Adaptive Hypermedia System in Education: A User ... - IEEE Xplore

Session T2D

Adaptive Hypermedia System in Education: A User Model and Tracking Strategy Proposal Francesco Colace, Massimo De Santo Dipartimento di Ingegneria dell’Informazione e Ingegneria Elettrica - DIIIE Università degli Studi di Salerno, Via Ponte don Melillo, 1 84084 Fisciano (SA), Italy {fcolace , desanto}@unisa.it Abstract - New technologies are quickly changing the traditional educational approaches and systems. In fact the integration of new technologies in the field of education offers new challenges and opportunities in distance learning, lifelong learning and e-learning in general. On the other hand e-Learning is an interesting application area for adaptive hypermedia system (AEH). Through the use of user models intelligent tutoring and students tracking techniques an AEH can recognize individual users and their needs adapting in an effective way the student learning path. This paper attempts to enhance student learning by addressing different learning styles by the use of the Adaptive Hypermedia System approach. In particular it addresses to define a standardized and simple user model and some tracking parameters and techniques in order to update the proposed learning path. The proposed system collects information on the user learning style by the use of various well known in literature test. We propose a mapping of this acquired information in some parameters defined in standard metadata user description like IMS LIP. The final goal is a student learning style representation by the use of a quantitative model. So we can develop a tracking strategy through the observation of the main model parameters. A similar approach could be used for the description of learning objects in order to provide the introduction of well defined metrics for the dynamic tailoring of the learning path to the student’s learning style and needs. Index Terms – Adaptive Hypermedia Systems, E-Learning, Metadata Standards INTRODUCTION

Obviously, this information can not explain all the aspects of student’s knowledge process and teachers can not support effectively them. In this context, a set of learning parameters has to be selected and used for tracking operation. In literature, many papers deal with this argument and offer several models whose target is the identification of these parameters in E-Learning process [1]. Some of them are based on the tracking of the student knowledge by using the formalism of the graphs where the nodes estimate the student’s knowledge [2]. Other approaches analyze the actions of the student during his learning process furnishing a detailed report to the tutor [3][4]. An interesting approach is proposed in [4]. This paper describes a model that builds the best students’ learning path starting from the analysis of some features outlining the main pedagogical characteristics. This approach is studentcentred and students’ parameters are selected according to three factors: • Test performance: this parameter gives the most direct information about the student’s knowledge. • Time performance: the time spent by student attending the various modules. • Reviewed topics: A topic’s review score records how many times the student has returned to review the topic. It is based on how many times the topic is reviewed and how much of the material is viewed each time. The above factors, described by an opportune mathematical model, indicates to teachers the learning level achieved by students. By analyzing these indexes, moreover, it is possible to establish if students may attend the next subject of the course. A good tool for this analysis is furnished by the use of an Adaptive Educational Hypermedia System (AEHS). An AEHS [5][6] is an approach whose target is to personalize the learning experience for the learner. In this way, by improving the learner satisfaction, it could be possible to enforce the learning experience. An AEHS could reach the aim by the management of fourth features: Knowledge Space (KS), User Model (UM), Observations of user behaviour (OBS), Adaptation Model (AM). Starting from this approach in this paper we propose a model for tracking student’s activities during his learning process. We have selected some indexes able to describe the student attitude during the learning process. In particular this approach updates the student’s profile (described according to IMS LIP standard) in order to adapt its learning path [7]. This paper is organized in this way: in section two we a have a brief description of an AEHS. The problem of student’s tracking is presented in section three. In section four the tracking indexes are introduced and the mathematical model of the student obtained with their combination is described. In section five the rules used to build the best learning path are analyzed. In section six some experimental results are showed.

