Explicitness and ellipsis as features of conversational style in British English and Ecuadorian Spanish

In this article I examine differences in conversational style between British English and Ecuadorian Spanish which can be the source of communication conflict among speakers of these two languages in telephone conversations, and, presumably in other types of interaction. I look at the language of mediated and non-mediated telephone conversations and examine one feature that interacts with indirectness, i.e., the degree of explicitness participants employ to realize similar acts or moves in the two languages. In non-mediated telephone interactions both British English and Ecuadorian Spanish speakers appear to display a preference for the use of explicitness in formulating various telephone management moves. On the other hand, in mediated interactions, while the British appear to favour explicitness, Ecuadorians in the present study, make use of elliptical forms. The latter, however, tend to be accompanied by deference markers. Differences in the use of explicit and elliptical utterances are interpreted as reflecting that, in certain types of interactions, Ecuadorians favour a style that can be characterized as fast and deferential, but possibly rather abrupt to the English, whereas the latter appear to favour a less hurried style which emphasizes the expression of consideration rather than deference

Understanding Interest And Self-Efficacy In The Reading And Writing Of Students With Persisting Specific Learning Disabilities During Middle Childhood And Early Adolescence

Three methodological approaches were applied to understand the role of interest and self-efficacy in reading and/or writing in students without and with persisting specific learning disabilities (SLDs) in literacy. For each approach students in grades 4 to 9 completed a survey in which they rated 10 reading items and 10 writing items on a Scale 1 to 5; all items were the same but domain varied. The first approach applied Principal Component Analysis with Varimax Rotation to a sample that varied in specific kinds of literacy achievement. The second approach applied bidirectional multiple regressions in a sample of students with diagnosed SLDs-WL to (a) predict literacy achievement from ratings on interest and self-efficacy survey items; and (b) predict ratings on interest and self-efficacy survey items from literacy achievement. The third approach correlated ratings on the surveys with BOLD activation on an fMRI word reading/spelling task in a brain region associated with approach/avoidance and affect in a sample with diagnosed SLDs-WL. The first approach identified two components for the reading items (each correlated differently with reading skills) and two components for the writing items (each correlated differently with writing skills), but the components were not the same for both domains. Multiple regressions supported predicting interest and self-efficacy ratings from current reading achievement, rather than predicting reading achievement from interest and self-efficacy ratings, but also bidirectional relationships between interest or self-efficacy in writing and writing achievement. The third approach found negative correlations with amygdala connectivity for 2 reading items, but 5 positive and 2 negative correlations with amygdala connectivity for writing items; negative correlations may reflect avoidance and positive correlations approach. Collectively results show the relevance and domain-specificity of interest and self-efficacy in reading and writing for students with persisting SLDs in literacy

What is behind a summary-evaluation decision?

Research in psychology has reported that, among the variety of possibilities for assessment methodologies, summary evaluation offers a particularly adequate context for inferring text comprehension and topic understanding. However, grades obtained in this methodology are hard to quantify objectively. Therefore, we carried out an empirical study to analyze the decisions underlying human summary-grading behavior. The task consisted of expert evaluation of summaries produced in critically relevant contexts of summarization development, and the resulting data were modeled by means of Bayesian networks using an application called Elvira, which allows for graphically observing the predictive power (if any) of the resultant variables. Thus, in this article, we analyzed summary-evaluation decision making in a computational framewor

Mobile-assisted language learning [Revised and updated version]

Mobile-assisted language learning (MALL) is the use of smartphones and other mobile technologies in language learning, especially in situations where portability and situated learning offer specific advantages. A key attraction of mobile learning is the ubiquity of mobile phones. Typical applications can support learners in reading, listening, speaking and writing in the target language, either individually or in collaboration with one another. Increasingly, MALL applications relate language learning to a person’s physical context when mobile, primarily to provide access to location-specific language material or to enable learners to capture aspects of language use in situ and share it with others. Mobile learning can be formal or informal, and mobile devices may form a bridge connecting in-class and out-of-class learning. When learning takes place outside the classroom, it is often beyond the reach and control of the teacher. This can be perceived as a threat, but it is also an opportunity to revitalize and rethink current approaches to teaching and learning. Mobile learning appeals to a wide range of people for a variety of reasons. It may exclude some learners but it is often a mechanism for inclusion. It is likely that the next generation of mobile learning will be more ubiquitous, which means that there will be smart systems everywhere for digital learning. Mobile learning is proving its potential to address authentic learner needs at the point at which they arise, and to deliver more flexible models of language learning

