Search results for: non-formal learning contexts

Authors: Mohsine Jebbour

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The ultimate aim of this study was to analyze a second-year baccalaureate textbook of English to see to what extent it includes elements of critical thinking. A further purpose was to assess the extent to which the teachers’ teaching practices help students develop some degree of critical thinking. The literature on critical thinking indicated that all the writers agree that critical thinking is skilled and dispositional oriented, and most of the definitions highlight the skill and disposition to select, collect, analyze and evaluate information effectively. In this study, two instruments were used, namely content analysis and questionnaire to ensure validity and reliability. The sample of this study, on the one hand, was a second year textbook of English, namely Ticket to English. The process of collecting data was carried out through designing a checklist to analyze the textbook of English. On the other hand, high school students (second baccalaureate grade) and teachers of English constituted the second sample. Two questionnaires were administered—One was completed by 28 high school teachers (18 males and10 females), and the other was completed by 51 students (26 males and 25 females) from Fez, Morocco. The items of the questionnaire tended to elicit both qualitative and quantitative data. An attempt was made to answer two research questions. One pertained to the extent to which the textbooks of English contain critical thinking elements (Critical thinking skills and dispositions, types of questions, language learning strategies, classroom activities); the second was concerned with whether the teaching practices of teachers of English help improve students’ critical thinking. The results demonstrated that the textbooks of English include elements of critical thinking, and the teachers’ teaching practices help the students develop some degree of critical thinking. Yet, the textbooks do not include problem-solving activities and media analysis and 86% of the teacher-respondents tended to skip activities in the textbooks, mainly the units dealing with Project Work and Study Skills which are necessary for enhancing critical thinking among the students. Therefore, the textbooks need to be designed around additional activities and the teachers are required to cover the units skipped so as to make the teaching of critical thinking effective.

Keywords: critical thinking, language learning strategies, language proficiency, teaching practices

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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Authors: S. B. Tende, H. L. Abubakar

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It is acknowledged that small and medium enterprises (SMEs) may encounter different ethical issues and pressures that could affect the way in which they strategize or make decisions concerning the outcome of their business. Therefore, this research aimed at assessing entrepreneurial ethics in the business of SMEs in Nigeria. Secondary data were adopted as source of corpus for the analysis. The findings conclude that a sound entrepreneurial ethics system has a significant effect on the level of performance of SMEs in Nigeria. The Nigerian Government needs to provide both guiding and physical structures; as well as learning systems that could inculcate these entrepreneurial ethics.

Keywords: culture, entrepreneurial ethics, performance, SME

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Authors: M. Shaheed Hartley

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There has been increasing pressure on education researchers and practitioners at higher education institutions to focus on the development of South Africa’s rural and peri-urban communities and improving their quality of life. Many tertiary institutions are obliged to review their outreach interventions in schools. To ensure that the support provided to schools is still relevant, a systemic evaluation of science educator needs is central to this process. These prioritised needs will serve as guide not only for the outreach projects of tertiary institutions, but also to service providers in general so that the process of addressing educators needs become coordinated, organised and delivered in a systemic manner. This paper describes one area of a broader needs assessment exercise to collect data regarding the needs of educators in a district of 45 secondary schools in the Western Cape Province of South Africa. This research focuses on the needs and challenges faced by science educators at these schools as articulated by the relevant stakeholders. The objectives of this investigation are two-fold: (1) to create a data base that will capture the needs and challenges identified by science educators of the selected secondary schools; and (2) to develop a needs profile for each of the participating secondary schools that will serve as a strategic asset to be shared with the various service providers as part of a community of practice whose core business is to support science educators and science education at large. The data was collected by a means of a needs assessment questionnaire (NAQ) which was developed in both actual and preferred versions. An open-ended questionnaire was also administered which allowed teachers to express their views. The categories of the questionnaire were predetermined by participating researchers, educators and education department officials. Group interviews were also held with the science teachers at each of the schools. An analysis of the data revealed important trends in terms of science educator needs and identified schools that can be clustered around priority needs, logistic reasoning and educator profiles. The needs database also provides opportunity for the community of practice to strategise and coordinate their interventions.

