DOI: 10.7763/IPEDR. 2014. V70. 19

An Empirical Study of Teaching Narrow Listening in College English

Chuanlin Zheng, Fangtao Kuang, Lili Zhang, and Wei Zeng

School of Foreign Languages, Southwest University, P.R. China

Abstract. By applying the method of mathematical statistics to analyzing students’ online listening data of one year, this paper aims to investigate the effects of teaching narrow listening based on autonomous online learning. Results indicate that narrow listening teaching is helpful in improving listening comprehension; because of the limited time in class, the practice of narrow listening after school is important. The criteria of autonomous online learning need be improved, such as, increasing the percentage of online time in the final online achievement. Students are allowed to do online exercise many times while the score getting at the first try should be recorded by the system which is beneficial to the data analysis. The study is hopefully of potential implications for teachers to carry on the English listening teaching efficiently with the autonomous online learning system.

Keywords: Teaching Narrow Listening, Autonomous Online Learning, Listening Training.

1. Introduction

Listening is one of four basic components of language skills. Internet and multimedia could assist teaching English listening, promote the English acquisition effectively, and improve the students' ability of listening comprehension[1]-[4]. Suqin Jiang [5] (2010) states that if multimedia assists English listening teaching, the learners' listening ability can be enhanced effectively; their autonomous learning ability can be cultivated. Through the comparative study (L2 listening comprehension research in China and abroad between 2000 and 2010), Lan Fang [6] discovers the results of domestic researches can be only used as references and can not be directly applied in teaching. Since the content of text and topic are transferred quickly in class, it is difficult for students to absorb the knowledge, and learning motivation would be decreased. But narrow listening can make up the shortage[7]. The case studies carried by Krashen [8] (2004) , Schmitt & Carter [9], Kimura[10] indicate the efficient of narrow teaching. With narrow listening theory, Caspion [11], Bessette [12] build the online narrow listening libraries and discover this kind of listening library has potential benefits for increasing students’ comprehension and motivation. So, with the former research, combining the setting of listening course of SWU China and the features of students, an empirical study has been carried on to find the effects of narrow listening teaching based on autonomous online learning.

2. The Study

2.1. Study questions

Based on the objectives of the study, the following study questions are proposed: 1) What are the effects of teaching narrow listening based on autonomous online learning? 2) What is the inspiration given by the study results to the teacher?

2.2. Objects of study

The data in this study comes from students in my two classes. All the students are teachers –to- be and in sophomore year. There are 78 students totally. They have 3 English courses each week; each course lasts 40 minutes. One of the three courses is used as practicing listening and speaking with the New Horizon College English (NHCE) book.

2.3. Teaching design

Corresponding author. Tel.: + 86 15683123114. E-mail address: zheng6816@qq.com.

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The idea of teaching narrow listening is used in the whole listening teaching process. Each unit of NHCE is about a topic. All the content in book is presented in NHCE college English online learning management system made by Foreign Language Teaching and Research Press. In class, first, students are encouraged to discus the topics which are enlightening and relative to unit. Meanwhile, to stimulate the interests of discussion and reduce the comprehension level in the future listening tasks, teacher would consciously and appropriately present some key words with marked feature of the background knowledge. Second, usually about two listening materials are listened in class. The topic of listening materials chosen by the teacher from the internet is related with the unit and the materials are mostly videos. Students listen to the materials repeatedly together and complete different exercises designed by the teacher to check their listening work after each or second time of listening. Last, the high frequency words, expressions are cleared. Since the time in class is limited, the practice after class is obvious. This part includes practicing the online exercises of targeted unit and the quiz related with the unit from the question bank of online management system.

Based on Kimura[10]’s idea, students’ score of listening part in the two final term examinations are used to check the effects of narrow listening teaching.

2.4. Data collection

All the data analyzed in this study are got from students’ sophomore year, including their online time, the number and score of finished quizzes, sections tried, score of finished sections(practice score) and the score of listening parts in the final term examinations of two terms. To make the data more accurate, online time is counted by second. Quantitative statistical analysis, LISREL method[13], oneway analysis of variance, and standard deviation analysis[5] are all used for statistical analysis.

