Search results for: learning & teaching

Authors: Patsy Tan, Dana Baltutis

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This presentation demonstrates the effectiveness of music therapy in co-treatment with speech pathology and occupational therapy as an innovative way when working with children and adolescents with complex individual differences to facilitate communication, emotional, motor and social skills development. Each child with special needs and their carer has an individual profile which encompasses their visual-spatial, auditory, language, learning, mental health, family dynamic, sensory-motor, motor planning and sequencing profiles. The most common issues among children with special needs, especially those diagnosed with Autism Spectrum Disorder, are in the areas of regulation, communication, and social-emotional development. The ability of children living with challenges to communicate and use language and understand verbal and non-verbal information, as well as move their bodies to explore and interact with their environments in social situations, depends on the children being regulated both internally and externally and trusting their communication partners and understanding what is happening in the moment. For carers, it is about understanding the tempo, rhythm, pacing, and timing of their own individual profile, as well as the profile of the child they are interacting with, and how these can sync together. In this study, music therapy is used in co-treatment sessions with a speech pathologist and/or an occupational therapist using the DIRFloortime approach to facilitate the regulation, attention, engagement, reciprocity and social-emotional capacities of children presenting with complex individual differences. Documented changes in 10 domains of children’s development over a 12-month period using the Individual Music Therapy Assessment Profile (IMTAP) were observed. Children were assessed biannually, and results show significant improvements in the social-emotional, musicality and receptive language domains indicating that co-treatment with a music therapist using the DIRFloortime framework is highly effective. This presentation will highlight strategies that facilitate regulation, social-emotional and communication development for children and adolescents with complex individual profiles.

Keywords: communication, shared attention, regulation, social emotional

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Authors: Dacheng Li, Bo Huang, Qinjin Han, Ming Li

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Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions.

Keywords: spatiotemporal fusion, sparse representation, K-SVD algorithm, dictionary learning

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Authors: Muneera Mohammed Al-Dossary, Fatimah Mohammed Al-Dossary, Mashael Al-Shahrani, Amal Al-Tammemi

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Johns Hopkins Aramco Healthcare (JAHA), a joint venture between Saudi Aramco and Johns Hopkins Medicine, delivers comprehensive healthcare services to a diverse patient population. Despite achieving high patient satisfaction rates and surpassing several operational targets, JAHA faces challenges such as appointment delays and resource inefficiencies. These issues highlight the need for an advanced, integrated approach to operational management. This paper proposes a Business Intelligence (BI) framework to address these challenges, leveraging tools such as Epic electronic health records and Tableau dashboards. The framework focuses on data integration, real-time monitoring, and predictive analytics to streamline operations and enhance decision-making. Key outcomes include reduced wait times (e.g., a 23% reduction in specialty clinic wait times) and improved operating room efficiency (from 95.83% to 98% completion rates). These advancements align with JAHA’s strategic objectives of optimizing resource utilization and delivering superior patient care. The findings underscore the transformative potential of BI in healthcare, enabling a shift from reactive to proactive operations management. The success of this implementation lays the foundation for future innovations, including machine learning models for more precise demand forecasting and resource allocation.

Keywords: business intelligence, operational efficiency, healthcare management, predictive analytics, patient care improvement, data integration, real-time monitoring, resource optimization, Johns Hopkins Aramco Healthcare, electronic health records, Tableau dashboards, predictive modeling, efficiency metrics, resource utilization, patient satisfaction