Keywords: needs assessment, science and mathematics education, evaluation, teaching and learning, South Africa

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Authors: Asmaa Abazaid, Maram Al-Ostah, Nadeen Abu-Zahra, Ruba Bawab, Refaat Abdel-Razek

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Innovation audit is defined as a tool that can be used to reflect on how the innovation is managed in an organization. The aim of this study is to audit innovation in the second top Engineering Firms in the world, and one of the Small Medium Enterprises (SMEs) companies that are working in United Arab Emirates (UAE). The obtained results are then compared with four international companies from China and Brazil. The Diamond model has been used for auditing innovation in the two companies in UAE to evaluate their innovation management and to identify each company’s strengths and weaknesses from an innovation perspective. The results of the comparison between the two companies (Jacobs and Hyper General Contracting) revealed that Jacobs has support for innovation, its innovation processes are well managed, the company is committed to the development of its employees worldwide and the innovation system is flexible. Jacobs was doing best in all innovation management dimensions: strategy, process, organization, linkages and learning, while Hyper General Contracting did not score as Jacobs in any of the innovation dimensions. Furthermore, the audit results of both companies were compared with international companies to examine how well the two construction companies in UAE manage innovation relative to SABIC (Saudi company), Poly Easy and Arnious (Brazilian companies), Huagong tools and Guizohou Yibai (Chinese companies). The results revealed that Jacobs is doing best in learning and organization dimensions, while PolyEasy and Jacobs are equal in the linkage dimension. Huagong Tools scored the highest score in process dimension among all the compared companies. However, the highest score of strategy dimension was given to PolyEasy. On the other hand, Hyper General Contracting scored the lowest in all of the innovation management dimensions. It needs to improve its management of all the innovation management dimensions with special attention to be given to strategy, process, and linkage as they got scores below 4 out of 7 comparing with other dimensions. Jacobs scored the highest in three innovation management dimensions related to the six companies. However, the strategy dimension is considered low, and special attention is needed in this dimension.

Keywords: Brazil, China, innovation audit, innovation evaluation, innovation management, Saudi Arabia, United Arab Emirates

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Authors: Mane Varosyan, Diana Tumanyan, Agnesa Martirosyan

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There are many catastrophic events that end with dire consequences, and to avoid them, people should be well-armed with the necessary information about these situations. During the last years, Serious Games have increasingly gained popularity for training people for different types of emergencies. The major discussed problem is the usage of gamification in education. Moreover, it is mandatory to understand how and what kind of gamified e-learning modules promote engagement. As the theme is emergency, we also find out people’s behavior for creating the final approach. Our proposed solution is an educational video game, “EZZRA”.

Keywords: gamification, education, emergency, serious games, game design, virtual reality, digitalisation

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Authors: Kim Alvin De Lara

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The research aimed to assess the level of acceptability of the developed activity sheets in Science 5 integrating climate change adaptation of grade 5 science teachers in the District of Pililla school year 2016-2017. In this research, participants were able to recognize and understand the importance of environmental education in improving basic education and integrating them in lessons through localization and contextualization. The researcher conducted the study to develop a material to use by Science teachers in Grade 5. It served also as a self-learning resource for students. The respondents of the study were the thirteen Grade 5 teachers teaching Science 5 in the District of Pililla. Respondents were selected purposively and identified by the researcher. A descriptive method of research was utilized in the research. The main instrument was a checklist which includes items on the objectives, content, tasks, contextualization and localization of the developed activity sheets. The researcher developed a 2-week lesson in Science 5 for 4th Quarter based on the curriculum guide with integration of climate change adaptation. The findings revealed that majority of respondents are female, 31 years old and above, 10 years above in teaching science and have units in master’s degree. With regards to the level of acceptability, the study revealed developed activity sheets in science 5 is very much acceptable. In view of the findings, lessons in science 5 must be contextualized and localized to improve to make the curriculum responds, conforms, reflects, and be flexible to the needs of the learners, especially the 21st century learners who need to be holistically and skillfully developed. As revealed by the findings, it is more acceptable to localized and contextualized the learning materials for pupils. Policy formation and re-organization of the lessons and competencies in Science must be reviewed and re-evaluated. Lessons in science must also be integrated with climate change adaptation since nowadays, people are experiencing change in climate due to global warming and other factors. Through developed activity sheets, researcher strongly supports environmental education and believes this to serve as a way to instill environmental literacy to students.

Keywords: activity sheets, climate change adaptation, contextualization, localization

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Authors: Yanting Cao, Kazumitsu Nawata

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As smartphones upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described as more refined, complex, and detailed. In this context, we analyzed a set of experimental data obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model becomes extremely challenging. After a series of feature selection and parameters adjustment, a well-performed SVM classifier has been trained.

