Computing the vocabulary demands of L2 reading.

Linguistic computing can make two important contributions to second language (L2) reading instruction. One is to resolve longstanding research issues that are based on an insufficiency of data for the researcher, and the other is to resolve related pedagogical problems based on insufficiency of input for the learner. The research section of the paper addresses the question of whether reading alone can give learners enough vocabulary to read. When the computer's ability to process large amounts of both learner and linguistic data is applied to this question, it becomes clear that, for the vast majority of L2 learners, free or wide reading alone is not a sufficient source of vocabulary knowledge for reading. But computer processing also points to solutions to this problem. Through its ability to reorganize and link documents, the networked computer can increase the supply of vocabulary input that is available to the learner. The development section of the paper elaborates a principled role for computing in L2 reading pedagogy, with examples, in two broad areas, computer-based text design and computational enrichment of undesigned texts.

INTRODUCTION

There is a lexical paradox at the heart of reading in a second language. On one side, after decades of guesswork, there is now widespread agreement among researchers that text comprehension depends heavily on detailed knowledge of most of the words in a text. However, it is also clear that the words that occur in texts are mainly available for learning in texts themselves. That is because the lexis (vocabulary) of texts, at least in languages like English, is far more extensive than the lexis of conversation or other non-textual media. Thus prospective readers of English must bring to reading the same knowledge they are intended to get from reading. This paradox has been known in outline for some time, but in terms loose enough to allow opposite proposals for its resolution. On one hand, Nation (e.g., 2001) argues for explicit instruction of targeted vocabulary outside the reading context itself. On the other, Krashen (e.g., 1989) believes that all the lexis needed for reading can be acquired naturally through reading itself, in a second language as in a first. It is only recently that the dimensions of this paradox could be quantified, with the application of computer text analysis to questions in language learning. What this quantification shows is the extreme unlikelihood of developing an adequate L2 reading lexicon through reading alone, even in highly favourable circumstances. This case is made in the initial research part of the paper. The subsequent development section goes on to show how the text computing that defined the lexical paradox can be re-tooled to break it, with (1) research-based design of texts and (2) lexical enrichment of undesigned texts. Empirical support for computational tools will be provided where available; all tools referred to, both analytical and pedagogical, are publicly available at the Compleat Lexical Tutor website (www.lextutor.ca).

DEFINING THE LEXICAL PARADOX

In applied linguistics conversations, turn-taking can involve a delay of several years. An example is Krashen's (2003) paper entitled Free voluntary reading: Still a very good idea, which criticizes the findings of a study by Horst, Cobb and Meara (1998) that had called into question the amount of vocabulary acquisition that normally results from free, pleasurable, meaning-oriented extensive reading. This study found that even with all the usual variables of an empirical study of extensive L2 reading controlled rather more tightly than usual, the number of new words that are learned through the experience of reading a complete, motivating, level-appropriate book of about 20,000 running words is minimal, and does not indicate that reading itself can reasonably be seen as the only or even main source of an adult reading lexicon. The gist of Krashen's response (2003) was that such studies typically underestimate the amount of lexical growth that takes place as words are encountered and re-encountered in the course of free reading. To support this contention, he calculated an effect size from the Horst, Cobb and Meara data that he interpreted to show stronger learning than these researchers' conclusions had implied. But more importantly, beyond the data, he believes that many words and phrases are learned from reading that do not appear in the test results of this type of study, owing to the crude nature of the testing instruments employed, which typically cannot account for partial or incremental learning. According to this argument, word knowledge is bubbling invisibly under the surface as one reads, and may appear as a known item in a vocabulary test only some time later (1). This hidden vocabulary learning from reading is seen as extensive enough to "do the entire job" (Krashen, 1989, p. 448) of acquiring a second lexicon, an idea that Waring and Nation (2004, p. 11) describe as "now entrenched" within second and foreign language teaching. Similar claims are many (e.g. Elley's belief that children graduating from a book flood approach had learned "all the vocabulary and syntax they required from repeated interactions with good stories," 1991, pp. 378-79); but clear definitions of "the entire job" are few.

Krashen has taken part in a number of conventional vocabulary-from-reading studies that use conventional measures, but these studies have not provided empirical evidence of either the extent of such hidden learning, or its sufficiency as the source of a reading lexicon. Instead, he cites the "default explanation" for the size of the adult lexicon, an account borrowed from first language (L1) theorizing (e.g., Nagy, 1988; Sternberg, 1987), whereby the lexical paradox is resolved through the sheer volume of reading time available over the course of growing up in a language. According to this explanation, a lifetime of L1 reading must eventually succeed in doing the job--even if very little measurable vocabulary knowledge is registered in any one reading event--since there is no other plausible way to account for the large number of words that adult native speakers typically know.

The extension of research assumptions and procedures from L1 to the L2 learning contexts is questionable at best (2), particularly in the absence of empirical support. But as will be shown here, both the extent and sufficiency of hidden vocabulary learning can in fact be investigated empirically within the L2 context, without recourse to default arguments. Key to this undertaking are a research instrumentation, method, and technology for measuring small increments of lexical knowledge that can be applied to sufficient numbers of words over a sufficient length of time to be plausibly commensurate with the known vocabulary sizes of learners: roughly 17,000 English word families in the case of a typical literate adult L1 lexicon (as calculated by Goulden, Nation & Read, 1990), or the 5,000 most frequent word families in the case of L2 (proposed as minimal for effective L2 reading by Hirsch & Nation, 1992). That is to say, the experimentation requires substantially more than the handful of words normally tested in this type of research (typically between 10 and 30, as discussed in Horst et al., 1998) in order to arrive at a credible estimate of "the entire job."

Claim A: The Extent of Hidden Learning

An instrument capable of measuring incremental knowledge is Wesche and Paribakht's (1996) vocabulary knowledge scale, or VKS, which asks learners to rate their knowledge of words not in binary terms (I know/I don't know what this word means) but on a five-point scale (ranging from "I don't remember having seen this word before," to "I can use this word in a sentence.") But since the VKS requires learners to also demonstrate their knowledge (e.g. by writing sentences), it is cumbersome to use in measuring changes in the knowledge of large numbers of words over time through repeated encounters, as would be needed to test the claim of extensive amounts of hidden acquisition. Therefore, Horst and Meara (1999) and Horst (2000) devised the following ratings-only version, which was suitable for adaptation to computer.

0 = I definitely don't know what this word means

1 = I am not really sure what this word means

2 = I think I know what this word means

3 = I definitely know what this word means (Horst, 2000, Chapter 7, p. 149)

Following a reading of a text, learners can efficiently rate their knowledge of a large number of its words using a computer input that employs this scale and stores the number of words rated 0, 1, 2, and 3 for each learner and each reading. But the real innovation of the adaptation is the conversion of the scale to a matrix, which allows the comparison of ratings over two (or more) readings of the same text. The matrix (shown in Figure 1) is essentially the 4-point scale in two dimensions, so that each cell represents results at both time n and after a subsequent reading (time n+1). For example, the data in the first horizontal row shows that 75 words had been rated 0 after reading n and were still rated 0 (I don't know) after reading n+1, but that 27 words had moved from 0 to 1, nine words from 0 to 2, and three words from 0 to 3. The second row shows how words rated 1 (not sure) at time n were distributed at time n+1, and so on. In other words, the cell intersections capture the numbers of words that have changed or failed to change from one knowledge state to another as a result of a subsequent reading.