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Addressing the Testing Challenge with a Web-Based E - Assessment System that Tutors as it Assesses Nidhi Goel Course: CS 590 Instructor: Prof. Abbott.

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Presentation on theme: "Addressing the Testing Challenge with a Web-Based E - Assessment System that Tutors as it Assesses Nidhi Goel Course: CS 590 Instructor: Prof. Abbott."— Presentation transcript:

1 Addressing the Testing Challenge with a Web-Based E - Assessment System that Tutors as it Assesses Nidhi Goel Course: CS 590 Instructor: Prof. Abbott

2 Motivation Every hour spent in assessing students is an hour lost from instruction. Student assessment is not always accurate How to instruct a student at the same time of assessment

3 ASSISTment System E-Assessment + E-Learning ✔ Accurate assessment of a student ✔ Assist a student along with assessment No extra time is spent in assessment Examples of web based systems ✔ www.assistment.org ✔ Massachusetts Comprehensive Assessment System (MCAS)

4 Research questions Does the tutoring provide valuable assessment information Does this continuous assessment system do a better job than more traditional forms of assessment Can we track student learning over the course of the year

5 Cont'd....... Can we see what factors affect student learning Can we track the learning of individual skills

6 Log in Assistment system

7 Original question Hint 1 st scaffolding question 2 nd scaffolding question Buggy Message

8 Data Collected Out of 600, 417 students of two middle school who have ✔ Score of MCAS test which is taken in May 2005 ✔ Results of 2 paper practise test which is taken on September 2004 and June 2005 ✔ Results of ASSISTment system which is used every other week from September 2004 to June 2005

9 Online Measures

10 Algorithm Step1: Select different variables among the 15 online measures to define the model. Step2: Calculate the coefficients for corresponding variables of a model using linear regression analysis to best match the obtained MCAS scores by the students. Step3: Calculate R2 using the calculated coefficients and sample data of students. R2 is a measure of correlation between curve fit values obtained from regression analysis equation and the sample data. Step4: Using R2 calculate BIC (Bayesian Information Criterion) = n*log(1- R2) + p(log(n)) where n=417 (number of samples), p=number of variables. BIC is a measure of how much information in terms of samples and number of variables is needed to get a reasonably good model. Higher BIC means more information is required, which implies the model is not very good. The authors select the model based on the following criteria ✔ Uses less number of independent variables ✔ Has large value of R2 The value of BIC gives a good assessment for such a model. Authors found that model IV was most significant.

11 Models

12 Conclusion Though model V has lowest BIC values, authors think model IV is better because it uses only six variables as compared to ten variables used in model V. Table 3 in this paper contains all included variables' coefficients calculated by regression analysis for model IV. These coefficients are useful in assessment of a student.


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