Keywords: smart sensors, human activity recognition, artificial intelligence, SVM

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Authors: Tracey Jean Boisseau

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Recently, the rise of concerted right-wing and authoritarian movements has put feminists as well as women, queer, trans, and non-binary folk, immigrants, refugees, the global poor, and people of color in their crosshairs. The U.S. is seeing unprecedented attacks on liberal democratic institutions, escalating “culture wars,” and increased anti-intellectual vitriol specifically targeting feminist and anti-racist educators and scholars. Such vitriol has fueled new legislation curtailing or outright banning of “gender studies” for its ideological commitment to theorizing gender identity as a cultural construct and an inherently political project rather than a “natural” binary that can not be contested or interrogated. At the same time, across the globe—in Afghanistan, Argentina, Brazil, France, Haiti, Hungary, Kenya, Nicaragua, Nigeria, Pakistan, the Philippines, Poland, Russia, South Korea, Sweden, Turkey, Uganda, the United Kingdom, and elsewhere—emergent anti-feminist, nativist, and white-supremacist political parties, as well as established autocratic and authoritarian regimes, have instituted blatantly misogynistic, anti-queer, and anti-trans legislation, often accompanied by governmental and extra-governmental policies explicitly intended to marginalize, erase, suppress, or extinguish gender studies as a legitimate academic discipline, topic of research, and teaching field. This paper considers the origins and effects of such legislation -as well as the strategies exhibited by practitioners of gender studies to counter these effects and resist erasure- from a cross-cultural perspective. The research underpinning this paper’s conclusions includes a survey of nearly 2000 gender studies programs in the U.S. and interviews with dozens of gender studies scholars and administrative leaders of gender-studies programs located worldwide. The goal of this paper is to illuminate distinctions, continuities, and global connections between anti-gender studies legislation that emanates from within national borders but arises from rightwing movements that supercede those borders, and that, ultimately, require globalist responses.

Keywords: anti-feminist, anti-LGBTQ, legislation, criminalization, authoritarianism, globalization

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Authors: V. Mantey, N. Findlay, I. Maddox

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The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

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Authors: Pushpinder Syal

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Literary writing carries the stamp of the current language of its time. In translating such texts, it becomes a challenge to capture such reflections which may be evident at several levels: the level of dialectal use of language by characters in stories, the alterations in syntax as tools of writers’ individual stylistic choices, the insertion of quasi-proverbial and gnomic utterances, and even the level of the pragmatics of narrative discourse. Discourse strategies may differ between earlier and later texts, reflecting changing relationships between narrators and readers in changed cultural and social contexts. This paper is a consideration of these features by an approach that combines historicity with a description, contextualizing language change within a discourse framework. The process of translating a collection of writings of Punjabi literature spanning 100 years was undertaken for this study and it was observed that the factor of the historicity of language was seen to play a role. While intended for contemporary readers, the translation of literature over the span of a century poses the dual challenge of needing to possess both accessibility and immediacy as well as adherence to the 'old world' styles of communicating and narrating. The linguistic changes may be observed in a more obvious sense in the difference of diction and word formation – with evidence of more hybridized and borrowed forms in modern and contemporary writings, as compared to the older writings. The latter not only contain vestiges of proverbs and folk sayings, but are also closer to oral speech styles. These will be presented and analysed in the form of chronological listing and by these means, the social process of translation from orality to written text can be seen as traceable in the above-mentioned works. More subtle and underlying shifts can be seen through the analysis of speech acts and implicatures in the same literature, in which the social relationships underlying language use are evident as discourse systems of belief and understanding. They present distinct shifts in worldview as seen at different points in time. However, some continuities of language and style are also clearly visible, and these aid the translator in putting together a set of thematic links which identify the literature of a region and community, and constitute essential outcomes in the effort to preserve its distinctive nature.

Keywords: cultural change, dialect, historicity, stylistic variation

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Authors: Addin Osman, Anwar Ali Yahya, Mohammed Basit Kamal

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Datasets or collections are becoming important assets by themselves and now they can be accepted as a primary intellectual output of a research. The quality and usage of the datasets depend mainly on the context under which they have been collected, processed, analyzed, validated, and interpreted. This paper aims to present a collection of program educational objectives mapped to student’s outcomes collected from self-study reports prepared by 32 engineering programs accredited by ABET. The manual mapping (classification) of this data is a notoriously tedious, time consuming process. In addition, it requires experts in the area, which are mostly not available. It has been shown the operational settings under which the collection has been produced. The collection has been cleansed, preprocessed, some features have been selected and preliminary exploratory data analysis has been performed so as to illustrate the properties and usefulness of the collection. At the end, the collection has been benchmarked using nine of the most widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k-Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. To recap, the benchmark has achieved promising results by utilizing preliminary exploratory data analysis performed on the collection, proposing new trends for research and providing a baseline for future studies.

