6,375 research outputs found

    Comparative Multiple Case Study into the Teaching of Problem-Solving Competence in Lebanese Middle Schools

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    This multiple case study investigates how problem-solving competence is integrated into teaching practices in private schools in Lebanon. Its purpose is to compare instructional approaches to problem-solving across three different programs: the American (Common Core State Standards and New Generation Science Standards), French (Socle Commun de Connaissances, de Compétences et de Culture), and Lebanese with a focus on middle school (grades 7, 8, and 9). The project was conducted in nine schools equally distributed among three categories based on the programs they offered: category 1 schools offered the Lebanese program, category 2 the French and Lebanese programs, and category 3 the American and Lebanese programs. Each school was treated as a separate case. Structured observation data were collected using observation logs that focused on lesson objectives and specific cognitive problem-solving processes. The two logs were created based on a document review of the requirements for the three programs. Structured observations were followed by semi-structured interviews that were conducted to explore teachers' beliefs and understandings of problem-solving competence. The comparative analysis of within-category structured observations revealed an instruction ranging from teacher-led practices, particularly in category 1 schools, to more student-centered approaches in categories 2 and 3. The cross-category analysis showed a reliance on cognitive processes primarily promoting exploration, understanding, and demonstrating understanding, with less emphasis on planning and executing, monitoring and reflecting, thus uncovering a weakness in addressing these processes. The findings of the post-observation semi-structured interviews disclosed a range of definitions of problem-solving competence prevalent amongst teachers with clear divergences across the three school categories. This research is unique in that it compares problem-solving teaching approaches across three different programs and explores underlying teachers' beliefs and understandings of problem-solving competence in the Lebanese context. It is hoped that this project will inform curriculum developers about future directions and much-anticipated reforms of the Lebanese program and practitioners about areas that need to be addressed to further improve the teaching of problem-solving competence

    Spanish Corpora of tweets about COVID-19 vaccination for automatic stance detection

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    The paper presents new annotated corpora for performing stance detection on Spanish Twitter data, most notably Health-related tweets. The objectives of this research are threefold: (1) to develop a manually annotated benchmark corpus for emotion recognition taking into account different variants of Spanish in social posts; (2) to evaluate the efficiency of semi-supervised models for extending such corpus with unlabelled posts; and (3) to describe such short text corpora via specialised topic modelling. A corpus of 2,801 tweets about COVID-19 vaccination was annotated by three native speakers to be in favour (904), against (674) or neither (1,223) with a 0.725 Fleiss’ kappa score. Results show that the self-training method with SVM base estimator can alleviate annotation work while ensuring high model performance. The self-training model outperformed the other approaches and produced a corpus of 11,204 tweets with a macro averaged f1 score of 0.94. The combination of sentence-level deep learning embeddings and density-based clustering was applied to explore the contents of both corpora. Topic quality was measured in terms of the trustworthiness and the validation index.Agencia Estatal de Investigación | Ref. PID2020–113673RB-I00Xunta de Galicia | Ref. ED431C2018/55Fundação para a Ciência e a Tecnologia | Ref. UIDB/04469/2020Financiado para publicación en acceso aberto: Universidade de Vigo/CISU

    Enabling Cross-lingual Information Retrieval for African Languages

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    Language diversity in NLP is critical in enabling the development of tools for a wide range of users. However, there are limited resources for building such tools for many languages, particularly those spoken in Africa. For search, most existing datasets feature few to no African languages, directly impacting researchers’ ability to build and improve information access capabilities in those languages. Motivated by this, we created AfriCLIRMatrix, a test collection for cross-lingual information retrieval research in 15 diverse African languages automatically created from Wikipedia. The dataset comprises 6 million queries in English and 23 million relevance judgments automatically extracted from Wikipedia inter-language links. We extract 13,050 test queries with relevant judgments across 15 languages, covering a significantly broader range of African languages than other existing information retrieval test collections. In addition to providing a much-needed resource for researchers, we also release BM25, dense retrieval, and sparse-dense hybrid baselines to establish a starting point for the development of future systems. We hope that our efforts will stimulate further research in information retrieval for African languages and lead to the creation of more effective tools for the benefit of users

    Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classification

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    The performance of learning models heavily relies on the availability and adequacy of training data. To address the dataset adequacy issue, researchers have extensively explored data augmentation (DA) as a promising approach. DA generates new data instances through transformations applied to the available data, thereby increasing dataset size and variability. This approach has enhanced model performance and accuracy, particularly in addressing class imbalance problems in classification tasks. However, few studies have explored DA for the Arabic language, relying on traditional approaches such as paraphrasing or noising-based techniques. In this paper, we propose a new Arabic DA method that employs the recent powerful modeling technique, namely the AraGPT-2, for the augmentation process. The generated sentences are evaluated in terms of context, semantics, diversity, and novelty using the Euclidean, cosine, Jaccard, and BLEU distances. Finally, the AraBERT transformer is used on sentiment classification tasks to evaluate the classification performance of the augmented Arabic dataset. The experiments were conducted on four sentiment Arabic datasets: AraSarcasm, ASTD, ATT, and MOVIE. The selected datasets vary in size, label number, and unbalanced classes. The results show that the proposed methodology enhanced the Arabic sentiment text classification on all datasets with an increase in F1 score by 4% in AraSarcasm, 6% in ASTD, 9% in ATT, and 13% in MOVIE.Comment: 15 pages, 16 Figures, this work has been submitted to the IEEE Access Journal for possible publicatio

    Religion, Education, and the ‘East’. Addressing Orientalism and Interculturality in Religious Education Through Japanese and East Asian Religions

