QUANTITATIVE DATA GATHERING – USE OF THE READABILITY FORMULAE

The relationship between the readability of the textbook and its semantics (vocabulary and sentence length) can be investigated using credible readability formula. These have been comprehensibly reviewed in Chapter 2, and are a valid base for examining readability quantitatively.

I used three standardised readability instruments namely: the Fry Readability Graph, the New Dale-Chall Readability Formula and the Flesch-Kincaid Reading Formula. To improve the accuracy of the study, the three readability methods incorporated two different readability measures: two that made use of formulae and one that utilized a graph.

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Average number of syllables/100 words

A rendition of the Fry Graph

The grade reading level (or reading difficulty level) is calculated by the average number of sentences (y-axis) and syllables (x-axis) per hundred words. These averages are plotted onto a specific graph; the intersection of the average number of sentences and the average number of syllables determines the reading level of the content.

The grade level score is calculated by selecting separated 100 word passages and counting the number of syllables in the sample. The number of sentences in the sample is then counted, estimated to the nearest tenth. Finally the average sentence length and average number of syllables are plotted against each other as described above.

3.4.2. THE NEW DALE-CHALL READABILITY FORMULA

The grade level or readability level is calculated by means of a formula. The following steps are applied:

1. A 100 word sample is selected.

2. The average sentence length (ASL) is computed by dividing the number of words by the number of sentences.

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4. Using the tables – both reading level tables and cloze score tables – the reading level of the sample is then read off. For example a sample with 34% unfamiliar or difficult words and an ASL of 6, would give a reading grade level ranging between 13 and 15.

3.4.3. THE FLESCH-KINCAID READABILITY FORMULA

The Flesch-Kincaid Reading Formula (FKRF) also uses one-hundred-word samples. The average sentence length is computed by dividing the 100 word sample by the number of sentences in the sample (ASL), and the average number of syllables per word is computed by dividing the number of syllables in the sample of words by 100 (ASW). These averages are then transferred to the following formula to give an appropriate reading level:

Grade Level = (.39 x ASL) + (11.8 x ASW) - 15.59

The Flesch formula has correlations as high as .98 with other reading formula.

3.4.4. FORMULA DISCREPANCIES

The discrepancy between the scores of different formulae is often cited by critics as an indication of their lack of precision (DuBay 2004:54). However, what is important is not how the formulae agree or disagree on a particular text but their degree of consistency in predicting difficulty over a range of graded texts.

The formulae used in this study – like reading tests – simply do not have a common zero point (Klare 1982). They are based on combinations of different variables as described in the above section as well as different criterion scores used in their development. This criterion score is the required level of comprehension indicating reading success as indicated by the percentage of correct answers on a reading test. For example, a formula can predict the level of reading skill required to answer correctly 75% of the questions on a reading test based on a criterion passage.

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The formulae developed with the higher criterion scores tend to predict higher scores, while those with high validity correlations, for example, Dale-Chall and Flesch, tend to predict lower scores. The Flesch and the Dale-Chall formulae use a 75% score.

Finally, the range of scores provided by different formulae, are reminders that they are not perfect predictors. They provide probability statements or rather estimates of difficulty (DuBay, 2004:55).

3.4.5. THE PROBLEM WITH OPTIMAL DIFFICULTY

Different uses of a text require different levels of difficulty. Bormuth (1967) indicated that the 35% close score was the point of optimum learning gain for assisted classroom reading.

This is in tune with Vygotsky’s (1978) claim that when learning is mediated by a teacher, optimal level of difficulty is slightly above a learner’s current level of development and not below; it is within what Vygotsky termed the zone of proximal development. Thus materials intended for assisted reading when a teacher is available should be somewhat harder than the readers’ tested reading level.

Paul (2003) found that independent reading requires at least 92% comprehension for advanced readers.

3.4.6. QUANTITATIVE DATA GATHERING : THE COMPREHENSION OF NON-TECHNICAL VOCABULARY

Quantitative data collection was also used when assessing the understanding of the learners using reading the non-technical vocabulary found in the textbooks. Earlier studies reviewed in Chapter 2, had shown certain words, to be as problematic in the comprehension of science language as the scientific terminology itself. A multiple choice questionnaire testing these words, [similar to that used in prior studies (Appendix C)] was

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given to 300 learners in three schools. These learners all actively use the textbooks being studied. The data was then analysed as follows:

The Facility Value (FV) was computed: that is the fraction of the class making the correct choice. I used the results from two first language English classes (n=60) and six second language English classes – home language being Xhosa (n=360). The two groups were then compared in the following way:

Secondly, the commonest distracters were determined. These are the commonest wrong word chosen.

In taking the results to my textbook analysis, I highlight the continual use of this non- technical vocabulary and stress the need to support it in teaching and in the text.