ABSTRACTUser modeling helps information systems know about their userbehavior to provide better services. An educational socialnetwork, called SoNITS, has been developed for IT students. Thispaper introduces an ontology-based student model that is used inSoNITS. Issues about constructing and managing student modelsare discussed. Applications that utilize these user models are alsodiscussed.

Categories and Subject DescriptorsH.1.2 [Human information processing]; J.1 [Education]; K.3.2[Computer and Information Science Education]

General TermsMeasurement, Human Factors

KeywordsUser Modeling, Social Network, Information Technology,Ontology

1. INTRODUCTIONUser modeling has been applied in various fields of applications.In education, student models has been constructed to assistprofessors running Intelligence Tutoring Systems (ITS) [1] moreefficiency or providing personalization and adaptation in learningfor learners [2][3]. Life-long learner profile can support learnersmanage their competencies and education managers plan forcontinuing training courses [4]. Personal profile can help usersenjoy a better service in using digital libraries [5]. User modelsalso support navigation systems enable personalized pathselections for users [6]. Understanding user interest can bring e-commerce web sites recommend proper products to theircustomers [7][8]. Finally, user profiles are constructed to supportusers finding interesting groups and information in socialnetworks and online communities [9][10][11][12].

There are different methods to represent user models. Usingontology to represent user models is the most popular methods[6][13][14][15][16][17][18]. Pedagogical models of studentswere developed in educational systems [1][19]. Other methodsconsist of the usage of temporally-tagged relational graphstructures in RDF [20], bipartite or conceptual graphs [21][22], aset of policies in social networks [12]. In these approaches, usingontology has some advantages about user model sharing andinteroperability [23].The usage of ontologies to represent student models are muchdifferent in various researches. Some studies [17] focus on theupper-level ontology that can be used to store certificates, skills orcompetencies. In these studies, user attributes are stored in a non-hierarchical structure and the relationship between attributes isnot adequately facilitated. In other papers [13][14][16],ontologies for particular fields are introduced. User attributes,specified by clear facts such as certificates or previous jobexperience, are classified in an ontological tree based on theirsemantic. In addition, the quantitative measurement of userattributes is also introduced [6][18]. However, the used reasoningon user models is based on the descriptive logic so it can notutilize these numerous values.An ontology to represent student models cannot be properlyconstructed by previously mentioned methods. Students’ skills orcompetences, the main attributes of student models, can not beclearly determined by a particular certificate. Their skills are alsoincreased throughout their studying program so that they have tobe quantitatively measured and properly reasoned. In addition,skills trained in their studying program are often related to eachothers to support a pre-specified career, so that a particulardomain ontology with well-classified skills is more useful forconstructing and using student models.Different with life-long learner modeling [4], students just live aperiod of their life with a clear goal of finishing their currentlytaking programs. However, this period is long enough to create along-term understand about students and able to track theirprogress through their four years in the university.This paper introduces an ontology-based student model used in aSocial Network for Information Technology Students (SoNITS),that is deployed in the International University – VNU HCM,Vietnam. This light-weight ontology only focused on theInformation Technology (IT) field, including industrial IT skills,due to the complexity of the ontology and the time limit of theproject. Each skill is measured by a ten-degree scale to supportquantitative reasoning. The process of constructing studentmodels and the relationship between skills are also described.

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2. STUDENT MODELING IN SoNITSTo represent a student, three following parts are stored in astudent model:

General user demographics and mental characteristic: asin GUMO [14].Competencies and Interests: mostly about IT skills.Policy and connectivity in social networks.

In this paper, only the competence part, that consists of IT skills,of student models is discussed in details. In the SoNITS’sontology, IT terms are organized in a light-weight ontology, a partof this ontology is shown in Figure 1. Each node represents for anIT skills. Beside these IT skills, there are four other groups of IT-related skills: ITManagement, ITProblemSolving, ITSoftSkill andNatural Science. Only 214 popular IT terms that are selected fromthe IT curriculum or IT job descriptions in SoNITS.

Each node in the ontology has a skill-meter that represents for thelevel of knowledge and experience of as student on that skill. Aten-degree scale is used to describe for those levels, fromunknown to the expert levels. This scale is enough to represent theability of a particular skill of a student, from unknown level at thebeginning of his/her studying program until very experiencedlevels which he/she can reach to in a few years after graduation.The description of this ten-degree scale is described in Table 1.

Figure 1. A part of the IT-Skill Ontology

Table 1. Scales of knowledge and experience on a skill

Scale Level of Knowledge & Experience0 No knowledge1 A little2 Limited3 Understanding fundamentals4 Applicable5 Mastery of fundamental knowledge6 Some practical experience7 Experienced8 Very experienced9 Expert

The studying result of a student is used to fill the leaf nodes of thestudent model of that student. In the curriculum design, eachcourse of the program trains a set of skills for students in asupportive or a highly supportive modes. In another side, a skillcan be trained in different levels and aspects by several coursesthat are organized in different years of the program. In general, theskill-meter of a skill is determined by the training mode and theacademic level of courses that train that skill.

The studying result only helps to fill a part of student models. Thefewer number of courses the student studied, the lower number ofleaf nodes in the student model the system can fill. Thus, there areseveral skills in student models having a empty skill-meter. Inpractice, IT skills are often related to each others. It means thatwhen a student are good on a skill, he/she has some knowledge onrelated skills of that one. Therefore, skills with a empty skill-metercan be induced from known skills.

