May 2018 by Editor IJITCE

ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING MAY 2018 VOL- 8 NO-5

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.5 MAY 2018

UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: editor@ijitce.co.uk Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.5 MAY 2018

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International Journal of Innovative Technology & Creative Engineering Vol.8 No.5 May 2018

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From Editor's Desk Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month.

Every autumn, a quiet mountain pass in the Swiss Alps turns into an insect superhighway. For a couple of months, the air thickens as millions of migrating flies, moths and butterflies make their way through a narrow opening in the mountains. For Myles Menz, it’s a front-row seat to one of the greatest movements in the animal kingdom. Menz, an ecologist at the University of Bern in Switzerland, leads an international team of scientists who descend on the pass for a few months each year. By day, they switch on radar instruments and raise webbed nets to track and capture some of the insects buzzing south. At sunset, they break out drinks and snacks and wait for nocturnal life to arrive. That’s when they lure enormous furry moths from the sky into sampling nets, snagging them like salmon from a stream.Scientists like Menz are fanning out across the globe to track butterflies, moths, hoverflies and other insects on their great journeys. Among the new discoveries: Painted lady butterflies time their round trips between Africa and Europe to coincide within days of their favorite flowers’ first blossoms. Hoverflies navigate unerringly across Europe for more than 100 kilometers per day, chowing down on aphids that suck the juice out of greening shoots. What’s more, some agricultural pests that ravage crops in Texas and other U.S. farmlands are now visible using ordinary weather radar, giving farmers a better chance of fighting off the pests. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.

Thanks, Editorial Team IJITCE

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.5 MAY 2018

Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.

Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.5 MAY 2018 Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.5 MAY 2018 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar)01332-000, São Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.5 MAY 2018

Contents Statistical Dimension Identification and Implementation for Student Progression System Harkiran Kaur, Aanchal Phutela ……………………………. [480]

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.5 MAY 2018

Statistical Dimension Identification and Implementation for Student Progression System Harkiran Kaur Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, (Deemed to be University, Patiala),India Aanchal Phutela Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, (Deemed to be University, Patiala),India Abstract- Descriptive Analytics is the summarization of the past data and generates some useful patterns from that data. This work focuses on analyzing and querying large academic dataset for generating Student Progression using visualization and dashboards. Presently projects on Progression Systems exist but no descriptive or predictive analytics has been performed on these datasets. The proposed system collects data from different departments of University, store data into the large data warehouse of the University and generate validated set of KPIs, based on the past dataset of University’s department. These KPIs are obtained after applying Statistical techniques on various sets of dimension in the academic datasets. After completion of this step, analysis of the data has been achieved with Online Analytical Processing (OLAP) operations, which have been show cased with the help of visualization and dashboards. Keywords- KPIs, Progression System, SPSS tool, Descriptive Analytics, OLAP.

1. INTRODUCTION Descriptive Analytics as the name suggests, ‘describe’ or summarize the data into some useful information and possibly formulate the data for further analysis that is comprehensible by humans. Some common techniques engaged in Descriptive Analytics are observations, case studies, and surveys. In the proposed work the given dataset is analyzed by applying statistical methods on the academic dataset using IBM SPSS Statistics tool. The main agenda of this study is to create student progression system. For this purpose, the descriptive analytics must be performed on only those features of the academic dataset which have a huge impact on the projected goal and these features are commonly called Key Performance Indicators (KPI’s). In its simplest form, a KPI is a type of measurement that helps you to recognize how your organization or department is carrying out. A good KPI will help you and your team to recognize whether you’re choosing the right path in the direction of your planned goals or not. A KPI must be effective if it should follow the SMART criteria. SMART refers to Specific, Measurable, Achievable, Relevant, and Time-bound [1]. The present study perform analytics on academic dataset of students in a University, which can be done by descriptive analytics (the introductory stage of the data analysis)