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Authors: Sousan Arafeh

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Educational systems and schools worldwide are increasingly reliant on information and communication technology (ICT). Unfortunately, most educational leadership development programs do not offer formal curricular and/or field experiences that prepare students for managing ICT resources, personnel, and processes. The result is a steep learning curve for the leader and his/her staff and dissipated organizational energy that compromises desired outcomes. To address this gap in education leaders’ development, Arafeh’s Integrated Technology Leadership Model (AITLM) was created. It is a conceptual model and tool that educational leadership students can use to better understand the ICT ecology that exists within their schools. The AITL Model consists of six 'infrastructure types' where ICT activity takes place: technical infrastructure, communications infrastructure, core business infrastructure, context infrastructure, resources infrastructure, and human infrastructure. These six infrastructures are further divided into 16 key areas that need management attention. The AITL Model was created by critically analyzing existing technology/ICT leadership models and working to make something more authentic and comprehensive regarding school leaders’ purview and experience. The AITL Model then served as a tool when it was distributed to over 150 educational leadership students who were asked to review it and qualitatively share their reactions. Students said the model presented crucial areas of consideration that they had not been exposed to before and that the exercise of reviewing and discussing the AITL Model as a group was useful for identifying areas of growth that they could pursue in the leadership development program and in their professional settings. While development in all infrastructures and key areas was important for students’ understanding of ICT, they noted that they were least aware of the importance of the intangible area of the resources infrastructure. The AITL Model will be presented and session participants will have an opportunity to review and reflect on its impact and utility. Ultimately, the AITL Model is one that could have significant policy and practice implications. At the very least, it might help shape ICT content in educational leadership development programs through curricular and pedagogical updates.

Keywords: education leadership, information and communications technology, ICT, leadership capacity building, leadership development

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Authors: F. Nasser-Abu Alhija

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In many countries, much efforts and resources are devoted to empowering and integrating minorities within the mainstream population. Major ventures in this route are crafted in higher education institutions where different outreach programs and methods such as lenient entry requirements, monitory incentives, learning skills workshops, tutoring and mentoring, are utilized. Although there is some information regarding these programs, their effectiveness still needs to be thoroughly examined. The Ethiopian community In Israel is one of the minority groups that has been targeted by sponsoring foundations and higher education institutions with the aim to ease the access, persistence and success of its young people in higher education and later in the job market. The evaluation study we propose to present focuses on the implementation of a program designed for this purpose. This program offers relevant candidates for study at a prestigious university a variety of generous incentives that include tuitions, livening allowance, tutoring, mentoring, skills and empowerment workshops and cultural meetings. Ten students were selected for the program and they started their studies in different subject areas before three and half years. A longitudinal evaluation has been conducted since the implementation of the program. Data were collected from different sources: participating students, program coordinator, mentors, tutors, program documents and university records. Questionnaires and interviews were used for collecting data on the different components of the program and on participants' perception of their effectiveness. Participants indicate that the lenient entry requirements and the monitory incentives are critical for starting their studies. During the first year, skills and empowering workshops, torturing and mentoring were evaluated as very important for persistence and success in studies. Tutoring was perceived as very important also at the second year but less importance is attributed to mentoring. Mixed results regarding integration in the Israeli culture emerged. The results are discussed with reference to findings from different settings around the world.

Keywords: access to higher education, minority groups, monitory incentives, torturing, mentoring

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Authors: Tomiwa, Akinyemi Clement

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Tropical regions experience diverse and complex precipitation patterns, posing significant challenges for accurate rainfall estimation and forecasting. This study addresses the problem of effectively classifying tropical rain types and refining the Z-R (Reflectivity-Rain Rate) relationship to enhance rainfall estimation accuracy. Through a combination of remote sensing, meteorological analysis, and machine learning, the research aims to develop an advanced classification framework capable of distinguishing between different types of tropical rain based on their unique characteristics. This involves utilizing high-resolution satellite imagery, radar data, and atmospheric parameters to categorize precipitation events into distinct classes, providing a comprehensive understanding of tropical rain systems. Additionally, the study seeks to improve the Z-R relationship, a crucial aspect of rainfall estimation. One year of rainfall data was analyzed using a Micro Rain Radar (MRR) located at The Federal University of Technology Akure, Nigeria, measuring rainfall parameters from ground level to a height of 4.8 km with a vertical resolution of 0.16 km. Rain rates were classified into low (stratiform) and high (convective) based on various microstructural attributes such as rain rates, liquid water content, Drop Size Distribution (DSD), average fall speed of the drops, and radar reflectivity. By integrating diverse datasets and employing advanced statistical techniques, the study aims to enhance the precision of Z-R models, offering a more reliable means of estimating rainfall rates from radar reflectivity data. This refined Z-R relationship holds significant potential for improving our understanding of tropical rain systems and enhancing forecasting accuracy in regions prone to heavy precipitation.

