional objects related to this specific goal presented to him/her by the system (Fig. 7 shows the study for Topic 1). The relation degree of the current object that the student is studying for, with its goal, user id, goal id, related object id, relation degree, the efficiency of the materials belonging to this instructional object, user id, material id, degree suitability, and the complexity degree of the exams, user id, exam id, complexity degree are represented in SM as written above. In case of the sample shown in Figure 7, while the student is studying for “topic 1” related to “goal 1,” he/she can answer the survey questions related to “topic 1” at the same time and these data are saved within SM. Feedback is an important data in the educational environment [9,29]. Filtering Rules. Data obtained from the beginners that work on the application or the users that do not study the domain objects for sufficient time period and do not answer enough of the questions may result in incorrect inferences [25,29]. Therefore, data collection from the UMs and questionnaires must be limited
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Figure 7 A basic instruction page template for SAHM. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
by certain rules. Three features are taken into consideration during the filtering period. The considered features are to be the degree of study time (t), the degree of exam performance (e), the knowledge level (k) of users about an instructional object in the DM. The user data are tested according to the assessment issues given in Table 3, whether it complies with the rules defined for these three features. Data belonging to the student meeting the filtering rule in the last column of Table 3 are included in the evaluation process given in the first column of Table 3.
Intelligent Decision-Making System. An intelligent system can advice for instructional objects, their relations with other objects, the effectiveness, and the suitability of their contents by processing the obtained useful data from the filtering process concerning these objects of DM, in various machine-learning algorithms. Machine-learning algorithms are selected after being evaluated according to various criteria such as suitability for tasks of real-time process, classification/estimation/advice/filtering/ clustering, fast performance, giving acceptable results with minimum data, comprehensibleness, ability to work with labeled and unlabelled data. Clustering methods organize the unclassified samples on the basis of instructional data. k-Nearest Neighbor (k-NN), Self-Organizing Maps (SOM), Neural Networks (NN), Explanation-Based Retrieval (EBR), and Fuzzy Clustering (FC) are the algorithms used for modeling/classification tasks in AEHS [27,30–34]. The real-time updating ability of these methods, their ability to classify, their performance considering the data intensively, their learning ability with or without counseling service, and their reaction time are the basic features [27,33]. In AEHSs, the machine-learning algorithms are generally used for modeling the user data. Bayesian Network (BN) [35],
Naive Bayes Classifier (NBC) [36], and k-NN [34] are commonly used algorithms for real-time modeling tasks in AEHSs. NN requires a long-term training period in order to fulfill an efficient estimation task. FC gives effective results in applications that do not require dynamic UMs. BN and NBC fulfill the estimation and classification tasks quickly and efficiently and the last but not the least they enable real-time operation [9]. The k-NN algorithm, for detecting similar templates and clusters, SOM [30] which is a NN method without any counseling and FC clustering algorithms are commonly used in literature studies. The main data source for user modeling algorithms is the answers for the tests [2,31,34,37]. In SAHM, the most reliable information which shows the efficacy of the user data is the exam results. NBC is used in MANIC, in order to know the choices of the students, estimate where the content of the instructional objects shall be displayed and which objects shall be saved and also to predict whether the students want to see the materials [38]. One of the most common algorithms in AEHSs that is used for the task of modeling the user data is NBC. NBC produces successful results in classification problems where independent features are effective on only one output. Owing to this success of NBC, together with its practicability and comprehensibleness, NBC is used in developed decision-making mechanism for evaluating the collected data from filtering process [9,36].
