A fuzzy system for evaluating students’ learning achievement

Saleh, Ibrahim A.; Kim, Seong-in

doi:10.1016/j.eswa.2008.07.088

Cited by 74 publications

(55 citation statements)

References 10 publications

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“…Most of these methods aim to assess learners compared with a group, i.e., assign grades to fit a predefined reference curve. They are known as grading on a curve methods (Bai & Chen, 2008a;Bai & Chen, 2008b;Law, 1996;Li & Chen, 2009;Saleh & Kim, 2009). Other authors report methods for assessing learners directly.…”

Section: State Of the Art Of Fuzzy Assessmentmentioning

confidence: 99%

An approach to automatic learning assessment based on the computational theory of perceptions

Sánchez-Torrubia

Torres-Blanc

Triviño

2012

Expert Systems with Applications

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E-learning systems output a huge quantity of data on a learning process. However, it takes a lot of specialist human resources to manually process these data and generate an assessment report. Additionally, for formative assessment, the report should state the attainment level of the learning goals defined by the instructor.This paper describes the use of the granular linguistic model of a phenomenon (GLMP) to model the assessment of the learning process and implement the automated generation of an assessment report. GLMP is based on fuzzy logic and the computational theory of perceptions. This technique is useful for implementing complex assessment criteria using inference systems based on linguistic rules. Apart from the grade, the model also generates a detailed natural language progress report on the achieved proficiency level, based exclusively on the objective data gathered from correct and incorrect responses. This is illustrated by applying the model to the assessment of Dijkstra's algorithm learning using a visual simulation-based graph algorithm learning environment, called GRAPHS.

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Section: State Of the Art Of Fuzzy Assessmentmentioning

confidence: 99%

An approach to automatic learning assessment based on the computational theory of perceptions

Sánchez-Torrubia

Torres-Blanc

Triviño

2012

Expert Systems with Applications

View full text Add to dashboard Cite

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“…It is worth mentioning that fuzzy set theory is an efficient and effective method to represent uncertainties [18,19]. Comparing with methods based on numerical grading scores [6,7,12], fuzzy set theory offers an alternative to linguistic evaluation in which "fuzzy" words, instead of numerals, are used during the assessment procedure [9,[18][19][20][21][22][23]. Besides that, "computing with words", as coined by Zadeh, is also a methodology related to fuzzy set theory, whereby the objects of computation are words and propositions drawn from a natural language [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy set theory has been used in education assessment [9,[18][19][20][21][22][23]. It is useful to deal with linguistic grades such as "didn't contribute", "willing but not successful", and "average" in a grading system, which involve a substantial amount of fuzziness and vagueness [18].…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the success of fuzzy set theory in education assessment [9,[18][19][20][21][22][23], this paper aims to propose a fuzzy peer assessment methodology that evaluates each student's contribution in a group work. The proposed methodology is a synthesis of perceptual computing (Per-C) [25][26][27][28][29] and a fuzzy ranking algorithm that uses fuzzy preference relations [30].…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, Per-C is able to handle subjectivity, vagueness, imprecision, and uncertainty while achieving tractability and robustness in modelling human decision-making behaviours [25][26][27][28][29]. Comparing with type-1 fuzzy models [9,[18][19][20][21][22][23], Per-C adopts interval type-2 fuzzy sets (IT2FSs) in tackling a decision making problem [25][26][27][28][29]. IT2FS has more flexibility in preserving and processing uncertainties than type-1 fuzzy set [25].…”

Section: Introductionmentioning

confidence: 99%

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A new fuzzy peer assessment methodology for cooperative learning of students

Chai

Tay

Lim

2015

Applied Soft Computing

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a b s t r a c tIn this paper, a new fuzzy peer assessment methodology that considers vagueness and imprecision of words used throughout the evaluation process in a cooperative learning environment is proposed. Instead of numerals, words are used in the evaluation process, in order to provide greater flexibility. The proposed methodology is a synthesis of perceptual computing (Per-C) and a fuzzy ranking algorithm. Per-C is adopted because it allows uncertainties of words to be considered in the evaluation process. Meanwhile, the fuzzy ranking algorithm is deployed to obtain appropriate performance indices that reflect a student's contribution in a group, and subsequently rank the student accordingly. A case study to demonstrate the effectiveness of the proposed methodology is described. Implications of the results are analyzed and discussed. The outcomes clearly demonstrate that the proposed fuzzy peer assessment methodology can be deployed as an effective evaluation tool for cooperative learning of students.

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