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

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)

Content

1.  General Objectives

2.  Background and Motivation

3.  Proposed Diagnostic Test Methodology

4.  Conclusions

5.  Future Work


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.

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.

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 re-

orientation of the reinforcement activities.

− The individual recognition of the level of learning disabilities and

misconceptions.

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

Proposed Diagnos,c Assesment Methodology


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,

or the test liability is the same for all the

participants (as a property of the test)

Similar level of assessment accuracy

for all different participants.

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.



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



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.



An inference example (probability and statistics)



Diagnostic Methodology

Tool used: R

h,p://www.r-‐project.org/

Proposed Diagnos,c: Learning Paths


Diagnostic Methodology

Clustering


Cluster Generation

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 :

Clustering


Cluster Generation

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)

Conclusions


Regarding the Diagnostic Methodology

A software for diagnostic was implemented:

•  Process answers of the examinees ( Deficient and Minimum) to generate the weakly-

understood 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.

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

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


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

Education

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