NBC. NBC enables a given sample’s properties that determine its class, to be evaluated independently from each other. This situation is also known as class conditional independence [39]. Each one of the data samples is represented by a class label, which corresponds to itself. This forms the learning set of NBC. Class of a new sample state can be found after training with a small
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Table 3 Generation of the Filtering Rule Minimum values of the features of the student data required for the participation in the evaluation
Evaluation subjects Relations degrees in among the objects Degree of suitability of the materials Degree of complexity of the exams
t > insufficient
—
k > intermediate
t > average
e > insufficient
—
t > average
e > insufficient
k > intermediate
number of data sample. Due to this feature of it, NBC is also known as a supervised learning algorithm. In the education set, each data sample is denoted as a n-dimensional property vector a1 , a2 , a3 ,. . . ,an and its corresponding class label “C” is also denoted as m-dimensional. In order a new sample to be classified in NBC, the Equation (1) shows the class with highest possibility that the sample belongs to depending on the property values that define that sample. This method is called as “maximum posterior probability (MAP)” [9,36]: CMAP = argmax P(cm |a1 , a2 , a3 , ..., an )
(1)
cm ∈C
If Equation (1) is re-written according to Bayes theorem: CMAP = argmax cm ∈C
Filtering rule
P(a1 , a2 , a3 , ..., an |cm )P(cm ) P(a1 , a2 , a3 , ..., an )
= argmax P(a1 , a2 , a3 , ..., an |cm )P(cm )
(2)
If (t > insufficient and k > intermediate) If (t > average and e > insufficient) If (t > average, e > insufficient, and k > intermediate)
related to. Each of elements y1 , y2 , y3 ,. . . ,yn of relation degree set shown in Figure 3e represents the goal values whose classes (related/little related/unrelated) need to be determined for NBC. As for in Figure 3b, c, and d the features Xr , Xp , Xt which are effective over the classes of the goal values and the label values of these features are given as “related,” “little related,” “unrelated” for Xr , “advanced,” “intermediate,” “beginner” for Xp , and lastly “advanced,” “average,” “insufficient,” “poor” for Xt , respectively. NBC determines the relation degree of each object in set Xo with the object o . Table 4 shows the “conditional probability distribution for the relation degrees” that is determined by using data from the users or expert and edited by the domain expert. Table 4 is formed by using a learning set composed of 36 states. The properties of the learning samples set are assigned to the appropriate labels within the UM Xr , Xp , Xt . According to these three property values, NBC algorithm classifies the relation degree of each object in set Xo with the object o as “related,” “little related,” “unrelated.”
(3)
cm ∈C
While calculating the target value in NBC, it is assumed that each property value a1 , a2 , a3 ,. . . ,an has an independent effect from each other. Depending on that assumption, calculating the probability values separately for this class value and comparing the results, the class that target sample belongs to is calculated as follows: CNB = argmax P(cm ) cm ∈C
n
(ai |cm )
(4)
Sample Case. The relation degree yn between the object o and the object on which is in the set Xo shall be determined. The labels assigned to the properties Xr , Xp , Xt after the process step shown in Figure 6 for the object on are given in Table 5. NBC will be applied for finding the class of the target value yn for sample case “C” in Table 5. As given below, an NBC classifies the given sample state in Table 5, depending on the learning data of Table 4. In order to estimate the class (related, little related, unrelated) of the target value yn , Equation (4) is used:
i=1
CNB = Processing the Useful Information Through NBC. Figure 3a shows an object o in the DM and the set Xo that this object is
argmax cm ∈(unrelated,littlerelated,related)
P(cm )P(Xr : unrelated|cm )
× P(Xp : intermediate|cm )P(Xt : average/cm )
Table 4 Distribution of Conditional Probability for the Relation Degrees Classes of goal (relation degrees) Name of the property Xr
Xp
Xt
The frequency of goal classes
Labels of property
Related
Little related
Unrelated
Related Little related Unrelated Advanced Intermediate Beginner Advanced Average Insufficient Poor
12/22 7/22 3/22 5/22 7/22 10/22 4/22 4/22 6/22 8/22 22/36
0 5/10 5/10 5/10 3/10 2/10 3/10 3/10 3/10 1/10 10/36
0 0 4/4 2/4 2/4 0 2/4 2/4 0 0 4/36
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Table 5 Property Values for the Sample State Object on
Xr
Xp
Xt
yn
Unrelated
Intermediate
Average
?
In order to calculate the probable class and probability level of the target value yn , the probability of the properties Xr , Xp , Xt for each class must be calculated. In that case, nine probability values are calculated for three different classes of three property values Punrelated = (4/4) × (2/4) × (2/4) × (4/36) = 0.0277 Plittlerelated = (5/10) × (3/10) × (3/10) × (10/36) = 0.0125 Prelated = (3/22) × (7/22) × (4/22) × (22/36) = 0.0048 If the max value of probability value is normalized: (0.0277)/(0.0277 + 0.0125 + 0.0048) = 0.6155. According to the calculation above, the class of the object for the target value yn is “unrelated.” The system submits these data to the application developer as “the relation degree between the object on and the object o is unrelated.” The degrees of suitability and difficulty rates of the materials given in Figure 4 are the goals whose classes are to be determined. The properties that are effective over the classes of these goal values and possible labels that can be assigned to these properties are given in Figure 4b–e. In a similar way, the degree of complexity given in Figure 5 represents the goal value whose class is to be determined. Figure 5b,c shows the properties that have affected the goal class and also the possible labels that these properties can get. Also for the classification process of the goal values in Figures 4 and 5, conditional probability distribution tables are formed as in Table 4.