Web-based learning in the field of empirical research methods

This study focuses on the development of a complex web-based learning environment aimed at promoting the acquisition of applicable knowledge in the context of studying empirical research methods at university. This learning environment was then modified further on an empirical basis. The main focus of the present article is to describe the conceptualisation of the learning environment and research activities which were guided by an integrative research paradigm. The learning environment consisted of highly structured, complex texts in which the process of empirical research was illustrated in a detailed manner. By combining these texts with other instructional measures, the learning environment is given a flexible hypertext-structure. The effectiveness of the learning environment as a whole was investigated in three studies (two evaluation studies in the field and one experimental study in the laboratory). It was demonstrated that the additional instructional measures (e.g. a specific feedback-guidance and time-management measures) were not effective. The importance of cognitive, motivational and emotional learning prerequisites for the successful utilisation of the learning environment was highlighted. The implementation of special training and additional preparatory modules is recommended in order to optimise the fit between students' prerequisites and learning environmIm Zentrum der vorliegenden Arbeit steht zum einen die Konzeptualisierung einer Lernumgebung zur Förderung des Erwerbs anwendbaren Wissens im Kontext der universitären Ausbildung in empirischen Forschungsmethoden. Zum anderen werden ausgehend von einem integrativen Forschungsparadigma Forschungsaktivitäten beschrieben, die die empirische Basis zur Weiterentwicklung der Lernumgebung bereitstellen. Die Lernumgebung besteht aus hoch strukturierten, komplexen Texten, in welchen der Prozess empirischer Forschung auf detaillierte Weise veranschaulicht wird. Diese Texte wurden mit anderen instruktionalen Maßnahmen kombiniert, wodurch die Lernumgebung eine flexible, hypertextartige Struktur bekam. Die Effektivität der gesamten Lernumgebung wurde im Rahmen dreier empirischer Studien untersucht, von denen zwei als Evaluationsstudien im Feld durchgeführt wurden; die dritte war eine experimentelle Laborstudie. Es wurde gezeigt, dass die zusätzlichen instruktionalen Maßnahmen (z. B. eine spezifische Feedback-Anleitung und eine Zeitmanagement-Maßnahme) nicht wirksam waren. Die Bedeutung kognitiver, motivationaler und emotionaler Lernvoraussetzungen für die erfolgreiche Nutzung der Lernumgebung konnte nachgewiesen werden. Um die Passung zwischen den Eingangsvoraussetzungen der Studierenden und der Lernumgebung zu verbessern, wurde die Implementation eines speziellen Trainings und eines zusätzlichen vorbereitenden Moduls vorgeschlag

Analyzing short-answer questions and their automatic scoring - studies on semantic relations in reading comprehension and the reduction of human annotation effort

