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A cluster-based analysis to diagnose students’
learning achievements

Luz Stella Robles Pedrozo, U. Tecnológica (Cartagena...
Content

1.  General Objectives
2.  Background and Motivation
3.  Proposed Diagnostic Test Methodology
4.  Conclusions
5. ...
General	
  Objec,ves	
  	
  

Scope
Recognizing Learning Disabilities trough Testing: Clustering Based Methodology and Rel...
Background	
  and	
  Mo,va,on	
  

û  Problems with prior knowledge diagnostic assessment using standardized tests with m...
Background	
  and	
  Mo,va,on:	
  tests	
  
û  Disadvantages of traditional tests : The same test, with a fixed
number of...
Background	
  and	
  Mo,va,on:	
  feedback	
  

A diagnostic assessment methodology that provides a classification score, ...
Proposed	
  Diagnos,c	
  Assesment	
  Methodology	
  
Item Response Theory (ITR) [Thurstone, 1925], [Lord, 1952, 1968]
ITR...
Proposed	
  Diagnos,c	
  Assesment	
  Methodology	
  
ITR Models
û  1, 2 and 3 parameters unidimensional logistic models
...
Proposed	
  Diagnos,c	
  Assesment	
  Methodology	
  

Diagnostic Methodology : Item selection
û  At least one assessment...
Proposed	
  Diagnos,c	
  Assesment	
  Methodology	
  
An inference example (probability and statistics)

IEEE EDUCON 2013 ...
Proposed	
  Diagnos,c	
  Assesment	
  Methodology	
  

IEEE EDUCON 2013 (Berlin)
Proposed	
  Diagnos,c	
  Assesment	
  Methodology	
  
Diagnostic Methodology

Tool	
  used:	
  R	
  
h,p://www.r-­‐project...
Proposed	
  Diagnos,c:	
  Learning	
  Paths	
  
Diagnostic Methodology

IEEE EDUCON 2013 (Berlin)
Clustering	
  
Cluster Generation

IEEE EDUCON 2013 (Berlin)
Clustering	
  

Cluster Generation
û  List of weakly-understood concepts per each examinee
û  Total weight of each weakl...
Clustering	
  
Cluster Generation

IEEE EDUCON 2013 (Berlin)
Clustering	
  
Cluster Generation

IEEE EDUCON 2013 (Berlin)
Conclusions	
  

Psychometric aspects
û  The Item Response Theory (IRT) was selected for this work after a proper underst...
Conclusions	
  
Regarding the Diagnostic Methodology
A software for diagnostic was implemented:
•  Process answers of the ...
Conclusions	
  

û  This work is useful for public education institutions in Colombia because it serves as a solution
for...
A cluster-based analysis to diagnose students’
learning achievements
THANKS!
Luz Stella Robles Pedrozo, U. Tecnológica (Ca...
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Learninig Analytics Special Track: A cluster-based analisys to diagnose student's learning achievements

IEEE EDUCON 2013 Conference Berlin

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Learninig Analytics Special Track: A cluster-based analisys to diagnose student's learning achievements

