university of pedagogy

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University of Pedagogy. Department of English. Instructor: Mr. Khương. Contribution for language teaching. Contrastive. Analasys. Group 2. C.A in Pedagogy. Prediction. Traditional Applications. Scales of Difficulty. Diagnosis of Error. Prediction. Things C.A can predict:. - PowerPoint PPT Presentation

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University of PedagogyDepartment of English

Contribution for

language teaching

Instructor: Mr. Kh ng ươ

Group 2

ContrastiveAnalasy

s

Diagnosis of Error

Scales of Difficulty

Prediction

Traditional Applications

C.A in Pedagogy

Prediction

What aspect can cause problems.

Difficulty

Error

the tenacity of certain errors.

Things C.A can predict:

Prediction

Predict of Error

The existence of error

The form of error

Prediction

C.A can predict a limitation on the number of error

Interlingual errors: result of L1 interference

Ex: He usually go to school late.

Intralingual errors: effect of L2 asymmetries

Ex: She has a hat beautiful.

Scales of Difficulty

The notions of positive and negative transfer potential

Conditions for transfers assumed to be statable in terms of the relations between rules of L1 and L2.

Based on:

Scales of Difficulty

inter-lingual rule relationships:

L1 has rule and L2 has equivalent.

L1 has rule but L2 has no equivalent.

L2 has rule but L1 has no equivalent.

Scales of Difficulty

Identify types of choices: 3 types Optional (Op): the possible selection

among phonemes

Obligatory (Ob): phonological choice involving little freedom

Zero (Ø): the absence in one of the language while it is available in the other

Scales of Difficulty

Different availabilities of choice allow eight kinds of relationship between L1 & L2

eight-point hierarchy of difficulty

Scales of Difficulty

1 ……… Ø Ob2 ……… Ø Op3 ……… Op Ob4 ………. Ob Op5 ………. Ob Ø6 ………. Op Ø7 ………. Op Op8 ………. Ob Ob

Order of difficulty Comparison of Choice TypeL1 L2Most

Least

I

II

III

A scale of three order of difficulty by coalescing

Diagnosis of Error

Teacher (monitor, assessor)

Student ‘s errors

Avoiding the same errors

recognize errors organize feedback

Self-correct

CA Hypothesis

diagnostic functions (tenable)

predictor errors (not tenable)

Diagnosis of Error

1. My class has a good boy, name call Ninja.Ming jiao (Chinese) - name call (English)

There are some errors NOT related to L1 components.

Diagnosis of Error

There are some errors related to L1 components.

2. I very love you.

Ex:

Ex: A: How are you?B: I’m fine, thanks. And you?

Thanks for ^oo^ Your Time

^oo^

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