In the last years, distance learning is becoming one of the most interesting topics in scientific literature and researchers are starting to take a real interest in the design of value-added services. In particular, ELearning environment should not only be limited to transfer the content of the didactic units to the student but also to support a new concept of didactic whose final objective is to increase the contribution of the traditional teaching thanks to Information and Communication Technology. Among these services, one of the most interesting is the student activities tracking service. In fact the main criticism to ELearning approach is the lack of direct contact between teachers and students: by tracking services teachers can track and support students in their learning process. However, new E-Learning platforms can collect and manage a large size of data concerning the student’s learning process. But this very impressive number of information can bewilder teachers that don’t use fully them. Usually a teacher uses few information: the students’ results at the final or end-unit tests. 1-4244-1084-3/07/$25.00 ©2007 IEEE October 10 – 13, 2007, Milwaukee, WI 37th ASEE/IEEE Frontiers in Education Conference T2D-18

Session T2D ADAPTIVE EDUCATIONAL HYPERMEDIA SYSTEM: USER MODEL PROPOSAL An Adaptive Educational Hypermedia System (AEHS) [5][6] is an approach whose target is to personalize the learning experience for the learner. In this way, by improving the learner satisfaction, it could be possible to enforce the learning experience. In [9] a general definition of an AEHS, reflecting the current state ofthe-art, is described: • Knowledge Space (KS), subdivided into the Media Space, containing the educational resources and associated descriptive information (e.g. metadata attributes, usage attributes etc.) and the Domain Mode, containing graphs that describe the structure of the domain knowledge in-hand and the associated learning goals. • User Model (UM), that describes information and data about an individual learner, such as knowledge status, learning style preferences, etc. The User Model contains two distinct submodels, one for representing the learner’s state of knowledge, and another one for representing learner’s cognitive characteristics and learning preferences (such as learning style, working memory capacity etc.). This distinction is made due to the fact that the first model (Learner Knowledge Space) can be frequently updated based on the interactions of the learner with the AEHS. On the other hand, learner’s cognitive characteristics and learning preferences are more static, having the same property values during a significant time period. • Observations (OBS) which are the result of monitoring learner’s interactions with the AEHS at runtime. Typical examples of such observations are: whether a user has visited a resource, the amount of time spent interacting with a given resource, etc. Observations related with learner’s behaviour are used for updating the User Model. • Adaptation Model (AM), that contains the rules for describing the runtime behaviour of the AEHS. These rules contain Concept Selection Rules which are used for selecting appropriate concepts from the Domain Model to be covered, as well as, Content Selection Rules which are used for selecting appropriate resources from the Media Space. These rule sets represent the implied didactic approach of an AEHS. From the above definition, it is clear that in order to define the runtime behaviour of the AEHS, the definition of how learner’s characteristics influence the selection of concepts to be presented from the domain model (Concept Selection Rules), as well as the selection of appropriate resources (Content Selection Rules), is required. The design of the student model that we will adopt in this paper is carefully described in [8] where a quintuple of characterization is provided. This model takes into account the learner’s learning style, his background knowledge, his preferences, so represented: • Format (f): type of media the learner prefers to study a learning resource • Bandwidth (b): the type of link used by the learner to connect to the internet • Interactivity (i): the level of interactivity used by the learner to interact with the learning resource • Difficulty (d): the level of preparation of the student • Time (t) the time of study the learner spends to study a lesson