Keywords: ABET, accreditation, benchmark collection, machine learning, program educational objectives, student outcomes, supervised multi-class classification, text mining

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Authors: Madrine King'endo

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This study dealt with the influence of spelling errors on English language performance among learners with dysgraphia in public primary schools in West Embu, Embu County, Kenya. The study purposed to investigate the influence of spelling errors on the English language performance among the class three pupils with dysgraphia in public primary schools. The objectives of the study were to identify the spelling errors that learners with dysgraphia make when writing English words and classify the spelling errors they make. Further, the study will establish how the spelling errors affect the performance of the language among the study participants, and suggest the remediation strategies that teachers could use to address the errors. The study could provide the stakeholders with relevant information in writing skills that could help in developing a responsive curriculum to accommodate the teaching and learning needs of learners with dysgraphia, and probably ensure training of teachers in teacher training colleges is tailored within the writing needs of the pupils with dysgraphia. The study was carried out in Embu county because the researcher did not find any study in related literature review concerning the influence of spelling errors on English language performance among learners with dysgraphia in public primary schools done in the area. Moreover, besides being relatively populated enough for a sample population of the study, the area was fairly cosmopolitan to allow a generalization of the study findings. The study assumed the sampled schools will had class three pupils with dysgraphia who exhibited written spelling errors. The study was guided by two spelling approaches: the connectionist stimulation of spelling process and orthographic autonomy hypothesis with a view to explain how participants with learning disabilities spell written words. Data were collected through interviews, pupils’ exercise books, and progress records, and a spelling test made by the researcher based on the spelling scope set for class three pupils by the ministry of education in the primary education syllabus. The study relied on random sampling techniques in identifying general and specific participants. Since the study used children in schools as participants, voluntary consent was sought from themselves, their teachers and the school head teachers who were their caretakers in a school setting.

Keywords: dysgraphia, writing, language, performance

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Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

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Authors: Ahmed Makhoukh

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Quality assurance (QA) has recently become a common practice, which is endorsed by most Higher Education (HE) institutions worldwide, due to the pressure of internal and external forces. One of the aims of this quality movement is to make the contribution of university education to socio-economic development highly significant. This entails that graduates are currently required have a high-quality profile, i.e., to be competent and master the 21st-century skills needed in the labor market. This wave of change, mostly imposed by globalization, has the effect that university education should be learner-centered in order to satisfy the different needs of students and meet the expectations of other stakeholders. Such a shift of focus on the student learning outcomes has led HE institutions to reconsider their strategic planning, their mission, the curriculum, the pedagogical competence of the academic staff, among other elements. To ensure that the overall institutional performance is on the right way, a QA system should be established to assume this task of checking regularly the extent to which the set of standards of evaluation are strictly respected as expected. This operation of QA has the advantage of proving the accountability of the institution, gaining the trust of the public with transparency and enjoying an international recognition. This is the case of Doha Institute (DI) for Graduate Studies, in Qatar, the object of the present study. The significance of this contribution is to show that the conception of quality has changed in this digital age, and the need to integrate a department responsible for QA in every HE institution to ensure educational quality, enhance learners and achieve academic leadership. Thus, to undertake the issue of QA in DI for Graduate Studies, an elite university (in the academic sense) that focuses on a small and selected number of students, a qualitative method will be adopted in the description and analysis of the data (document analysis). In an attempt to investigate the extent to which QA is achieved in Doha Institute for Graduate Studies, three broad indicators will be evaluated (input, process and learning outcomes). This investigation will be carried out in line with the UK Quality Code for Higher Education represented by Quality Assurance Agency (QAA).