Short-answer questions are a wide-spread exercise type in many educational areas. Answers given by learners to such questions are scored by teachers based on their content alone ignoring their linguistic correctness as far as possible. They typically have a length of up to a few sentences. Manual scoring is a time-consuming task, so that automatic scoring of short-answer questions using natural language processing techniques has become an important task. This thesis focuses on two aspects of short-answer questions and their scoring: First, we concentrate on a reading comprehension scenario for learners of German as a foreign language, where students answer questions about a reading text. Within this scenario, we examine the multiple relations between reading texts, learner answers and teacher-specified target answers. Second, we investigate how to reduce human scoring workload by both fully automatic and computer-assisted scoring. The latter is a scenario where scoring is not done entirely automatically, but where a teacher receives scoring support, for example, by means of clustering similar answers together. Addressing the first aspect, we conduct a series of corpus annotation studies which highlight the relations between pairs of learner answers and target answers, as well as between both types of answers and the reading text they refer to. We annotate sentences from the reading text that were potentially used by learners or teachers for constructing answers and observe that, unsurprisingly, most correct answers can easily be linked to the text; incorrect answers often link to the text as well, but are often backed up by a part of the text not relevant to answer the question. Based on these findings, we create a new baseline scoring model which considers for correctness whether learners looked for an answer in the right place or not. After identifying those links into the text, we label the relation between learner answers and target answers as well as between reading texts and answers by annotating entailment relations. In contrast to the widespread assumption that scoring can be fully mapped to the task of recognizing textual entailment, we find the two tasks to be only closely related and not completely equivalent. Correct answers do often, but not always, entail the target answer, as well as part of the related text, and incorrect answers do most of the time not stand in an entailment relation to the target answer, but often have some overlap with the text. This close relatedness allows us to use gold-standard entailment information to improve the performance of automatic scoring. We also use links between learner answers and both reading texts and target answers in a statistical alignment-based scoring approach using methods from machine translation and reach a performance comparable to an existing knowledge-based alignment approach. Our investigations into how human scoring effort can be reduced when learner answers are manually scored by teachers are based on two methods: active learning and clustering. In the active learning approach, we score particularly informative items first, i.e., items from which a classifier can learn most, identifying them using uncertainty-based sample selection. In this way, we reach a higher performance with a given number of annotation steps compared to randomly selected answers. In the second research strand, we use clustering methods to group similar answers together, such that groups of answers can be scored in one scoring step. In doing so, the number of necessary labeling steps can be substantially reduced. When comparing clustering-based scoring to classical supervised machine learning setups, where the human annotations are used to train a classifier, supervised machine learning is still in the lead in terms of performance, whereas clusters provide the advantage of structured output. However, we are able to close part of the performance gap by means of supervised feature selection and semi-supervised clustering. In an additional study, we investigate the automatic processing of learner language with respect to the performance of part-of-speech (POS) tagging tools. We manually annotate a German reading comprehension corpus both with spelling normalization and POS information and find that the performance of automatic POS tagging can be improved by spell-checking the data using the reading text as additional evidence for lexical material intended in a learner answer.Short-Answer-Fragen sind ein weit verbreiteter Aufgabentyp in vielen Bildungsbereichen. Die Antworten, die Lerner zu solchen Aufgaben geben, werden von Lehrenden allein auf Grundlage ihres Inhalts bewertet; linguistische Korrektheit wird soweit möglich ignoriert. Diese Doktorarbeit legt ihren Schwerpunkt auf zwei Aspekte im Zusammenhang mit Short- Answer-Fragen und ihrer Bewertung: Zum einen betrachten wir ein Leseverständnisszenario, bei dem Studenten Fragen zu Lesetexten beantworten. Dabei untersuchen wir insbesondere die verschiedenen Beziehungen, die es zwischen Lesetexten, Lernerantworten und vom Lehrer erstellten Musterantworten gibt. Zum anderen untersuchen wir, wie der menschliche Bewertungsaufwand durch voll-automatisches und computergestütztes Bewerten reduziert werden kann. Bei letzterem handelt es sich um ein Szenario, in dem Lehrer bei der Bewertung unterstützt werden, z.B. indem ähnliche Antworten automatisch gruppiert werden. Zur Untersuchung des ersten Aspekts unternehmen wir eine Reihe von Korpusannotationsstudien, die sowohl die Beziehungen zwischen Lerner- und Musterantworten beleuchten, als auch die Beziehung zwischen diesen Antworten und dem Lesetext, auf den sie sich beziehen. Wir annotieren Sätze aus dem Lesetext, die vermutlich bei der Formulierung einer Antwort benutzt wurden und machen die zu erwartende Beobachtung, dass die meisten korrekten Antworten problemlos mit bestimmten Textpassagen in Verbindung gebracht werden können. Inkorrekte Antworten haben ebenfalls oft eine Verbindung zu bestimmten Textpassagen, die aber oft für die jeweilige Frage nicht relevant sind. Auf Grundlage dieser Erkenntnisse entwerfen wir ein neues Baseline-Bewertungsmodell, das für die Korrektheit einer Antwort nur in Betracht zieht, ob der Lerner die Antwort an der richtigen Stelle im Lesetext gesucht hat oder nicht. Nachdem wir diese Verbindungen in den Text identifiziert haben, annotieren wir die Relation zwischen Lerner- und Musterantworten und zwischen Texten und Antworten mit Entailment- Relationen. Im Gegensatz zur der weitverbreiteten Annahme, dass das Bewerten von Short- Answer-Fragen und das Erkennen von Textual-Entailment-Relationen zwischen Lerner und Musterantworten sich direkt entsprechen, finden wir heraus, dass die beiden Aufgaben nur nahe verwandt aber nicht vollständig äquivalent sind. Korrekte Antworten entailen meistens, aber nicht immer, die Musterantwort und auch den entsprechenden Satz im Lesetext. Inkorrekte Antworten stehen meist in keiner Entailmentrelation mit der Musterantwort, haben aber oft zumindest teilweisen Overlap mit dem Text. Diese nahe Verwandtschaft erlaubt es uns, Goldstandard-Entailmentinformation zu benutzen, um die Performanz beim automatischen Bewerten zu verbessern. Wir benutzen die annotierten Verbindungen zwischen Lesetexten und Antworten auch in einem Scoringansatz, der auf statistischem Alignment basiert und Methoden aus dem Bereich der maschinellen Übersetzung nutzt. Dabei erreichen wir eine Scoringgenauigkeit, die mit Ansätzen, die ein existierendes wissensbasiertes Alignment nutzen, vergleichbar ist. Unsere Untersuchungen, wie der Bewertungsaufwand beim Menschen verringert werden kann, wenn Antworten vom Lehrer manuell bewertet werden, basieren auf zwei Methoden: Active Learning und Clustering. Beim Active-Learning-Ansatz werden besonders informative Antworten vorrangig zur Bewertung ausgewählt, d.h. solche Antworten, von denen ein Klassifikator besonders viel lernen kann. Wir identifizieren solche Antworten durch Uncertainty-Sampling- Methoden und erreichen dadurch mit einer gegebenen Anzahl von Annotationsschritten eine höhere Klassifikationsgenauigkeit als mit zufällig ausgewählten Antworten. In unserem zweiten Forschungszweig nutzen wir Clusteringmethoden um ähnliche Antworten zu gruppieren, so dass Gruppen von Antworten in einem Annotationsschritt bewertet werden können. Dadurch kann die Anzahl der insgesamt nötigen Bewertungsschritte drastisch reduziert werden. Beim Vergleich zwischen clusteringbasierten Bewertungsverfahren und klassischem überwachten maschinellen Lernen, bei dem menschliche Annotationen dazu genutzt werden, einen Klassifikator zu trainieren, erbringen überwachte maschinelle Lernverfahren immer noch eine höhere Bewertungsgenauigkeit. Demgegenüber bringen Cluster den Vorteil eines strukturierten Outputs mit sich. Wir sind jedoch in der Lage, einen Teil diese Genauigkeitslücke zu schließen, in dem wir überwachte Featureauswahl und halbüberwachtes Clustering anwenden. In einer zusätzlichen Studie untersuchen wir die automatische Verarbeitung von Lernersprache im Hinblick auf die Performanz vonWerkzeugen für dasWortarten-Tagging. Wir annotieren ein deutsches Leseverstehenskorpus manuell sowohl mit Normalisierungsinformation in Bezug auf Rechtschreibung als auch mit Wortartinformation. Als Ergebnis der Studie finden wir, dass die Performanz bei der automatischen Wortartenzuweisung durch Rechtschreibkorrektur verbessert werden kann, insbesondere wenn wir den Lesetext als zusätzliche Evidenz dafür verwenden, welche Wörter der Leser in einer Antwort vermutlich benutzen wollte