Keywords: remote sensing, precipitation, drop size distribution, micro rain radar

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Authors: Ashlie Latiolais

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Architecture, as a discipline, is on a trajectory of extension beyond the boundaries of buildings and, more increasingly, is coupled with research that connects to alternative and typically disjointed disciplines. A “both/and” approach and (expanded) definition of architecture, as depicted here, expands the margins that contain the profession. Figuratively, architecture is a series of edges, events, and occurrences that establishes a choreography or stage by which humanity exists. The way in which architecture controls and suggests the movement through these spaces, being within a landscape, city, or building, can be viewed as a datum by which the “dance” of everyday life occurs. This submission views the realm of architecture through the lens of movement and dance as a cross-fertilizer of collaboration, tectonic, and spatial geometry investigations. “Designing on digital programs puts architects at a distance from the spaces they imagine. While this has obvious advantages, it also means that they lose the lived, embodied experience of feeling what is needed in space—meaning that some design ideas that work in theory ultimately fail in practice.” By studying the body in motion through real-time performance, a more holistic understanding of architectural space surfaces and new prospects for theoretical teaching pedagogies emerge. The atypical intersection rethinks how architecture is considered, created, and tested, similar to how “dance artists often do this by thinking through the body, opening pathways and possibilities that might not otherwise be accessible” –this is the essence of this poster submission as explained through unFOLDED, a creative performance work. A new languageismaterialized through unFOLDED, a dynamic occupiable installation by which architecture is investigated through dance, movement, and body analysis. The entry unfolds a collaboration of an architect, dance choreographer, musicians, video artist, and lighting designers to re-create one of the first documented avant-garde performing arts collaborations (Matisse, Satie, Massine, Picasso) from the Ballet Russes in 1917, entitled Parade. Architecturally, this interdisciplinary project orients and suggests motion through structure, tectonic, lightness, darkness, and shadow as it questions the navigation of the dark space (stage) surrounding the installation. Artificial light via theatrical lighting and video graphics brought the blank canvas to life – where the sensitive mix of musicality coordinated with the structure’s movement sequencing was certainly a challenge. The upstage light from the video projections created both flickered contextual imagery and shadowed figures. When the dancers were either upstage or downstage of the structure, both silhouetted figures and revealed bodies are experienced as dancer-controlled installation manipulations occurred throughout the performance. The experimental performance, through structure, prompted moving (dancing) bodies in space, where the architecture served as a key component to the choreography itself. The tectonic of the delicate steel structure allowed for the dancers to interact with the installation, which created a variety of spatial conditions – the contained box of three-dimensional space, to a wall, and various abstracted geometries in between. The development of this research unveils the new role of an Architect as a Choreographer of the built environment.

Keywords: dance, architecture, choreography, installation, architect, choreographer, space

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Authors: Chinwe Susan Oguejiofor, Daniel Osamwonyi Iyioha

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Over the last two decades, Nigeria has witnessed an upsurge in graduate unemployment occasioned by the lack of industries and proliferation of tertiary institutions churning out thousands of graduates every year to compete for the few available job space. The astronomical rise in the unemployment rate amongst Nigerian graduates however, is principally assumed to be the defective curricula of the universities and other tertiary institutions whose focus is on training for white-collar jobs. Although graduate unemployment has become a global scourge, its adverse economic impact is believed to be more in developing economies like Nigeria with a huge young population within the working age who cannot seem to find gainful employment to make out a respectable livelihood. Thus, higher institutions especially Universities found itself under pressure and intense competition to produce graduates who can think outside the box and create jobs; hence there was the need to focus on instilling hands-on practical job skills into their students that will make them job creators rather than job seekers on graduation. In the same vein stakeholders in education have continued to lend their voices to the philosophy that the undergraduate curricula should be completely overhauled to accomodate the development of hand-on practical skills and innovative capacity relevant to creating solutions to societal problems. In a bid to correct this anomaly, the Federal Government of Nigeria in conjunction with the Ministry of Commerce, Industry and Investment inaugurated a programme tagged “University Entrepreneurship Development Programme” (UNEDEP) whose objective was basically to promote self-employment among the youth right from the institutions of higher learning. But the question is whether the objectives of the programme have actually been achieved. Despite the inclusion in Nigerian educational curriculum close to two decades now,, one wonder if the essence has been aborted. Thus, the paper focused on the concept of entrepreneurship education, objectives of entrepreneurship education, Graduates unemployment, rethinking entrepreneurship education programme in tertiary institution for employment generation , role of entrepreneurship in job creation, challenges of entrepreneurship education in tertiary institution in Nigeria, conclusion and recommendations were drawn accordingly.