Keywords: accreditation, higher education, quality, quality assurance, standards

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Authors: Rossella Resi

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This paper describes a project carried out in an Italian school with English learning pupils combining three didactic tools which are attested to be relevant for the success of young learner’s language curriculum: the use of technology, the intralingual and interlingual mediation (according to CEFR) and the cultural dimension. Aim of this project was to test a technological hands-on translation activity like subtitling in a formal teaching context and to exploit its potential as motivational tool for developing listening and writing, translation and cross-cultural skills among language learners. The activities proposed involved the use of professional subtitling software called Aegisub and culture-specific films. The workshop was optional so motivation was entirely based on the pleasure of engaging in the use of a realistic subtitling program and on the challenge of meeting the constraints that a real life/work situation might involve. Twelve pupils in the age between 16 and 18 have attended the afternoon workshop. The workshop was organized in three parts: (i) An introduction where the learners were opened up to the concept and constraints of subtitling and provided with few basic rules on spotting and segmentation. During this session learners had also the time to familiarize with the main software features. (ii) The second part involved three subtitling activities in plenum or in groups. In the first activity the learners experienced the technical dimensions of subtitling. They were provided with a short video segment together with its transcription to be segmented and time-spotted. The second activity involved also oral comprehension. Learners had to understand and transcribe a video segment before subtitling it. The third activity embedded a translation activity of a provided transcription including segmentation and spotting of subtitles. (iii) The workshop ended with a small final project. At this point learners were able to master a short subtitling assignment (transcription, translation, segmenting and spotting) on their own with a similar video interview. The results of these assignments were above expectations since the learners were highly motivated by the authentic and original nature of the assignment. The subtitled videos were evaluated and watched in the regular classroom together with other students who did not take part to the workshop.

Keywords: ICT, L2, language learning, language mediation, subtitling

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Authors: Derek O Reilly, Piotr Milczarski, Shane Dowdall, Artur Hłobaż, Krzysztof Podlaski, Hiram Bollaert

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Learning through creation of contextual games is a very promising way/tool for interdisciplinary and international group projects. During 2013 and 2014 we took part and organized two intensive students projects in different conditions. The projects enrolled 68 students and 12 mentors from 5 countries. In the paper we want to share our experience how to strengthen the chances to succeed in short (12-15 days long) student projects. In our case almost all teams prepared working prototype and the results were highly appreciated by external experts.

Keywords: contextual games, mobile games, GGULIVRR, walkabout, Erasmus intensive programme

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Authors: Ahlem Bouziri, Chiraz Latiri, Eric Gaussier

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The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to extract dependencies between terms, namely a generic basis of association rules between terms. The key feature of our approach is a better trade off between the size of the mining result and the conveyed knowledge. Thus, face to the huge number of derived association rules and in order to select the optimal combination of query terms from the generic basis, we propose to model the problem as a classification problem and solve it using a supervised learning algorithm such as SVM or k-means. For this purpose, we first generate a training set using a genetic algorithm based approach that explores the association rules space in order to find an optimal set of expansion terms, improving the MAP of the search results. The experiments were performed on SDA 95 collection, a data collection for information retrieval. It was found that the results were better in both terms of MAP and NDCG. The main observation is that the hybridization of text mining techniques and query expansion in an intelligent way allows us to incorporate the good features of all of them. As this is a preliminary attempt in this direction, there is a large scope for enhancing the proposed method.

Keywords: supervised leaning, classification, query expansion, association rules

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Authors: Amanda Aliende da Matta

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In different fields, there are people who have something that stands out. In the educational world, for example, it is clear when some teachers have something: they are the best teachers, but this is not directly attributed to their disciplines, methodologies, etc. It is that they have something that captivates, inspires, and motivates. But we also find this something in other contexts. In this thesis, the interest is in something that some marginalized people, such as Ab (fictitious name), have. Ab was born in a rural community and saw the lifestyle of his family change drastically as a consequence of structural changes in his village. The community became impoverished, and together with a group of teenagers, he decided to migrate to Spain in search of opportunities. His best friend drowned during the crossing. After arriving, he lived in indecent conditions and felt unsafe. He now suffers from anxiety and frequently faints from it. Yet, he’s linked to Joves x la pau (a Christian project, although he is a Muslim), distributing food for people who live on the streets every Thursday afternoon. When he asked about what happens on cold and rainy days, he explained simply: "if it rains, I distribute the food, and immediately I get home, take a bath, and sleep warm under my roof. That is when we most have to go." This something he has will be called caring. And one of the general objectives of the thesis is to discover what are the meaning structures of this caring what is the lived experience of this caring. In this communication, preliminary results of an Applied Hermeneutic Phenomenology (AHP) study on the lived experience of caring as a vulnerable person are presented. The research means to answer what is the lived experience of caring as a vulnerable person. That is, to describe and explain what it is like to caregive for a vulnerable person, what it is, essentially, to caregive for a vulnerable person, what makes the lived experience of caregiving for a vulnerable person different from any other. In order to investigate the meaning of the phenomenon of caregiving as a vulnerable person, as already stated, the method used will be Applied Hermeneutic Phenomenology (AHP). We base ourselves, initially, on the proposal of Raquel Ayala-Carabajo and Max Van Manen. As Van Manen (1990) explains, AHP is a method that works essentially through fieldwork, with the collection of data on lived experience (experiential material). It is a phenomenology of practice. We here present the provisional themes we found: caregiving as a vulnerable person is seeing yourself in the other, identifying with the care-receiver; Caregiving as a vulnerable person is putting the other’s need before oneself’s; Caregiving as a vulnerable person is temporarily overcoming your weaknesses to make yourself strong for the other; Caregiving as a vulnerable person is going beyond the conventional approach; and Caregiving as a vulnerable person is taking responsibility even if it’s not yours.