COMPARISONS AHAM, Munich, LAOS, and CAM models have been the reference models for the development of AEH applications to develop adaptive educational environment for the students and for application developers as authoring tools. Therefore, basically, domain, user, and AMs are defined to store required objects permanently in the database layer of these reference models. AEHSs based on these reference models focus on developing some tools. AEHSs such as AHA!, MOT, SmexWeb, and GRAPPLE were developed for the application developers to define various pedagogic rules in the AM and also developing DM by using a graphical or form-based interface. Despite of all these studies, apparently, AEH applications have not become widespread and have not reached a wider user group as much as non-adaptive (traditional) web-based educational applications. Development of the authoring tools for developing AEH applications, the necessity of the authors to be a domain (instructional area) expert, and to have knowledge about AEH technology are still an obligation. In other words, although AEH systems have the required models and tools to develop DM, generally they do not have the models and tools to enable any test or measure the accuracy. Whereas the most important factor that influences the success of an AEH application is necessary to define the domain objects, to determine the relations and the relation degrees among the instructional objects, to prepare content objects (tests, samples, materials, etc.) that are used in teaching of the objects properly to the users with different knowledge levels and preferences.
In SAHM, apart from storing the user data, the objects of DM and the adaptation rules permanently within the database layer, various test and evaluation data regarding the domain objects are also needed to be stored within the SM of database layer. The stored data are used for determining the “relation degree,” “the effectiveness and suitability of the materials,” and “the exams in terms of users with different knowledge levels and instructional goals.” The possible values of these features under different conditions are shown in Table 2. The application developers might use the required data provided by the evaluation of data stored within SM and in common with the data stored within UM through a developed intelligent system.
CONCLUSIONS AND FURTHER WORK This study introduces successfully a new model called SAHM to solve the problems encountered in AEH applications. Together with SAHM, the storage layer of the existing hypermedia models such as Dexter, AHAM, LAOS, Munich, and CAM have been extended. SM was also defined in the storage layer. Its structure is a copy of DM having different functionalities and DM to be tested and analyzed. The structure of SM is determined to be the copy of DM. Therefore, instructional objects in the DM are transferred to the SM by inheritance in object-oriented programming. However, the relations among instructional objects and the properties of objects have not been re-defined in SM. Apart from DM, new properties belonging to the objects have been defined in SM. These properties represent the relation degrees between the objects and sufficiency and suitability degrees of the contents (materials, samples, etc.) and the exams. Two resources, UMs and questionnaires, were recommended for the determination of the values of objects’ properties in SM. It was approved to evaluate data obtained from the both resources independently and to save these data into different tables. The designed model asserts (comes up with the idea that) that a DM of AEH application can be organized by the system with the evaluation of the stored data in SM. While existing reference models do not offer the application developers such a facility for improving the disabilities and the encountered mistakes of DM, SAHM model helps even inexperienced application developers or the ones having lack of knowledge for organizing DM. Accordingly, upcoming AEH studies are expected to involve some specific studies in respect of SM. By means of the designed SAHM model, it is also expected that the applications in AEH field would become widespread and new studies would be made concerning DM to be automatically developed by the system. In further study, the evaluation process of data stored within SM by means of a rule-based and intelligent application environment will be developed. Generating the filtering rules, building up intelligent inference mechanisms and decision-making processes about the features of domain objects will also be deeply searched in further studies.
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BIOGRAPHIES H. Tolga Kahraman received his Master of Science (MSc) in 2004 from the Institute of Science and Technology of Gazi University, Turkey. He received the PhD degree in 2009 from the Institute of Science and Technology of Gazi University, Turkey. His main research area covers adaptive hypermedia systems, intelligent tutoring systems, user modeling, and web-based applications.
Seref Sagiroglu received the BS degree in electronic engineering from Erciyes University, Kayseri, Turkey, in 1987, and the PhD degree in system engineering from the University of Wales College of Cardiff, UK, in 1994. He is now Professor at the Department of Computer Engineering and head of the department, Gazi University. His research interests include modern heuristic optimization techniques (genetic algorithms, tabu search, and simulated annealing), artificial neural networks, fuzzy logic, intelligent system identification, modeling and control, and robotics, computer and information security, web-based applications.
Ilhami Colak graduated from the Department of Electrical and Electronics Education of Gazi University in 1985. He received his Master of Science (MSc) Degree from the Institute of Science and Technology of Gazi University in 1988 and his Master of Philosophy (MPhil) Degree from the Department of Electrical and Electronics Engineering of Birmingham University in Birmingham, UK in 1991 and his Doctor of Philosophy (PhD) Degree from the Department of Electrical Engineering of Aston University in Birmingham, UK in 1994. He became a full Professor at Gazi University in 2005. His main research area covers electrical machines, power electronics, distance education, artificial neural networks, alternating energy sources and automatic control.