Each parameter assumes values in the range [1,10] coherently with the IMS-LIP metadata fields. In order to have an objective starting point we propose a matching between the results obtained by the use of ILS questionnaires and the proposed model. ILS is an instrument used to assess preferences in four dimensions (active/reflective, sensing/intuitive, visual/verbal and sequential/global) of a learning style model and was designed by Richard M. Felder and Linda K. Silverman. In this work a project for an AEH system is developed. In particular, starting by the proposed works in literature, each phase is accurately analyzed. Now we are going to examine the student model. The need to use a student model arises from the observation that knowing the student behaviour and preferences can improve his/her learning performance. In fact, nowadays, the main issue in the E-Learning environment is that it is not easy to monitor students' learning behaviours. Knowing user knowledge, goals, interests, and other features can enable the system to distinguish among different users. An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user. The user model is used to provide an adaptation effect, that is, to tailor interaction to different users in the same context. Whereas an adaptive system automatically adapts a user model to a given user, an adaptable system requires the user to specify exactly how the system should be different, for example, by tailoring the sports section to provide information about a favourite team. Target of the here proposed student model is to collect important data about learners. Target of this study is to consider some learner information that could be compatible with the learner metadata IMS-LIP. For instance, it could be necessary to know with which type of media the user usually works, for example a simple text document, or a video, or a presentation. It could be necessary to know if the student prefers to interact with the lesson, for example he could prefer a simple lesson or an exercise. All this information is related to the user’s learning style. In order to take into account information about learning style, an accurate study has been developed. In WWW there are a lot of systems that provide tools to characterize the learning style. Usually these systems ask the user to enter personal data and, later, to compile a given questionnaire. Once the questionnaire is completed, the system analyzes the answers of the student and provides the learning style. The learning style tool we have used in this work is ILS (Index of Learning Styles). Information about the learning style is subdivided into four dimensions. The first dimension is sensing/intuition. Sensing learners tend to like learning facts and to be patient with details and good at memorizing facts and doing hands-on (laboratory) work. Intuitive learners often prefer discovering possibilities and relationships and may be better at grasping new concepts and are often more comfortable with abstraction and mathematical formulation than sensing users. The second dimension is active/reflective. Active learners tend to retain and understand information best by doing something active with it— discussing or applying it or explaining it to others. Active learners tend to like group work more than reflective learners, who prefer working alone. Reflective learners prefer to think about it quietly first. “Let’s try it out and see how it works” is an active learner’s phrase; “Let’s think it through first” is the reflective learner’s response. The third dimension is sequential/global. Sequential learners tend to gain understanding in linear steps, with each step following logically from the previous one. Global learners tend to learn in large jumps, absorbing material almost

1-4244-1084-3/07/$25.00 ©2007 IEEE October 10 – 13, 2007, Milwaukee, WI 37th ASEE/IEEE Frontiers in Education Conference T2D-19

Session T2D randomly without seeing connections, and then suddenly “getting it.” The fourth dimension is visual/verbal. Visual learners remember best what they see - pictures, diagrams, flow charts, time lines, films, and demonstrations. Verbal learners get more out of words - written and spoken explanations. The ILS learning style is subdivided into four dimensions and each dimension is evaluated in the range [1,11]. Starting from ILS information, an accurate analysis has been done in order to adapt them to the metadata standard. In particular, from the active/reflective dimension the interactivity level of the student has been extracted. Then, from the visual/verbal dimension the type of media the learner typically uses has been extracted. In order to complete the student model, further information is necessary. In particular, it could be necessary to know which is his/her level of preparation on a particular argument, how much time he/she usually spends to study a lesson, how many times he/she usually repeats a lesson, and so on. This information cannot be obtained with the learning style, but should be considered separately. The complete student model is so characterized: Type of media

Interactivity level

Bandwidth

ILS (Visual/Verbal)

ILS Further (Active/Reflective) Information Figure 1: User Model

Difficulty level LIP fields

Time of study LIP fields

where the third parameter, the bandwidth, takes into account which device the student typically uses to connect to the internet and to download the lessons. Also for the learning resource an analogous description is furnished, exception for the Bandwidth variable, where the Dimension parameter is considered. The approach we propose in this work focuses on the design of an opportune methodology of tracking on learner activity and on the adaptation model. In this way it is possible to personalize the learning path of the student by providing the most adequate learning resource by taking into account up to date information of his characteristics. In the next sections we will describe the tracking strategy and the adopted approach.

