Development of smoothness

In order to be understood and not make too many disruptions in a text that could disturb the listener’s comprehension and in extreme situations even reduce the cohesion of an utterance, the speaker is faced with the need to speak smoothly, i.e. not make too many pauses nor pauses that are too long as well as not make too many revisions of his or her text, nor revisions that are overly extensive. As was mentioned above, production of a written text takes place in different circumstances to those in which a spoken one is produced and a direct comparison is not possible. But even an analysis of how smoothly a text emerges can help us answer the question of how the third aspect of fluency, namely smoothness in text production, develops.

In the present study the way a  smooth text is produced will be ex-amined using the Mean length of burst. This value reflects the number of tokens between pauses or such editing activities as deletions or revi-sions and it can be seen as an indicator of smoothness in text production.

Only online activities have been taken into account, i.e. pauses in reading a text after it had been produced or self-repairs that were the result of such a re-reading were excluded from the analysis. The time criterion for a pause was 2 seconds, which is in line with previous studies on L2-writ-ing (1995; Severinsson-Eklundh & Kollberg, 1996; Strömqvist & Ahlsén, 1999; Wengelin, 1999a; 2002; Wengelin, 2007). Furthermore, as in the analysis of spelling errors, corrections of typos were excluded from the analysis. Gunnarsson (2012) also made such an exclusion in her study on the development of complexity, accuracy and fluency of L2-learners of French where the same tool (ScriptLog) was used.

As was illustrated in Figure 7.5 the average values indicate a steady increase in smooth text units during the first two years of learning

Swed-ish, after which fluency stabilized at the level of 13 tokens (about two words) written in one course. The biggest increase occurred during the first year of learning, when the mean growth rate between the first and the second experiment was 28%, after which it steadily declined. This pattern corresponds with those observed for the other aspects of flu-ency, i.e. automaticity and rapidity, where the mean development was continuous during the first two years of learning before approaching an attractor state. Also the correlation between the mean values of au-tomaticity (1/ TT), rapidity (Words written per minute) and smooth-ness (mlb) were very high ( .93≤ r 1.0)¹⁰, which is an additional factor confirming how strongly all three aspects of fluency are interconnected with one other.

The individual developmental paths are by no means as uniform as the mean curve would suggest. In many learners fluency changes very rapidly. In fact, the first three semesters of learning were characterized by a  significant increase, with growth rates exceeding 20% (see Fig-ures 7.6a and 7.6b). However, the system was far from stable and devel-oped continuously in a visibly non-linear way. Even after this clear im-provement in fluency had been achieved at the beginning of the learning period many students made continued progress with high growth rates.

In no learner was the mean length of burst shorter after three years of second language instruction. Even those with very high levels of fluen-cy at the beginning of the study continuously improved, although with much lower growth rates.

10 p = 0.007 and p = 0.000, respectively.

Figure 7.5. Mean development of fluency (smoothness)

8 10 11 13 13 13

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Just as occurred with text production rapidity, so also in the case of smoothness those learners who were more careful and did not focus on fluency at the beginning of the study made the greatest progress in the long-term. The correlation between the level of fluency in the first experi-ment and the general growth rate after three years of learning was ρ = .57 (p = 0.02), which unequivocally shows that the lower the fluency level at the beginning of the learning period the more progress can be expected in the long-term. To learn fast does not automatically mean to learn best.

Second language teachers often label students who develop more slow-ly as “poor” learners and in light of this and other previousslow-ly described outcomes such stigmatization can lead to teachers disregarding the hard

Figure 7.6a. Substantial growth in fluency during the first year

Figure 7.6b. Substantial growth in fluency during the third semester –60

work and the great improvement that these learners actually make. The slower learning students did not develop as dynamically and variably as their faster learning fellow students. As Figure 7.7 shows their develop-mental paths resemble much more a line than a polygonal chain, which, on the other hand, is a characteristic feature of more fluent learners (Fig-ure 7.87). There are two possible reasons for such discrepancies. The first results from the dichotomy between the functional and optimal levels.

A chaotic developmental curve with many peaks may suggest that the learner oscillated between his or her optimal and functional levels at the data collection point. A more line-like pattern, on the other hand, may be interpreted as meaning that these learners’ optimal and functional levels tend to lie close to each other. In other words – the development of lower achieving learners proceeds more smoothly. The second reason for such polarization in developmental patterns may lie in the general develop-mental behaviour of students. Regardless of the language structure, skill or proficiency dimension, some learners tend to learn more rapidly and may develop a “risk-taker” profile, while others develop in smaller steps without dramatic changes. Furthermore, such differences may be inter-connected with personality, which, however, requires a separate study.

Figure 7.7. Mean length of burst in slower developing learners

0

As far as accuracy is concerned, the extreme spurts achieved by two learners ought to be mentioned here. These exceptional peaks in fluency were observed in students S3 and S15 (see Figure 7.8), whose growth rates in one experiment were 139% (S3) and 79% (S15). The very

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cant increase in fluency achieved by writer S15 during the sixth semester was clearly a consequence of her learning environment. She was one of the students who spent one semester on a scholarship in Sweden, which surely contributed to substantial improvement in fluency. Again we can see how strongly systems are interconnected in the process of second lan-guage learning.

