De carlo rizk 2010 icelw

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This document summarizes a study on developing an expert system called W-CAT (Witty Cat) to analyze educational data and generate rules to provide feedback to instructors. It describes collecting student survey data on exam preparation activities and results. Association rule learning was used to generate rules from the data, such as students who viewed review videos performed poorly on exams. The study found the rules provided useful insights for instructors. Further development of W-CAT is ongoing to automate rule generation and provide human-readable explanations of results.

Presenter: Paul DeCarlo Professor: Nouhad Rizk Date: 6 / 11/ 2010 The Design and Development of an Expert System Prototype for Enhancing Exam Quality DeCarlo, P., & Rizk, N. (2010). The Design and Development of an Expert System Prototype for Enhancing Exam Quality . International Conference on Electronic Learning in the Workplace.

Introduction ,[object Object],[object Object],[object Object]

Study Purpose ,[object Object],[object Object],[object Object],[object Object]

What is Association Rule Learning? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Data Collection Driven by W-CAT model ,[object Object],[object Object],[object Object]

Example Survey Questions & Results ,[object Object],[object Object]

The RapidMiner Process Tree ,[object Object],[object Object],[object Object],[object Object]

Rules generated in our case study Results from 50 students enrolled in a College level Computer Organization and Design Course at the University of Houston Fall 2009.

Impact of Pilot Study on Instructor Methodology ,[object Object],[object Object],[object Object]

Development Issues raised in Pilot Study ,[object Object],[object Object]

Current state of WittyCat ,[object Object],[object Object],[object Object]

Overview of the Desired Final Product A dynamic, adaptive, specified, intelligent, assessment tool with expert adaptation.

How you can contribute ,[object Object],[object Object],[object Object],[object Object]

With the growth of voluminous amount of data in educational institutes’, the need is to mine the large dataset to produce some useful information out of it. In this research we focused on to form a decision support system for the educational institutes’ which can help them to know about the placement possibility of students. Our research is not limited to find out placement possibility but we did multi-level analysis on student performance dataset which will predict that what level of interview process a student is likely to pass. For this we have applied Naïve Bayes and Improved Naïve Bayes which is integrated with relief feature selection technique to obtain the prediction. Data analysis was done using NetBeans and WEKA. For this our proposed technique gave better accuracy than existing naïve Bayes which was 84.7% and naïve Bayes gave 80.96% accuracy.

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