has a priori assigned for that Learning Object by using an appropriate rational function Gv. In this way, if the student has obtained a good mark, his profile is updated and the successive adapted didactic unit is located, otherwise a unit with the same content but less difficult is chosen for him (also in this case it is necessary to update the student profile). In the next sub-sections we will describe in details the various phases of our approach. TRACKING OF STUDENT ACTIVITIES MODULE This module observes the student activity during his period of studying of a learning resource. Two main targets of this methodology are: • to maintain up-to-date information about student model’s parameters. The information observed during learner’s activity studying are: o the studying time. More precisely, this parameter evaluates the average of time used to study a learning resource and time for the first repetition. o level of preparation o interest for determined type of media • to provide an evaluation of the learner action related to his entire learning path by using information acquired during the observation activity. In this way it is possible to evaluate the learner performance by providing a global assessment usually based only on the final test mark. By denoting with the subscript u information related to the student and with r those related to the learning resource, it is supposed to know some parameters the tutor initially sets: • time of studying of his learning resource, tr •

a time parameter t x , generally a percentage of tr

•

the fair mark vr for that learning resource.

In this way, once the student learning time, tu is acquired, it is possibile to compare it by using the evaluation function:

THE TRACKING PROBLEM In this section we describe an approach for tracking the student‘s preparation during his learning activity. To this aim, it is necessary to design a method for tracking the student in a more useful and concrete way in order to help the docent in an effective evaluation. The proposed approach takes into account the difficulties that the student meets when he faces a didactic unit and furnishes to each student the most adapted didactic unit to his actual knowledge. Our approach is able to watch how much he is learning, which topics are very difficult for him and how it is possible to give to him the appropriate feedbacks. From this point of view, the system helps the docent by furnishing the best pedagogical contribution for the learning process of each student. How it is possible to choose the best learning path for each student? It is supposed to know how time the student spends when he faces a Learning Object (tk) and the mark he obtains in the final test (vk). The time student tk is matched with a reference learning time that the docent has a priori assigned, trk., for the k-th Learning Object. This matching is made by using an appropriate rational function Gt. The goal of Gt is, by setting opportunely its parameters, to give the right weight if the student has spent a lot of or little time in the making use of a lesson. Moreover, the mark student vk is matched with the reference mark vrk. the docent

Gt = 2 +

(t k − t k ) 2 − t x 2 u

r

k

(t − t k ) 2 + t x 2 u

∈ [1,3]

r

The minimum value (i.e. 1) is assumed when the estimated time student corresponds with that expected, that is tu ∈ [tr − t x , tr + t x ] . Moreover the tracking module is able to take into account how many times the student repeats the same lesson. This occurrence is considered by evaluating the function: 1 Tk (i) = ∈ ]0,1] 1 + a(i − 1)

where i = 1,2,3,... counts the number of repetitions of the same lesson. The function has a hyperbolic progress that assumes the maximum value when i = 1 and decreases when i increases. The parameter a sets the decrement rate and is equal to: a=

du 2dr

In this way, if the resource is more difficult than the learner preparation level, the decrement rate does not heavily penalize the learner, and vice versa. The second target of the tracking module is providing a student evaluation by using information acquired during his studying activity.


Session T2D The purpose is to assess the learner performance focusing attention to his complete studying activity. To this end, the learner assessment is a weighted average that takes into account two terms: the first is relative to the present state activity, A(k ) , where k denotes the k-th learning resource just tackled by the learner, and a term relative to his past learning activity. The student assessment evaluation is then:

Scorek = μ A(k ) + (1 − μ ) A (k )

where: A (k ) = f ( A(1), A(2),..., A(k − 1)) = 1

0 ≤ μ ≤1 k −1

∑ A(i)

k − 1 i =1

updated. The analysis we have performed, it results that Score = 0 denotes the threshold value. UPDATING PROFILE PHASE The updating process considers two parameters: the preparation level of the learner and his studying time. The other parameters of the student profile, relative to his preferences, can be considered constant in a long period of time.

Let us examine the A(k ) term. This term is in turn expressed as a weighted average:

A(k ) = α G(vk ) + (1 − α )( L( pr − pu ) + FOBS )

0 ≤ α ≤1

where: - G (vk ) is a term relative to the student grade vk obtained in the final test.