  1. 1. A cluster-based analysis to diagnose students’ learning achievements Luz Stella Robles Pedrozo, U. Tecnológica (Cartagena, Colombia) Miguel Rodríguez Artacho, UNED University (Madrid) IEEE EDUCON 2013 (Berlin)
  2. 2. Content 1.  General Objectives 2.  Background and Motivation 3.  Proposed Diagnostic Test Methodology 4.  Conclusions 5.  Future Work IEEE EDUCON 2013 (Berlin)
  3. 3. General  Objec,ves     Scope Recognizing Learning Disabilities trough Testing: Clustering Based Methodology and Reliability General Objective The design and implementation of a methodology for learning disabilities diagnosis and assessment based on: û  Adaptive feedback to the students in order to individually identify learning weaknesses and misconceptions about a topic right after assessment through testing. û  Classification of the students via clustering of the detected learning disabilities, as a support for the design of feedback strategies and activities for improving their academic performance. IEEE EDUCON 2013 (Berlin)
  4. 4. Background  and  Mo,va,on   û  Problems with prior knowledge diagnostic assessment using standardized tests with manual scoring: Type I ICFES multiple choice questions with only one correct answer. This kind of questions are used for: Midterm exams, SABER 5th, 9th, 11th and SABER PRO mandatory state tests in Colombia. û  The traditional education system uses pass/fail scoring scale based written exams for assessment à The score does not provide enough information about learning that can be used for performance improving. û  The recognition of learning disabilities and misconceptions is key and complex process that has to be manually performed. IEEE EDUCON 2013 (Berlin)
  5. 5. Background  and  Mo,va,on:  tests   û  Disadvantages of traditional tests : The same test, with a fixed number of items, is given to all test takers. They have limited answer choices. The test is long in order to make it more accurate. û  The assessment uses traditional methodologies which do not allow : −  Identification of systematic misconceptions and weak understanding of concepts in order to plan strategies to improve their academic performance. −  The classification and grouping of the students to undertake a reorientation of the reinforcement activities. −  The individual recognition of the level of learning disabilities and misconceptions. IEEE EDUCON 2013 (Berlin)
  6. 6. Background  and  Mo,va,on:  feedback   A diagnostic assessment methodology that provides a classification score, identifies learning disabilities, misconceptions and weak understanding of concepts, allowing to group the students with similar problems in clusters, is required. Structure of the proposed diagnostic assessment methodology: û  Item Response Theory (IRT) is used as the method to obtain the skill level of each concept. û  The use of a system of interrelated concepts and dependences to identify cognitive disabilities (misconceptions and weak understanding of concepts) û  The use of Clustering to classify the students in groups with similar disabilities IEEE EDUCON 2013 (Berlin)
  7. 7. Proposed  Diagnos,c  Assesment  Methodology   Item Response Theory (ITR) [Thurstone, 1925], [Lord, 1952, 1968] ITR allows invariant measured variables that are independent with respect to the examinees and the used test instruments. CTT ITR Lack of invariance in the properties of the tests with respect to the test subjects. The characteristics of the items depend on the group of persons. Different tests can be comparable, as the skill level trend to be the same between different item sets Asumes the same error level for all subjects, Similar level of assessment accuracy or the test liability is the same for all the for all different participants. participants (as a property of the test) IEEE EDUCON 2013 (Berlin)
  8. 8. Proposed  Diagnos,c  Assesment  Methodology   ITR Models û  1, 2 and 3 parameters unidimensional logistic models û  Dichotomous answer format (only one answer) û  Performance and skills assessment ITR – Model proofing The test instrument, with the items containing the object variable, is applied to û  Validate the ITR assumptions û  Select the optimum models based on statistical analysis ITR – Once the model is selected … û  Estimate the parameters of the selected model û  Calculate the skill or proficiency level of the test subjects û  Identify learning disabilities in the test subjects IEEE EDUCON 2013 (Berlin)
  9. 9. Proposed  Diagnos,c  Assesment  Methodology   Diagnostic Methodology : Item selection û  At least one assessment item assigned to each node of the framework. û  The knowledge domain to be evaluated, categorized into sub-topics and pre-requisites. û  The dependences between the items and the concepts (concepts for the assessment in each item). û  The weight of the concepts in each item. IEEE EDUCON 2013 (Berlin)
  10. 10. Proposed  Diagnos,c  Assesment  Methodology   An inference example (probability and statistics) IEEE EDUCON 2013 (Berlin)
  11. 11. Proposed  Diagnos,c  Assesment  Methodology   IEEE EDUCON 2013 (Berlin)
  12. 12. Proposed  Diagnos,c  Assesment  Methodology   Diagnostic Methodology Tool  used:  R   h,p://www.r-­‐project.org/   IEEE EDUCON 2013 (Berlin)
  13. 13. Proposed  Diagnos,c:  Learning  Paths   Diagnostic Methodology IEEE EDUCON 2013 (Berlin)
  14. 14. Clustering   Cluster Generation IEEE EDUCON 2013 (Berlin)
  15. 15. Clustering   Cluster Generation û  List of weakly-understood concepts per each examinee û  Total weight of each weakly-understood concept in the test (TP CI d) û  Calculate the total weight of the weakly-understood concepts in the test (PTcd) per each examinee, as in : IEEE EDUCON 2013 (Berlin)
  16. 16. Clustering   Cluster Generation IEEE EDUCON 2013 (Berlin)
  17. 17. Clustering   Cluster Generation IEEE EDUCON 2013 (Berlin)
  18. 18. Conclusions   Psychometric aspects û  The Item Response Theory (IRT) was selected for this work after a proper understanding of its advantages with respect to the Classical Test Theory (CTT). û  An statistical procedure was proposed to select and validate the optimum model to use with the obtained data from the tests used in this work. A computer program was designed on the R language for analysis purposes . û  A comparative studied was performed between the score for the skills level of a group of examinees obtained with the classical test theory (TCT, average score) and that obtained with the IRT model (unidimensional 3 parameters logistic model 3PL) IEEE EDUCON 2013 (Berlin)
  19. 19. Conclusions   Regarding the Diagnostic Methodology A software for diagnostic was implemented: •  Process answers of the examinees ( Deficient and Minimum) to generate the weaklyunderstood concepts per student •  Represent the suggested leaning paths for each examinee. •  An index representing the total weight (or total sum of weigths) of the weakly-understood concepts in the test per examinee is generated. Regarding the Cluster A computer program was implemented in R in order to generate a list classifying the examinees in groups with similar misconceptions or learning disabilities. à Data mining tools can be as useful as Intelligent Systems in certain domains to diagnose student models. IEEE EDUCON 2013 (Berlin)
  20. 20. Conclusions   û  This work is useful for public education institutions in Colombia because it serves as a solution for the efficient diagnostic of the learning disabilities in students by using a test. û  The design and implementation of the diagnostic procedure, suppported with IRT and clustering procedures, allow to perform a comprehensive diagnostic of the learning disabilities, misconceptions and weak understanding of concepts in students. û  The work provides the students with a tool for the easy identification of their learning and cognitive disabilities, and the suggested self-learning path to improve their academic performance à Provide feedback IEEE EDUCON 2013 (Berlin)
  21. 21. A cluster-based analysis to diagnose students’ learning achievements THANKS! Luz Stella Robles Pedrozo, U. Tecnológica (Cartagena, Colombia) Miguel Rodríguez Artacho, UNED University (Madrid) Learning Technologies and Collaborative Systems http://ltcs.uned.es IEEE EDUCON 2013 (Berlin)

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