A Corpus-Based Analysis of Cohesion in L2 Writing by Undergraduates in Ecuador

In finding out the nature of cohesion in L2 writing, the present study set out to address three research questions: (1) What types of cohesion relations occur in L2 writing at the sentence, paragraph, and whole-text levels? (2) What is the relationship between lexico-grammatical cohesion features and teachers’ judgements of writing quality? (3) Do expectations of cohesion suggested by the CEFR match what is found in student writing? To answer those questions, a corpus of 240 essays and 240 emails from college- level students learning English as a foreign language in Ecuador enabled the analysis of cohesion. Each text included the scores, or teachers’ judgements of writing quality aligned to the upper-intermediate level (or B2) as proposed by the Common European Framework of Reference for learning, teaching, and assessing English as a foreign language. Lexical and grammatical items used by L2 students to build relationships of meaning in sentences, paragraphs, and the entire text were considered to analyse cohesion in L2 writing. Utilising Natural Language Processing tools (e.g., TAACO, TextInspector, NVivo), the analysis focused on determining which cohesion features (e.g., word repetition/overlap, semantical similarity, connective words) predicted the teachers’ judgements of writing quality in the collected essays and emails. The findings indicate that L2 writing is characterised by word overlap and synonyms occurring at the paragraph level and, to a lesser degree, cohesion between sentences and the entire text (e.g., connective words). Whilst these cohesion features positively and negatively predicted the teachers’ scores, a cautious interpretation of these findings is required, as many other factors beyond cohesion features must have also influenced the allocation of scores in L2 writing

Different forms, different meanings? A lexicographic and corpus-based study of four sets of English near-synonyms

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Google Translate: A helpful aide? A mixed method sequential explanatory study on the usage and effects of Google Translate in three Norwegian EFL classes

This thesis investigates how learners in Norway use Google Translate to aid them in EFL writing as well as how the usage of Google Translate affects the quality of the texts they write. A mixed method study was used in three Norwegian EFL classes situated in the same school along with learner stimulated recall interviews and teacher interviews. The aim of the study was to determine how effective Google Translate is at helping Norwegian learners at different performance levels and to see how dependant these learners are on Google Translate to help them in EFL writing. Two writing sessions were conducted to gather data on learners’ usage of Google Translate and determine whether the translation tool had any effect on the quality of their written products. The first writing session consisted of learners using dictionaries to help them translate from Norwegian to English, and in total, 33 learners participated in this writing session. In the second writing session, learners were encouraged to use Google Translate as they would normally to help in EFL writing, and 33 learners participated in this writing session, 22 of whom used Google Translate. By comparing vocabulary, syntax errors, subject-verbal concord errors, essay length, and spelling mistakes between the texts from the two writing sessions of learners who used Google Translate in the second writing session, comments could be made on the effectiveness Google Translate has on the quality of learners’ texts. A separate analysis was conducted of how Google Translate was being used by learners in the second writing session, i.e. how many times Google Translate was used to translate words, phrases, sentences, entire texts or for other purposes. Based on the analysis comparing two sets of 22 learners’ screen recorded videos and the learners’ writing along with information from stimulated recall interviews and teacher interviews, it seems that learners’ current usage of Google Translate does not affect the quality of their written product in a positive nor negative way, except for syntax related errors which lowered for all learner groups from the first to the second writing session. Google Translate mostly serves as a quicker alternative to dictionaries as it provides translations at a much fast speed compared to dictionaries. Further, learners mostly use Google Translate to aid in the translation of words and phrases, rarely using it to translate whole sentences or longer texts. However, there is a disparity between lower performing learners, average performing learners, and higher performing learners in how much they use the tool. Furthermore, there is a lack of training given to learners and teachers on how to use Google Translate as an efficient translation tool. Both learners and teachers that participated in the study reported a lack of training received on proper usage of the tool and reviewing 31 screen recorded videos from the second writing session, it was clear that learners lacked knowledge of the many capabilities the tool has to offer. There has previously been concern amongst teachers that Google Translate hinders learners in learning English as the translation tool produces incorrect output or that learners use the tool to translate large amounts of text. However, statements from teacher interviews reveal that these teachers seem to have become more acceptant of the translator being used in their class. Previous studies also reveal that Google Translate has reached the point where it has the capability of providing output equivalent to the minimum level of accuracy required for university entrance, thereby providing output better than what most learners in primary school could produce themselves (Mundt & Groves, 2015; Stapleton & Leung, 2019). Finally, based on the analysis of 64 screen recordings, the author of this thesis argues that Google Docs and other word processors should be a greater concern for teachers than Google Translate and other tools that aid in translation. This is due to a substantial number of learners who participated in the study being heavily dependent on the grammatical and spelling correction tools that the software provides, making it difficult for teachers to assess whether learners know various grammatical rules and possess the ability to apply these rules in written texts