3. Statistical Model and Analysis

3.1. Data exploration

SPSS’ function of data exploration is used to estimate the normality of variables. Kolmogorov-Smirnov test is obtained to test the variables selected by system; because the number of samples is less than 50, the Shapiro– Wilk test is used too. Also the Q – Q plot is adopted to estimate the normality of variables. The results of the data exploration are shown in Table 1.

Table 1: Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig. Online Time .207 38 .000 .739 38 .000 Quiz Score .114 38 .200* .971 38 .430 Section Tried .172 38 .006 .892 38 .002 Practice Score .117 38 .200* .947 38 .072 Final Score4 .081 38 .200* .975 38 .538 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction

From Table 1, Sig. of quiz score, practice score, final score 4(score of listening part in the final exam of

second term in sophomore year) are greater than 5%, through the Kolmogorov –Smirnov and Shapiro – Wilk tests; normality is significant. But the Sig. of online time and the sections tried are obviously far less than 5%; normality is clearly not obvious. Meanwhile, with the nature of the Q - Q plot, in the figure 1 b) d) e)，the basic distribution points of quiz score, online practice score and final score are all in the vicinity of the straight line. So, these data accord with normal distribution. On the contrary, in figure 1 a) c), data about online time and the section tried in Q - Q distribution figure scatter outside the line, and the distance from the straight line is a little long. Thus, these partial data do not accord with normal distribution.

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Fig.1. Normality test.

3.2. Counting the data’ correlation Since the data of quiz score, practice score, final score 4 all accord with normal distribution, the method

of Pearson’ correlation coefficient is used to do further analysis. Pearson’ correlation coefficient is usually expressed in R, and if the variables X and Y are surveyed, a set of data ix , iy (i=1,2,…,n）are got, the correlation coefficient formula between X and Y is:

2 2

( )( )

( ) ( )

i i

xy

i i

x x y yR

x x y y

If 0xyR , X and Y are not related; When 1xyR , Y is associated with X completely. The closer

the xyR is to 1, the more significant of correlation is between X and Y. The results of data analysis are shown in Table 2.

Table 2: Correlation Test

Quiz Score

Practice Score

Final Score4

Quiz Score

Pearson Correlation 1 .385* .170 Sig. (2-tailed) .017 .309 N 38 38 38

Practice Score

Pearson Correlation .385* 1 -.176 Sig. (2-tailed) .017 .290 N 38 38 38

Final Score4

Pearson Correlation .170 -.176 1 Sig. (2-tailed) .309 .290

N 38 38 38 *. Correlation is significant at the 0.05 level (2-tailed).

From the test results in Table 2, quiz score and practice score are significantly correlated, but the

correlation among the quiz score, practice score and final score 4 is not obvious. The specific reasons will be analyzed in part 4.

For the two variable data, online time and the section tried do not conform to the normal distribution, so Spearman’ rank correlation method which focuses on studying the correlation between two variables is used

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in the test. From Table 3, the relationship among online time, section tried and the final score4 is significant; the relationship between the section tried and the final score4 is not significant.

Table 3: The Relationship among Online Time

Online Time

Section Tried

Final Score4

Spearman's rho

Online Time

Correlation Coefficient 1.000 .390* .387*

Sig. (2-tailed) . .016 .016 N 38 38 38

Section Tried

Correlation Coefficient .390* 1.000 .150 Sig. (2-tailed) .016 . .368 N 38 38 38

Final Score4

Correlation Coefficient .387* .150 1.000 Sig. (2-tailed) .016 .368 . N 38 38 38

*. Correlation is significant at the 0.05 level (2-tailed).

Further statistical analysis is made to search the relationship between each variable and

“improvement”(students’ rank change in class comparing their score of listening part in the final term exam of second term with score of listening part in the final term exam of first term) as shown in table4（the table4 is put in 3.3 part）. From Table 4, online time, final score3, final score4 are significantly correlated with improvement.

