WO2015114658A2

WO2015114658A2 - An assessment system and method for assessing educational institutes

Info

Publication number: WO2015114658A2
Application number: PCT/IN2015/000036
Authority: WO
Inventors: Manoj Potdar Manisha
Original assignee: Manoj Potdar Manisha
Priority date: 2014-01-22
Filing date: 2015-01-21
Publication date: 2015-08-06
Also published as: WO2015114658A3

Abstract

An assessment system for assessing educational institutes comprising: aspects correlated to educational institute; factors correlated to aspects; parameters correlated to aspects, wherein a group of parameters form one factor; sub- parameters correlated parameters, wherein a group of sub-parameters form parameters; sources being correlated to parameters and / or sub-parameters; database of tests in order to assess each of said aspects in terms of its factors by assessing correlated parameters and optionally correlated sub-parameters using source per parameter and optionally correlated source per sub-parameter; and scoring mechanism adapted to score each of said parameters, sub-parameters based on at least a source, to score each of said factors, to score each of said aspects from a parameter-specific performance based on correlating tests, said scores being first derived at a parameter level, then at a factor level, and then at an aspect level, said scores being cumulated to form a report of said educational institute.

Description

AN ASSESSMENT SYSTEM AND METHOD FOR ASSESSING EDUCATIONAL INSTITUTES

Field of the Invention:

This invention relates to the field of computational systems.

Particularly, this invention relates to an assessment system and method for assessing educational institutes.

Background of the Invention:

Education is of paramount importance in today's day and age. It is one of the cornerstones of an individual's personality, standing in society, individual's working capability and ability, and finally at least in relation to the individual's contribution to society, at large. Education defines a person.

An educational institution is a place where people of different ages gain an education, including preschools, childcare, elementary schools, and universities. For an educational institute, there are at least five pillars of support: a) Students; b) Teachers; c) Parents; d) Institute; and e) Supporting environment.

Typically, educations institutes provide school degrees, high school degrees, diplomas, degrees, certificates, after a course of curriculum is taught. An educational institute is subjectively ranked in terms of output of students in a particular grade or in a particular sport or in a particular extra-curricular activity.

The hallmark of each centre of educational institute or school or centre of institution is the quality of the coursework or curriculum, the teachers available to teach it, the facilities available for curricular and extra-curricular activities. There are many and more of such parameters. Some are tracked, randomly. Some are left alone. There is no holistic and objective mechanism to rate an educational institute. There is no channel or platform which adjudges an educational institute on these parameters and / or displays them.

Hence, many institutes and parents are not aware of highlighting features of an educational institute or on a ranking or assessment score of an educational institute. Therefore, parents cannot make an informed decision regarding admissions and institutes cannot take informed decision for their growth.

The criteria for better education are not limited only to academics but also include intelligence, creativity, curiosity, social values, and professional values. When assessing these parameters emphasis is on understanding strengths and creating awareness about not-so-stronger traits.

Even though certain institutes have understood the importance of measuring the performance of an educational institute, the factors considered, according to the prior art are not complete or holistic in nature. The performance should be based on certain indicators linked with the educational institute. This not only aids parents and students but also enables an educational institute to perform their own SWOT analysis and further to improve themselves. The efficient operation of educational institutes is dependent not only on the identification of indicators but also on how to use these indicators as a measure of score in relative or absolute terms.

Furthermore, most of the assessments are based on individual parameters without considering other affecting parameters; e.g. IQ tests or Personality tests. These tests are carried out in isolation. This eliminates other factors responsible for it and therefore it is insufficient to find the underlying reasons. Also, this fails to give an over-all trend across the institute. Some of the institute assessments are based only on academic results of students. Accreditation measures the efficiency of the institute based on the resources provided by the institute, i.e. accreditations mainly look for infrastructure that a school can provide. Accreditation does not provide any clue or understanding of actual quality learning happening within the institute. These accreditations are more related to compliance rather than quality learning. Some of the assessment works only of measuring certain skill-sets such as improving academic subjects, memory or IQ etc. Lot of software resources are available that help the institute management to share the data among parents, students and teachers. Those tools are a platform to share the data hassle free and not focused on the learning outcomes and efficiency of the institute.

Hence, there is a need for a system and method to allow for an assessment platform which identifies key parameters in relation to an educational institute. This system and method should be able to correlate an educational institute's absolute or relative score with the key parameters.

Objects of the Invention:

An object of the invention is to provide a system and method for an assessment model and solution that assesses the strengths and developmental areas of an educational institute system in order to achieve quality learning outcomes for students and identify areas of growth for the institute.

Another object of the invention is to provide a system and method which aids in comprehensive and authentic 360° analysis about an educational institute and its performance. Yet another object of the invention is to provide a system and method which aids in at least a SWOT analysis for an educational institute.

Still another object of the invention is to provide a system and method which bases its analysis about an educational institute based on at least the institute's assets, which includes institute's brand and infrastructure.

An additional object of the invention is to provide a system and method which bases its analysis about an educational institute based on at least the teaching quality, which assesses current teaching capabilities and gaps in teaching and teachers' professional growth potential, and teachers' competency and life skills.

Yet an additional object of the invention is to provide a system and method which bases its analysis about an educational institute based on at least its students' performance, which includes assessment of students' competency and life skills and their relation to their academic performance.

Still an additional object of the invention is to provide a system and method which bases its analysis about an educational institute based on at least supporting data of students and teachers, which help in quality learning.

Another additional object of the invention is to provide a system and method which bases its analysis about an educational institute's pedagogical ability.

Yet another additional object of the invention is to provide a system and method for a rigorous, end-to-end quality learning model for quality learning and the growth of an educational institute. Still another additional object of the invention is to provide a system and method for assessing an educational institute that is factual and data driven.

Another additional object of the invention is to provide a system and method which provides a quantifiable number for an institute's growth.

Summary of the Invention:

According to this invention, there is provided an assessment system for assessing educational institutes comprising:

- at least a database of pre-defined aspects adapted to be correlated to an educational institute;

- at least a database of pre-defined factors, each of said factors being correlated to at least one of said pre-defined aspects;

- at least a database of pre-defined parameters being correlated to at least one of said pre-defined aspects, wherein a group of pre-defined parameters form one of said pre-defined factors;

- at least a database of pre-defined sub-parameters being correlated to at least one of said pre-defined parameters, wherein a group of sub-parameters form one of said pre-defined parameters;

- at least a database of pre-defined sources being correlated to at least one of said pre-defined parameters and / or at least one of said pre-defined sub- parameters;

- at least a database of tests in order to assess each of said aspects in terms of its factors by assessing correlated pre-defined parameters and optionally correlated pre-defined sub-parameters using at least a source per parameter and optionally correlated at least a source per sub-parameter; and

- at least a scoring mechanism adapted to score each of said pre-defined sub- parameters based on at least a source, to score each of said pre-defined parameters based on at least a source, to score each of said factors cumulative to said group of pre-defined parameters, to score each of said aspects from a parameter-specific performance based on said correlating tests conducted for each of said aspects, characterised in that, said scores being first derived at a parameter level (level 3), then at a factor level (level 2), and then at an aspect level (level 1), said scores being cumulated to form a report of said educational institute.

