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A Fully Objective Assessment Methodology for Evaluating Students and Teachers' Performance in an Educational Institute

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A Fully Objective Assessment Methodology for Evaluating Students and Teachers' Performance in an Educational Institute
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  A Fully Objective Assessment Methodology for Evaluating Students and Teachers’ Performance in an Educational Institute Pradipta Biswas, S.K. Ghosh School of Information Technology Indian Institute of Technology, Kharagpur 721302, India Email : {pbiswas,skg}@sit.iitkgp.ernet.in Abstract Evaluation is an unavoidable feature in any teaching or learning scenario. The Assessment and evaluation strategy of students differs widely throughout the world. Most of the institute does not use any objective technique to assess the teaching performance of a teacher. The present paper attempts to establish a  performance metric for student and teacher evaluation and also discusses the methodology of calculating the metrics. In a decision making scenario, the metrics may not be solely used, but it will provde enough insight into the assimilation capability of students and teaching capability of teachers. Once measured  properly for adequate length of time, the metrics can also provide many other useful information like utility of a course modification, institutional performance etc.  Keywords : Education Technology, Data Warehouse, Performance Metric, Ontology 1 Introduction In any educational institution, besides regular teaching and learning activities, evaluation is a matter of utmost importance. It is not only important from the students’ point of view only; rather the performance of the whole institute depends on the Assessment techniques. Unfortunately, there is no universal mechanism invented yet which can give the best Assessment. The evaluation or Assessment techniques depend heavily on the course and also the institution itself. One of the important focuses on the field of education technology is student Assessment technique. In [1], the variance of student performance is shown to be dependent on the standard on question papers and subjects. So it can be inferred that an evaluation technique should use different weightages for different subjects and questions. The idea of accurate weightage for practical examinations and internal Assessments is more explored in [2]. A novel approach of using Analytical Hierarchical Process for calculating accurate weightage for theory and practical examination is discussed. This approach is claimed to be universal i.e. it is irrespective of an individual institution performance. In [3], an evaluation technique is presented which is based on student model. The concept of student model is widely used in Intelligent Tutoring Systems. The evaluation technique in [3], mainly concerns with the extent of assimilation of concepts. The inference mechanism of the system is  based on Bayesian Network. Currently the system is demonstrated for physics only and it deals with 290  physics rules. The problem of the approach is that it does not seem to be scalable for a large number of concepts. The existing system has the main focus on the Assessment techniques of students. They ignore two important things • Assessment of teachers • Defining a universal metric to measure the performance of students and teachers. Both of these facts are very important from an institutional point of view. It is a common observation that students prefer some teachers than another based on various reasons. So there should be an objective mechanism to measure the teacher’s teaching performance also, along with the students’ performance. Finally a universal metric of performance is needed very much for a comparative study of students, and teachers throughout a large number of institutions. 1  The present paper discusses about some universal performance metrics for teachers and students and the methodology for measuring them. The metrics are viewed both from theoretical point of view and implementation point of view. From theoretical point of view, the significance of the metrics is given and their definitions are expressed through propositional calculus statements. From implementation point of view, scheme diagrams for an online database and a data warehouse have been described for storing enough information about a curriculum as well as efficient measurement of the performance metrics. The scope of the metrics presented in this paper is not only confined to performance evaluation, rather more sophisticated decisions regarding course modification, institution performance etc. can easily be taken from the metrics. The organization of the paper is as follows. In the next section a brief overview of data warehouse is given. In sec 3 the proposed system is discussed. Section 3.1 concerns with the operational overview of the  proposed methodology. In sec 3.2 the schemes of an online database and data warehouse have been  presented. In section 3.3 we have described the point calculation system in an examination adopted in the  proposed system. In section 3.4 the performance metrics are defined. In section 3.5 some other utilities derivable from the proposed performance metrics are discussed. 2 Preliminaries  Data Warehouse Operational database helps business people to take low-level operational decisions and also some tactical decisions. But they can not help much to take a strategic decision which depends on a huge amount of  business data organized in a proper fashion. In this regard Datawarehousing comes into picture. According to W.H. Inmon it is a subject oriented, time variant, non-volatile, integrated repository of data [4]. This data warehouse now becomes an unavoidable tool for better decision-making process. While there are many types of data warehouses, based on different design methodologies and philosophical approaches, they all have these common traits: • Information in a data warehouse is organized around the major subjects of the enterprise (for example, customers, products, sales, or vendors), reflecting a data-driven design. • Raw data is gathered from nonintegrated operational and legacy applications, cleansed, and then summarized and presented in a way that makes sense to business users. The main differences of data warehouse with traditional database include the followings [5]. • Utilization : A traditional database is used for daily operational processing. The unit of work is short transactions. It is used for tracking daily operations at a detailed level. Data warehouse is used to store summarized historical data for analysis purpose. Its main focus is on generating information to aid strategic decision-making. • Access Pattern : The access pattern of database is short, atomic transactions. Accesses to data warehouse mainly consist of complex read-only queries. • Size : The size of a data warehouse generally ten times larger than an operational database. 