Keywords: rethinking, entrepreneurship education, remedy, unemployment, job creation

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Authors: Annika Stechemesser, Rachel Pounds, Emma Lucas, Chris Dawson, Julia Lipecki, Pavle Vrljicak, Jan Brosens, Sean Kehoe, Jason Yap, Lawrence Young, Sascha Ott

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Advances in technology make it now possible to retrieve the genetic information of thousands of single cancerous cells. One of the key challenges in single cell analysis of cancerous tissue is to determine the number of different cell types and their characteristic genes within the sample to better understand the tumors and their reaction to different treatments. For this analysis to be possible, it is crucial to filter out background noise as it can severely blur the downstream analysis and give misleading results. In-depth analysis of the state-of-the-art filtering methods for single cell data showed that they do, in some cases, not separate noisy and normal cells sufficiently. We introduced an algorithm that filters and clusters single cell data simultaneously without relying on certain genes or thresholds chosen by eye. It detects communities in a Shared Nearest Neighbor similarity network, which captures the similarities and dissimilarities of the cells by optimizing the modularity and then identifies and removes vertices with a weak clustering belonging. This strategy is based on the fact that noisy data instances are very likely to be similar to true cell types but do not match any of these wells. Once the clustering is complete, we apply a set of evaluation metrics on the cluster level and accept or reject clusters based on the outcome. The performance of our algorithm was tested on three datasets and led to convincing results. We were able to replicate the results on a Peripheral Blood Mononuclear Cells dataset. Furthermore, we applied the algorithm to two samples of ovarian cancer from the same patient before and after chemotherapy. Comparing the standard approach to our algorithm, we found a hidden cell type in the ovarian postchemotherapy data with interesting marker genes that are potentially relevant for medical research.

Keywords: cancer research, graph theory, machine learning, single cell analysis

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Authors: Lisa Fox, Jill Aylott

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There is a global need to identify ways to engage doctors in non-clinical matters such as medical leadership, service improvement and health system transformation. Using the core principles of Realistic Evaluation (RE), this study examined what works, for doctors of different grades, specialities and experience in an acute NHS Hospital Trust in the UK. Realistic Evaluation is an alternative to more traditional cause and effect evaluation models and seeks to understand the interdependencies of Context, Mechanism and Outcome proposing that Context (C) + Mechanism (M) = Outcome (O). In this study, the context, mechanism and outcome were examined from within individual medical leaders to determine what enables levels of medical engagement in a specific improvement project to reduce hospital inpatient mortality. Five qualitative case studies were undertaken with consultants who had regularly completed mortality reviews over a six month period. The case studies involved semi-structured interviews to test the theory behind the drivers for medical engagement. The interviews were analysed using a theory-driven thematic analysis to identify CMO configurations to explain what works, for whom and in what circumstances. The findings showed that consultants with a longer length of service became more engaged if there were opportunities to be involved in the beginning of an improvement project, with more opportunities to affect the design. Those that are new to a consultant role were more engaged if they felt able to apply any learning directly into their own settings or if they could use it as an opportunity to understand more about the organisation they are working in. This study concludes that RE is a useful methodology for better understanding the complexities of motivation and consultant engagement in a trust wide service improvement project. The study showed that there should be differentiated and bespoke training programmes to maximise each individual doctor’s propensity for medical engagement. The RE identified that there are different ways to ensure that doctors have the right skills to feel confident in service improvement projects.