Teaching adults to read better and faster : results from an experiment in Burkina Faso

Two cognitively oriented methods were tested in Burkina Faso to help illiterates learn to read more efficiently. These were (a) speeded reading of increasingly larger word units and (b) phonological awareness training to help connect letters to speech. Learners were given reading tests and a computerized reaction time test. Although the literacy courses were shortened by the arrival of rains and government delays, the piloted methods helped adults read better than those in the standard"control"classes. Learners enrolled in the experimental classes performed better on the outcome tests than did learners enrolled in control classes. Ninety percent of the possible comparisons between treatment classes and control classes favored classes receiving treatments, and 72 percent of the measurements in favor of treatments were statistically significant. The evidence suggests that phonological awareness training is particularly effective in situations where the training period was short, and that rapid reading was more advantageous in longer training situations. Overall, the results are indicative of the potential that scientifically backed methods have in making adult literacy instruction more effective. However, due to the short duration of the classes (3-4 months) learners apparently did not receive sufficient practice to consolidate skills. Literacy skills may still be prone to being forgotten if readers do not learn to read automatically and if opportunities to read are few.Curriculum&Instruction,Teaching and Learning,Nonformal Education,Primary Education,ICT Policy and Strategies,Nonformal Education,ICT Policy and Strategies,Primary Education,Teaching and Learning,Curriculum&Instruction

EXAMINING THE RELATIONSHIP BETWEEN AUTOMATICITY AND ORAL READING COMPREHENSION IN ENGLISH LEARNERS

This doctoral dissertation examined the relationship between automaticity and oral reading comprehension in English Learners (ELs) by comparing outcomes with non-English Learners. High fluency rate, or automaticity, is often used as a predictor of reading comprehension in students. Much of the prior research conducted on the relationship between reading rates and oral reading comprehension involved monolingual populations. Few studies have investigated this correlation among EL populations. In this present study, secondary assessment data were retrieved for third-grade students (N = 1,583) across 13 public schools within a single diverse school district in southern Colorado during the 2017-2018 and 2018-2019 school years. The school district includes 20.8% EL students. The researcher chose this approach as most appropriate to examine the relationship between oral reading rate and reading comprehension in ELs and non-English learners. The Dynamic Indicators of Basic Early Literacy Skills (DIBELS) Oral Reading Fluency (DORF) was utilized as the measure for assessing third-grade reading fluency (word-level decoding and accuracy) and the Colorado Measures of Academic Success (CMAS) was used as the measure for assessing third-grade reading comprehension in language arts. Results indicated that ELs who read at a high automatic rate still scored significantly lower on reading comprehension than non-English learners who read at the same rate. Future research should consider conducting additional studies that analyze EL comprehension levels within the context of automaticity