Supporting Collocation Learning

Collocations are of great importance for second language learners. Knowledge of them plays a key role in producing language accurately and fluently. But such knowledge is difficult to acquire, simply because there is so much of it. Collocation resources for learners are limited. Printed dictionaries are restricted in size, and only provide rudimentary search and retrieval options. Free online resources are rare, and learners find the language data they offer hard to interpret. Online collocation exercises are inadequate and scattered, making it difficult to acquire collocations in a systematic way. This thesis makes two claims: (1) corpus data can be presented in different ways to facilitate effective collocation learning, and (2) a computer system can be constructed to help learners systematically strengthen and enhance their collocation knowledge. To investigate the first claim, an enormous Web-derived corpus was processed, filtered, and organized into three searchable digital library collections that support different aspects of collocation learning. Each of these constitutes a vast concordance whose entries are presented in ways that help students use collocations more effectively in their writing. To provide extended context, concordance data is linked to illustrative sample sentences, both on the live Web and in the British National Corpus. Two evaluations were conducted, both of which suggest that these collections can and do help improve student writing. For the second claim, a system was built that automatically identifies collocations in texts that teachers or students provide, using natural language processing techniques. Students study, collect and store collocations of interest while reading. Teachers construct collocation exercises to consolidate what students have learned and amplify their knowledge. The system was evaluated with teachers and students in classroom settings, and positive outcomes were demonstrated. We believe that the deployment of computer-based collocation learning systems is an exciting development that will transform language learning

Working with synonyms in the process of teaching foreign languages to translation students

Обоснована необходимость целенаправленной и профессионально-ориентированной работы с синонимами в процессе иноязычной подготовки переводчиков. Предложен комплекс упражнений, апробированный на занятиях по практике иностранного языка со студентами-переводчиками. В качестве положительных результатов отмечаются повышение грамотности и уверенности студентов в использовании синонимов; приобретение ими необходимых навыков работы c данным типом лексических единиц; перенос соответствующей последовательности действий, составляющих оптимальный алгоритм работы с синонимами, в самостоятельную работу студентов

An Attention-Based Model for Predicting Contextual Informativeness and Curriculum Learning Applications

Both humans and machines learn the meaning of unknown words through contextual information in a sentence, but not all contexts are equally helpful for learning. We introduce an effective method for capturing the level of contextual informativeness with respect to a given target word. Our study makes three main contributions. First, we develop models for estimating contextual informativeness, focusing on the instructional aspect of sentences. Our attention-based approach using pre-trained embeddings demonstrates state-of-the-art performance on our single-context dataset and an existing multi-sentence context dataset. Second, we show how our model identifies key contextual elements in a sentence that are likely to contribute most to a reader's understanding of the target word. Third, we examine how our contextual informativeness model, originally developed for vocabulary learning applications for students, can be used for developing better training curricula for word embedding models in batch learning and few-shot machine learning settings. We believe our results open new possibilities for applications that support language learning for both human and machine learner

LearningQ: a large-scale dataset for educational question generation

We present LearningQ, a challenging educational question generation dataset containing over 230K document-question pairs. It includes 7K instructor-designed questions assessing knowledge concepts being taught and 223K learner-generated questions seeking in-depth understanding of the taught concepts. We show that, compared to existing datasets that can be used to generate educational questions, LearningQ (i) covers a wide range of educational topics and (ii) contains long and cognitively demanding documents for which question generation requires reasoning over the relationships between sentences and paragraphs. As a result, a significant percentage of LearningQ questions (~30%) require higher-order cognitive skills to solve (such as applying, analyzing), in contrast to existing question-generation datasets that are designed mostly for the lowest cognitive skill level (i.e. remembering). To understand the effectiveness of existing question generation methods in producing educational questions, we evaluate both rule-based and deep neural network based methods on LearningQ. Extensive experiments show that state-of-the-art methods which perform well on existing datasets cannot generate useful educational questions. This implies that LearningQ is a challenging test bed for the generation of high-quality educational questions and worth further investigation. We open-source the dataset and our codes at https://dataverse.mpi-sws.org/dataverse/icwsm18