Mobile collaborative language learning: State of the art

This paper presents a review of mobile collaborative language learning studies published in 2012–16 with the aim to improve understanding of how mobile technologies have been used to support collaborative learning among second and foreign language students. We identify affordances, general pedagogical approaches, second- and foreign-language pedagogical approaches, second language acquisition (SLA) principles and affective designs. The results indicate that affordances such as flexible use, continuity of use, timely feedback, personalisation, socialisation, self-evaluation, active participation, peer coaching, sources of inspiration outdoors and cultural authenticity have been emphasised. These affordances were found to be particularly suited to promote social constructivism, which is often sustained by game-based, task based and seamless learning. In terms of second and foreign language pedagogical approaches, the combination of individualised and collaborative learning prevails, along with task based, situated and communicative language learning, and raising orthographic awareness. Among SLA principles, negotiation of meaning and opportunities for feedback are highlighted. Affective aspects include increases in motivation, engagement and enjoyment, mutual encouragement, reduction in nervousness and embarrassment, and a few negative reports of risk of distraction, safety concerns, feelings of uncertainty and technical problems. The reviewed studies present a convincing case for the benefits of collaboration in mobile language learning

Using of CALL Method in Writing

This paper shares about using of CALL method in increasing English Writing Skill. The implementation of CALL method brings the new atmospheres in classroom and offers benefit to the increasing of four English skills. CALL method is an alternative tool to assist teacher or lecturer and student language learning. In spite of the benefits, the implementation of CALL method also has limitation. Therefore, teacher or lecturer should remind that CALL method can be applied depends on the situation suitable for the learners and their nee

A review of recent methodologies, technologies and usability in English language content delivery

English Language Teaching (ELT) and content delivery have undergone vast shift in this era of modernization. With analogue content digitized as a common form of knowledge delivery, methodologies equipped with current technologies have produced new perspectives on English Language Learning. This paper reviews the status, context, teaching parameters, assessment parameters, teaching strategies and usability in the current research capacity of ELT, highlighting the current works with technologies in their content delivery methods. Emerging technologies in ELT has also inspires the other spectrum of study involving the usability of technological interfaces, which has evolved constantly with the progression of human and computer interactivity. The aim of this research is to rediscover usability evolution surrounding the technologies in ELT and to redefine the gap existed in between English learning and tools interactivity. Current technologies and usability measures used in ELT will be discussed, highlighting the current trends in gauging interface interaction. A summary of comparative results in the aforementioned works will also be highlighted in this review paper, together with the categorization of reviewed parameters, variables and metrics in ELT. The reviews conducted have shown that there are still many unexplored areas in ELT, ELT technologies and usability in ELT

Computer-Aided Knowledge Engineering for Corporate Information Retrieval

In 1987, Digital Equipment Corporation's internal Madret Information Services Group I Information Access Services (lAS) decided to build a single thesaurus system to support production and retrieval of multiple applications. This system TIMS (Thesaurus I Indexing Management System) bad to be dynamic and allow for easy modification and merging of volatile business terminology. A faceted approach was used for knowledge-base building and semantic representation. 1be system allowed the knowledge engineer to determine a classification structure and to develop relation types suited to a specific application's requirements

KARL: A Knowledge-Assisted Retrieval Language

Data classification and storage are tasks typically performed by application specialists. In contrast, information users are primarily non-computer specialists who use information in their decision-making and other activities. Interaction efficiency between such users and the computer is often reduced by machine requirements and resulting user reluctance to use the system. This thesis examines the problems associated with information retrieval for non-computer specialist users, and proposes a method for communicating in restricted English that uses knowledge of the entities involved, relationships between entities, and basic English language syntax and semantics to translate the user requests into formal queries. The proposed method includes an intelligent dictionary, syntax and semantic verifiers, and a formal query generator. In addition, the proposed system has a learning capability that can improve portability and performance. With the increasing demand for efficient human-machine communication, the significance of this thesis becomes apparent. As human resources become more valuable, software systems that will assist in improving the human-machine interface will be needed and research addressing new solutions will be of utmost importance. This thesis presents an initial design and implementation as a foundation for further research and development into the emerging field of natural language database query systems