The said report is also generated using the Relationship Builder Mechanism (RB) which consumes the scores of parameter level (LeveB), Factor level (Level 2) and Aspect level (Level 1).

Typically, said system comprises at least a pre-defined assessment matrix to assess quality of learning being imparted in said educational institute, said predefined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism.

Typically, said system comprises at least a pre-defined assessment matrix to assess quality of learning being imparted in said educational institute, said predefined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism, characterized in that, said assessment mechanism being configured to collect inputs for each of said pre-defined set of parameters and each of said sub-parameters from said correlating tests.

Typically, said system comprises at least a pre-defined assessment matrix to assess quality of learning being imparted in said educational institute, said predefined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism, characterized in that, said scoring mechanism comprising: - at least a parameter scoring mechanism (PSM) configured to provide single parameter score that is generated through at least one source which is derived from at least one pre-defined test, said mechanism comprising a hierarchal model using pre-defined percentage (μ);

- at least a factor scoring mechanism (FSM) configured to provide a single factor score that is generated by combination of one or more parameter scores of related parameters or sub parameters; and

- at least an aspect scoring mechanism (ASM) configured to provide a single aspect score that is generated by combination of one or more factor scores of related factors.

Typically, said aspects are evaluation functions relating to an educational institute, characterised in that, said aspects comprising at least one or more of the following items:

i) The learner or a student;

ii) Teacher or learning facilitator (human or non-human);

iii) The system or/and infrastructure in which the learning takes place;

iv) Supporting parameters of learners and teachers; and

v) Parents.

Typically, said database of tests comprises at least a test item selected from a group of test items consisting of feedback archiving test items, data archiving tests, ability diagnosing test items, written assessment test items, interview test items, activities test items, games test items, and audit test items.

Typically, each of said parameters is a weight-assigned attribute which quantifies subjective and objective content in relation to assessing the learning system or educational institute. Typically, each of said sub-parameters is a weight-assigned attribute which quantifies subjective and objective content in relation to assessing the learning system or educational institute.

Typically, said scoring mechanism comprises at least a segregation mechanism in order to segregate inputs in the form of primary data, secondary data, tertiary data, and such hierarchy based data, characterised in that, each input is converted into a single parameter-factor-aspect score by using pre-defined percentage (μ) and / or pre-defined weights (δ).

Typically, said scoring mechanism comprises at least an aspect scoring mechanism such that the scores of interrelated or standalone factors are processed in this mechanism in order to generate an aspect score (level 1) further wherein, at least a weight assignment mechanism is used in a correlational manner with said aspect scoring mechanism in order to provide a weighted aspect score (level 1).

Typically, said scoring mechanism comprises at least a Factor Scoring Mechanism (FSM) such that the parameter score is converted to a factor score (level 2) using pre-defined weights (δ) which is assigned to each parameter further wherein, at least a weight assignment mechanism is used in a correlational manner with said factor scoring mechanism in order to provide a weighted factor score (level 2).

Typically, said scoring mechanism comprises at least a Factor Scoring Mechanism (FSM) such that the parameter score is converted to a factor score (level 2) using pre-defined weights (δ) characterised in that said factor score is obtained by normalizing parameter scores based on weights assigned to sources using which parameter and optionally to sub-parameter scores. Typically, said scoring mechanism comprises at least a Factor Scoring Mechanism (FSM) such that the parameter score is converted to a factor score (level 2) using pre-defined weights (δ) characterised in that said factor score is obtained by normalizing parameter scores based on weights assigned to sources using which parameter and optionally to sub-parameter scores, said normalization function being:

Factor Score = {[δ* (parameter A Score)] + [5 '* (parameter B Score)] + [5"*(parameter C Score)] + ... }

Typically, said scoring mechanism comprises at least a Parameter Scoring Mechanism (PSM) such that data is converted to parameter score (level 3) using pre-defined percentages (μ) further wherein, at least a weight assignment mechanism is used in a correlational manner with said parameter scoring mechanism in order to provide a weighted parameter score (level 3).

Typically, said scoring mechanism comprises at least a Parameter Scoring Mechanism (PSM) such that data is converted to parameter score (level 3) using pre-defined percentages (μ) characterised in that said parameter score is obtained by normalizing test scores based on weights assigned to sources using which test scores are obtained.

Typically, said scoring mechanism comprises at least a Parameter Scoring Mechanism (PSM) such that data is converted to parameter score (level 3) using pre-defined percentages (μ) characterised in that said parameter score is obtained by normalizing test scores based on weights assigned to sources using which test scores are obtained, said normalization function being: Parameter Score = [(Primary Τ8*μ)+(86ϋοηα^ TSV)+ (tertiary TS* μ")+...]/Νο. of sources

TS=Test Score

Typically, said system comprises at least a pre-defined assessment matrix to assess quality of learning being imparted in said educational institute, said predefined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism, characterised in that, said relationship builder mechanism is configured to consume scores from said scoring mechanism and further configured to provide various relationship based data, said scores from relationship based data also comprising reports at parameter level, sub-parameter level, factor level, and / or aspect level and individual teacher and student level.

Typically, said system comprises a weight assignment mechanism configured to assign weights to each of said pre-defined parameters, said pre-defined sub- parameters, said pre-defined factors, and said pre-defined aspects , said weight being dependent on the importance and priority of said parameter, said sub- parameter weight being dependent on the importance and priority of said sub- parameter, said factor weight being dependent on the importance and priority of said factor, said aspect weight being dependent on the importance and priority of said aspect.

Typically, said scoring mechanism is characterised by at least a pre-defined percentage (μ) which is the percentage factor applied to inputs from at least an assessment mechanism in order to get at least a parameter score and which score varies from 0-100% depending on parameter. Typically, said scoring mechanism is characterised by at least a pre-weighted number of function (δ) which is a factor applied to inputs from at least an assessment mechanism in order to get at least a factor score.

