Web-based peer assessment: feedback for students with various thinking-styles

Abstract This study used aptitude treatment interaction design to examine how feedback formats (specific vs. holistic) and executive thinking styles (high vs. low) affect web-based peer assessment. An Internet-based (anonymous) peer-assessment system was developed and used by 58 computer science students who submitted assignments for peer review. The results indicated that while students with high executive thinking styles significantly improved over two rounds of peer assessment, low executive students did not improve through the cycles. In addition, high executive students contributed substantially better feedback than their low executive counterparts. In the second round of peer assessment, thinking style and feedback format interactively affected student learning. Low executive students receiving specific feedback significantly outperformed those receiving holistic feedback. In receiving holistic feedback, high executive thinkers outperformed their low executive counterparts. This study suggests that future web-based peer assessment adopts a specific feedback format for all students.     

A similarity measure for fuzzy rulebases based on linguistic gradients

Fuzzy rulebases are an approximate representation of some interesting system. As such, they could potentially be used for indexing and searching candidate solutions in case-based reasoning (CBR) systems in a variety of application areas. However, there is currently no method for directly and efficiently determining the similarity between two rulebases, which is a necessary element of their use in any CBR system. We propose a method for measuring similarity between two linguistic fuzzy rulebases, based on the granular computing technique of linguistic gradients. The proposed similarity measure is based on comparing the linguistic structure of two rulebases, using the linguistic gradient operator to reveal that structure. Our algorithm operates at the level of linguistic rulebases, rather than a defuzzified reasoning surface, and thus belongs to the Computing-with-Words paradigm. In a validation experiment, we compare our new method with the root-mean-square difference between reasoning surfaces for 603 pairs of fuzzy rulebases drawn from the literature. A Spearman correlation analysis shows that our new linguistic method is consistent with the numerical RMS results, while being theoretically and empirically faster than computing an accurate RMS difference.     

Reliability and validity of Web-based portfolio peer assessment: A case study for a senior high school’s students taking computer course

This study examined the reliability and validity of Web-based portfolio peer assessment. Participants were 72 second-grade students from a senior high school taking a computer course. The results indicated that: 1) there was a lack of consistency across various student raters on a portfolio, or inter-rater reliability; 2) two-thirds of the raters demonstrated inconsistency assessing different portfolios, i.e. inner-rater reliability; 3) peer-assessment scores were not consistent with teacher-assessment scores (criterion-related validity); 4) significant differences were found between peer-assessment scores and end-of-course examination scores, implying that Web-based portfolio peer assessment failed to reflect learning achievements (criterion-related validity). In short, Web-based portfolio peer assessment was not a reliable and valid method.     

A computational method of forecasting based on fuzzy time series

In this paper, a computational method of forecasting based on fuzzy time series have been developed to provide improved forecasting results to cope up the situation containing higher uncertainty due to large fluctuations in consecutive year's values in the time series data and having no visualization of trend or periodicity. The proposed model is of order three and uses a time variant difference parameter on current state to forecast the next state. The developed model has been tested on the historical student enrollments, University of Alabama to have comparison with the existing methods and has been implemented for forecasting of a crop production system of lahi crop, containing higher uncertainty. The suitability of the developed model has been examined in comparison with the other models to show its superiority.     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.     

A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology

We propose an inventory classification system based on the fuzzy analytic hierarchy process (AHP), a commonly used tool for multi-criteria decision making problems. We integrate fuzzy concepts with real inventory data and design a decision support system assisting a sensible multi-criteria inventory classification. We report on a study conducted in a small electrical appliances company and validate the design of the proposed multi-criteria inventory classification system and its underlying fuzzy AHP model.     

A weight adaptation method for fuzzy cognitive map learning

Fuzzy cognitive maps (FCMs) constitute an attractive modeling approach that encompasses advantageous features. The most pronounces are the flexibility in system design, model and control, the comprehensive operation and the abstractive representation of complex systems. The main deficiencies of FCMs are the critical dependence on the initial experts beliefs, the recalculation of the weights corresponding to each concept every time a new strategy is adopted and the potential convergence to undesired equilibrium states. In order to update the initial knowledge of human experts and to combine the human experts structural knowledge with the training from data, a learning methodology for FCMs is proposed. This learning method, based on nonlinear Hebbian-type learning algorithm, is used to adapt the cause–effect relationships of the FCM model improving the efficiency and robustness of FCMs. A process control problem is presented and its process is investigated using the proposed weight adaptation technique.     

