Team formation is important in industrial and academic institutions because the success of teams

depend on assembling the right combination of team members.Prior academic achievement is one of the factors that affect teams’ performances.Therefore, it is important to identify an effective technique that can determine equality amongst teams based on prior academic achievements.Having team with similar prior academic achievements can increase equality, reliability and validity in team formation before embarking on any research

studies.This can be achieved by applying fuzzy rule-based technique.Fuzzy rule-based technique is suitable for this study because this technique allow analyzing of imprecise data and classifying selected criteria.Initial evaluation of this technique showed that it can indicate whether every team has equal distribution of prior

academic achievements.By incorporating this technique in a team formation model, each team can be guaranteed to have equal chances to perform effectively.This technique can facilitate decision makers when forming highly productive project teams

Mohd Idris, Mohd Fazril Izhar

Omar, Mazni

Syed-Abdullah, Sharifah-Lailee

UUM Repository

World Applied Sciences Journal 15 (3): 359-363, 2011ISSN 1818-4952© IDOSI Publications, 2011Corresponding Author: Mazni Omar, Department of Computer Sciences, Faculty of Computer and Mathematical Sciences,Universiti Teknologi MARA, 02600 Arau, Perlis, Malaysia.Tel: (+604)9284735.359Team Achievements Equality Using Fuzzy Rule-based TechniqueSharifah-Lailee Syed-Abdullah, Mazni Omar and Mohd Fazril Izhar Mohd IdrisDepartment of Computer Sciences, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 02600 Arau, Perlis, MalaysiaAbstract: Team formation is important in industrial and academic institutions because the success of teamsdepend on assembling the right combination of team members. Prior academic achievement is one of the factorsthat affect teams’ performances. Therefore, it is important to identify an effective technique that can determineequality amongst teams based on prior academic achievements. Having team with similar prior academicachievements can increase equality, reliability and validity in team formation before embarking on any researchstudies. This can be achieved by applying fuzzy rule-based technique. Fuzzy rule-based technique is suitablefor this study because this technique allow analyzing of imprecise data and classifying selected criteria. Initialevaluation of this technique showed that it can indicate whether every team has equal distribution of prioracademic achievements. By incorporating this technique in a team formation model, each team can beguaranteed to have equal chances to perform effectively. This technique can facilitate decision makers whenforming highly productive project teams. Key words: Team equality  Team achievements  Team formation  Fuzzy rule-basedINTRODUCTION prior academic achievements amongst team members. ThisIn this era of globalisation and competitiveness, compete with each others. teamwork becomes a central part of every workplace. Biasness may exist if issue in inequality of academicWorking as team is vital in ensuring successful achievements amongst team members is not resolved.organisational goals and strategies are met. In many This is because members faced difficulty to performworkplaces, employee works as a team in industries such effectively as a team. This may limit members’ ability inas engineering, manufacturing and construction. Many engaging and learning during teamwork activities.jobs cannot rely on one-man-show tasks because Therefore, this study aims to determine a technique thatindividuals have limited knowledge and capability. can ascertain equality of team members based onTherefore, working in teams expedites the completion of student’s prior academic achievements. To develop aworks by cultivating more ideas [1] and creating synergy model, a fuzzy approach using rule-based technique wasamongst members in teams [2] thereby increasing teams selected because it is allow imprecise data to be analysedproductivity. and selected criteria to be classified.The success of teams depends on the grouping ofthe right members in a team [3-4]. However, to form a team Related Worksthat has members with similar academic achievements [5], The Importance of Teamwork: Teams that have commonexperiences [6], or even personality types [3] is a characteristics are able to exchange ideas actively andchallenging task. According to Ounnas et al. [7], most thus able to create more interest amongst team members.existing group formations used self-selecting approach According to [8], cooperative teams achieve higher levelswhereby the students choose their own team members or of thought and retain information longer than those whoinstructor-selecting approach, where the instructors work quietly as individuals. Vygotsky [9] argued thatinitiate the group formation. Nonetheless, these people are capable of performing at higher intellectualapproaches often lead to a team that consists of unequal levels when working in collaborative situations than wheninequality leads to teams not having equal chances toWorld Appl. Sci. J., 15 (3): 359-363, 2011360working individually. The shared learning environments rule-based technique [17]. Association rule and roughallow