Keywords: realistic evaluation, medical leadership, medical engagement, patient safety, service improvement

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Authors: Vishal Raj, Noorhan Abbas

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Ambiguity in NLP (Natural language processing) refers to the ability of a word, phrase, sentence, or text to have multiple meanings. This results in various kinds of ambiguities such as lexical, syntactic, semantic, anaphoric and referential am-biguities. This study is focused mainly on solving the issue of Lexical ambiguity. Word Sense Disambiguation (WSD) is an NLP technique that aims to resolve lexical ambiguity by determining the correct meaning of a word within a given context. Most WSD solutions rely on words for training and testing, but we have used lemma and Part of Speech (POS) tokens of words for training and testing. Lemma adds generality and POS adds properties of word into token. We have designed a novel method to create an affinity matrix to calculate the affinity be-tween any pair of lemma_POS (a token where lemma and POS of word are joined by underscore) of given training set. Additionally, we have devised an al-gorithm to create the sense clusters of tokens using affinity matrix under hierar-chy of POS of lemma. Furthermore, three different mechanisms to predict the sense of target word using the affinity/similarity value are devised. Each contex-tual token contributes to the sense of target word with some value and whichever sense gets higher value becomes the sense of target word. So, contextual tokens play a key role in creating sense clusters and predicting the sense of target word, hence, the model is named Contextual SenSe Model (CSM). CSM exhibits a noteworthy simplicity and explication lucidity in contrast to contemporary deep learning models characterized by intricacy, time-intensive processes, and chal-lenging explication. CSM is trained on SemCor training data and evaluated on SemEval test dataset. The results indicate that despite the naivety of the method, it achieves promising results when compared to the Most Frequent Sense (MFS) model.

Keywords: word sense disambiguation (wsd), contextual sense model (csm), most frequent sense (mfs), part of speech (pos), natural language processing (nlp), oov (out of vocabulary), lemma_pos (a token where lemma and pos of word are joined by underscore), information retrieval (ir), machine translation (mt)

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Authors: Afshin Gharib

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Often, instructors prefer to bring into class a guest lecturer who can provide an “experiential” perspective on a particular topic. The assumption is that the personal experience brought into the classroom makes the material resonate more with students and that students would have a preference for material being taught from an experiential perspective. The question we asked in the present study was whether a guest lecture from an “experiential” expert with a disability (e.g. a guest suffering from cone-rod dystrophy lecturing on vision, or a dyslexic lecturing on the psychology of reading) would be more effective than the course instructor in capturing students attention and conveying information in an Introduction to Psychology class. Students in two sections of Introduction to Psychology (N = 25 in each section) listened to guest lecturers with disabilities lecturing on a topic related to their disability, one in the area of Sensation and Perception (the guest lecturer is vision impaired) and one in the area of Language Development (the guest lecturer is dyslexic). The Guest lecturers lectured on the same topic in both sections, however, each lecturer used their own experiences to highlight the topics they cover in one section but not the other (counterbalanced between sections), providing students in one section with experiential testimony. Following each of the 4 lectures (two experiential, two non-experiential) students rated the lecture on several dimensions including overall quality, level of engagement, and performance. In addition, students in both sections were tested on the same test items from the lecture material to ascertain degree of learning, and given identical “pop” quizzes two weeks after the exam to measure retention. It was hypothesized that students would find the experiential lectures from lecturers talking about their disabilities more engaging, learn more from them, and retain the material for longer. We found that students in fact preferred the course instructor to the guests, regardless of whether the guests included a discussion of their own disability in their lectures. Performance on the exam questions and the pop quiz items were not different between “experiential” and “non-experiential” lectures, suggesting that guest lecturers who discuss their own disabilities in lecture are not more effective in conveying material and students are not more likely to retain material delivered by “experiential” guests. In future research we hope to explore the reasons for students preference for their regular instructor over guest lecturers.

Keywords: guest lecturer, student perception, retention, experiential

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Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae

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Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.

