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Numerous conflicting claims exist concerning the nature of L1 influence. This article argues that much of the confusion could be eliminated if a unified framework were established for this area of inquiry. Such a framework would minimally require transfer studies to consider at least 3 potential effects of L1 influence: (a) intra-L1-group similarities, (b) inter-L1-group differences, and (c) L1-IL performance similarities. This study examines all three types of evidence in the English lexical reference of Finnish-speaking and Swedish-speaking Finns at multiple levels of age and L2 exposure in three different but related elicitation tasks. The results suggest a subtle yet demonstrable presence for L1 influence in this area of interlanguage performance.

A reliable index of lexical diversity (LD) has remained stubbornly elusive for over 60 years. Meanwhile, researchers in fields as varied as stylistics, neuropathology, language acquisition, and even forensics continue to use flawed LD indices — often ignorant that their results are questionable and in some cases potentially dangerous. Recently, an LD measurement instrument known as vocd has become the virtual tool of the LD trade. In this paper, we report both theoretical and empirical evidence that calls into question the rationale for vocd and also indicates that its reliability is not optimal. Although our evidence shows that vocd's output (D) is a relatively robust indicator of the aggregate probabilities of word occurrences in a text, we show that these probabilities — and thus also D — are affected by text length. Malvern, Richards, Chipere and Durán (2004) acknowledge that D (as calculated by vocd's default method) can be affected by text length, but claim that the effects are not significant for the ranges of text lengths with which they are concerned. In this paper, we explain why D is affected by text length, and demonstrate with an extensive empirical analysis that the effects of text length are significant over certain ranges, which we identify.

Following up on recent work by Malvern and Richards (1997, this issue; McKee et al., 2000) concerning the measurement of lexical diversity through curve fitting, the present study compares the accuracy of five formulae in terms of their ability to model the type-token curves of written texts produced by learners and native speakers. The most accurate models are then used to consider unresolved issues that have been at the forefront of past research on lexical diversity: the relationship between lexical diversity and age, second language (L2) instruction, L2 proficiency, first language (L1) background, writing quality and vocabulary knowledge. The participants in the study comprise 140 Finnish-speaking and 70 Swedish-speakinglearners of English, and an additional group of 66 native English speakers. The data include written narrative descriptions of a silent film, and the results show that two of the curve-fitting formulae provide accurate models of the type-token curves of over 90% of the texts. The texts for which accurate models were obtained were subjected to further analyses, and the results indicate a clear relationship between lexical diversity and amount of instruction, but a more complicated relationship between lexical diversity and L1 background, writing quality and vocabulary knowledge.

The effect of animation and concreteness of visuals on immediate recall and long-term comprehension when learning the basic principles and laws of motion.

indices Predicting lexical proficiency in language learner texts using computational Published by: http://www.sagepublications.com can be found at: Language Testing Additional services and information for AbstractThe authors present a model of lexical proficiency based on lexical indices related to vocabulary size, depth of lexical knowledge, and accessibility to core lexical items. The lexical indices used in this study come from the computational tool Coh-Metrix and include word length scores, lexical diversity values, word frequency counts, hypernymy values, polysemy values, semantic coreferentiality, word meaningfulness, word concreteness, word imagability, and word familiarity. Human raters evaluated a corpus of 240 written texts using a standardized rubric of lexical proficiency. To ensure a variety of text levels, the corpus comprised 60 texts each from beginning, intermediate, and advanced second language (L2) adult English learners. The L2 texts were collected longitudinally from 10 English learners. In addition, 60 texts from native English speakers were collected. The holistic scores from the trained human raters were then correlated to a variety of lexical indices. The researchers found that lexical diversity, word hypernymy values and content word frequency explain 44% of the variance of the human evaluations of lexical proficiency in the examined writing samples. The findings represent an important step in the development of a model of lexical proficiency that incorporates both vocabulary size and depth of lexical knowledge features.

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