people opportunity to engage in depth discussions sets techniques are more appropriate when dealing withand thus become critical thinkers. Critical thinkers can be empirical data. These techniques are more suitable whenachieved when members in teams brainstormed and searching interesting patterns and strongest rules fromshared ideas to accomplish specific goals effectively [10]. complex problems. Nevertheless, fuzzy rule-basedDespite the benefits of teamwork, issues concerning technique can deal with linguistic variables which wereteamwork are unavoidable. Issues observed such as ‘free the case in this study. Therefore, fuzzy rule-basedriders’, lack of cooperation, inadequate rewards and technique was chosen to analyse each predefinedconflicting task become major threats that can affect collection rules that can be employed by using Mamdaniteams’ performance. These problems may arise because fuzzy inference system. skills and experiences amongst team members are not Zadeh [17] [18] introduced fuzzy logic forequally distributed amongst team members. Therefore, representing and manipulating fuzzy terms. He later usedresearchers need to investigate strategies for selecting fuzzy algorithm for complex systems and fuzzy rules forsuitable members to form effective teams. capturing human knowledge that can formulate maps fromTechniques in Team Formation: Automated tool for given a input to stated a output. The mapping provides ateam formation is valuable for decision makers when basis for decisions or patterns to be identified. Theassigning members to a team. Therefore, more researchers process of fuzzy inference involves membershipare investigating several techniques for automating group functions, fuzzy logic operators and if-then rules. Thereformation using computerized modeling techniques [11- are two types of fuzzy inference systems that can be14]. These techniques varied according to team implemented in the Fuzzy Logic Toolbox namely thecharacteristics under investigation. Redmond [11] used Mamdani-type and Sugeno-type. These two types ofgreedy algorithm to assign members in a team based on inference systems vary according to the determinedcompatible time slots and student projects. The tool output.supports group schedule compatibility, however, the Mamdani's fuzzy inference method is the mostinstructor still needs to check manually members in each commonly used fuzzy technique. Mamdani and Assilianteam to ensure they the prior grade are equally [19] used fuzzy set theory to control a steam engine anddistributed. Study by Doyle, et al. [12], introduce a web- boiler combination by synthesizing a set of linguisticbased system for creating team in industry and academic control rules obtained from experienced human operators.institutions. The system used divergent and convergent Researches using Fuzzy Logic approach have beenalgorithms to form team based on member’s preferences. applied in medical for diagnosing heart diseases [20],These preferences include time, location and experience. industry for controlling materials in steel productionTobar and Freitas [13] proposed a rule-based team process [21] and automotive suspension system [22],assignment tool based on students’ IMS learner internet shopping for assessing consumer behavioursinformation package (IMS LIP) specification such as pattern [23], decision making [24] and in education forgender. The rule defined can be created and reused based evaluating students’ performance [25-27]. on instructor’s specific preferences for forming a team. Numerous studies demonstrated the ability of fuzzyTechnique proposed by Ounnas, et al. [7, 14] used logic in determining students’ grade achievements [25-27].semantic web technologies and logic programming to deal Bai and Chen [26] used fuzzy membership and fuzzy ruleswith incomplete data when assigning members to a team. for distinguishing students’ grades based on theMost of the above techniques have problems with difficulty, importance and complexity of questions. Chenmissing data and unidentified pattern; therefore studies and Li [25] used fuzzy rules and fuzzy reasoning capabilityare now focusing on fuzzy logic approach. to automatically assign weightage to six (6) attributes;Fuzzy Logic Approach: In real world applications, accuracy rate, time rate, difficulty, complexity, answer costsolving problems always deal with uncertainty and and importance of fuzzy rules. Montero et al. [27]imprecise data. Reviews of literature have shown that introduced a fuzzy method to automatically evaluatemuch research has been carried out describing technique teamwork based on grade homogeneity, gradeto deals with this type of data. This could be achieved by improvement and attendance. This approach allowsapplying rule-based approach. This approach includes researchers to ensure equal assessment amongst membersassociation rule mining [15], rough sets [16] and fuzzy in a team.,2x n group is balanceA B n if x n group is averagex n group is unbalance >+ = = <World Appl. Sci. J., 15 (3): 359-363, 2011361MATERIALS AND METHODSIn this paper, fuzzy ruled-based was used todetermine whether each team has equal prior academicachievements. Data from 26 software engineering (SE)teams was collected to test and evaluate the technique.This was done to ensure that every team has equalcharacteristic. Four input