h3 acid balance tsai-wei huang

7/31/2019 H3 Acid Balance Tsai-Wei Huang

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Examining the effect of classifyingresponse patterns on an acid-base test by a

Guttman-based person-fit index set

Tsai-Wei Huang Shuh-Wang Tsai

National Chiayi University

08/11/2012


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Introduction

The first time sixth grade students learn

the concept of acid-base balance.

Two-tier multiple-choice questions (know-

what then know-how) are effective todiagnose misconceptions.

Persons response patterns prevalently

were used to analyze students learningmisconceptions.


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Questions

Q1 : How do the classified clusters based

on the BW indices be verified?

Q2 : What characteristics of the classifiedclusters will reveal?

Q3 : What characteristics will change forindividuals between the know-what testand know-how test?


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Rationales of person-fit index

Guttman model

BW family indices


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000000000000001.93.071#10

000000000000101.87.132#7

000001000000111.73.274#8

000000010111110.60.406#2000101101110100.53.477#5

010010101001111.47.538#1

001000011111011.40.609#4

000101110111011.33.6710#9

000011111111111.27.7311#3111010011111111.20.8012#6

stTID

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.10.20.20.20.30.40.40.50.50.60.60.60.70.70.80p=

104812111351761593214Item #

Illustration of Guttman-based Response Patterns (N=10 persons, K=15 Items).


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BW family indices

Person-facetW (Carelessness)

B (Guessing)

C (Capability)

M (Misconception)

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Method

Subject

67 valid 6th grade students in Taiwan.

41 boys (61.19%) and 26 girls (38.81%).


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Instrument

20 two-tier multiple-choice acid-base questions

Item difficultyEasy : .65


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Example 1Know-what question (Answer)( ) According to the weather report, rain water from most parts of the

world is acidic rain. What do you think the nature of acidic rain is? (1) Acidic (2) Neutral (3) Basic (4) Uncertain

Know-how question (Reason)

( ) What causes it to possess such property? (1) Because of openings in the ozone layer. (2) Because factories, vehicles and scooters release acidic

substances into the air, therefore rain water is acidic. (3) Because factories release basic substances into the air,

therefore rain water is basic. (4) Whether the nature of rain water is acidic, neutral or basic, it

depends on the substances in the air at the moment.


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Example 2

Know-what question (Answer)

( )There are many types of aqueous solutions in our dailylives and one of them has a slippery feeling whentouched. What kind of property might it posses?

(1) Acidic (2) Basic (3) Neutral

Know-how question (Reason)

( ) What do you think the best reason for this is?

(1) Soap water feels slippery (2) Tap water feels slippery

(3) Peeling pears feel slippery


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Scoring systemS1. Dichotomous scoring for 1st tier items as kk1S2. Dichotomous scoring for 2nd tier items as kk2

S3. Coding cognitive stylestruly known: kcogn = 1, if (kk1 = 1 & kk2 = 1)guessing: kcogn = 2, if (kk1 = 1 & kk2 = 0)careless: kcogn = 3, if (kk1 = 0 & kk2 = 1)

unknown: kcogn = 4, if (kk1 = 0 & kk2 = 0)S4: Summing the number of each cognitive type across 20 items withinsubject, and create four variables as TK (truly known), GS(guessing), CR (careless), and (UK) unknown to serve as thepredictors of discriminant analysis and multinominal logisticregression procedures on the criteria of group classifications fromclustering.


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Four cognitive styles of the two tier items


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Classification by BW indices


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AnalysisBW indices estimation The BW indices (W,B,C,M) were calculated through the first and second tier

items (i.e., right or wrong). Changes from the two tiers of test can becompared through standardized indices.

Classification: Three cluster analysis methods based on would classify samples into 2-4

groups based on the BW indices by Between method (Average linkage between groups) Ward method K-mean method

Verification:

The four variables (TK, GS, CR, UK) would serve as the predictors on thecriteria of group classifications from clustering by two identifying approaches Discriminant Analysis (DA)~ linearly prediction Multinominal Logistic Regression (MLR) ~ nonlinearly prediction


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RESULTS

Both identifying approaches confirmed the BW-based classifications were valid withvery high hit rates of predictions, especially the nonlinear multinominal logistic

regression.

Since the small number of cases (n=2) clustered by the Between method in the firsttwo types of groups (Group = 2 and 3) and that (n=4) by the K-mean and Ward

methods in the final group (Group =4) for both DA and MLR procedures, we will

discuss the properties of clusters by the K-mean and Ward methods in the first twotypes of groups.

Classification


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Properties of 2 clusters

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Properties of 3 clusters

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Change between know-what and know-how

H1H1 H1A3

H1L1 L1H1


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Conclusion & Discussion

1. Two and three groups of classification by the

methods of K-mean and Ward weresatisfactory.

2. Two-tier test (answer/reason) design

confirmed the BW-based classifications werevalid with very high hit rates of predictions,especially predicted by the nonlinear

multinominal logistic regression. This impliesthe BW indices can effectively predict studentspractical cognitive styles of acid-base learning.


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3. Properties of clusters by the methods of K-mean

and Ward were similar; High- level cognitive students with low misconceptions

tend to be less guessing and careless than the lowcognition students do in 2- group classification.

Low- level cognitive students tend to guess; middle-level ones tend to slip.

4. Examining the changes of standardized person-fit indices between the know-what test and theknow-how test can provide much diagnosticinformation.


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Limitations and Suggestions

The BW indices can effectively predict students

practical cognitive styles of acid-base learning,but it still need more essential evidence.

Although a two-tier test is not easy designed (in

this study, the discriminations were notsatisfactory), it still possesses rich diagnosticinformation. Future studies might apply this

device to other subjects for more diagnosingstudents learning misconceptions, especiallycombined with the WB person-fit indices.


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Thanks for your listening!

h3 acid balance tsai-wei huang

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