Science Wall’s Data Analysis Research Paper

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Introduction

Teaching scientific language to ELL students can be difficult. Words often have multiple meanings that need to be memorized, and previous knowledge of linguistics does not help the student to understand those terms better. However, various strategies exist that can help students build scientific term vocabulary to understand instructions better. One of such strategies is the use of interactive word walls that have visual representations of words to create associations for better understanding. This paper examines the data gathered on a class that had such a board installed.

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Background

For the purposes of research, two classes were selected. One served as the experiment class where the science wall with images and corresponding words was installed. It contained ten ESL students and five non-ESL students. In the gathered data this class is labeled as “A.” The second class was chosen as the control class. While the space for the science wall was prepared, it was not installed, and the students had to rely on the standard methods of vocabulary development. The class contained seven ESL students and eight non-ESL students. In the data, the class is labeled as “R.” To test the effectiveness of the Science wall, two tests were performed. The first was done at the beginning of the year and the second one at the end of it.

Collected Data

The students of class A have achieved a mean average score of 80 during the test at the beginning of the year. ESL students on their own scored a mean average of 79 on the test, while the non-ESL students scored an average of 80. The highest score among ESL students of class A was 96, and among non-ESL students, it was 92. On the other hand, class R students scored a mean average of 89. The ESL students of the R class separately scored an average of 90, and non-ESL students scored a mean score of 88. The highest ESL student score was 95, and the non-ESL score was 97. For added clarity, these statistics are presented in Table 1.

ClassMean ScoreMean Score ESLMean Score Non-ESLHighest Score ESLHighest Score Non-ESL
A8079809692
R8990889597

Table 1. Beginning of the year test scores.

The end of the year test presented a completely different set of data with the quality of the scores changing dramatically between classes. The mean average score of the A class rose to 94 with the scores of ESL and non-ESL students averaging to 93 and 97 respectively. Highest scores among the students also rose with both groups having multiple students scoring full 100 marks. The average scores of class R however, lowered to 86. ESL students of the class scored an average of 85 and non-ESL students having a mean score of 86. The highest scores also reduced with the ESL group having the highest of 93 and non-ESL showing 94. The data can be seen in Table 2.

ClassMean ScoreMean Score ESLMean Score Non-ESLHighest Score ESLHighest Score Non-ESL
A949397100100
R8685869394

Table 2. End of the year test scores.

Analysis of the Data

The data was analyzed by the method of direct comparison of the average growth of students between two classes. The growth of ESL and non-ESL students was calculated by subtracting the early test results from the later ones for each student. Then the average for each group was calculated. In class A the average score growth of ESL students was recorded as being 14, while non-ESL students of the class improved by 16 marks. Class R has shown a lesser average improvement among the groups with ESL students showing an average score growth of 7 and non-ESL students improving by an average of 8 points. The data can be seen in Table 3.

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Average Score Growth
ClassESL StudentsNon-ESL Students
A1416
R78

Table 3. Average score growth of the students.

While both classes have shown an average improvement among the students, it was not the case with all the students. All of the students in the A class experienced an improvement in at least a single point with the best cases increasing by 30 points. The students of the R class, however, have shown a decrease in scores up to 13 points. The reduction can be seen in students of both groups. These results suggest that the implementation of the Science wall was not only beneficial for the ESL students but also for the non-ESL students. Perhaps the continuous interaction with the science wall created better familiarity with the terms among both groups. For the ESL students, it allowed for easier vocabulary building and for the Non-ESL students it might have served as a reminder of various terms that they encountered in the test.

Conclusion

Scientific instruction is an essential part of the educational process. However, it is often a problematic topic for ESL students due to the specificity of the words. While this research was based on a relatively small sample of participants, it nonetheless shows the potential of the science board to teach scientific vocabulary to the ESL students.

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IvyPanda. (2020, December 30). Science Wall's Data Analysis. https://ivypanda.com/essays/science-walls-data-analysis/

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"Science Wall's Data Analysis." IvyPanda, 30 Dec. 2020, ivypanda.com/essays/science-walls-data-analysis/.

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IvyPanda. (2020) 'Science Wall's Data Analysis'. 30 December.

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IvyPanda. 2020. "Science Wall's Data Analysis." December 30, 2020. https://ivypanda.com/essays/science-walls-data-analysis/.

1. IvyPanda. "Science Wall's Data Analysis." December 30, 2020. https://ivypanda.com/essays/science-walls-data-analysis/.


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