A fuzzy inference system-based criterion-referenced assessment model

Tay, Kai Meng; Lim, Chee Peng

doi:10.1016/j.eswa.2011.02.158

Cited by 29 publications

(25 citation statements)

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“…In this method, both the assessment criteria and the grades assigned to each project are decided by means of fuzzy group decision making techniques. Recently, Tay and Lim (2011) presented an assessment system, based on fuzzy IF-THEN rules with rule refinement, to obtain a finer-tuned assessment. One weakness of all these methods is that instructors have to participate in each and every assessment, as it is they that provide the fuzzy system input data.…”

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

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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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An Intelligent Computing Approach to Evaluating the Contribution Rate of Talent on Economic Growth

Gao

Liao

2015

Comput Econ

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We set out in this study to develop an intelligent computing method for the evaluation of the 'economic contribution rate of talent' (ECRT). We begin by constructing an indicator system for comprehensive evaluation of the talent environment and then go on to classify our (country or region) target system using our proposed GA-DE-FCM methodology. We subsequently identify total 'human capital' as comprising of 'talent capital' and 'general labor', which, along with 'fixed assets', are used as the input variables of the economic system, whilst the corresponding gross domestic product is used as the output variable. The mapping between the inputs and output is modeled in this study by a 'fuzzy artificial neural network' from which several fuzzy rules can be extracted. Having extracted these fuzzy rules, we subsequently go on to investigate the effect of each input factor (fixed assets, talent capital and general labor) on the level of economic growth within each category (obtained in Step 1), and then carry out an examination of the ECRT within each category, as well as that within the whole target system. The traditional methods of evaluating ECRT are not regarded as satisfactory, given that the ECRT problem is non-linear and involves lags; however, we argue that based upon intelligent computing, the model proposed here can effectively deal with these issues. The results, based upon a 2001-2010 sample of 31 provinces in mainland China, indicate that during this period, China could be classified into three categories according to the talent environment. The first category (high level talent environment) comprises of just two regions, with an average ECRT of 44.61 per cent, whilst the second category (median level talent environment) comprises of five B Nuo Liao 123 Y. He et al. regions, with an average ECRT of 37.57 per cent, and the third category (low level talent environment) comprises of 24 regions, with an average ECRT of 14.8 per cent. The average ECRT for China as a whole is 25.67 per cent.

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A fuzzy inference system-based criterion-referenced assessment model

Cited by 29 publications

References 29 publications

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

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

A new fuzzy peer assessment methodology for cooperative learning of students

An Intelligent Computing Approach to Evaluating the Contribution Rate of Talent on Economic Growth

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