According to this invention, there is also provided an assessment method for assessing educational institutes comprising the steps of:

- storing pre-defined aspects adapted to be correlated to an educational institute;

- storing pre-defined factors, each of said factors being correlated to at least one of said pre-defined aspects;

- storing pre-defined parameters being correlated to at least one of said predefined aspects, wherein a group of pre-defined parameters form one of said pre-defined factors;

- storing pre-defined sub-parameters being correlated to at least one of said predefined parameters, wherein a group of sub-parameters form one of said predefined parameters;

- storing pre-defined sources being correlated to at least one of said pre-defined parameters and / or at least one of said pre-defined sub-parameters;

- storing tests in order to assess each of said aspects in terms of its factors by assessing correlated pre-defined parameters and optionally correlated predefined sub-parameters using at least a source per parameter and optionally correlated at least a source per sub-parameter; and

- scoring each of said pre-defined sub-parameters based on at least a source, to score each of said pre-defined parameters based on at least a source, scoring each of said factors cumulative to said group of pre-defined parameters, scoring each of said aspects from a parameter-specific performance based on said correlating tests conducted for each of said aspects, characterised in that, said scores being first derived at a parameter level (level 3), then at a factor level (level 2), and then at an aspect level (level 1), said scores being cumulated to form a report of said educational institute. The said report is also generated through Relationship Builder Mechanism (RB).

Typically, said method comprises a step of assessing quality of learning being imparted in said educational institute, using at least a pre-defined assessment matrix, said pre-defined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism.

Typically, said method comprises a step of assessing quality of learning being imparted in said educational institute, using at least a pre-defined assessment matrix, said pre-defined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism, characterized in that, said step of assessing further comprising a step of collecting inputs for each of said pre-defined set of parameters and each of said sub-parameters from said correlating tests.

Typically, said method comprises a step of assessing quality of learning being imparted in said educational institute, using at least a pre-defined assessment matrix, said pre-defined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism, characterized in that, said scoring mechanism further being configured to perform the steps comprising:

- providing single parameter score generated through at least one source which is derived from at least one pre-defined test, said mechanism comprising a hierarchal model using pre-defined percentage (μ);

- providing a single factor score is generated by combination of one or

more parameter scores of related parameters or sub parameters; and - providing a single aspect score is generated by combination of one or more factor scores of related factors.

i) The learner or a student;

ii) Teacher or learning facilitator (human or non-human);

iii) The system or/and infrastructure in which the learning takes place;

iv) Supporting parameters of learners and teachers; and

v) Parents.

Typically, said step of storing tests comprises a step of selecting a test item from a group of tests consisting of feedback archiving test items, data archiving tests, ability diagnosing test items, written assessment test items, interview test items, activities test items, games test items, and audit test items.

Typically, each of said parameters is a weight-assigned attribute which quantifies subjective and objective content in relation to assessing the learning system or educational institute.

Typically, each of said sub-parameters is a weight-assigned attribute which quantifies subjective and objective content in relation to assessing the learning system or educational institute.

Typically, said step of scoring comprises an additional step of segregating inputs in the form of primary data, secondary data, tertiary data, and such hierarchy based data, characterised in that, each input is converted into a single parameter-factor-aspect score by using pre-defined percentage (μ) and / or predefined weights (δ).

Typically, said step of scoring comprises an additional step of processing scores of interrelated or standalone factors are processed in this mechanism in order to generate an aspect score (level 1) further wherein, at least a weight assignment mechanism is used in a correlational manner with said aspect scoring mechanism in order to provide a weighted aspect score (level 1).

Typically, said step of scoring comprises an additional step of converting parameter score to a factor score (level 2) using pre-defined weights (δ) which is assigned to each parameter further wherein, at least a weight assignment mechanism is used in a correlational manner with said factor scoring mechanism in order to provide a weighted factor score (level 2).

Typically, said step of scoring comprises an additional step of converting parameter score to a factor score (level 2) using pre-defined weights (δ) characterised in that said factor score is obtained by normalizing parameter scores based on weights assigned to sources using which parameter and optionally to sub-parameter scores.

Typically, said step of scoring comprises an additional step of converting parameter score to a factor score (level 2) using pre-defined weights (δ) characterised in that said factor score is obtained by normalizing parameter scores based on weights assigned to sources using which parameter and optionally to sub-parameter scores, said normalization function being:

Factor Score = {[δ* (parameter A Score)] + [δ'* (parameter B Score)] + [δ"* (parameter C Score)] + ... } Typically, said step of scoring comprises an additional step of converting data to parameter score (level 3) using pre- defined percentages (μ) further wherein, at least a weight assignment mechanism is used in a correlational manner with said parameter scoring mechanism in order to provide a weighted parameter score (level 3).

Typically, said step of scoring comprises an additional step of converting data is to parameter score (level 3) using pre- defined percentages (μ) characterised in that said parameter score is obtained by normalizing test scores based on weights assigned to sources using which test scores are obtained.

Typically, said step of scoring comprises an additional step of converting data to parameter score (level 3) using pre- defined percentages (μ) characterised in that said parameter score is obtained by normalizing test scores based on weights assigned to sources using which test scores are obtained, said normalization function being:

Parameter Score = [(Primary TS*μ)+(Secondary Τ8*μ')+ (tertiary TS* μ")+· ·,]/Νο. of sources

TS^Test Score

Typically, said method comprises a step of assessing quality of learning being imparted in said educational institute, using at least a pre-defined assessment matrix, said pre-defined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism, characterised in that, said relationship builder mechanism being configured to perform a step of consuming scores from said scoring mechanism and further configured to provide various relationship based data, said scores from relationship based data also comprising reports at parameter level, sub- parameter level, factor level, and / or aspect level and individual teacher and student level.

Typically, said method comprises a step of assigning weights to each of said pre-defined parameters, said pre-defined sub-parameters, said pre-defined factors, and said pre-defined aspects, said weight being dependent on the importance and priority of said parameter, said sub-parameter weight being dependent on the importance and priority of said sub-parameter, said factor weight being dependent on the importance and priority of said factor, said aspect weight being dependent on the importance and priority of said aspect.

Typically, said step of scoring is characterised by at least a pre- defined percentage (μ) which is the percentage factor applied to inputs from at least an assessment mechanism in order to get at least a parameter score and which score varies from 0-100% depending on parameter.

Typically, said step of scoring is characterised by at least a pre-weighted number of function (δ) which is a factor applied to inputs from at least an ^' assessment mechanism in order to get at least a factor score.

Brief Description of the Accompanying Drawings:

The invention will now be described in relation to the accompanying drawings, in which:

Figure 1 illustrates a schematic block diagram for an assessment system and method for assessing educational institutes;

Figure 2 illustrates a schematic block diagram of an assessment mechanism; Figure 3 illustrates a graphical diagram of aspects, factors, and parameters / sub- parameters and levels associated, thereof;

Figure 4 illustrates a flowchart relating to generation of report using a relationship builder mechanism and data from an assessment mechanism and from a scoring mechanism; and

Figure 5 illustrates that a report is generated by scores received from a Parameter Scoring Mechanism, Factor Scoring Mechanism, Aspect Scoring Mechanism, and Relationship Builder Mechanism.

Detailed Description of the Accompanying Drawings:

For the purposes of this invention, the term, "educational institute", is intended to mean any educational institution such as a school, a university, a graduate school, a college for any stream of study, or the like.