Keywords: canny pruning, hand recognition, machine learning, skin tracking

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Authors: Antonella Falanga, Armando Cartenì

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Today, an unprecedented amount of data coming from several sources, including mobile devices, sensors, tracking systems, and online platforms, characterizes our lives. The term “big data” not only refers to the quantity of data but also to the variety and speed of data generation. These data hold valuable insights that, when extracted and analyzed, facilitate informed decision-making. The 4Vs of big data - velocity, volume, variety, and value - highlight essential aspects, showcasing the rapid generation, vast quantities, diverse sources, and potential value addition of these kinds of data. This surge of information has revolutionized many sectors, such as business for improving decision-making processes, healthcare for clinical record analysis and medical research, education for enhancing teaching methodologies, agriculture for optimizing crop management, finance for risk assessment and fraud detection, media and entertainment for personalized content recommendations, emergency for a real-time response during crisis/events, and also mobility for the urban planning and for the design/management of public and private transport services. Big data's pervasive impact enhances societal aspects, elevating the quality of life, service efficiency, and problem-solving capacities. However, during this transformative era, new challenges arise, including data quality, privacy, data security, cybersecurity, interoperability, the need for advanced infrastructures, and staff training. Within the transportation sector (the one investigated in this research), applications span planning, designing, and managing systems and mobility services. Among the most common big data applications within the transport sector are, for example, real-time traffic monitoring, bus/freight vehicle route optimization, vehicle maintenance, road safety and all the autonomous and connected vehicles applications. Benefits include a reduction in travel times, road accidents and pollutant emissions. Within these issues, the proper transport demand estimation is crucial for sustainable transportation planning. Evaluating the impact of sustainable mobility policies starts with a quantitative analysis of travel demand. Achieving transportation decarbonization goals hinges on precise estimations of demand for individual transport modes. Emerging technologies, offering substantial big data at lower costs than traditional methods, play a pivotal role in this context. Starting from these considerations, this study explores the usefulness impact of big data within transport demand estimation. This research focuses on leveraging (big) data collected during the COVID-19 pandemic to estimate the evolution of the mobility demand in Italy. Estimation results reveal in the post-COVID-19 era, more than 96 million national daily trips, about 2.6 trips per capita, with a mobile population of more than 37.6 million Italian travelers per day. Overall, this research allows us to conclude that big data better enhances rational decision-making for mobility demand estimation, which is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, cloud computing, decision-making, mobility demand, transportation

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Authors: Ahn Christine Myunghee, Sim Wonjin, Cho Kristina, Ahn Mira, Hong Yeju, Kwok Jihae, Lim Sooyeon, Park Hansol

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With a sharp increase in the prevalence rate for mental health issues in Korea, there is a need for specific and effective intervention strategies in counseling and psychotherapy for use with Korean clients. With the cultural emphasis on restraining emotional expression and not disclosing personal and familial problems to outsiders, clients often find it difficult to discuss their emotional issues even to therapists. Exploring a client’s internal psychological processes bypassing this culture-specific mode of therapeutic communication often becomes a challenge in the therapeutic setting. Given this socio-cultural context, the purpose of the current study was to investigate the effectiveness of using dream work to individuals in Korea. The current study conducted one 60-90 minute dream session and analyzed the dream content of 39 Korean young adults to evaluate the effectiveness of the Hill dream model in accessing the intra-psychic materials, determining essential emotional themes, and learning how the individuals interpreted the contents of their dreams. The transcribed data, which included a total of 39 sessions from 39 volunteer university students, were analyzed by the Consensus Qualitative Research (CQR) approach in terms of domains and core ideas. Self-report measures on Dream Salience, Gains from Dream Interpretations and the Session Evaluation Scale were administered before and after each of their dream sessions. The results indicated that dream work appears to be an effective way to understand unconscious motivations, thoughts, and feelings related to a person’s sense of self, and also how these people relate to other people. Current findings need to be replicated with clients referred for counseling and psychotherapy to determine if the dream work is an appropriate and useful intervention in counseling settings. Limitations of the current study and suggestions for future follow-ups are included in the discussion.