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    This work addresses the theme of Japanese religions in order to rethink theories and practices pertaining to the field of Religious Education. Through an interdisciplinary framework that combines the study of religions, didactics and intercultural education, this book puts the case study of Religious Education in England in front of two ‘challenges’ in order to reveal hidden spots, tackle unquestioned assumptions and highlight problematic areas. These ‘challenges’, while focusing primarily on Japanese religions, are addressed within the wider contexts of other East Asian traditions and of the modern historical exchanges with the Euro-American societies. As result, a model for teaching Japanese and other East Asian religions is discussed and proposed in order to fruitfully engage issues such as orientalism, occidentalism, interculturality and critical thinking

    Examining the Use of Expressive Arts Therapies in Neurorehabilitation Treatment Planning

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    Those undergoing neurorehabilitation after stroke and traumatic brain injury report a diminished sense of overall wellness. This paper examines the conceivable benefits of introducing expressive arts therapies, which is the therapeutic use and combination of the visual arts, movement, drama, music, writing and other intermodal creative processes, into physical therapy and neurorehabilitation treatment planning. Expressive arts therapies have the capacity to engage with an individual’s physical, emotional, social and spiritual states concurrently. They simultaneously offer the ability to promote an increased sense of well-being, address mind-body disconnects, and process trauma non-verbally. The sections of this narrative literature review focus on the following neurological rehabilitation treatment goals: identity development and building self-esteem; coping with chronic pain and disability; improving communication and motor control; increasing cognition and memory. Qualitative, quantitative and arts-based research articles are included to support the inclusion of expressive arts therapies interventions that support and increase progress in neurorehabilitation treatment completion. The majority of research to date has indicated that a variety of positive outcomes occur, with little negative effect, when expressive arts therapies interventions are employed with stroke and traumatic brain injury survivors. There is a pronounced need for further research about the benefits of pairing expressive arts therapies with physical and neurological rehabilitation and this paper acts as supporting evidence to that statement

    The automatic processing of multiword expressions in Irish

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    It is well-documented that Multiword Expressions (MWEs) pose a unique challenge to a variety of NLP tasks such as machine translation, parsing, information retrieval, and more. For low-resource languages such as Irish, these challenges can be exacerbated by the scarcity of data, and a lack of research in this topic. In order to improve handling of MWEs in various NLP tasks for Irish, this thesis will address both the lack of resources specifically targeting MWEs in Irish, and examine how these resources can be applied to said NLP tasks. We report on the creation and analysis of a number of lexical resources as part of this PhD research. Ilfhocail, a lexicon of Irish MWEs, is created through extract- ing MWEs from other lexical resources such as dictionaries. A corpus annotated with verbal MWEs in Irish is created for the inclusion of Irish in the PARSEME Shared Task 1.2. Additionally, MWEs were tagged in a bilingual EN-GA corpus for inclusion in experiments in machine translation. For the purposes of annotation, a categorisation scheme for nine categories of MWEs in Irish is created, based on combining linguistic analysis on these types of constructions and cross-lingual frameworks for defining MWEs. A case study in applying MWEs to NLP tasks is undertaken, with the exploration of incorporating MWE information while training Neural Machine Translation systems. Finally, the topic of automatic identification of Irish MWEs is explored, documenting the training of a system capable of automatically identifying Irish MWEs from a variety of categories, and the challenges associated with developing such a system. This research contributes towards a greater understanding of Irish MWEs and their applications in NLP, and provides a foundation for future work in exploring other methods for the automatic discovery and identification of Irish MWEs, and further developing the MWE resources described above

    Researchers eye-view of sarcasm detection in social media textual content

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    The enormous use of sarcastic text in all forms of communication in social media will have a physiological effect on target users. Each user has a different approach to misusing and recognising sarcasm. Sarcasm detection is difficult even for users, and this will depend on many things such as perspective, context, special symbols. So, that will be a challenging task for machines to differentiate sarcastic sentences from non-sarcastic sentences. There are no exact rules based on which model will accurately detect sarcasm from many text corpus in the current situation. So, one needs to focus on optimistic and forthcoming approaches in the sarcasm detection domain. This paper discusses various sarcasm detection techniques and concludes with some approaches, related datasets with optimal features, and the researcher's challenges.Comment: 8 page

    tieval: An Evaluation Framework for Temporal Information Extraction Systems

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    Temporal information extraction (TIE) has attracted a great deal of interest over the last two decades, leading to the development of a significant number of datasets. Despite its benefits, having access to a large volume of corpora makes it difficult when it comes to benchmark TIE systems. On the one hand, different datasets have different annotation schemes, thus hindering the comparison between competitors across different corpora. On the other hand, the fact that each corpus is commonly disseminated in a different format requires a considerable engineering effort for a researcher/practitioner to develop parsers for all of them. This constraint forces researchers to select a limited amount of datasets to evaluate their systems which consequently limits the comparability of the systems. Yet another obstacle that hinders the comparability of the TIE systems is the evaluation metric employed. While most research works adopt traditional metrics such as precision, recall, and F1F_1, a few others prefer temporal awareness -- a metric tailored to be more comprehensive on the evaluation of temporal systems. Although the reason for the absence of temporal awareness in the evaluation of most systems is not clear, one of the factors that certainly weights this decision is the necessity to implement the temporal closure algorithm in order to compute temporal awareness, which is not straightforward to implement neither is currently easily available. All in all, these problems have limited the fair comparison between approaches and consequently, the development of temporal extraction systems. To mitigate these problems, we have developed tieval, a Python library that provides a concise interface for importing different corpora and facilitates system evaluation. In this paper, we present the first public release of tieval and highlight its most relevant features.Comment: 10 page
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