For the purposes of this invention, the term, "aspect", relates to an entity relating to an educational institution which is to be assessed using the system and method of this invention. Typically, aspects comprise teacher, students, educational institutions, and the like. Each of these aspects can be individually assessed.

For the purposes of this invention, the term, "factor", relates to pre-defined items that are to be assessed in relation to a selected aspect. Typically, factors comprise parameters and sub-parameters, as described below. Typically, factors comprise a list of skills that are required for development of an aspect and these skills are to be evaluated using the system and method of this invention. For the purposes of this invention, the term, "parameter", relates to an item which corresponds towards development and rating of an aspect. Typically, parameters comprise a list of sub-skills that are required or contribute towards each skill i.e. towards each factor for development of an aspect and these sub- skills are to be evaluated using the system and method of this invention.

For the purposes of this invention, the term, "sub-parameter", relates to sub- skills that are required or contribute towards each skill or Parameter i.e. towards each factor for development of an aspect and these sub-skills are to be evaluated using the system and method of this invention. Typically, sub- parameters comprise a list of items which are used in order to assess a parameter and, in turn, a factor.

For the purposes of this invention, the term, "source", relates to an origin or item from where data is collated or from which data is extracted using tests.

For the purposes of this invention, the term, "tests", relates to tests, feedbacks / activities / audits / administrative data, academic data, and the like.

According to this invention, there is provided an assessment system and method for assessing educational institutes.

Figure 1 illustrates a schematic block diagram for an assessment system and method for assessing educational institutes.

In accordance with an embodiment of this invention, there is provided a database of pre-defined Aspects (PA) adapted to be correlated to an educational institute. These aspects are items relating to an educational institute. Typically, there are plurality of such aspects. In at least one embodiment, this list of pre-defined aspects is classified as follows: i) educational institute's assets, which includes institute's brand and infrastructure; ii) teacher and / or teaching quality, which assesses current teaching capabilities and competency and life skills and gaps in teaching and teachers' professional growth potential; iii) student's performance, which includes assessment of student's competency and life skills; and iv) supporting data of students and teachers, which help in quality learning process. Under each of these aspects, several parameters are included, which in turn is a combination of sub-parameters and / or data. A group of related parameters form Factors. Each of these parameters and sub- parameters (which are normally skills / sub-skills displayed through learning) thereof, are assessed using at least these three forms of use: tests, feedbacks, and data. In at least one embodiment, tests comprise the use of written assessment, interviews, activities, games, and the like. In at least one embodiment, feedbacks comprise the use of written assessment, and interviews. In at least one embodiment, data comprises factual data / non-personal data or data from audit conducted. Furthermore, written tests, interviews, projects work, team exercise, and personal interviews may be used but are not limited only to these modes of assessment. The essence is to capture level of expertise on these aspects over a scale. Depending on the type of educational institute, parameters or skills and sub-skills are added or deleted. Competency skills of students are defined as the skill-set required for a student to learn. Competency skills of a teacher are defined as the skill-set required for a teacher to convert information (text book or reference books) into knowledge and delivering it effectively to students so that quality learning can take place. The term, 'teacher', is not limited only to teaching staff; it includes faculty members who contribute to the learning process. The term 'Teacher' is additionally used for teaching aid or a virtual teaching assistant, which can be a human or non human entity. This pre-defined set of aspects / factors / parameters / sub-parameters is used to assess a learning system or an educational institute. This set of aspects is used in the following assessment process.

For any learning system or educational institute, a set of aspects are defined and are stored in an aspects database (PA). Each defined aspect is a weight-assigned entity of the learning system or educational institute. The essential aspects are, but not limited to: - 1) The learner or a student; 2) Teacher or learning facilitator (human or non-human); 3) the system or/and infrastructure in which the learning takes place; 4) Supporting parameters of learners and teachers; and 5) Parents.

In at least one embodiment, supporting parameters comprise a set of Parameters and Sub-parameters which are directly or indirectly associated with the process of learning and rolls up to the Factors and Aspects. The Parameters and Sub-parameters include the one that are in action within an education institute and also the one that are in action outside of the education institute.

For any learning system or educational institute, a set of factors are defined and are stored in a factors database (FD).

Each Aspect has various sub-aspects called as factors. Each defined factor is a weight-assigned attribute which quantifies subjective and objective content in relation to assessing the learning system or educational institute. Each factor is related to a particular aspect and directly or indirectly impacts users of the learning system or educational institute. Each factor is a group of related parameters. For any learning system or educational institute, a set of parameters and sub- parameters are defined and stored respectively in a parameters database (PD) and a sub-parameters database (SPD). The defined parameter and sub-parameter are further comprised of various skills / traits / facilities / processes / values / learning aids, involved or supporting in the learning process or educational institute. Each defined parameter and sub-parameter is a weight-assigned attribute which quantifies subjective and objective content in relation to assessing the learning system or educational institute. First a parameter is defined which is to be assessed. Based on the parameter, a set of sub-parameters are defined which cumulatively contribute to the parameter.

For any learning system or educational institute, a set of sources is defined and stored in a sources database (SD). These sources are items such as persons or facts collection methods through which data is gathered. For each parameter, a set of sources may be defined from which data is to be gathered.

For any learning system or educational institute, a set of tests is defined and stored in a tests database (TD). These tests are used to evaluate parameters and sub-parameters.

The present invention provides a matrix to assess if quality learning is being imparted in a given system. Since, this is a subjective question with a subjective answer. This invention objectifies and quantifies the data in the form of an assessment matrix with weight-assigned attributes and with associated functions, thereof, in order to provide a repeatable holistic view of the learning process or educational institute. In other words, the matrix provides a 360° view in relation to the strengths and development areas of a given system with an objective to benefit the entire learning system or educational institute. In accordance with another embodiment of this invention, there is provided a scoring mechanism (SM) adapted to provide a score for each of said predefined parameters from a parameter-specific performance based on written, verbal, and activity-based tests conducted for students, teachers, and the institute. These scores are then marked in a pre-defined scoring system. Typically, a scorecard for each of the Parameters, each of the Factors, and each of the Aspects (mentioned above) is generated.

In the present invention, quality learning in a system is defined as a cumulative score of the scores of all aspects involved in the learning process or educational institute. This assessment matrix broadly has at least three major mechanisms - Assessment Mechanism (AM), Scoring Mechanism (SM) [includes, but is not limited to, Parameter Scoring Mechanism (PSM), Factor Scoring Mechanism (FSM), Aspect Scoring Mechanism (ASM)] , and Relationship Builder Mechanism (RB). Figure 2 illustrates a schematic block diagram of an scoring mechanism.

Figure 5 illustrates that a report is generated by scores received from a Parameter Scoring Mechanism (PSM), Factor Scoring Mechanism (FSM), Aspect Scoring Mechanism (ASM), and Relationship Builder Mechanism (RB).

All sub-mechanisms of the scoring mechanism (SM) use pre-defined weights to generate scores.