On-line peer assessment and the role of the peer feedback: A study of high school computer course

The purposes of this study were to explore the effects and the validity of on-line peer assessment in high schools and to analyze effects of various types of peer feedback on students. The participating students, a total of 184 10th graders, developed their individual course projects by involving the on-line peer assessment learning activities in a computer course. The peer assessment activities consisted of three rounds, and each of the students acted as an author and a reviewer. Research data as evaluated from peers and experts indicated that students significantly improved their projects as involving the peer assessment activities. The scores determined by the learning peers were highly correlated with those marked by the experts, indicating that peer assessment in high school could be perceived as a valid assessment method. Moreover, this study also examined the relationships between the types of peer feedbacks in which students obtained from peer assessment and the subsequent performance of their projects. We categorized peer feedbacks into four types: Reinforcing, Didactic, Corrective and Suggestive. It was found that Reinforcing peer feedback was useful in helping students’ development of better projects; however, Didactic feedback and perhaps Corrective feedback provided by peers might play an unfavorable role for subsequent improvement of students’ projects. The Suggestive feedback may be helpful in the beginning of peer assessment activities; however, in the later parts of peer assessment, the effect of this type of feedback on learning might not be significant.     

一种新型的认知无线电协作检测算法研究

依据无线传感网络分簇协议,提出了一种算法。簇内采用能量自适应双门限检测,簇间通过Beta模型来并自动更新动态分配每一个簇头在数据融合中的权重因子,从而有效减小信任度较低的簇头对判决结果的影响,增强信任度较高的簇头参与度。理论分析和仿真结果表明,算法的复杂度和检测性能均优于传统的协作检测算法和分簇算法。     

A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information

Both academic and corporate interest in sustainable supply chains has increased in recent years. Supplier selection process is one of the key operational tasks for sustainable supply chain management. This paper examines the problem of identifying an effective model based on sustainability principles for supplier selection operations in supply chains. Due to its multi-criteria nature, the sustainable supplier evaluation process requires an appropriate multi-criteria analysis and solution approach. The approach should also consider that decision makers might face situations such as time pressure, lack of expertise in related issue, etc., during the evaluation process. The paper develops a novel approach based on fuzzy analytic network process within multi-person decision-making schema under incomplete preference relations. The method not only makes sufficient evaluations using the provided preference information, but also maintains the consistency level of the evaluations. Finally, the paper analyzes the sustainability of a number of suppliers in a real-life problem to demonstrate the validity of the proposed evaluation model.     

A tool to design fuzzy decision trees for sustainability assessment

Assessment of sustainability is a multicriteria problem that addresses several criteria belonging to various themes structured in sustainability dimensions. There is the need for a relevant aggregation method in addition to the disaggregated indicators. To face this challenge, we developed a new aggregation method, CONTRA, which is based on a decision tree using fuzzy sets. We attempted to combine the advantages of previous tools like DEXi and FisPro that ensure simplicity, flexibility and transparency while limiting subjectivity in the design of decision trees. The results of two examples of implementation in the agricultural sector and a sensitivity test highlight the functionalities of the tool and its discrimination potential. The possibility offered by the tool to correct predetermined decision rules makes it possible to cope with compensation between input variables, an important issue in aggregation. The next step will be the finalisation of the Excel prototype in a user-friendly software.     