On the other hand, the explanation for student S3’s fluency spurt is not so obvious. This learner was not in Sweden after the third experi-ment. Nor were her accuracy or complexity levels lower at the objective data collection point, which might have been expected in cases involving such a great improvement in fluency due to the Trade-Off Hypothesis.

She clearly improved in all other dimensions of the language, but not as remarkably as in fluency. Her within-word automaticity or rapidity in text production increased, but not as dramatically as it did in the case of smoothness. In fact, she wrote a very long text (one of the longest) and the length seems to be the key factor here. The topic for the fourth ex-periment was “An adventure on my holidays” and presumably it was such an interesting subject for her that she wrote her text very fluently, with-out long pauses and withwith-out extensive revision – nevertheless her story was lexically and syntactically complex and accurate. There was one more learner (S9) who also peaked in the fourth experiment (with a growth rate of 66%) and whose text was as long as her (S3) text. In general, text length may be seen as a condition of fluency. When a learner toils over the drafting of a text he or she will not write at length nor fluently. A long

Figure 7.8. Mean length of burst in dynamically developing learners 0

text is an outcome of many factors, of which interest in the topic and sufficient language skills are the most crucial. Learner S3 presumably possessed both in the fourth experiment when she wrote down her holi-day memories. A strong interconnection between length and fluency was also confirmed by measuring the correlation coefficient for length and fluency level in all learners. The Spearman rho ranged between ρ = .45 and ρ = .72, which indicates that the relationship between text length and fluency is strong. Only in the first experiment was this interconnec-tion a little weaker (ρ = .30), which may be explained by a lower level of automatiziation and internalization of linguistic features in the second language. Due to the fact that participants in the present study had no time limit for writing their the narrative and thus could produce both long and short texts, the general conclusion to be drawn is that when we lack such tools as ScriptLog, which can provide insights into the en-tire writing process, the length of a written text can serve as an indirect indicator of fluency. This can of course be much more evident in cases where writers do not have so much time at their disposal. However, even in situations where no such limit is set a long text is the outcome or an indicator of high fluency.

The interplay of systems is evident not only in vocabulary growth or text length. All three aspects of fluency also strongly interact with one other. The interconnection between automaticity and rapidity in text production has already been discussed earlier in the chapter. However, also the smoothness of a text is clearly dependent on other aspects of fluency.

The table below (Table 7.2) presents the interplay of all three aspects of fluency, i.e. automaticity, rapidity and smoothness. The first column recalls Table 7.1, but for transparency reasons all subsystems have been presented together. This stems from the juxtaposition below, which shows there is a very strong interconnection between all aspects of fluency. The correlation coefficient (Pearson’s r) is higher than ±.56, which demon-strates an at least moderately strong interplay during the developmental period of a second language. However, in the majority of learners this interplay between all the features is quite strong and oscillates around r = ±.80. Also in those students in whom no correlation was observed between within-word automaticity and rapidity in text production (S10, S12 and S14), a strong interplay occurs between at least one of the other features. This result is further proof of the need to investigate more sys-tems. To consider only one feature of second language development

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essarily deprives the complexity of its fundamental characteristics. And as an evident consequence of it a simplification occurs which on the one hand can provide us with quick answers, and on the other hand, however, gives rise to the risk that the research results will be flattened out and interpreted only in a cursory manner.

Table 7.2. Correlation between subsystems of fluency Automaticity/rapidity

Dynamic systems are complex systems. As such system fluency, which involves automaticity, ease and rapidity in retrieving linguistic items from long-term memory, as well as smoothness, undoubtedly behaves like other complex systems. All the above discussed features of fluency develop chaotically, undergo unpredictable changes and are sensitive to initial conditions: a significant change in one parameter in the early phases of development triggers random behaviour in the entire system, such that predicting a learner’s developmental pattern is not possible. All the subsystems are inherently interconnected with one other. The first of these – automaticity – enables the learner to produce utterances faster, and this developing rapidity in text production in turn results in fewer

disruptions and pauses so that the learner speaks or writes smoothly, which in turn gives the listener the impression that the speaker/writ-er has uttspeaker/writ-erance fluency. This scenario assumes that automaticity is an enabling condition for other fluency features and after achieving it the learner can produce texts faster and more smoothly. This hypothesis has in fact been confirmed in the present study. Automaticity was the only feature for which an attractor state was approached in all participants. It developed faster than rapidity and smoothness and was not exposed to such rapid changes as the two other indicators. The stability of this sys-tem enables other syssys-tems to reorganize and reach more mature stages.

In the case of fluency these stages involve faster writing and thinking in a second language in longer and longer chunks.