Keywords: applied hermeneutic phenomenology, care ethics, hermeneutics, phenomenology

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Victor Abu

Abstract:

Background: Social justice ideals are considered as the foundation of nursing practice. These ideals are not always clearly integrated into nursing professional standards or curricula. This hinders concerted global nursing agendas for becoming aware of social injustice or engaging in action for social justice to improve the health of individuals and groups. Aim and objectives: The aim was to create an educational framework for empowering nursing students for social justice awareness and action. This purpose was attained by understanding the meaning of social justice, the effect of social injustice, the visibility of social justice learning, and ways of integrating social justice in nursing education and practice. Methods: Critical interpretive methodologies and constructivist grounded theory research designs guided the processes of recruiting nursing students (n = 11) and nurse educators (n = 11) at a London nursing university to participate in interviews and focus groups, which were analysed by coding systems. Findings: Firstly, social justice was described as ethical practices that enable individuals and groups to have good access to health resources. Secondly, social injustice was understood as unfair practices that caused minimal access to resources, social deprivation, and poor health. Thirdly, social justice learning was considered to be invisible in nursing education due to a lack of explicit modules, educator knowledge, and organisational support. Lastly, explicit modules, educating educators, and attracting leaders’ support were suggested as approaches for the visible integration of social justice in nursing education and practice. Discussion: This research proposes approaches for nursing awareness and action for the development of critical active nurse-learner, critical conscious nurse-educator, and servant nurse leader. The framework on Awareness for Social Justice Action (ASJA) created in this research is an approach for empowering nursing students for social justice practices. Conclusion: This research contributes to and advocates for greater nursing scholarship to raise the spotlight on social justice in the profession.

Keywords: social justice, nursing practice, nursing education, nursing curriculum, social justice awareness, social justice action, constructivist grounded theory

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Authors: C. Fortin, D. Tokmeninova, O. Ushakova

Abstract:

Among 500 Russian universities Skolkovo Institute of Science and Technology (Skoltech) is one of the youngest (established in 2011), quite small and vastly international, comprising 20 percent of international students and 70 percent of faculty with significant academic experience at top-100 universities (QS, THE). The institute has emerged from close collaboration with MIT and leading Russian universities. Skoltech is an entirely English speaking environment. Skoltech curriculum plans of ten Master programs are based on the CDIO learning outcomes model. However, despite the Institute’s unique focus on industrial innovations and startups, one of the main challenges has become an evident large proportion of nearly half of MSc graduates entering PhD programs at Skoltech or other universities rather than industry or entrepreneurship. In order to increase the share of students joining the industrial sector after graduation, Skoltech started implementing a number of unique practices with a focus on employers’ expectations incorporated into the curriculum redesign. In this sense, extended knowledge exchange with industrial partners via collaboration in learning activities, industrial projects and assessments became essential for students’ headway into industrial and entrepreneurship pathways. Current academic curriculum includes the following types of components based on extended knowledge exchange with industrial partners: innovation workshop, industrial immersion, special industrial tracks, MSc defenses. Innovation workshop is a 4 week full time diving into the Skoltech vibrant ecosystem designed to foster innovators, focuses on teamwork, group projects, and sparks entrepreneurial instincts from the very first days of study. From 2019 the number of mentors from industry and startups significantly increased to guide students across these sectors’ demands. Industrial immersion is an exclusive part of Skoltech curriculum where students after the first year of study spend 8 weeks in an industrial company carrying out an individual or team project and are guided jointly by both Skoltech and company supervisors. The aim of the industrial immersion is to familiarize students with relevant needs of Russian industry and to prepare graduates for job placement. During the immersion a company plays the role of a challenge provider for students. Skoltech has started a special industrial track comprising deep collaboration with IPG Photonics – a leading R&D company and manufacturer of high-performance fiber lasers and amplifiers for diverse applications. The track is aimed to train a new cohort of engineers and includes a variety of activities for students within the “Photonics” MSc program. It is expected to be a successful story and used as an example for similar initiatives with other Russian high-tech companies. One of the pathways of extended knowledge exchange with industrial partners is an active involvement of potential employers in MSc Defense Committees to review and assess MSc thesis projects and to participate in defense procedures. The paper will evaluate the effect and results of the above undertaken measures.