Automatic Scaling of Text for Training Second Language Reading Comprehension

For children learning their first language, reading is one of the most effective ways to acquire new vocabulary. Studies link students who read more with larger and more complex vocabularies. For second language learners, there is a substantial barrier to reading. Even the books written for early first language readers assume a base vocabulary of nearly 7000 word families and a nuanced understanding of grammar. This project will look at ways that technology can help second language learners overcome this high barrier to entry, and the effectiveness of learning through reading for adults acquiring a foreign language. Through the implementation of Dokusha, an automatic graded reader generator for Japanese, this project will explore how advancements in natural language processing can be used to automatically simplify text for extensive reading in Japanese as a foreign language

A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports

Knowledge graph visualization in ultrasound reports is essential for enhancing medical decision making and the efficiency and accuracy of computer-aided analysis tools. This study aims to propose an intelligent method for analyzing ultrasound reports through knowledge graph visualization. Firstly, we provide a novel method for extracting key term networks from the narrative text in ultrasound reports with high accuracy, enabling the identification and annotation of clinical concepts within the report. Secondly, a knowledge representation framework based on ultrasound reports is proposed, which enables the structured and intuitive visualization of ultrasound report knowledge. Finally, we propose a knowledge graph completion model to address the lack of entities in physicians’ writing habits and improve the accuracy of visualizing ultrasound knowledge. In comparison to traditional methods, our proposed approach outperforms the extraction of knowledge from complex ultrasound reports, achieving a significantly higher extraction index (η) of 2.69, surpassing the general pattern-matching method (2.12). In comparison to other state-of-the-art methods, our approach achieves the highest P (0.85), R (0.89), and F1 (0.87) across three testing datasets. The proposed method can effectively utilize the knowledge embedded in ultrasound reports to obtain relevant clinical information and improve the accuracy of using ultrasound knowledge

Understanding and Supporting Vocabulary Learners via Machine Learning on Behavioral and Linguistic Data

This dissertation presents various machine learning applications for predicting different cognitive states of students while they are using a vocabulary tutoring system, DSCoVAR. We conduct four studies, each of which includes a comprehensive analysis of behavioral and linguistic data and provides data-driven evidence for designing personalized features for the system. The first study presents how behavioral and linguistic interactions from the vocabulary tutoring system can be used to predict students' off-task states. The study identifies which predictive features from interaction signals are more important and examines different types of off-task behaviors. The second study investigates how to automatically evaluate students' partial word knowledge from open-ended responses to definition questions. We present a technique that augments modern word-embedding techniques with a classic semantic differential scaling method from cognitive psychology. We then use this interpretable semantic scale method for predicting students' short- and long-term learning. The third and fourth studies show how to develop a model that can generate more efficient training curricula for both human and machine vocabulary learners. The third study illustrates a deep-learning model to score sentences for a contextual vocabulary learning curriculum. We use pre-trained language models, such as ELMo or BERT, and an additional attention layer to capture how the context words are less or more important with respect to the meaning of the target word. The fourth study examines how the contextual informativeness model, originally designed to develop curricula for human vocabulary learning, can also be used for developing curricula for various word embedding models. We identify sentences predicted as low informative for human learners are also less helpful for machine learning algorithms. Having a rich understanding of user behaviors, responses, and learning stimuli is imperative to develop an intelligent online system. Our studies demonstrate interpretable methods with cross-disciplinary approaches to understand various cognitive states of students during learning. The analysis results provide data-driven evidence for designing personalized features that can maximize learning outcomes. Datasets we collected from the studies will be shared publicly to promote future studies related to online tutoring systems. And these findings can also be applied to represent different user states observed in other online systems. In the future, we believe our findings can help to implement a more personalized vocabulary learning system, to develop a system that uses non-English texts or different types of inputs, and to investigate how the machine learning outputs interact with students.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162999/1/sjnam_1.pd