In accordance with another embodiment of this invention, an Assessment Mechanism (AM) collects inputs for the pre-defined set of parameters and sub- parameters from mechanisms such as, but not limited to, actual tests, feedbacks, discussions, observations, audits, data, and the like. The pre-defined sets of parameters and sub-parameters are all parameters associated with a learning system or educational institute and are essential for quality learning and efficient functioning of the learning system or educational institute. The current invention connects / facilitates collection of inputs from multiple sources for a parameter or sub-parameter. The inputs from the assessment mechanism (AM) are then sent to the scoring mechanism (SM). In the Scoring Mechanism (SM), the inputs are segregated in form of Primary data (PD), Secondary data (SD), Tertiary data (TD), and so on. Each input (primary, secondary, tertiary data) is converted into a single parameter-factor-aspect score by using pre-defined percentage (μ) or / and pre-defined weights (δ).

The scoring mechanism (SM) comprises at least an aspect scoring mechanism (ASM), at least a factor scoring mechanism (FSM), and at least a parameter scoring mechanism (PSM).

In at least one embodiment of the scoring mechanism, there is provided an Aspect Scoring Mechanism (ASM). The scores of interrelated or standalone factors are processed in this mechanism to generate an aspect score (level 1). At least a weight assignment mechanism (WAM) is used in a correlational manner with the aspect scoring mechanism (ASM) in order to provide a weighted aspect score (level 1).

Typically, the Aspect Scoring Mechanism (ASM) is a scoring mechanism through which a single aspect score is generated by combination of one or more factor scores of related factors

In at least one embodiment of the scoring mechanism, there is provided a Factor Scoring Mechanism (FSM). The parameter score is converted to a factor score (level 2) using pre-defined weights (δ) which is assigned to each parameter. At least a weight assignment mechanism (WAM) is used in a correlational manner with the factor scoring mechanism (FSM) in order to provide a weighted factor score (level 2).

Typically, the factor scoring mechanism (FSM) is a scoring mechanism through which a single factor score is generated by combination of one or more parameter scores of related parameters or sub parameters.

In at least one embodiment of the scoring mechanism, there is provided a Parameter Scoring Mechanism (PSM). In the parameter scoring mechanism, inputs are segregated in to primary secondary, tertiary data, and the like. This data is converted to parameter score (level 3) using pre-defined percentages (μ). If learning matrix is applied to more than one learners, then Average Parameter Score is generated for the learning system or educational institute. At least a weight assignment mechanism (WAM) is used in a correlational manner with the parameter scoring mechanism (PSM) in order to provide a weighted parameter score (level 3).

Typically, the parameter scoring mechanism (PSM) is a scoring mechanism through which a single parameter score is generated using one or more sources which are derived from which are derived from tests as defined in this specification. These may be in the form of written tests, interview tests, game tests, activity tests, and the like. A hierarchal model using pre-defined percentage (μ) is used.

Figure 3 illustrates a graphical diagram of aspects, factors, and parameters / sub- parameters and levels associated, thereof.

Scores are first derived at Parameter level (level 3), then at Factor (level 2), and then at Aspect level (level 1). They are cumulated as they are derived from Parameter level (level 3) to Factor level (level 2) to Aspect level (level 1) in order to form a report (R).

As seen in Figure 3 of the accompanying drawings, parameters and sub- parameters may comprise conceptual learning (al . l), PSS (al .2), brain flexibility (a 1.3), analytical reasoning (al .4), creativity (al .5), curiosity (al .6), interpersonal skills (a2.1), communication skills (a2.2), hard working (a2.3), and the like. Further, as seen in Figure 3 of the accompanying drawings, factors may comprise competency skills for a student (al ), life skills for a student (a2), competency skills for a teacher (bl), and professional skills for a teacher (b2). Yet further, as seen in Figure 3 of the accompanying drawings, aspects may comprise student performance (A), teacher contribution (B), support factor for teachers and students (C), school infrastructure (D), and school brand (E).

In accordance with another embodiment of this invention, a relationship builder mechanism (RB) consumes scores in correlation with parameters, factors, and aspects i.e. from the parameter scoring mechanism (PSM), the factor scoring mechanism (FSM), and the aspects scoring mechanism (ASM). Further, the relationship builder mechanism (RB) establishes correlations and trends based on, but not limited to, various data analysis, data mining, data interpretation techniques such as, but not limited to, statistical, cognitive models, linear and non-linear modeling, predictive techniques, and so on. The relations can be built at Parameter, Factor, and / or Aspect level.

Figure 4 illustrates a flowchart relating to generation of report using a relationship builder mechanism and data from an assessment mechanism and from a scoring mechanism. In the Relationship Builder mechanism (RB), the scores correlating to Aspects, Factors, and Parameters are used to derive meaningful correlations, trends, and statistical insights. The insights are derived based on, but not limited to, various data analysis, data mining, data interpretation techniques such as, but not limited to, statistical, cognitive models, linear and non-linear modeling, predictive techniques, and so on. The insights are used to bring out the strengths, development areas, possible risks and opportunities for learner, learning facilitator, and the learning system as a whole.

The insights from the relationship builder mechanism (RB) are then compiled into a report. The current invention facilitates to generate report at a plurality of levels; such as individual level, parameter level, factor level, aspect level, and / or learning system level. It also allows the report at much wider level that covers more than one learning systems.

In accordance with yet another embodiment of this invention, there is provided a weight assignment mechanism (WAM) adapted to assign weights to each of the pre-defined parameters. A parameter weight is dependent on the importance and priority of the parameter. This weight assignment mechanism provides a non-equal model for assessment so that the parameters are graded and not given equal weightage. Pre-defined set of weights in the assessment depend on their importance and can range from 0-100%. The parameters and sub-parameters are analyzed based on pre-defined set of weights built into the assessment model. Each parameter, skill, or sub-skill has unique pre-defined weight which is used to compute the score of the parameter.

After conducting the assessment, the score of each parameter and sub- parameter, thereof is calculated using the assessment model. These scores are used to generate an overall score of the institute. After this computation, a report (R) is generated depicting the parameter score, general trend across the educational institute, correlation of skills, and interpretation of the trends and scores.

Report (R) of the analysis is generated at individual student and teacher level, Parameter level (level 1), Factor level (level 2), and / or Aspect level (level 3) with the appropriate data representation techniques.

Primary / Secondary / tertiary data of each of the parameters or the sub- parameters is the actual score obtained by assessment through methods such as, but not limited to, written test, feedback, discussion, activities, observation, and audit.

The parameters or the sub-parameters (Level 3) can be of living and non-living entities, such as but not limited to, physical asset or technology or personal traits / skills / learning aids / processes / facility which helps or is a requirement in learning process. The number of parameters included can be from zero (or 1) to any number.