L( pr − pu ) takes into account how learning resource’s parameters ( pr ) are distant respect to learner’s ( pu ). - FOBS term takes account of the terms evaluated during learner activity. Note that, by expressing A(k ) in this way, if α = 1

-

the learner assessment takes into account only his grade, as it is usually considered. The term:

v −v G (vk ) = k r vr

considers the variance of learner’s grade respect to the fair mark vr , normalized to this value. The second term: i −i dr − du 0 ≤ γ ≤1 L( pr − pu ) = γ + (1− γ ) r u Max(dr , du ) Max(ir , iu ) is expressed as weighted average of the two more important learner’s parameters, the preparation level d and the interactivity level i . When the learning resource is more difficult respect to the learner’s preparation level or it has an interactivity level not expected to the learner’s preferences, L( pr − pu ) assumes a positive term. Note that in this case the variance of the two parameters is calculated respect to its relative maximum values. The third term, finally:

FOBS =

1+Sgn(L( pr − pu )) Tk (i) 1−Sgn(L( pr − pu )) ⎛ Tk (i) ⎞ − ⎜1− ⎟ 2 Gt 2 ⎝ Gt ⎠

takes account of the learner’s studying activity. Hence:

LEARNER PREPARATION LEVEL If the learner has a preparation level greater than the k-th learning resource’s difficulty one, his score assessment is not fair and he failures the final test twice, his preparation level is decreased. ⎧ du > d r ⎪ ⎨ Scorek < 0 ⇒ du = du − 1 ⎪ nfail = 2 k ⎩

Otherwise, if learner has reached at least a fair assessment having studied a learning resource more difficult respect to his preparation level, he has not rejected either once, and finally his total number of fails is limited. This limitation value is related to the number that identifies the k-th learning object belonging to the student learning path1. ⎧ du < d r ⎪ Score > 0 ⎪ k ⇒ du = du + 1 ⎨ ⎪nfailk = 0 ⎪⎩nfailtot < id k − 2

LEARNER STUDYING TIME The learner’s learning time is so updated: ⎧tu > tr 1 k −1 i ⇒ tu = ⎨ ∑ tu k − 1 i =1 ⎩ Scorek < 0

⎧tu < tr tk + tk ⇒ tu = u r ⎨ 2 ⎩ Scorek < 0

Once the learner has completed the k-th learning resource, the Scorek is evaluated, and so, at the end of the learning path, the complete learner assessment can be evaluated:

⎧⎪dr > du ⇒ Sgn (L( pr − pu )) = 1 ⇒ FOBS = Tk (i) Gt ⎨ ⎪⎩dr < du ⇒ Sgn (L( pr − pu )) = −1 ⇒ FOBS = 1− Tk (i) Gt

Score =

1 M

M

∑ Score i =1

i

where Sgn (•) denotes the sign function. By analyzing each single element of the Scorek term, we can realize that if Scorek assumes a negative value, learner assessment is not fair, and the learner is forced to repeat the same lesson. Otherwise, he can approach to the following learning resource. In any case, the student profile parameters are

1

At the k-th step, learner is processing the k-th learning resource, denoted with k −1

id k , and nfailtot = ∑ nfaili

.

i =1


Session T2D The information updated in the IMS-LIP metadata fields are showed in table 1. Learner metadata fields

Semantic Assessment value relative to the j-th learning resource Assessment value relative to the complete learning path Overcoming relative to a learning resource

Activity.evaluation.result.result[i].fieldata = Scorei Activity.evaluation.result.score = Score Goal.status = completed

Table 1 IMS-LIP Metadata fields updating related to the assessment information THE ADAPTATION MODEL In [10] two distinct areas of adaptation are distinguished: content level adaptation or adaptive presentation and link level adaptation or adaptive navigation support. This work is focused on the design of an adaptive presentation model by starting on learner and learning resource information profile. In particular, the proposed module is developed in three steps: Step 1: matching of parameters profiles The learners profile’s and learning resource’s parameters are considered. For each homonym parameter a matching function is considered. The minimum value corresponds to the best matching for that parameter, otherwise the resource parameter is very distant from learner’s. the function matching design are: Interactivity: M I = I r − I u ∈ [0,9] Difficulty: M = D − D ∈ [0,9] D r u Type_of_Media:

M F = Fr − Fu ∈ [0,9]

Time_of_Studying: Bandwidth:

In this way, the minimum value of Ind defines the nearest learning resource to the learner characteristics, namely: IndOPT = Min Indi i

EXPERIMENTAL RESULTS In order to evaluate our approach two kind of experimentations are reported. Object of the first experimentation is to demonstrate the effectiveness of our model and the second is to compare the results of this system with another in the case there is no possibilities to adapt the learning resource to the learner characteristics. The First Experimentation In this experimentation the learning path of a single learner is reported. In particular the starting learner profile is [4, 5, 7, 3, 5], that is a learner who usually prefers to learn with .doc or .pdf documents, uses a good connection to internet, has a high level of interactivity, has a low level of preparation and generally studies a lesson in two hours (we have supposed Δ = 20 minutes). The experimentation parameters we have used are: α = 0.85, γ = 0.85. μ = 0.9 and, for the adaptation model, a1 = 0.1, a2 = 0.2, a3 = 0.1, a4 = 0.4, a5 = 0.2 . The selected domain for our experimentations is the Computer Science Domain. For the description of the subject domain concepts we have extracted the ontology from the ACM Computing Curricula 2001 for Computer Science [11]. The extracted ontology is organized in two areas, two units for each area and ten lessons. Furthermore, it is supposed that twenty learning objects are available to explain every lesson of the course. In fig. 2 the graphic describing the Ind values of matching for each lesson submitted to the student is reported. 1,8 1,6

M T = Tr − Tu ∈ [0,9]

1,4 1,2

M B = Br − Bu ∈ [0,9]

1 0,8 0,6

Step 2: Evaluation of the characteristic functions

0,4 0,2 0

Once the matching functions are evaluated, the educational ( Ce ) and technical ( Ct ) characteristics can be considered. To this end a normalized weighted average mechanism is considered: Ct =

a1M F + a2 M B a1 + a2

with

∑ ai = 1

∈ [0,9]

Ce =

a3 M I + a4 M D + a5 M T a3 + a4 + a5

∈ [0,9]

5

i =1

Step 3: Evaluation of the global matching index The final step consists in the evaluation of the distance between the learner and the learning resource in term of educational and technical characteristics. To this end, the global index Ind is so calculated: Ind =

Ce2 + Ct2 2

∈ [0,9]

1

1

1

2

3

4

5

5

6

7

7

7

8

9

10

10

Figure 2 Ind values of matching for each lesson Along the y-axis the Ind valued are reported and on x-axis the number indicate the progress of the lesson submitted to the learner. In our experimentation the learner fails twice in the lessons 1, 7 and once in the lessons 5 and 10. As we can see, the adaptation model works well, in facts the Ind values are relatively low values, by furnishing to the learner the most adapted learning resource to his characteristics. This result produces a better level of preparation of the student. The Second Experimentation In this simulation we have reported a comparison of twenty classes of twenty learners with a real ILS profile. In particular, we have submitted the ILS test in order to consider a real preference of a learner. By starting from these values, we have simulated the learning profile and the learning path for arch learner. In particular, for each learner we have simulated the learning path in the case it is available the course adopted in the above simulation. In the second time we have simulated the


Session T2D learning path in the case it is available only one learning object for each lesson. That is, a not adapted learning path, because there is no possibilities to adapt the lesson to the learner characteristics. In table 1 it is showed these results with a simulation of twenty classes. As we can read, the above result is confirmed on the average for each class we have simulated.