Table 4: Correlations between Each Variable and the Improvement

Online Time

Quiz Score

Section Tried

Practice Score

Final Score4

Final Score3

Impro- vement

Impr- Ove- ment

Pearson Correlation

.325* .206 .191 -.195 .594** -.547** 1

Sig. (2-tailed)

.047 .214 .250 .240 .000 .000

N 38 38 38 38 38 38 38

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

3.3. Regression analysis

Students' autonomous learning achievement is assessed by online learning work. Thus scientific criteria are important. Multiple linear regressions are adopted to look for the coefficient. The dependent variable is the final score; the independent variables are the rank of online time in whole class, quiz score, section tried and practice score. The results of the regression are shown in Table 5.

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Table 5: Regression Analysis

Modela Unstandardized Coefficients Standardized Coefficients

t Sig.

B Std. Error Beta

1 Constant 16.263 1.218 13.350 .000 Online Rank

-.142 .054 -.398 -2.602 .013

a. Dependent Variable: FinalScore4

4. Results Analysis and Its Implications for Teaching

4.1. Variables of normality

Variables of quiz score, practice score and the final score4 are significant. Quiz score, practice score are assessed by autonomous learning system. Final examinations are unified proposition, unified checking. So, those three results are all objective and it is reasonable for them to obey the normal distribution. The unit of online time is small, the numerical value of online time is large, and the variance is big. These lead to the normality of online time is not obvious. The normality of section tried variable is not obvious too. The main reason is that the number of sections is only 60, not sufficient for data analysis. Many students have completed over half of the sections; some students have completed all 60 sections. The partial quantity statistics do not accord with normal distribution.

4.2. Correlation analysis

As a rule, the quiz score, practice score and the final score should be highly linear correlation. But from the table 2 and 3 that is not the case. Main reason is that final score is got by one time. But quiz and practice are allowed to answer many times. Through the effort the standard answer can be found, the highest score can be got. There is other reason: to pursue the higher score, some students may find the reference answer first and “do” the practice second. Therefore, the practice score and the final score are not correlation.

Table 3 shows the longer of online time, the better of final score4. This is unexpected. According to the design of system, teacher can not ensure that online time is all spent on doing autonomous learning by students. Because even if students login system and don't do online learning, they also get the cumulative online time in the system. So there was once thought that the statistic of online time is no reference value. But after the data analysis, a new sense of this variable is found. This shows the authenticity of students' online time, and students do not deliberately cheating.

4.3. Enlightenment to teaching narrow listening

First, this study proves that teaching narrow listening based on autonomous online learning is effective. But because the teaching time in classroom is limited, the narrow listening practice after class should be strengthened. It’s better for teachers to encourage students to do online listening practice of each unit several times. At the same time, teachers strengthen the inspections in class, such as designing some relative exercises in class to check the students’ autonomous online learning. Meanwhile, teacher may make full use of “team work” in the network platform, so students can learn from each other and supervise each other. Then the inspections in class can be developed by team as the unit.

Second, evaluation criterion of online learning should be developed. For example, increasing the proportion of online time is worth. Now we describe students’ achievements with multiform assessments. The proportion of online learning achievement is 20%. These 20% score should be judged by quiz score, section tried, practice score and the online time. Before the study, quiz score, section tried and the practice score are mainly considered to evaluate the online listening achievement. But after regression analysis of multiple factors, it is found that online time should be more considered. The calculation model should be:

16.263 0.142y x and X is for online time ranking in the class. 101

Third, although the autonomous learning system allows students to do exercises many times, only the score got in the first time practice should be recorded. Thus the test atmosphere is simulated and it is conducive to scientific study and analysis.

5. Conclusion

This paper is an empirical study of teaching narrow listening in college English. The study finds the effect of teaching narrow listening is remarkable. The narrow listening practice based on autonomous online learning after class plays a very important role, which provides a direction for the future teaching. The study also provides the basis for the criteria of evaluating online listening practice, makes guidance for the future design of autonomous online learning system. The shortcomings of the survey are clear, such as the short time of survey, the narrow range of study subjects. What are needed do in the further in-depth work are improving teaching and collecting more parameters, such as exercise types, contents etc.

6. Acknowledgements

This paper is part achievement of the project supported by the Fundamental Research Funds for the

Central Universities (SWU1309242).

7. References

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