In the scoring mechanism (SM), the pre-defined percentage (μ) is the percentage factor applied to inputs from the assessment mechanism (AM) to get parameter score and can vary from 0-100% depending on parameter. It can also be a mathematical expression used as a factor. The number of primary / secondary / tertiary may vary.

The Parameter or sub-parameter Score for an individual can represented as:

Parameter / Sub-Parameter Score= [(Primary TS*μ)+(Secondary Τ8*μ')+ (Tertiary Τ8*μ")+...]/Νο. of sources Primary TS: Primary Test Score

Secondary TS: Secondary Test Score

Tertiary TS: Tertiary Test Score

No. Of Sources: Is the No. of sources data or test to generate parameter or Sub- parameter score

In factor scoring mechanism (FSM), the parameter score is processed with pre- weighted factor (δ). Mathematically, it can be represented for an individual as:

Factor Score .= {[6*(parameter A Score)]+ [δ'* (parameter B Score)]+ [5"*(parameter C Score)]+ ... }

The symbol, 'δ', involved in calculating the factor score can be a number or a mathematical function or expression.

The mathematical representation can be used for more than one individual to derive population-level insights.

A few educational institutes that assess students' and teachers' performance on non-academic parameters use subjective methods. The use of the present invention's assessment mechanism is that it gives factual data that provides objectivity to the assessment. For example, communication skills of a teacher are normally assessed based on general and communication with students in a class. This might not necessary tell the level of expertise of a teacher. In at least one embodiment of this invention, this parameter is assessed on verbal and written communication test. The results of these parameters are validated with students' learning parameters. This gives a factual data on which teacher can relate and work on so that there is improvement in skills, and hence in the score related to that parameter. The use of this invention also provides assessment that is output based i.e. the assessment mechanism assesses the output of an educational institute rather than inputs (such as resources). For example, most of the accreditations (of the prior art) assess the educational institute based on the resources available at the institute. The present invention measures the quality learning with these resources and compares these outcomes with efficiency of operation of an educational institute.

The use of this present invention provides assessment which helps an educational institute to identify its strengths and challenging areas. In this assessment mechanism, students' and teachers' performances are assessed based on the parameter specific tests carried out using written tests, discussions (interviews), projects, and games which can be activity based.

The INVENTIVE STEP of this invention lies in providing a system and method which provides a holistic, dynamic, and customized assessment for educational institutes based on a plurality of parameters (including skills and sub-skills) and their correlations, thereof. This provides fact based assessment which may include both subjective as well as objective assessment from output based perspective of the educational institute. The assessment contains at least these four aspects: i) institute's assets, which includes institute's brand and infrastructure; ii) teaching quality, which asses current teaching capabilities, teachers' competency and life skills, gaps in teaching and teachers' professional growth potential; iii) students' performance, which includes assessment of student's competency and, life skills; and iv) supporting data of students and teachers, which help in quality learning process. The system and method of this invention also provide a methodical correlation of parameters and sub- parameters which gives analytical insights that helps students / institute in actual learning process. It also helps schools to understand their overall trends and helps schools towards their overall (or 360 degree) growth.

The data, in each of the components, means, modules, mechanisms, units, devices of the system and method may be 'encrypted' and suitably 'decrypted' when required.

The systems described herein can be made accessible through a portal or an interface which is a part of, or may be connected to, an internal network or an external network, such as the Internet or any similar portal. The portals or interfaces are accessed by one or more of users through an electronic device, whereby the user may send and receive data to the portal or interface which gets stored in at least one memory device or at least one data storage device or at least one server, and utilises at least one processing unit. The portal or interface in combination with one or more of memory device, data storage device, processing unit and serves, form an embedded computing setup, and may be used by, or used in, one or more of a non-transitory, computer readable medium. In at least one embodiment, the embedded computing setup and optionally one or more of a non-transitory, computer readable medium, in relation with, and in combination with the said portal or interface forms one of the systems of the invention. Typical examples of a portal or interface may be selected from but is not limited to a website, an executable software program or a software application.

The systems and methods may simultaneously involve more than one user or more than one data storage device or more than one host server or any combination thereof. A user may provide user input through any suitable input device or input mechanism such as but not limited to a keyboard, a mouse, a joystick, a touchpad, a virtual keyboard, a virtual data entry user interface, a virtual dial pad, a software or a program, a scanner, a remote device, a microphone, a webcam, a camera, a fingerprint scanner, a cave, pointing stick

The systems and methods can be practiced using any electronic device which may be connected to one or more of other electronic device with wires or wirelessly which may use technologies such as but not limited to, NFC, Bluetooth, Wi-Fi, Wimax. This will also extend to use of the aforesaid technologies to provide an authentication key or access key or electronic device based unique key or any combination thereof.

In at least one embodiment, one or more user can be blocked or denied access to one or more of the aspects of the invention.

Encryption can be accomplished using any encryption technology, such as the process of converting digital information into a new form using a key or a code or a program, wherein the new form is unintelligible or indecipherable to a user or a thief or a hacker or a spammer. The term 'encryption' includes encoding, compressing, or any other translating of the digital content. The encryption of the digital media content can be performed in accordance with any technology including utilizing an encryption algorithm. The encryption algorithm utilized is not hardware dependent and may change depending on the digital content. For example, a different algorithm may be utilized for different websites or programs. The term 'encryption' further includes one or more aspects of authentication, entitlement, data integrity, access control, confidentiality, segmentation, information control, and combinations thereof. The described embodiments may be implemented as a system, method, apparatus or article of manufacture using standard programming and/or engineering techniques related to software, firmware, hardware, or any combination thereof. The described operations may be implemented as code maintained in a "non-transitory, computer readable medium", where a processor may read and execute the code from the non-transitory, computer readable medium. A non-transitory, computer readable medium may comprise media such as magnetic storage medium (e.g., hard disk drives, floppy disks, tape, etc.), optical storage (CD-ROMs, DVDs, optical disks, etc.), volatile and nonvolatile memory devices (e.g., EEPROMs, ROMs, PROMs, RAMs, DRAMs, SRAMs, Flash Memory, firmware, programmable logic, etc.), etc. The code implementing the described operations may further be implemented in hardware logic (e.g., an integrated circuit chip, Programmable Gate Array (PGA), Application Specific Integrated Circuit (ASIC), etc.).

Still further, the code implementing the described operations may be implemented in "transmission signals", where transmission signals may propagate through space or through a transmission media, such as an optical fibre, copper wire, etc. The transmission signals in which the code or logic is encoded may further comprise a wireless signal, satellite transmission, radio waves, infrared signals, Bluetooth, etc. The transmission signals in which the code or logic is encoded is capable of being transmitted by a transmitting station and received by a receiving station, where the code or logic encoded in the transmission signal may be decoded and stored in hardware or a non-transitory, computer readable medium at the receiving and transmitting stations or devices. An "article of manufacture" comprises non-transitory, computer readable medium or hardware logic, and/or transmission signals in which code may be implemented. A device in which the code implementing the described embodiments of operations is encoded may comprise a non-transitory, computer readable medium or hardware logic. Of course, those skilled in the art will recognize that many modifications may be made to this configuration without departing from the scope of the present invention, and that the article of manufacture may comprise suitable information bearing medium known in the art.