There are three cases of skill induction. Firstly, in the siblinginduction, a skill on a node can be induced from its sibling skills.Secondly, in the upward induction, a skill can be induced from itsknown children. Thirdly, in the downward induction, childrenskills of a filled skill can be induced from their super-skill. Thesethree kinds of induction are differently developed in SoNITS (seeTable 2).

Table 2. Three induction rules on the skill ontologySample Tree

A (V, D)o A1(V1, D1)o A2(V2, D2)o …o Ai(Vi, Di)

In the sample tree, A is name of skill, V is value of skill meter,and D is value of dissimilarity. A1, A2, … Ai are children nodes ofA.Siblinginductionrule

Sibling induction rule is used to estimate the skill-meter value of a node from its sibling skills.Select parameter k=2.“k-max of a set” means the sub-set k of biggestmembers from that setSuppose that A1, A2, A3 are the known skills withV1>V2>V3.The skill-meter value of remaining skills A4,A5,…,Ai are:

V4 = V5 = … = Vi = Median of k-max of {V1,V2, V3} – fd (D);

Upwardinductionrule

Upward induction rule is used to estimate theskill-meter a skill from its children skills.Suppose that A1, A2, …, Ai are the known skills.Skill-meter value of skill A is:

V = average (V1,V2,…,Vi);Downwardinductionrule

Downward induction rule is used to estimate theskill-meter of children skills from the known skill.Suppose that A is the known skills.Skill-meter value of skill A is:

V1 = V2 = … = Vi = VFunction fd(x) is used to calculate dissimilarity points from adissimilarity value x.

3,42,21,1

)(xx

x

xfd

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The sibling induction depends on the similarity between thesibling skills. The more similar they are, the smaller the differencebetween the induced value and known skill-meters is. Forinstance, high-level programming languages are very similar intheir manner, so that if an engineer is very skillful about “C++programming”, he can have a good programming skill on similarlanguages, such as “C”, “Java” or “C#”. In other wise, systemadministration skill is much different in various operatingsystems, so that a very experienced user of Windows systems maynot know much about Unix or Linux systems. Therefore, adissimilarity value is introduced in each middle node of the skillontology to support the sibling induction.In upward induction, a more general skill can be induced fromtheir children skills. The “Programming” skill of a student canconcluded from his skills on all particular programminglanguages. In otherwise, in downward induction, a unknown skillcan be induced from its super-skill. For example, if a student isgood in “System Administration” but there is no informationabout his skills on a particular operating system, it can beassumed that he is good in “System Administration on Unixsystems” or “System Administration on Windows systems”.These three skill induction methods are constructed based on thehuman reasoning in practice. When only able collecting a part ofinformation about a person, the other information of that personcan be estimated based on the organization and similarity ofinformation. This process is popularly used when screeningcurriculum vitas or interviewing candidates for job vacancies. Theorder of applying induction methods is sibling, upward and thendownward.

3. EVALUATION3.1 Evaluation methodsScrutability of profiles is one of the essential requirements ofusers [4]. Because of the complicate of the proposed ontology,two special visual methods have been developed in SoNITS tohelp students browse their student models. A basic tree-viewdisplay is constructed as in Figure 2. A student model is shown ona tree structure. Each node represents for a skill with itscorrespondent skill-meter. The skill-meter can be a transferredvalue or a induced value. When the student clicks on the“Reasoning” link, the reasoning of the value is displayed (seeFigure 3).

Figure 2. Tree view of student model

Figure 3. The explanation of a skill-meterTo help student discover their main attributes, a radar-view charthas been developed (see Figure 4). Four radar-view charts(General, Software, Hardware, Network) are developed.

Figure 4. Student model by Radar Chart

3.2 Feedback from studentsA survey is carried out to get the feedback from students. In thissurvey, the questionnaire consists of five following questions:

(1) Does the General Chart appropriately reflect yourmajor?

(2) Does skill-group IT Management in General Chartappropriately reflect your capabilities?

(3) Does skill-group IT Soft Skill in General Chartappropriately reflect your capabilities?

(4) Does skill-group IT Natural Science in General Chartappropriately reflect your capabilities?

(5) Does your major chart (Software/Hardware/Network)appropriately reflect your capabilities?

Table 3. Student feedbacks (%)Questionnumber

Stronglydisagreed

Disagreed Undecided Agreed StronglyAgreed

1 0 2.78 22.22 72.22 2.78

2 0 5.56 30.56 61.10 2.78

3 0 2.78 27.78 66.66 2.78

4 0 2.78 11.11 75.00 11.11

5 0 5.56 19.44 69.44 5.56

Average 0 3.89 22.22 68.88 5.00

In the survey, there are 36 feedbacks from third year students.Table 1 shows the result of the evaluation. From the feedback,more than 73% of students agree with the display mentioned bythe charts. Only around 4% of students disagree with the result.Therefore, the student models have represented a good overviewabout the students.

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However, the percentages of “Undecided” feedback from allquestions, except question 4, are quite high. This can be explainedthat the charts carry too much information and are quitecomplicated for students, so they should be improved in the futureabout its visualization.

4. CONCLUSION AND DISCUSSIONFocusing on the studying period in the university and having thesupport of a social network, a light-weighted ontology has beendeveloped to represent user models for IT students. In thisontology, IT skills are organized in a tree-based taxonomy to helpthe organization of knowledge and the reasoning on skillrelationships. In each skill, a skill-meter is proposed to measurethe knowledge and experience of students. The methods totransfer student studying result to student models is described.The skill induction is also introduced.The proposed student models have a good feedback fromstudents. It means that they are able to represent main attributes ofstudents.

5. ACKNOWLEDGMENTSAuthors’ thanks to Vietnam National University have funded forthis paper.

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