and creates the summary of historical data and generate some useful information from the past trends in the dataset. The vital success of an organization is its ability to analyze the data, find some major facts in it and take some actions towards the changes. The task of finding major facts in these dataset is performed by validated selection of KPI. Further the corresponding actions are taken as per the data analytic technique applied on these KPIs contained in the dataset. 2. KPI CLASSIFICATION AND IDENTIFICATION METHOD The major issue in KPIs identification is that, there are N number of KPIs to choose from. If we are choosing the wide of the mark, then we are calculating something that doesn’t line up with our objectives. Every organization needs to analyze the results for their respective business problems and consider just those set of KPIs which are most relevant for an organization’s further monitoring, evaluating, planning and decision making. Therefore, it is a prerequisite to identify a set of KPIs for a specific organization for its mission, vision and values on the subject of an organization’s approach. A KPI should be: relevant, realistic, specific, attainable, measurable, and used to identify trends, timely, understood, agreed, reported, governed or resourced [1]. There are several methods and techniques you can use for selecting subset of features which helps your model to perform better and efficiently. These include: Pearson’s Correlation, Linear Discriminant Analysis (LDA), ANOVA and Chi-Square Test. These statistical filter methods are used as a preprocessing step for identifying KPIs. In this the selection feature is independent from any machine learning algorithm. Fig 1 describes the implementation of filter methods.

Fig.1. Filter Methods [4] Some other methods include: Forward Selection, Backward Elimination and Recursive Feature elimination, these are Wrapper Methods. Figure 2 demonstrates the processing of wrapper methods, in which learning algorithms are used for the selection of best subset of features [4].

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Fig. 2. Wrapper Methods [4] There are also some mehods which combine the qualities’ of Filter and Wrapper Methods that are called Embedded Methods which includes LASSO and RIDGE Regression Methods. Figure 3 illustsrates the processing of embedded methods. In which the best subset of features are selected from set of all features.

Fig. 3. Embedded Methods [4] 3. RELATED WORK Ratapol Wudhikarn et al. [10] proposed a work for intellectual capital management for which the author used the Delphi Method. Mainly, the research has been focused on the identification of important indicators, which were significant for the business performance. Since the research has been done on Delphi Method, it will help the organization to manage the intellectual capital in the business logistics more effectively. This method is better than the completely depending upon expert’s advice and also helped in determining important KPIs in business logistics. Key performance indicator (KPI) framework proposed by Sangeeta M. Joshi [3] can be used by an educational institute to meet the developmental necessities across the world. In this work, the authors have developed a performance management system (PMS). Firstly the authors categorizes the KPIs in different areas and then evaluate performance of faculty by identifying the KPIs. The PMS will help to produce faculty ranking and also helps the institutes to rise their quality standards. Dwi Al Aji Suseno et al. [9] proposed a work of determining bonus in enterprise resource planning using KPI. In this work, the authors define how to calculate bonus by calculating the weight and analyzing the KPIs. In general, there are many ways to determine the bonus in the company but it leads to some drawbacks for the company. This is because there is no clear idea that determines the significant factors for the

organization. SMART criteria had been used by the authors for analysis and determining the bonus in the company. The authors concluded that, determining bonus depends upon KPI is more efficient than seniority-based distribution of bonus among the employees. Another observation was that, if the employee can achieve the KPI goal he will get more bonus. According to Paulo Roberto Martins de Andrade et al. [8] having a system like Business Indicator Management helps to meet their needs in terms of information availability and agility as well. The proposed work gives the integrated approach to manage the KPIs, through which real-time information about organizations can be obtained and further the actual situation of the companies can be analyzed. This helps the organization to increase their productivity and also decision making efficiency. Jinsoo Park et al. [6] proposed a work for manufacturing scheduling, it is almost unmanageable for schedulers to study all the constraints. So, the authors have proposed an approach of simulation-based advanced planning and scheduling (APS). In this work, the authors propose a new method that meet the features of any process by selecting appropriate KPIs. The authors verify this method with empirical analysis whether they meet the requirements of KPIs or not. In which the authors associate the outputs derived from simulation-based APS with modifications of domain experts and propose a framework to identify the appropriate KPIs. David Plandor et al. [7] proposed an article, in which the authors invent an application for producing KPIs. First of all, the authors initiated with the results of the company’s business diagnostic process. Formerly, the results was ready for the KPIs analysis, which was stored in the database. The author’s objective is to create a set of KPIs, for which they used genetic algorithm. The author developed a brand new software application as a tool for KPI collection. Worarat Krathu et al. [5] proposed an analysis for inter-organizational relations (IOR’s) that are important for collaborations between businesses. In this work, the authors proposed KPIS for measuring success factors. For this work, the authors presented a method for identifying IOR’s. The proposed methods takes the semantics and data types of both data elements come to precise results. The authors applied this technique on real-world industry MIGs and offered a set of inter-organizational KPIs. The KPIs offered can be used for the evaluation of IOR’s. 4. PROPOSED WORK The proposed work applies statistical filter methods: ANOVA test and correlation testing on academic dataset, to obtain validated set of KPIs. In this research, authors of this paper used two steps for creating Student Progression System. These steps are: STEP 1: Evaluation of applied statistical technique and select the KPIs This step further involve following sub steps. The steps are: a) define candidate KPIs, b) define null hypothesis and alternate hypothesis for the selected candidate KPIs in first step, c) perform descriptive analytics to identify their correlation, d) formulation of condense list of KPIs.