The term network means a system allowing interaction between two or more electronic devices, and includes any form of inter/intra enterprise environment such as the world wide web, Local Area Network (LAN) , Wide Area Network (WAN) , Storage Area Network (SAN) or any form of Intranet and Internet.

The systems and methods can be practiced using any electronic device. An electronic device for the purpose of this invention is selected from any device capable of processing or representing data to a user and providing access to a network or any system similar to the internet, wherein the electronic device may be selected from but not limited to, personal computers, tablet computers, mobile phones, laptop computers, palmtops, portable media players, and personal digital assistants. In an embodiment, the computer readable medium data storage unit or data storage device is selected from a set of but not limited to USB flash drive (pen drive), memory card, optical data storage discs, hard disk drive, magnetic disk, magnetic tape data storage device, data server and molecular memory.

The process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and algorithms may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously, in parallel, or concurrently.

While the present invention is susceptible of embodiment in various forms, there is shown in the drawings and will hereinafter be described a presently preferred embodiment with the understanding that the present disclosure is to be considered an exemplification of the invention and is not intended to limit the invention to the specific embodiments illustrated. The use of "including", "comprising" or "having" and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude or rule out the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

While this detailed description has disclosed certain specific embodiments for illustrative purposes, various modifications will be apparent to those skilled in the art which do not constitute departures from the spirit and scope of the invention as defined in the following claims, and it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.

Claims

Claims,

1. An assessment system for assessing educational institutes comprising:

2. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said system comprising at least a pre-defined assessment matrix to assess quality of learning being imparted in said educational institute, said pre-defined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism.

3. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said system comprising at least a pre-defined assessment matrix to assess quality of learning being imparted in said educational institute, said pre-defined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism, characterized in that, said assessment mechanism being configured to collect inputs for each of said pre-defined set of parameters and each of said sub-parameters from said correlating tests.

4. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said system comprising at least a pre-defined assessment matrix to assess quality of learning being imparted in said educational institute, said pre-defined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism, characterized in that, said scoring mechanism comprising:

- at least a parameter scoring mechanism (PSM) configured to provide single parameter score that is generated through at least one source which is derived from at least one pre-defined test, said mechanism comprising a hierarchal model using pre-defined percentage (μ); - at least a factor scoring mechanism (FSM) configured to provide a single factor score that is generated by combination of one or more parameter scores of related parameters or sub parameters; and

5. An assessment system as claimed in claim 1 wherein, said aspects are evaluation functions relating to an educational institute, characterised in that, said aspects comprising at least one or more of the following items:

i) The learner or a student;

ii) Teacher or learning facilitator (human or non-human);

iii) The system or/and infrastructure in which the learning takes place;

iv) Supporting parameters of learners and teachers; and

v) Parents.

6. An assessment system as claimed in claim 1 wherein, said database of tests comprising at least a test item selected from a group of test items consisting of feedback archiving test items, data archiving tests, ability diagnosing test items, written assessment test items, interview test items, activities test items, games test items, and audit test items.

7. An assessment system as claimed in claim 1 wherein, each of said parameters is a weight-assigned attribute which quantifies subjective and objective content in relation to assessing the learning system or educational institute.

8. An assessment system as claimed in claim 1 wherein, each of said sub- parameters is a weight-assigned attribute which quantifies subjective and objective content in relation to assessing the learning system or educational institute.

9. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said scoring mechanism comprising at least a segregation mechanism in order to segregate inputs in the form of primary data, secondary data, tertiary data, and such hierarchy based data, characterised in that, each input is converted into a single parameter-factor-aspect score by using pre-defined percentage (μ) and / or pre-defined weights (δ).

10. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said scoring mechanism comprising at least an aspect scoring mechanism such that the scores of interrelated or standalone factors are processed in this mechanism in order to generate an aspect score (level 1) further wherein, at least a weight assignment mechanism is used in a correlational manner with said aspect scoring mechanism in order to provide a weighted aspect score (level 1).

11. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said scoring mechanism comprising at least a Factor Scoring Mechanism (FSM) such that the parameter score is converted to a factor score (level 2) using pre-defined weights (δ) which is assigned to each parameter further wherein, at least a weight assignment mechanism is used in a correlational manner with said factor scoring mechanism in order to provide a weighted factor score (level 2).

12. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said scoring mechanism comprising at least a Factor Scoring Mechanism (FSM) such that the parameter score is converted to a factor score (level 2) using pre-defined weights (δ) characterised in that said factor score is obtained by normalizing parameter scores based on weights assigned to sources using which parameter and optionally to sub-parameter scores.

13. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said scoring mechanism comprising at least a Factor Scoring Mechanism (FSM) such that the parameter score is converted to a factor score (level 2) using pre-defined weights (δ) characterised in that said factor score is obtained by normalizing parameter scores based on weights assigned to sources using which parameter and optionally to sub-parameter scores, said normalization function being:

Factor Score = {[δ* (parameter A Score)] + [δ'* (parameter B Score)] + [5"*(parameter C Score)] + ... }

14. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said scoring mechanism comprising at least a Parameter Scoring Mechanism (PSM) such that data is converted to parameter score (level 3) using pre-defined percentages (μ) further wherein, at least a weight assignment mechanism is used in a correlational manner with said parameter scoring mechanism in order to provide a weighted parameter score (level 3).

15. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said scoring mechanism comprising at least a Parameter Scoring Mechanism (PSM) such that data is converted to parameter score (level 3) using pre-defined percentages (μ) characterised in that said parameter score is obtained by normalizing test scores based on weights assigned to sources using which test scores are obtained.

16. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said scoring mechanism comprising at least a Parameter Scoring Mechanism (PSM) such that data is converted to parameter score (level 3) using pre-defined percentages (μ) characterised in that said parameter score is obtained by normalizing test scores based on weights assigned to sources using which test scores are obtained, said normalization function being:

Parameter Score = [(Primary TS*μ)+(Secondary Τ8*μ')+ (tertiary TS* μ")+...]/Νο. of sources

TS=Test Score

17. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said system comprising at least a pre-defined assessment matrix to assess quality of learning being imparted in said educational institute, said pre-defined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism, characterised in that, said relationship builder mechanism is configured to consume scores from said scoring mechanism and further configured to provide various relationship based data, said scores from relationship based data also comprising reports at parameter level, sub- parameter level, factor level, and / or aspect level and individual teacher and student level.