Keywords: Curriculum redesign, knowledge exchange model, learning outcomes framework, stakeholder engagement

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Authors: Tatiana Ivushkina

Abstract:

The paper is aimed at studying the use of literary words of foreign origin in modern fiction from a sociolinguistic point of view, which presupposes establishing correlation between this category of words in a speech portrayal or narrative and a social status of the speaker, verifying that it bears social implications and serves as a social marker or index of socially privileged identity in the British literature of the 21-st century. To this end, there were selected literary words of foreign origin in context (60 contexts) and subjected to careful examination. The study is carried out on two novels by Jeffrey Archer – Not a Penny More, Not a Penny Less and A Prisoner of Birth – who, being a graduate from Oxford, represents socially privileged classes himself and gives a wide depiction of characters with different social backgrounds and statuses. The analysis of the novels enabled us to categorize the selected words into four relevant groups. The first represented by terms (commodity, debenture, recuperation, syringe, luminescence, umpire, etc.) serves to unambiguously indicate education, occupation, a field of knowledge in which a character is involved or a situation of communication. The second group is formed of words used in conjunction with their Germanic counterparts (perspiration – sweat, padre – priest, convivial – friendly) to contrast social position of the characters: literary words serving as social indices of upper class speakers whereas their synonyms of Germanic origin characterize middle or lower class speech portrayals. The third class of words comprises socially marked words (verbs, nouns, and adjectives), or U-words (the term first coined by Allan Ross and Nancy Mitford), the status acquired in the course of social history development (elegant, excellent, sophistication, authoritative, preposterous, etc.). The fourth includes words used in a humorous or ironic meaning to convey the narrator’s attitude to the characters or situation itself (ministrations, histrionic, etc.). Words of this group are perceived as 'alien', stylistically distant as they create incongruity between style and subject matter. Social implication of the selected words is enhanced by French words and phrases often accompanying them.

Keywords: British literature of the XXI century, literary words of foreign origin, social context, social meaning

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Authors: Rubina Jahan, Mohammad Zayeed Bin Alam, Razia Sultana, Md Faroque Miah

Abstract:

Mental health remains a significant global public health challenge, profoundly affecting millions worldwide. In Bangladesh, the situation is dire, with the National Mental Health Survey 2018-19 indicating that 19% of adults suffer from any kind of mental disorders, including severe mental disorder of around 2%. Despite these high prevalence rates, there is a substantial treatment gap in low- and middle-income countries, including Bangladesh, where up to 92% of individuals with mental illnesses do not receive adequate care. This gap is exacerbated by social barriers such as stigma, discrimination, social exclusion, poverty, homelessness, and human rights violations. To address these challenges, the SAJIDA Foundation launched the Proshanti in November 2022. Proshanti is a community based supportive housing intervention designed to provide cost-effective, sustainable, long-term care for individuals with chronic mental illnesses. It aims to rehabilitate participants by improving their mental health, quality of life, and equipping them with skills necessary for independent living and social mobility. Currently, Proshanti operates seven houses in Manikganj and Habiganj districts of Bangladesh, accommodating up to 40 individuals. Over a two-year period, individuals have received personalized support from trained personal assistants and care coordinators, regular health checkups, and opportunities for vocational training and community engagement. In this presentation, we will present the outcome of such intervention on individual’s functionality, quality of life and psychological health generated from 24 months of journey. Additionally, a qualitative approach will be employed to understand the facilitators and barriers of program implementation. The Proshanti program represents a promising model for addressing the significant mental health treatment gap in Bangladesh at the community level. Our findings will provide crucial insights into the program's feasibility, effectiveness, and the factors influencing its implementation, potentially guiding future mental health interventions in similar contexts.