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Step a. Define candidate KPIs This study utilizes Academic Dataset of the students from the University Database which includes different factors of students like number of backlogs, number of semesters, regularity, Extra curriculum activities, projects done, research work done, their grades or CGPA or many other dimensions. These are the candidate KPIs for the selected domain. TABLE I CATEGORY-WISE CANDIDATE KPIs. Sr. No. Candidate KPIs Category 1.

No. of Backlogs

Quantitative KPI

Extra curriculum activities

Leading KPI

Regularity

Actionable KPI

CGPA

Outcome KPI

State

Quantitative KPI

Projects

Research Work

Quantitative KPI Qualitative and Quantitative KPI Leading KPI

Fig.4. Results of ANOVA Test Now, according to the Fig 4, the results shows that pvalue is less than 0.05 then, the null hypothesis will be rejected and the alternate hypothesis will be accepted. It means that Regularity has significant effect on Extra Curriculum Activities. Set 2: Regularity and CGPA of students. The corresponding hypothesis for this set them would be H0: Regularity have no significant effect on CGPA of students. H1: Regularity have significant effect on CGPA of students.

All Rounder Score Number of Semester in 9. Quantitative KPI the course 10. Number of Subjects Quantitative KPI Step b. Define Null Hypothesis (H0) and Alternate Hypothesis (H1) for the selected candidate KPIs in first step In the proposed work we are using filter statistical techniques for feature selection (significant factors), including ANOVA, Correlation Test and from the list of given factors. These tests are applied on various combination of candidate KPIs. Some of them have been showcased in the coming paragraphs. Let us take the Null and Alternate Hypothesis for two factors that is: H0: First factor have no significant effect on Second factor. H1: First factor have significant effect on Second factor. Where H0 represents the Null Hypothesis and H1 represents the Alternate Hypothesis. These tests has been conducted under 0.05 significant level. a. ANOVA Test One-way Analysis Of Variance(ANOVA) will be performed on these factors, which will determine whether there is any statistically significant differences between the means of two or more independent (unrelated) groups or not. If the results would be less than the p-value then the null hypothesis will be rejected and alternate hypothesis will be accepted and vice- versa. ANOVA test has been performed on several pair of factors as shown below: Set 1: Regularity and Extra Curriculum Activities The corresponding hypothesis would be: H0: Regularity have no significant effect on extra curriculum activities. H1: Regularity have significant effect on extra curriculum activities.

Fig.5. Results of ANOVA Test Now, according to the Fig 5, the results shows that pvalue is less than 0.05 then, the Null Hypothesis will be rejected and the Alternate Hypothesis will be accepted. It means that Regularity have significant effect on CGPA. Set 3: Number of semester in the course and CGPA if students. The corresponding hypothesis for this set would be: H0: Number of semester have no significant effect on CGPA of students. H1: Number of semester have significant effect on CGPA of student.

Fig.6. Results of ANOVA Test Now, according to the Fig 6, the results shows that p-value is greater than 0.05 then, the Null Hypothesis will be accepted and the Alternate Hypothesis will be rejected. It means that Number of Semester in a Course has no significant effect on CGPA of students.