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The interplay of Complexity, Accuracy and Fluency

The dynamics of development and the complexity of systems involved in second language learning necessarily imply some form of interplay between the systems in action. As has been described earlier in this book this interconnection is supposed to proceed in accordance with the Trade-Off Hypothesis proposed by Skehan (2009), which suggests that all three proficiency dimensions, i.e. Complexity, Accuracy and Fluency, do not develop in parallel and focusing more on one of them at the ex-pense of the other two will weaken the performance of the latter. In other words: second language learners are unable to handle all three simulta-neously and when they pay more attention to, for example, fluency they will automatically concentrate less on accuracy and complexity, which may lead to a  situation where they will speak quickly but at the same time their texts will be rather simple and contain many errors. When we base our observations on the averages for each dimension and look at the linear correlation between them (see Table 8.1) the interplay between all three cannot be ignored. In each case the correlation is at least moderate-ly strong with the lowest interconnectedness between accuracy and syn-tactic complexity (subordination ratio) r = .58 and the highest between fluency (rapidity measured as the number of words written per minute) and syntactic diversity (number of different clauses per sentence) r = .92.

When it comes to the interplay between complexity, accuracy and fluency for the group as a whole we could reject the Trade-Off Hypothesis. The outcomes presented below tend to confirm the continuing interplay of all systems and provide proof that the development of these proficiency dimensions generally occurs simultaneously. However, a more in-depth analysis of the data reveals an interesting regularity. Although in all three cases the correlation is strong or moderately strong a common pattern

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can be observed, namely that in general interconnection is weakest be-tween accuracy and syntactic complexity (both in terms of the diversity of syntactic structures and the ratio of subordinated clauses), as well as between fluency and lexical complexity, i.e. the ability to connect words in more complex phrases. This observation could also lead to a re-formu-lation of the Trade-Off Hypothesis in the sense that learners can focus on two dimensions at the same time rather than only on one and, for ex-ample, improvements in fluency and accuracy go hand in hand with one another and only complexity is more neglected. This suggests the need to analyse developmental paths as a whole rather than focus on linear relationships.

Table 8.1. Correlation (Pearsons r) between Complexity, Accuracy and Fluency (for mean values)

Accuracy Fluency

Automaticity Rapidity Smoothness

Complexity

Lexical diversity .85 .80 .86 .89

Lexical complexity .90 .70 .66 .68

Syntactic diversity .68 .84 .92 .88

Syntactic

complexity .58 .78 .75 .70

Fluency Automaticity .87

Rapidity .85

Smoothness .84

The most striking observation concerning the development of all facets of complexity, accuracy and fluency is the uniform direction they take at the beginning of the learning period – in the first three semesters (Figure 8.1). All the systems make steady progress, albeit at different rates. As was stated earlier in this book the most progress was achieved in fluency and accuracy, with complexity being the slowest developing dimension in this respect. From the end of the second year of learning the students’

developmental paths diverge more and more from each other and here the most remarkable reorganization processes begin. The interplay of systems becomes increasingly dynamic.

Figure 8.1. Development of mean Complexity, Accuracy and Fluency¹¹ Due to one of the basic features of Dynamic Systems Theory, namely its analysis of complex systems, we cannot base our statements exclusive-ly on data revealed by average values. As has been shown earlier in the book, mean values can be used only as a reference point for group-level development at a given point and not as an indicator of the general de-velopmental path. The complexity of every component (in this case, for example, every learner) requires studying individual development and on the basis of such paths we can try to find regularities that, on the other hand, shed light on the unique development of individuals. This is espe-cially important in light of the above discussion on the dynamic behav-iour of systems demonstrated by mean values, which automatically leads to the assumption that the interplay of complexity, accuracy and fluency in individuals may reveal highly divergent patterns.

In order to refute or confirm the Trade-Off Hypothesis we should state what precisely such refutation or confirmation might indicate. A focus on one dimension that results in less attention being paid to the other two may mean that one aspect of proficiency will increase during the learning period while the other two will decrease. Due to the nonlinear character of dynamic system behaviour another assumption may be that all dimensions will develop in the same direction, i.e. they will increase or decrease simultaneously, but with different dynamics. A greater focus on one may mean that this particular aspect will develop faster or more dynamically than the other two, which can be observed in higher growth

11 Due to the spread of numeric values for the respective dimensions the values have been recalculated to 0−1 in order to present them in one diagram.

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rates during the learning period. However, as the outcomes for mean val-ues have shown, it may happen that learners pay attention not simply to one dimension at a time, but actually manage two dimensions in tandem, only one of which can be treated as less important at a given point.

The justification for the Trade-Off Hypothesis will thus include confir-mation of the following assumptions:

1) An increase in one dimension leads to a decrease in the other two and/or

2) An increase in one dimension is greater than the increase in the other two and/or

3) An increase in two dimensions leads to a decrease in the third and/or 4) An increase in two dimensions is greater than the increase in the third.

These postulates can thus appear in a stronger version, i.e. the dimen-sions compete with each other, which will result in a dichotomy, i.e. an increase in one or two of the dimensions is accompanied by a decrease in the other dimension(s), or they can appear in a softer version, where the growth rate in one or two of the dimensions is higher than the growth rate in the other dimension(s).

The analysis of complexity, accuracy and fluency has shown that in general fluency and accuracy develop more dynamically than complexity and that this strong development continues for the first three semesters.

For complexity to develop, on the other hand, a longer period is needed.

For complexity to develop, on the other hand, a longer period is needed.