Generalized Net Model for Adaptive Electronic Assessment, Using Intuitionistic Fuzzy Estimations

Shannon, A. G.; Orozova, Daniela; Sotirova, Evdokia; Atanassov, Krassimir; Melo‐Pinto, Pedro; Kim, Taekyun

doi:10.1007/3-540-31182-3_26

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…In a series of research papers, the authors have studied some of the most important processes of functioning of universities [2][3][4][5][6][7][8][9][10]. In particular, Generalized Nets [11], are used to describe the process of student assessment [2,6,7], where the assessments can be represented in an intuitionistic fuzzy and in an interval valued intuitionistic fuzzy form. The concept of intuitionistic fuzzy set is described in details in [12,13] and of interval valued intuitionistic fuzzy set in [12].…”

Section: Introductionmentioning

confidence: 99%

“…In [6], the authors constructed a model, which describes the process of evaluation by lecturers. Next, a model that describes the standardization of the process of evaluation by lecturers was constructed [7]. In the next steps of the educational processes there are models of the evaluation of the lecturers themselves [8], and the assessment of the course [9].…”

Section: Introductionmentioning

confidence: 99%

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Interval Valued Intuitionistic Fuzzy Evaluations for Analysis of a Student’s Knowledge in University e-Learning Courses

Kim¹,

Sotirova²,

Shannon³

et al. 2018

IJFIS

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In the paper a method is proposed for evaluation of the students' knowledge obtained in the university e-learning courses and an evaluation of the whole student class. For the assessment of the student's solution of the respective assessment units the theory of intuitionistic fuzzy sets is used, while for the class evaluation, interval valued intuitionistic fuzzy sets is used. The obtained intuitionistic fuzzy estimations reflect the degree of each student's good or poor performances, for each assessment unit. The interval valued intuitionistic fuzzy evaluations are based on the separate student's evaluations. We also consider a degree of uncertainty that represents such cases wherein the student is currently unable to solve the problem. The method presented here provides the possibility for the algorithmization of the process of forming the student's evaluations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interval Valued Intuitionistic Fuzzy Evaluations for Analysis of a Student’s Knowledge in University e-Learning Courses

Kim¹,

Sotirova²,

Shannon³

et al. 2018

IJFIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Human decision-making can be modeled and simulated through soft computing approaches in an E-learning system [6,19]. Generalized nets are used as a tool for modeling to analyze the process of how universities function [7][8][9][10][11][12][13][14][15]. They are also used to analyze the process of administration, servicing, producing a timetable, the logical ordering of the subjects for students, and the assessment of students via Atanassov's intuitionistic fuzzy evaluations.…”

Section: Introductionmentioning

confidence: 99%

An Intuitionistic Fuzzy Approach to Classify the User Based on an Assessment of the Learner’s Knowledge Level in E-Learning Decision-Making

2014

J Inf Process Syst

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In this paper, Atanassov's intuitionistic fuzzy set theory is used to handle the uncertainty of students' knowledgeon domain concepts in an E-learning system. Their knowledge on these domain concepts has been collected from tests that were conducted during their learning phase. Atanassov's intuitionistic fuzzy user model is proposed to deal with vagueness in the user's knowledge description in domain concepts. The user model uses Atanassov's intuitionistic fuzzy sets for knowledge representation and linguistic rules for updating the user model. The scores obtained by each student were collected in this model and the decision about the students' knowledge acquisition for each concept whether completely learned, completely known, partially known or completely unknown were placed into the information table. Finally, it has been found that the proposed scheme is more appropriate than the fuzzy scheme.

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“…Digital libraries have been incorporated into many environments, but we focused on the academic context. Specifically, we honed in on University Digital Libraries (UDLs), which supply several services and information resources to the users in an environment where research, teaching, and learning come together [6,7].…”

mentioning

confidence: 99%

“…The use of automated tools would help them to deal with and manage the large amount of information available to them in digital format online [8]. Moreover, the web influences other forms of information media, such as newspapers, journals, books, etc., and more specifically, the development and growth of academic digital libraries [7]. The major problem that these libraries have to address is the exponential growth of information because their employees have problems executing the tasks of delivering the information to users.…”

mentioning

confidence: 99%

Fuzzy Linguistic Recommender Systems for the Selective Diffusion of Information in Digital Libraries

Porcel¹,

Ching-López²,

Bernabé-Moreno³

et al. 2017

J Inf Process Syst

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The significant advances in information and communication technologies are changing the process of how information is accessed. The internet is a very important source of information and it influences the development of other media. Furthermore, the growth of digital content is a big problem for academic digital libraries, so that similar tools can be applied in this scope to provide users with access to the information. Given the importance of this, we have reviewed and analyzed several proposals that improve the processes of disseminating information in these university digital libraries and that promote access to information of interest. These proposals manage to adapt a user's access to information according to his or her needs and preferences. As seen in the literature one of the techniques with the best results, is the application of recommender systems. These are tools whose objective is to evaluate and filter the vast amount of digital information that is accessible online in order to help users in their processes of accessing information. In particular, we are focused on the analysis of the fuzzy linguistic recommender systems (i.e., recommender systems that use fuzzy linguistic modeling tools to manage the user's preferences and the uncertainty of the system in a qualitative way). Thus, in this work, we analyzed some proposals based on fuzzy linguistic recommender systems to help researchers, students, and teachers access resources of interest and thus, improve and complement the services provided by academic digital libraries.

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Generalized Net Model for Adaptive Electronic Assessment, Using Intuitionistic Fuzzy Estimations

Cited by 7 publications

References 2 publications

Interval Valued Intuitionistic Fuzzy Evaluations for Analysis of a Student’s Knowledge in University e-Learning Courses

Interval Valued Intuitionistic Fuzzy Evaluations for Analysis of a Student’s Knowledge in University e-Learning Courses

An Intuitionistic Fuzzy Approach to Classify the User Based on an Assessment of the Learner’s Knowledge Level in E-Learning Decision-Making

Fuzzy Linguistic Recommender Systems for the Selective Diffusion of Information in Digital Libraries

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