Class 1

starting adapted non adapted Number o difficulty level difficulty level difficulty level f fails (average) 4,2 4,88 4,05 7,65

Class 2

5,45

5,8

4,20

8,65

Class 3

5,55

5,76

4,70

8,2

Class 4

5,95

5,974

4,89

8,3

Class 5

5,8

5,68

4,44

8,8

Class 6

6,1

5,85

4,60

9,05

Class 7

5,4

5,31

4,30

8,8

Class 8

5,4

5,63

4,45

8,7

Class 9

4,75

5,04

3,51

9,1

Class 10

5,8

5,70

4,60

9,1

Class 11

5,95

5,93

4,01

11

Class 12

4,65

5,26

4,20

8,4

Class 13

5,3

5,61

4,41

8,1

Class 14

5,1

5,74

4,51

7,85

Class 15

5,55

6,20

4,38

8,5

Class 16

5,5

5,81

4,92

8,15

Class 17

5,75

6,05

4,83

7,85

Class 18

5,35

5,73

4,64

8

Class 19

5,25

5,68

4,57

7,9

Class 20

4,55

5,35

4,67

6,8

average

5,36

5,65

4,45

8,45

[2]

Specht M., Oppermann R., “ATS - Adaptive Teaching System a WWW-based ITS”, Workshop Adaptivität und Benutzermodellierung in Interaktiven Softwaresystemen, 1998

[3]

Greer J. E., Philip T., “Guided Navigation Through Hyperspace ”, Workshop Intelligent Educational Systems on the Word Wide Web, 1997.

[4]

Yi Shang, Hongchi Shi, Su-Shing Chen, “An Intelligent Distributed Environment for Active Learning”, Journal on Educational Resources in Computing (JERIC), 1, 4, 2001.

[5]

Brusilovsky, P. (2001). Adaptive hypermedia. Methods and techniques of adaptive hypermedia. International Journal of User Modeling and User-Adapted Interaction, 11 (1/2), 87-110.

[6]

De Bra, P., Aroyo, L., & Cristea, A. (2004). Adaptive Web-based Educational Hypermedia. In Levene, M. & Poulovassilis, A. (Eds.), Web Dynamics, Adaptive to Change in Content, Size, Topology and Use, Heidelberg, Germany: Springer, 387410.

[7]

Colace, F.; De Santo, M.; Molinara, M.; Percannella, G., “An automatic learning contents selector based on metadata standards”, Proceedings of ITRE 2003 IEEE International Conference, 2003

[8]

Colace F., De Santo M., Iacone M.: Intelligent Tutoring System: A Model for Student Tracking. ICEIS [5] 2006: 110-115

[9]

Henze, N., & Nejdl, W. (2004). A Logical Characterization of Adaptive Educational Hypermedia. New Review of Hypermedia and Multimedia (NRHM), 10 [1], 77-113.

[10] Brusilovsky, P. (1996) Methods and techniques of adaptive hypermedia, User Modeling and User Adapted Interaction, 1996, v 6, n 2-3, pp 87-129 [11] ACM. (2001). ACM Computing Curricula 2001 for Computer Science, Retrieved October 25, 2006, from http://www.acm.org/education/curricula.html.

Table 1 Difficulty levels for each class CONCLUSIONS In this paper we have showed a student tracking model based on the definition of a set of features related to the concepts, skills and attitudes the student is expected to assimilate by the end of a unit. Each feature is represented by means of appropriate mathematical functions, which are combined in a mathematical model devised to facilitate the course characterization and comparison and to provide support for diagnostics. In this paper we have showed the design and implementation of a software module for deducing the representative “vector” of a given student starting from the standard description of various resources (student profiles, content descriptions and so on). We have discussed some experimental results in using the quoted vectors to find the most suitable set of contents for each student profile, confirming the effectiveness of the here proposed student model. In the future we will extend this approach to a real classroom in order to test his real effectiveness. REFERENCES [1]

Zaitseva L., Boule C., "Student Models in Computer-Based Education", Proceedings of the third IEEE International Conference on Advanced Learning Technologies, 2003.