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5. RESULTS AND FINDINGS

Correlation Test The authors used two factors at a time and explored the correlation between them, as the value of correlation test tells about the dependency of factors upon each other. If the correlation test will give the positive value then it means factors are directly proportional with each other and if its value is negative then it means factors are inversely proportional with each other. In IBM SPSS tool, we the authors have implemented this technique on following set of candidate KPIs. Set 1: Regularity and Extra Curriculum Activities. In Fig 7, negative correlation between these factors will be observed which identifies the inversely proportional relationship between them. It means that when one parameter increases other will decreases and vice-versa.

Fig.7. Results of Correlation Test Set 2: Regularity and CGPA. In Fig 8, positive correlation between these factors has been observed which identifies the directly proportional relationship between them. It means that when regularity increases CGPA also increases and vice-versa is also true.

S. No.

TABLE II CONDENSED LIST OF KPIs Condense KPIs Category

No. of Backlogs

Quantitate KPI

Extra curriculum activities

Leading KPI

Regularity

Actionable KPI

CGPA

Outcome KPI

Projects

Quantitative KPI Qualitative and 6. Research Work Quantitative KPI 7. All Rounder Score Leading KPI In Table 2 describes the condense list of KPIs retrieved after applying step1 on set of candidate KPIs. By applying statistical methods on them. As the results shows some KPIs are significant for our goal so we will apply the descriptive techniques only on the condense list of the KPIs which will increase the performance of the system and make the decision making process easier. 6. VISUALIZATION In Fig10, the authors have presented some visualization on dashboards. In this figure the authors have shown that, the changes occurred in the dataset will reflect directly on the charts as shown in the figure. The authors have used a slicer in which slicing of data has been done and the changes were immediately reflects on the dashboards. As shown when the field M.tech from the slicer has chosen the change on resultant dataset occurred and it reflects on the visualization. In this way, we can make our data more visible and easier to understand for analysis.

Fig.8. Results of Correlation Test Set 3: Number of Semester in a course and CGPA. In figure 9, positive correlation between these factors has been observed which identifies the directly proportional relationship between them. It means that when number of semester increases CGPA will decreases and vice-versa. But the figure also shows that the significant value if greater than 0.05 which identifies there is no significance of number of semesters on the CGPA of the students.

Fig.10. Visualization of Academic KPIs

Fig.9. Results of Correlation Test

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7. CONCLUSION In the proposed work, the authors have implemented the Statistical techniques such as Hypothesis Testing and Correlation testing to generate the optimized and most significant set of KPIs for the Student Progression System. Further, descriptive analytics has been applied on the Academic dataset based on the obtained list of KPIs. This descriptive analysis is demonstrated with the help of visualization of KPIs on the Academic Dataset on the dashboards. These visualizations supports the course wise analysis of student progressions, based on the slicer feature of OLAP. This work can be further extended by applying more of the OLAP operations on these visualizations and generate multidimensional data views from the Academic dataset. REFERENCES [1] Andrade, P. R. (2017). Improving Business Decision Making based on KPI Management System. (pp. 12801285). IEEE International Conference on Systems, Man, and Cybernetics (SMC). [2] Hagan, G. (n.d.). HR- The Appraisal Process. [3] Joshi, S. M. (2016). Developing Key Performance Indicators framework for evaluating performance of engineering faculty. (pp. 8-11). IEEE Eighth International Conference on Technology for Education (T4E). [4] Kaushik, S. (2016). Introduction to Feature Selection methods with an example (or how to select the right variables?). [5] Krathu, W. (2013). Identifying inter-organizational key performance indicators from EDIFACT messages. IEEE 15th Conference on Business Informatics. [6] Park, J. (2015). New key performance indices for complex manufacturing scheduling. 2015-Janua, pp. 2384-2395. Proceedings of the Winter Simulation Conference 2014. [7] Plandor, D. (2012). Generating KPI sets using genetic algorithms. Proceedings of the 13th International Carpathian Control Conference (ICCC). [8] Roberto, P. (2017). Improving Business Decision Making based on KPI Management System. IEEE International Conference on Systems, Man, and Cybernetics (SMC). [9] Suseno, D. (2017). Determining bonus in Enterprise Resource Planning at Human Resource Management module using Key Performance Indicator. IEEE. [10] Wudhikarn, R. (2017). Determining key performance indicators of intellectual capital in logistics business using Delphi method. International Conference on Digital Arts, Media and Technology (ICDAMT).

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