18. An assessment system for assessing educational institutes as claimed n claim 1 wherein, said system comprising a weight assignment mechanism configured to assign weights to each of said pre-defined parameters, said pre-defined sub-parameters, said pre-defined factors, and said pre-defined aspects , said weight being dependent on the importance and priority of said parameter, said sub-parameter weight being dependent on the importance and priority of said sub-parameter, said factor weight being dependent on the importance and priority of said factor, said aspect weight being dependent on the importance and priority of said aspect.

19. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said scoring mechanism being characterised by at least a pre-defined percentage (μ) which is the percentage factor applied to inputs from at least an assessment mechanism in order to get at least a parameter score and which score varies from 0-100% depending on parameter.

20. An assessment system for assessing educational institutes as claimed in claim 1 wherein, said scoring mechanism being characterised by at least a pre-weighted number of function (δ) which is a factor applied to inputs from at least an assessment mechanism in order to get at least a factor score.

21. An assessment method for assessing educational institutes comprising the steps of:

- storing pre-defined sources being correlated to at least one of said pre-defined parameters and / or at least one of said pre-defined sub-parameters; - storing tests in order to assess each of said aspects in terms of its factors by assessing correlated pre-defined parameters and optionally correlated predefined sub-parameters using at least a source per parameter and optionally correlated at least a source per sub-parameter; and

- scoring each of said pre-defined sub-parameters based on at least a source, to score each of said pre-defined parameters based on at least a source, scoring each of said factors cumulative to said group of pre-defined parameters, scoring each of said aspects from a parameter-specific performance based on said correlating tests conducted for each of said aspects, characterised in that, said scores being first derived at a parameter level (level 3), then at a factor level (level 2), and then at an aspect level (level 1), said scores being cumulated to form a report of said educational institute.

22. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said method comprising a step of assessing quality of learning being imparted in said educational institute, using at least a predefined assessment matrix, said pre-defined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism.

23. An assessment method for assessing educational institutes as claimed in claim 21 herein, said method comprising a step of assessing quality of learning being imparted in said educational institute, using at least a predefined assessment matrix, said pre-defined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism, characterized in that, said step of assessing further comprising a step of collecting inputs for each of said pre-defined set of parameters and each of said sub-parameters from said correlating tests.

24. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said method comprising a step of assessing quality of learning being imparted in said educational institute, using at least a predefined assessment matrix, said pre-defined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism, characterized in that, said scoring mechanism further being configured to perform the steps comprising:

- providing a single factor score is generated by combination of one or

more parameter scores of related parameters or sub parameters; and

- providing a single aspect score is generated by combination of one or more factor scores of related factors.

25. An assessment method as claimed in claim 21 wherein, said aspects are evaluation functions relating to an educational institute, characterised in that, said aspects comprising at least one or more of the following items:

i) The learner or a student;

ii) Teacher or learning facilitator (human or non-human);

iii) The system or/and infrastructure in which the learning takes place;

iv) Supporting parameters of learners and teachers; and

v) Parents.

26. An assessment method as claimed in claim 21 wherein, said step of storing tests comprising a step of selecting a test item from a group of tests consisting of feedback archiving test items, data archiving tests, ability diagnosing test items, written assessment test items, interview test items, activities test items, games test items, and audit test items.

27. An assessment method as claimed in claim 21 wherein, each of said parameters is a weight-assigned attribute which quantifies subjective and objective content in relation to assessing the learning system or educational institute.

28. An assessment method as claimed in claim 21 wherein, each of said sub- parameters is a weight-assigned attribute which quantifies subjective and objective content in relation to assessing the learning system or educational institute.

29. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said step of scoring comprising an additional step of segregating inputs in the form of primary data, secondary data, tertiary data, and such hierarchy based data, characterised in that, each input is converted into a single parameter-factor-aspect score by using pre-defined percentage (μ) and / or pre-defined weights (δ).

30. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said step of scoring comprising an additional step of processing scores of interrelated or standalone factors are processed in this mechanism in order to generate an aspect score (level 1) further wherein, at least a weight assignment mechanism is used in a correlational manner with said aspect scoring mechanism in order to provide a weighted aspect score (level 1).

31.An assessment method for assessing educational institutes as claimed in claim 21 wherein, said step of scoring comprising an additional step of converting parameter score to a factor score (level 2) using pre-defined' weights (δ) which is assigned to each parameter further wherein, at least a weight assignment mechanism is used in a correlational manner with said factor scoring mechanism in order to provide a weighted factor score (level

32. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said step of scoring comprising an additional step of converting parameter score to a factor score (level 2) using pre-defined weights (δ) characterised in that said factor score is obtained by normalizing parameter scores based on weights assigned to sources using which parameter and optionally to sub-parameter scores.

33. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said step of scoring comprising an additional step of converting parameter score to a factor score (level 2) using pre-defined weights (δ) characterised in that said factor score is obtained by normalizing parameter scores based on weights assigned to sources using which parameter and optionally to sub-parameter scores, said normalization function being:

34. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said step of scoring comprising an additional step of converting data to parameter score (level 3) using pre- defined percentages (μ) further wherein, at least a weight assignment mechanism is used in a correlational manner with said parameter scoring mechanism in order to provide a weighted parameter score (level 3).

35. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said step of scoring comprising an additional step of converting data is to parameter score (level 3) using pre- defined percentages (μ) characterised in that said parameter score is obtained by normalizing test scores based on weights assigned to sources using which test scores are obtained.

36. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said step of scoring comprising an additional step of converting data to parameter score (level 3) using pre- defined percentages (μ) characterised in that said parameter score is obtained by normalizing test scores based on weights assigned to sources using which test scores are obtained, said normalization function being:

TS=Test Score

37. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said method comprising a step of assessing quality of learning being imparted in said educational institute, using at least a predefined assessment matrix, said pre-defined matrix comprising at least an assessment mechanism, at least a scoring mechanism, and at least a relationship builder mechanism, characterised in that, said relationship builder mechanism being configured to perform a step of consuming scores from said scoring mechanism and further configured to provide various relationship based data, said scores from relationship based data also comprising reports at parameter level, sub-parameter level, factor level, and / or aspect level and individual teacher and student level.

38. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said method comprising a step of assigning weights to each of said pre-defined parameters, said pre-defined sub-parameters, said pre-defined factors, and said pre-defined aspects, said weight being dependent on the importance and priority of said parameter, said sub- parameter weight being dependent on the importance and priority of said sub-parameter, said factor weight being dependent on the importance and priority of said factor, said aspect weight being dependent on the importance and priority of said aspect.

39. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said step of scoring being characterised by at least a predefined percentage (μ) which is the percentage factor applied to inputs from at least an assessment mechanism in order to get at least a parameter score and which score varies from 0-100% depending on parameter.

40. An assessment method for assessing educational institutes as claimed in claim 21 wherein, said step of scoring being characterised by at least a pre- weighted number of function (δ) which is a factor applied to inputs from at least an assessment mechanism in order to get at least a factor score.