Keywords: dream work, dream interpretation, Korean, young adults, CQR

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Authors: Elizabeth Howarth, Dawn Green, Sean Connolly, Geena Vabulas, Sara Smolley

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Through the EU Horizon 2020 Nuvoic Project, the project team recruited 70 individuals in the UK and Ireland to test the Voiceitt speech recognition app and provide user feedback to developers. The app is designed for people with dysarthric speech, to support communication with unfamiliar people and access to speech-driven technologies such as smart home equipment and smart assistants. Participants with atypical speech, due to a range of conditions such as cerebral palsy, acquired brain injury, Down syndrome, stroke and hearing impairment, were recruited, primarily through organisations supporting disabled people. Most had physical or learning disabilities in addition to dysarthric speech. The project team worked with individuals, their families and local support teams, to provide access to the app, including through additional assistive technologies where needed. Testing was user-led, with participants asked to identify and test use cases most relevant to their daily lives over a period of three months or more. Ongoing technical support and training were provided remotely and in-person throughout the testing period. Structured interviews were used to collect feedback on users' experiences, with delivery adapted to individuals' needs and preferences. Informal feedback was collected through ongoing contact between participants, their families and support teams and the project team. Focus groups were held to collect feedback on specific design proposals. User feedback shared with developers has led to improvements to the user interface and functionality, including faster voice training, simplified navigation, the introduction of gamification elements and of switch access as an alternative to touchscreen access, with other feature requests from users still in development. This work offers a case-study in successful and inclusive co-design with the disabled community.

Keywords: co-design, assistive technology, dysarthria, inclusive speech recognition

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Authors: Mythri G., Manjunath ML, Girish Babu M., Shireen Swaliha Quadri

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Background: The increase in diabetes among the elderly is of concern because in addition to the wide range of traditional diabetes complications, evidence has been growing that diabetes is associated with increased risk of cognitive decline. Aims and Objectives: To find out if there is any association between blood sugar levels and short-term and working memory status in patients of type 2 diabetes. Materials and Methods: The study was carried out in 200 individuals aged between 40-65 years consisting of 100 diagnosed cases of Type 2 Diabetes Mellitus and 100 non-diabetics from OPD of Mc Gann Hospital, Shivamogga. Rye’s Auditory Verbal Learning Test, Verbal Fluency Test and Visual Reproduction Test, Working Digit Span Test and Validation Span Test were used to assess short-term and working memory. Fasting and Post Prandial blood sugar levels were estimated. Statistical analysis was done using SPSS 21. Results: Memory test scores of type 2 diabetics were significantly reduced (p < 0.001) when compared to the memory scores of age and gender matched non-diabetics. Fasting blood sugar levels were found to have a negative correlation with memory scores for all 5 tests: AVLT (r=-0.837), VFT (r=-0.888), VRT(r=-0.787), WDST (r=-0.795) and VST (r=-0.943). Post- Prandial blood sugar levels were found to have a negative correlation with memory scores for all 5 tests: AVLT (r=-0.922), VFT (r=-0.848), VRT(r=-0.707),WDST (r=-0.729) and VST (r=-0.880) Memory scores in all 5 tests were found to be negatively correlated with the FBS and PPBS levels in diabetic patients (p < 0.001). Conclusion: The decreased memory status in diabetic patients may be due to many factors like hyperglycemia, vascular disease, insulin resistance, amyloid deposition and also some of the factor combine to produce additive effects like, type of diabetes, co-morbidities, age of onset, duration of the disease and type of therapy. These observed effects of blood sugar levels of diabetics on memory status are of potential clinical importance because even mild cognitive impairment could interfere with todays’ activities.

Keywords: diabetes, cognition, diabetes, HRV, respiratory medicine

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Authors: Mahdi Allaoua Seklab, Süleyman BaşTürk