A gamified peer assessment model for on-line learning environments in a competitive context

Peer Assessment (PA) offers a powerful solution that helps teachers in online learning environments to correct essays by distributing the workload among students. Nevertheless, the quality of the results in PA depends on good evaluations of reviewers. Thus, the main drawback for scaling up the use of PA is the presence of inadequate behaviours, such as being too harsh or too soft in the assessment, or even not offering a helpful feedback. This usually occurs due to the lack of motivation and engagement of students in the PA process. To deal with this problem, this paper proposes a gamified peer assessment model, where gamification elements are used to engage students in PA activities. Two experiments using the proposed model within an intelligent tutoring system called MeuTutor shows satisfactory outcomes. We verified that the average grade given by students to an essay are equivalent to those given by experts, but the time and costs to complete the assessments were largely reduced. Furthermore, the use of gamification helped to increase the amount of students' access to the system in 64.28%; increase in 10.53% the number of essays written and submitted; and improve the quantity and quality of assessments for each essay.     

A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand

The fuzzy logical relationships and the midpoints of interval have been used to determine the numerical in-out-samples forecast in the fuzzy time series modeling. However, the absolute percentage error is still yet significantly improved. This can be done where the linguistics time series values should be forecasted in the beginning before the numerical forecasted values obtained. This paper introduces the new approach in determining the linguistic out-sample forecast by using the index numbers of linguistics approach. Moreover, the weights of fuzzy logical relationships are also suggested to compensate the presence of bias in the forecasting. The daily load data from National Electricity Board (TNB) of Malaysia is used as an empirical study and the reliability of the proposed approach is compared with the approach proposed by Yu. The result indicates that the mean absolute percentage error (MAPE) of the proposed approach is smaller than that as proposed by Yu. By using this approach the linguistics time series forecasting and the numerical time series forecasting can be resolved.     

Fuzzy fusion for attending and responding assessment system of affective teaching goals in distance learning

Teachers can handle learning situations during activities in peer-to-peer classes, and assess student achievements associated with teaching goals in the affective domain. In distance learning, teachers cannot directly observe student states and assess achievement concurrently. Many distance education studies adopted frequency of interaction as the basis of student participation when assessing the student achievements in class. However, the number of times a student interacts is not equal to discussion quality. Although synchronous discussions during class can help teachers assess learning states, these discussions are not suited to all courses. If a teacher can supervise student images from computer’s webcam and observe student status, the teacher can assess achievement accurately. Image processing technology can be applied in an assessment system in distance learning, student states can be observed and these observational results can be combined with behavior detection to help teachers assess student achievement in terms of teaching goals in the affective domain.This study had analyzed the theory and method of assessing affective domain teaching goals. The assessment system had been implemented and simulated using image processing technology and records to analyze student achievement of attending and responding stages with a class period, via fuzzy logic and a fuzzy integral. Simulation results indicate that this assessment system can accurately assess student achievement in terms of attending and responding stages of affective domain teaching goals.     

A method for group decision making with multi-granularity linguistic assessment information

This paper proposes a method to solve the group decision making (GDM) problems with multi-granularity linguistic assessment information. In the method, the multi-granularity linguistic information provided by experts is firstly expressed in the form of fuzzy numbers. In order to make the collective opinion close to each expert’s opinion, a linear goal programming model is constructed to integrate the fuzzy assessment information and to directly compute the collective ranking values of alternatives without the need of information transformation. Then, a fuzzy preference relation on the pairwise comparisons of the collective ranking values of alternatives is constructed using the dominance possibility degree of the comparison between the fuzzy numbers. By applying a non-dominance choice degree to this fuzzy preference relation, the ranking of alternatives is determined and the most desirable alternative(s) is selected. An example is used to illustrate the applicability of the proposed method and its advantages.     

A two-layer fuzzy synthetic strategy for operational performance assessment of an industrial hydrocracking process

The operating performance may deteriorate from the optimal condition in industrial hydrocracking process due to changes in product requirements, equipment performance degradation, environmental noises, and random disturbances, etc. Therefore, it is necessary to carry out effective online operating performance assessment for it. In this paper, a two-layer fuzzy synthetic strategy is proposed for operating performance assessment of the hydrocracking process. First, an operating performance assessment index is constructed based on the concept of positive deviation of the quality variables. As there are many products in the process, an assessment system is further built based on the performance index. To calculate the indicators in the assessment index system in real-time, partial least squares is used for prediction. To solve the problem of limited labeled samples in the prediction, the process variables are classified into several subspace blocks according to their correlations with different key indicators. Then, a two-layer fuzzy synthetic evaluation strategy is designed to evaluate the actual operating performance of the hydrocracking process. The proposed strategy is finally illustrated with a series of cases on an industrial hydrocracking process in China.     

