Relationship Between Lexical Indexes and Holistic Scoring by Raters in an English Essay

本研究では，自由英作文から得られるさまざまな語彙の指標のうち，初級レベルの大学生EFL 学習者の自由英作文（エッセイ）の総合的評価と相関の強いものを調べ，重回帰分析により，総合的評価を予測する回帰式を作成し，その妥当性を検討することを目的とした。結果として，総語数以外では，使用語彙の難易度を示す指標や語彙の豊富さを示す指標などと，自由英作文の総合的評価の間に中程度以上の相関が見られた。重回帰分析では，各種指標を組み合わせることにより，総合的評価をある程度予測できる重回帰式が作成可能であることが明らかになった。This study investigated the relationship between the holistic rating of Englishessays written by false-beginner level university EFL learners and the lexical indexes obtained from their essays. Multiple regression analysis was employed in order to create a predictive multiple regression equation with those lexical indexes. The results show that a moderately high correlation existed between the lexical difficulty and richness indexes andthe essay score. It was also found that with multiple regression analysis, creating apredictive multiple regression equation by combining those lexical indexes would be possible to some extent

Initial Evaluation of AWSuM: A Pilot Study

This article reports on a pilot study to evaluate usability, effectiveness, and impact of Academic Word Suggestion Machine (AWSuM), a web-based, innovative tool, powered by the combination of rhetorical moves and lexical bundles with an auto-complete feature. Eight L2 writers participated in the study. Their open-ended comments after using the tool were qualitatively analyzed and classified in thematic categories. The results showed that the developed tool brought about beneficial effects that genre writing pedagogy explicitly aims to achieve. Implications are discussed in terms of the potential role that a tool like AWSuM could play in the teaching and learning of technology-enhanced genre writing.This study was made possible by a Grant-in-aid for Scientific Research (No. 17H02369)from the Japan Society for the Promotion of Science

マルチレベル分析

Multilevel analysis (or multilevel model), also known by names such as hierarchical linear model (HLM), linear mixed model, mixed-effect model, and random effects model, is being increasingly used as an alternative to conventional analyses in the field of language testing and applied linguistics in general.20th Anniversary Special Issue of JLTA Journal : 3.5.1

Corpus-Based Analysis of Lexical Bundles: Its Potential Applications in English Language Teaching

Lexical bundles, or multi-word sequences, play an essential role in creating discourse in spoken and written registers. Successful use of these bundles indicates the writer is in the discourse community, where experts use the “common language.” In this paper, I will first show the importance of lexical bundles in shaping the discourse by introducing some of the corpus-based research findings. I will then elaborate on the possibilities of applying corpus-based analysis of lexical bundles to English language teaching.《シンポジウム報告③

Effects of self-regulated vocabulary learning process on self-efficacy

Researchers, especially in the field of educational psychology, have argued that self-efficacy plays an important role in self-regulated learning. As such, teaching of self-regulated learning often focuses on enhancing self-efficacy. However, few studies have examined how the process of self-regulated learning might lead to the enhancement of self-efficacy. Therefore, the aim of this study was to investigate the effects of the self-regulated vocabulary learning process on self-efficacy. A total of 303 learners of English as a Foreign Language from two universities participated in the study. They completed a questionnaire measuring the process of self-regulated learning (i.e. forethought, performance or volitional control, and self-reflection). They also completed a vocabulary test as a measure of learning outcome. Correlation analysis was employed to evaluate the relationships between various variables investigated in the study. The results showed that self-regulated learning process can boost self-efficacy and increase vocabulary knowledge. The pedagogical implications of the current study are discussed mainly in terms of incorporating instructions aimed at enhancing self-efficacy and self-regulation in vocabulary teaching.This research was made possible by a Grant-in-aid for Scientific Research (No. 24720276)from the Japan Society for the Promotion of Science

Comparison of Multivariate Data Analysis Methodsin Corpus Linguistics: Principal Component Analysis vs. Correspondence Analysis

本研究では，コーパス言語学において，多変量解析の手法として使用されることの多い，(a) 主成分分析，(b) コレスポンデンス分析，(c) 行列データを転置して行う主分分析の3つの結果を比較した。その結果，コレスポンデンス分析が変数間の差異を調べるような研究ではふさわしいということが確認されたが，目的に応じて使い分けることが重要であるということも示唆された。This paper reports on a comparison of multivariate data analysis methods in corpus linguistics: principal component analysis, correspondence analysis, and principal component analysis with transposed matrix. The results show that correspondence analysis is the most suitable method for exploring the differences among variables

Enhancing Self-efficacy in Vocabulary Learning: A Self-regulated Learning Approach

The current study aimed to explore the effects of integrating a selfregulated learning approach on self-efficacy in vocabulary learning. A group of 115 English as a Foreign Language (EFL) learners from a university in Japan participated in this longitudinal study. The participants were assigned as the treatment group, the contrast group 1, and the contrast group 2. Only the treatment group received the intervention basedon the self-regulated learning approach. The participants completed a questionnaire on self-efficacy in vocabulary learning three times and a vocabulary test twice. Multilevel analysis of change was employed to examine the trajectories of change in the participants’ self-efficacy over the measurement occasions. The gain scores in the vocabulary test were submitted to analysis of variance. The results showed that the treatment group showed a steady increase in self-efficacy and vocabulary knowledgecompared with the other two contrast groups. The findings from the current study provide empirical evidence suggesting that through a selfregulated learning approach, it might be possible to enhance self-efficacy, which in turn may contribute to the development of vocabulary knowledge.This research was made possible by a Grant-in-aid for Scientific Research (No.24720276) from the Japan Society for the Promotion of Science

Applying Statistical Methods to Develop a Better Vocabulary List

これまでに数多くの学習語彙表が開発されているが，より良い語彙表を作るために，既存の語彙表における情報を統計的手法によって統合しているような研究はほとんどない。本研究では，基礎語の抽出のために，いくつかの語彙表などにおける語彙の特徴指標を統合し，より良い学習語彙表を開発するための統計的手法の検討を行うことを目的とした。まず，中学校主要英語教科書をコーパス化したものの頻度を基にした語彙表を作成し，いくつかの既存の語彙表や語に対する親密度，語の長さなどを語の特徴指標とし，それらをクラスター分析によってどのように分類できるのか確認し，因子分析によって潜在的な因子を調査した。いくつかの指標を統合するためには主成分分析が試された。結果として，これらの手法により，より良い学習語彙表が開発できるのではないかという可能性が示唆された。Many word lists for educational purposes have been developed thus far. However, no attempt has been made to integrate the information on the other word lists in the past with a statistical approach. The purpose of this study was thus to suggest statisticalmethods to incorporate several measures of individual words in order to develop a betterbasic word list. To this end, a junior high school textbook corpus was utilized to make afrequency-based word list out of it. The characteristics indices of vocabulary in this study include figures such as ranks or word levels in other word lists, word familiarity, and word length. With cluster analysis and factor analysis, categorization and latent factors behind these characteristics were examined. For the purpose of integrating the several measures for each word, principal component analysis was employed. The results suggest that the methods proposed here are promising in developing a better vocabulary list

語彙学習方略 : 理論と実践

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