parameters were used to monitorthe score of prior academic achievements. The teams’equality was evaluated using Fuzzy Inference System Fig. 1: Research Modelbased on four data on members, known as membershipfunction. Figure 1 shows the framework for this research.Membership Function for Input Parameters: The inputparameters represented the average grade of prioracademic achievement for team members: ‘A’, ‘B’, ‘C’ and‘D’. These four parameters were known as membershipfunctions. Each parameter was classified into four levels,which were ‘Gagal’ (Fail), ‘Lulus’ (Pass), ‘Kepujian’(Good) and ‘Cemerlang’ (Excellent). Figure 2 shows therepresentation of membership function for member ‘A’.Fuzzy Rule-Based Construction: During this stage, rulesconstruction was developed by calculating the meanvalue of total score for all members in the group. This wasachieved by representing all membership functions bynumbers. A fuzzy rule was constructed based on four Fig. 2: Membership Functions for “A”levels, which were;‘Fail’ = 1, ‘Pass’ = 2, ‘Good’ = 3, ‘Excellent’ = 4 (1)Mean value was calculated using the formula;(2)where of team members A-minimum scoreB-maximum scorex-total score of all members in the groupThe rule was constructed by using the algorithm asshown Figure 3. This algorithm was used for determiningthe combination value for each team members.Figure 4 shows a snapshot of the constructed rulescalculated from the above algorithm. The rules wereprocessed using Mamdani-style inference [19] in order toget crisp output indicating team equality. Fig. 4: Snapshot of the Constructed RuleFig. 3: Algorithm for determining the combination valuesWorld Appl. Sci. J., 15 (3): 359-363, 2011362Fig. 5: Assessment of Team EqualityFuzzy Rule-Based Evaluation: In order to evaluate themodel, the real data act as the parameter and the equalitycharacteristic of the team was calculated. The results werethen compared to the real performance of each team givenby project evaluators.Figure 5 shows student A having 3.4 pointer, studentB = 2.8, student C = 2.67 and student D = 2.47, the teamequality characteristic was calculated as 0.816. This value(0.816) indicates the level of team equality characteristicand shows that the team has equal prior academicachievements. Therefore, the team is considered ‘Fair’ toundertake any project effectively.RESULTS AND DISCUSSIONData from 26 software engineering (SE) teams werecollected to test and evaluate the fuzzy-rule basedtechnique. From Table 1, it was discovered that all groupshas equal characteristic for their prior academicachievements. This shows that all groups have an equalchance to perform effectively and therefore able to scorein their project tasks. If the equality score is less than 0.5mean value, the result indicates that the team willprobably has problem proceeding with their project. Italso shows that some members of the team have lowerprior academic achievements. Therefore, the team will notbe able to perform effectively and develop qualitysoftware application.Team equality results were then compared with realperformance results given by the instructors. Resultsindicated that all teams perform well in their softwareproject. This shows that fuzzy rule-based technique isable to indicate equality team prior academicachievements amongst team members.Table 1: Sample of Results for Team Members’ Prior AcademicAchievements EqualityMembers-------------------------------------------------------Team A B C D Equality Score1 3.33 3.46 3.4 3.2 0.8182 3.4 3.13 3.6 2.87 0.833 2.8 3 2.47 2.67 0.8164 2.73 3.07 3.67 2.67 0.8375 3.46 3.4 3 2.4 0.8186 3.4 2.8 2.67 2.87 0.8187 2.67 2.63 2.7 2.53 0.8148 2.73 3.2 3.27 3.53 0.8169 3.73 3.67 2.56 3.6 0.82310 3.47 3.06 3.6 2.47 0.816CONCLUSIONThis study proved that fuzzy rule-based techniquecan be used to solve complexity problem involvingestablishing whether team members have equal prioracademic achievements before embarking on comparativeresearch studies. This proved that fuzzy rule is capable forsolving complexity problems such as determining whethereach team contains members that have equal prioracademic achievements. It is important to be able to formteam with equal prior academic achievements because thiswill add validity and reliability of team formation. Thetechnique can be improved to accommodate moremembers for each team. Further improvement is requiredto incorporate this technique in team formation model. Byincorporating this technique in team formation model,each team can be guaranteed to have equal chances toperform effectively. This technique can facilitate decisionmakers when forming highly productive project teams.REFERENCES1. Kang, H.R.,  H.D.  Yang  and  C.  Rowley,  2006.Factors in team effectiveness: Cognitive anddemographic similarities of software developmentteam members. Human Relations, 59(12): 1681-710.2. Amato,    C.H.    and    L.H.    Amato,    2005.Enhancing  Student  Team  Effectiveness:Application  of  Myers-Briggs  PersonalityAssessment in Business Course. Journal ofMarketing Education, 27(41) :41-51.3. Mazni,  O.  and  S.A.  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Team Achievements Equality Using Fuzzy Rule-based Technique

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Team Achievements Equality Using Fuzzy Rule-based Technique

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