A comprehensive fuzzy DEA model for emerging market assessment and selection decisions

The changing economic conditions have challenged many financial institutions to search for more efficient and effective ways to assess emerging markets. Data envelopment analysis (DEA) is a widely used mathematical programming technique that compares the inputs and outputs of a set of homogenous decision making units (DMUs) by evaluating their relative efficiency. In the conventional DEA model, all the data are known precisely or given as crisp values. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. In addition, performance measurement in the conventional DEA method is based on the assumption that inputs should be minimized and outputs should be maximized. However, there are circumstances in real-world problems where some input variables should be maximized and/or some output variables should be minimized. Moreover, real-world problems often involve high-dimensional data with missing values. In this paper we present a comprehensive fuzzy DEA framework for solving performance evaluation problems with coexisting desirable input and undesirable output data in the presence of simultaneous input–output projection. The proposed framework is designed to handle high-dimensional data and missing values. A dimension-reduction method is used to improve the discrimination power of the DEA model and a preference ratio (PR) method is used to rank the interval efficiency scores in the resulting fuzzy environment. A real-life pilot study is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms in assessing emerging markets for international banking.     

A new type-2 fuzzy set of linguistic variables for the fuzzy analytic hierarchy process

The fuzzy analytic hierarchy process (FAHP) has been used to solve various multi-criteria decision-making problems where trapezoidal type-1 fuzzy sets are utilized in defining decision-makers’ linguistic judgment. Previous theories have suggested that interval type-2 fuzzy sets (IT2 FS) can offer an alternative that can handle vagueness and uncertainty. This paper proposes a new FAHP characterized by IT2 FS for linguistic variables. Differently from the typical FAHP, which directly utilizes trapezoidal type-1 fuzzy numbers, this method introduces IT2 FS to enhance judgment in the fuzzy decision-making environment. This new model includes linguistic variables in IT2 FS and a rank value method for normalizing upper and lower memberships of IT2 FS. The proposed model is illustrated by a numerical example of work safety evaluation. Comparable results are also presented to check the feasibility of the proposed method. It is shown that the ranking order of the proposed method is consistent with the other two methods despite difference in weight priorities.     

A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot

Ant Colony Optimization is a population-based meta-heuristic that exploits a form of past performance memory that is inspired by the foraging behavior of real ants. The behavior of the Ant Colony Optimization algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired optimization algorithms. The present paper explores a new fuzzy approach for diversity control in Ant Colony Optimization. The main idea is to avoid or slow down full convergence through the dynamic variation of a particular parameter. The performance of different variants of the Ant Colony Optimization algorithm is analyzed to choose one as the basis to the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence is created. Encouraging results on several traveling salesman problem instances and its application to the design of fuzzy controllers, in particular the optimization of membership functions for a unicycle mobile robot trajectory control are presented with the proposed method.     

A novel approach for combination of individual and group decisions based on fuzzy best-worst method

The process of decision-making in an enterprise may either keep the business on track or derail it. Thus, a senior decision maker often use a group of experts as the supportive team to ensure appropriate decisions. The experts often have different expertise level regarding their knowledge, talent, proficiency, and experience. In this study, we first extend the best-worst method based on the linguistic preferences of decision-makers about importance of attributes. These preferences are converted into triangular fuzzy numbers to be utilized in the linear programming model. That is, in contrast with the original best-worst method in which the preferences towards the attributes are crisp, fuzzy preferences are considered in the proposed method to reflect the imprecise comments of experts. Second, we propose a novel group decision making approach based on the fuzzy best-worst method to combine the opinion of senior decision-maker and the opinions of the experts. Indeed, our model helps the senior decision-maker to make a significant trade-off between democratic and autocratic decision-making styles. From sensitivity analyses on two numerical examples, we show that, when there is conflict between senior decision-maker and group of decision-makers, the consistency of group decision-making (democracy) will increase as it tends to individual decision-making (autocracy).