Keywords: mental health, community based supportive housing, treatment gap, bangladesh

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Authors: C. E. Laburu, M. A. Barros, A. F. Zompero, O. H. M. Silva

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The work presents a teaching strategy that employs syntagmatic and paradigmatic linguistic relations in order to monitor the understanding of physics students’ concepts. Syntagmatic and paradigmatic relations are theoretical elements of semiotics studies and our research circumstances and justified them within the research program of multi-modal representations. Among the multi-modal representations to learning scientific knowledge, the scope of action of syntagmatic and paradigmatic relations belongs to the discursive writing form. The use of such relations has the purpose to seek innovate didactic work with discourse representation in the write form before translate to another different representational form. The research was conducted with a sample of first year high school students. The students were asked to produce syntagmatic and paradigmatic of Newton’ first law statement. This statement was delivered in paper for each student that should individually write the relations. The student’s records were collected for analysis. It was possible observed in one student used here as example that their monemes replaced and rearrangements produced by, respectively, syntagmatic and paradigmatic relations, kept the original meaning of the law. In paradigmatic production he specified relevant significant units of the linguistic signs, the monemas, which constitute the first articulation and each word substituted kept equivalence to the original meaning of original monema. Also, it was noted a number of diverse and many monemas were chosen, with balanced combination of grammatical (grammatical monema is what changes the meaning of a word, in certain positions of the syntagma, along with a relatively small number of other monemes. It is the smallest linguistic unit that has grammatical meaning) and lexical (lexical monema is what belongs to unlimited inventories; is the monema endowed with lexical meaning) monemas. In syntagmatic production, monemas ordinations were syntactically coherent, being linked with semantic conservation and preserved number. In general, the results showed that the written representation mode based on linguistic relations paradigmatic and syntagmatic qualifies itself to be used in the classroom as a potential identifier and accompanist of meanings acquired from students in the process of scientific inquiry.

Keywords: semiotics, language, high school, physics teaching

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Authors: Abeer Karaali

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This paper will seek to clarify important key terms such as home schooling and home education as well as the legalities attached to such terms. It will reflect on the recent proposed changes to terminology in NSW, Australia. The various pedagogical approaches to home education will be explored including their prominence in the Australian context. There is a strong focus on literature from Australia. The historical background of home education in Australia will be explained as well as the difference between distance education and home education. The statistics related to home education in Australia will be explored in the scope and compared to the US. The future of home education in Australia will be discussed.

Keywords: alternative education, e-learning, home education, home schooling, online resources, technology

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Authors: Arlene Caney, Linda Fellag

Abstract:

To counter the adverse effect of Covid-19 on college student success, two faculty members at a US community college have used web-based assessment data to improve curricula and, thus, student outcomes. This case study exemplifies how “closing the loop” by analyzing outcome assessments in real time can improve student learning for academically underprepared students struggling during the pandemic. The purpose of the study was to develop ways to mitigate the negative impact of Covid-19 on student success of underprepared college students. Using the Assessment, Evaluation, Feedback and Intervention System (AEFIS) and other assessment tools provided by the college’s Office of Institutional Research, an English professor and a Music professor collected data in skill areas related to their curricula over four semesters, gaining insight into specific course sections and learners’ performance across different Covid-driven course formats—face-to-face, hybrid, synchronous, and asynchronous. Real-time data collection allowed faculty to shorten and close the assessment loop, and prompted faculty to enhance their curricula with engaging material, student-centered activities, and a variety of tech tools. Frequent communication, individualized study, constructive criticism, and encouragement were among other measures taken to enhance teaching and learning. As a result, even while student success rates were declining college-wide, student outcomes in these faculty members’ asynchronous and synchronous online classes improved or remained comparable to student outcomes in hybrid and face-to-face sections. These practices have demonstrated that even high-risk students who enter college with remedial level language and mathematics skills, interrupted education, work and family responsibilities, and language and cultural diversity can maintain positive outcomes in college across semesters, even during the pandemic.

Keywords: AEFIS, assessment, distance education, institutional research center

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Authors: Rabi Abdullahi Danjuma, Fatima Binta Attahir

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Classroom discourse is important, and it is worth examining what the phenomena is and how it helps both the teacher and students in a classroom situation. This paper looks at the classroom as a traditional social setting which has its own norms and values. The paper also explains what discourse is, as extended communication in speech or writing often interactively dealing with some particular topics. It also discusses classroom discourse as the language which teachers and students use to communicate with each other in a classroom situation. The paper also looks at some strategies for effective classroom discourse. Finally, implications and recommendations were drawn.

Keywords: classroom, discourse, learning, student, strategies, communication

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Authors: Selim M. Khan

Abstract:

Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

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