3 The Proposed Methodology The present paper discusses about an evaluation system, which can be used to evaluate the performance of  both students and teachers in an educational institution. The evaluation will be done based on online examinations held at different time of a course. The online examination is found to make no change in scoring compared to paper pencil tests [6]. The system is designed to be used for a long range of time. 2  Main emphasis is given on designing a database scheme for storing basic information about course curriculums and developing a data warehouse for storing consolidated information for a long range of time. The data warehouse will actually be used to evaluate students and teachers’ performance on a long range and also to take strategic decisions about a course design, improvement measurement etc. The system will not propose any decision regarding a student or teacher rather it will present necessary information in a comprehensible manner that will aid decision-making. 3.1 Operational Overview The proposed evaluation system will operate in three phases. These phases are 1. Initialization Phase 2. Running Phase 3. Assessment Phase These system phases conform to the regular course calendar. The initialization phase will take place before start of a course. The running phase will run with the course. After the end of the course, the students’ and teachers’ performance will be evaluated in the Assessment phase. The initialization phase mainly concerns with database fill up with curriculum details and demographic information. A course is broken up into a number of subjects. Each subject is further classified into chapters or topics. Now a topic is broken up into some concepts. As for example a secondary level science course can be divided into subjects like physics, chemistry, biological sciences and mathematics. Now  physics can be classified into topics like optics, magnetism, mechanics etc. The topic mechanics includes concepts like free body diagram, inclined plane, momentum etc. The ontology of a course can be defined as shown in Fig. 1 Subject Topic [Topic Difficulty Index] Concept [Concept Difficulty Index] Questions [Question Difficulty Index] [Expected Answer Time] Fig. 1 Ontology of the course As shown in Fig. 1 each topic, concept and question is given a difficulty index. Questions are associated with an expected answer time also. These difficulty indices will be used for Assessment of the student. In the running phase, the teacher can periodically evaluate the class performance by designing online examinations or quiz sessions. These examinations or quiz can be designed using the existing question-  3  answers within the database or by inserting new questions and answers. Even the course instructor can add new topics or concepts also during this running phase. Short term Assessment can also be carried out by manually analyzing the points scored by the students during an examination. The point calculation system is explained in the next section. After the end of the course, the final Assessment will be carried out. The final Assessment will not only consider the immediate performance of a student in a single course, but also takes care of historical data available about the students, teachers and subjects. Enough data will be maintained to calculate the  performance metrics defined in section 3.3. 3.2 Database Design The database is designed according to the operational phases of the system. The initialization and running  phase will deal with an online normalized database. In the Assessment phase, the content of the database will be analyzed and consolidated into a data warehouse. This data warehouse will store information that will facilitate to draw scoring curves at various levels of granularities and for various combinations of the dimensions. The schemes of the database and data warehouse are furnished below. 3.2.1 Database Tables Sl. No. Table Name Utility 1 Teacher Stores Teachers’ demographic information 2 Teacher_Allotment Stores the subjects taken by a teacher 3 Topic Stores Topic Details 4 Concept Stores Concept Details 5 Answer Stores the answer statements 6 Concept_Topic_Mapping Maps each Topic to a Concept 7 Examination Stores Examination information 8 Question Stores question related information 9 Student Stores Students’ demographic information 10 Question_Answer_Mapping Maps each answer to a question 11 Student_Session Stores examination details of individual student 12 Subject_Concept_Mapping Maps each Concept to a Subject 13 Subject Stores each subject information 3.2.2 Data warehouse Tables: The data warehouse has two fact tables and six dimensions. The fact is the points scored by student in an online examination or quiz session. The student and teacher fact table is separated to avoid any personal misunderstanding between an individual student and teacher. Sl. No. Table Name Utility 1 Concept Stores Concept Details 2 Examination Stores Examination information 3 Student Stores Students’ demographic information 4 Student_Fact_Table Table to assess Students’ learning Rate 5 Subject Stores each subject information 6 Teacher Stores Teachers’ demographic information 7 Teacher_Fact_Table To evaluate Teachers’ Performance 8 Topic Stores Topic Details 4  3.2.3 Database Table Relationships: Fig. 2 Online Database Scheme 3.2.4 Data warehouse Table Relationships: Fig. 3 Online Data warehouse Scheme 5
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