System for co-clustering of student assessment data

Abstract

A system and method for making use of formative assessment data collected is disclosed that identifies clusters of students and concurrently determines the characteristics of the student clusters. A decomposition of the data is performed with spectral theories of graphs and fuzzy logic algorithms to identify the clusters of students, clusters of assessment data and relationships between them. An actionable output is presented to teachers for the evaluation of educational progress.

What is claimed is: 
     
         1 . A system for educational assessment of students, comprising:
 a processor having a memory, wherein the processor has a clustering engine that includes a student identification module that identifies student clusters based on metadata having characteristics of students within each student cluster and is associated with a hierarchy of assessment data of one or more formative assessments, wherein the hierarchy of assessment data is related to different levels of demonstrated knowledge by students based on the formative assessments;   a display module that compiles the metadata related to the student clusters and the assessment data clusters, and provides relationships between the student clusters and the assessment data clusters with the metadata to a visible medium in response to co-clustering of student data and assessment data.   
     
     
         2 . The system of  claim 1 , wherein the different levels of demonstrated knowledge are based on a certain response to a particular question, or assessment evaluation, of the formative assessments in relation to other responses to the particular question or assessment evaluation among students, and the student clusters are identified by the student identification module according to the responses of students to a plurality of questions of the formative assessments that have one or more response possibilities for one or more of the plurality of questions. 
     
     
         3 . The system of  claim 1 , wherein the student clusters are assigned to hard clustering so that each student is associated with only one student cluster and the assessment data is assigned to soft clustering so that the assessment data is not confined to being associated with only one assessment cluster. 
     
     
         4 . The system of  claim 3 , wherein the assessment data includes data related to student responses to assessment evaluations of each formative assessment and each assessment evaluation includes a question for evaluation of each student that is associable with more than one assessment cluster. 
     
     
         5 . The system of  claim 1 , further comprising a transformation engine that compiles the assessment data from formative assessments provided to the plurality of students and creates bipartite graphs of student data for each student and assessment evaluations from the assessment data;
 an adjacency mapping engine that maps adjacency relationships between the students and the assessment data by creating at least one adjacency matrix from the bipartite graphs; and   a decomposition engine that performs spectral decomposition on the adjacency matrix and establishes weighted distances for each relationship.   
     
     
         6 . The system of  claim 5 , wherein the weighted distances are configured to be fixed according to a setting by a user.