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In the quest to enhance coastal environmental health, this study introduces a fully autonomous beach cleaning machine, a breakthrough in leveraging green energy and advanced artificial intelligence for ecological preservation. Designed to operate independently, the machine is propelled by a solar-powered system, underscoring a commitment to sustainability and the use of renewable energy in autonomous robotics. The vehicle's autonomous navigation is achieved through a sophisticated integration of LIDAR and a camera system, utilizing an SSD MobileNet V2 object detection model for accurate and real-time trash identification. The SSD framework, renowned for its efficiency in detecting objects in various scenarios, is coupled with the lightweight and precise highly MobileNet V2 architecture, making it particularly suited for the computational constraints of on-board processing in mobile robotics. Training of the SSD MobileNet V2 model was conducted on Google Colab, harnessing cloud-based GPU resources to facilitate a rapid and cost-effective learning process. The model was refined with an extensive dataset of annotated beach debris, optimizing the parameters using the Adam optimizer and a cross-entropy loss function to achieve high-precision trash detection. This capability allows the machine to intelligently categorize and target waste, leading to more effective cleaning operations. This paper details the design and functionality of the beach cleaning machine, emphasizing its autonomous operational capabilities and the novel application of AI in environmental robotics. The results showcase the potential of such technology to fill existing gaps in beach maintenance, offering a scalable and eco-friendly solution to the growing problem of coastal pollution. The deployment of this machine represents a significant advancement in the field, setting a new standard for the integration of autonomous systems in the service of environmental stewardship.

Keywords: autonomous beach cleaning machine, renewable energy systems, coastal management, environmental robotics

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Authors: Narash Narasimman, Calvin Leong Jia Jun, Raksha Karthik, Paul Englert

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Background: Project Grit, a 12-week youth resilience program implemented by Impart and Spartans Boxing Club, aimed to help at-risk adolescents develop resilience through psychoeducation and mental health techniques for dealing with everyday stressors and adversity. The programme consists of two parts-1.5 hours of group therapy followed by 1 hour of boxing. Due to the novelty of the study, 6 male participants, aged 13 to 18, were recruited to participate in the study. Aim: This case study aims to examine the effectiveness of Project Grit in building resilience among at-risk adolescents. Methods: A case study design was employed to capture the complexity and uniqueness of the intervention, without oversimplifying or generalizing it. A 15-year-old male participant with a history of behavioural challenges, delinquency and gang involvement was selected for the study. Teacher, parent and child versions of the Strengths and Difficulties Questionnaire (SDQ) were administered to the facilitators, parents and participants respectively before and after the programme. Relevant themes from the qualitative interviews will be discussed. Results: Scores from all raters revealed improvements in most domains of the SDQ. Total difficulties scores across all raters improved from “very high” to “close to average”. High interrater reliability was observed (κ= .81). The participant reported learning methods to effectively deal with his everyday concerns using healthy coping strategies, developing a supportive social network, and building on his self efficacy. Themes from the subject’s report concurred with the improvement in SDQ scores. Conclusions: The findings suggest that Project Grit is a promising intervention for promoting resilience among at-risk adolescents. The teleological behaviourism framework and the combination of sports engagement and future orientation may be particularly effective in fostering resilience among this population. Further studies need to be conducted with a larger sample size to further validate the effectiveness of Project Grit.

Keywords: resilience, project grit, adolescents, at-risk, boxing, future orientation

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Authors: Mehran Asadi, Afrand Agah

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In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.

Keywords: online social networks, data mining, social cloud computing, interaction and collaboration

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Authors: Michelangelo Sofo, Giuseppe Labianca

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In recent years, the scientific community's interest in the exploratory analysis of biomedical data has increased exponentially. Considering the field of research of nutritional biologists, the curative process, based on the analysis of clinical data, is a very delicate operation due to the fact that there are multiple solutions for the management of pathologies in the food sector (for example can recall intolerances and allergies, management of cholesterol metabolism, diabetic pathologies, arterial hypertension, up to obesity and breathing and sleep problems). In this regard, in this research work a system was created capable of evaluating various dietary regimes for specific patient pathologies. The system is founded on a mathematical-numerical model and has been created tailored for the real working needs of an expert in human nutrition using the human-centered design (ISO 9241-210), therefore it is in step with continuous scientific progress in the field and evolves through the experience of managed clinical cases (machine learning process). DietAdhoc® is a decision support system nutrition specialists for patients of both sexes (from 18 years of age) developed with an agile methodology. Its task consists in drawing up the biomedical and clinical profile of the specific patient by applying two algorithmic optimization approaches on nutritional data and a symbolic solution, obtained by transforming the relational database underlying the system into a deductive database. For all three solution approaches, particular emphasis has been given to the explainability of the suggested clinical decisions through flexible and customizable user interfaces. Furthermore, the system has multiple software modules based on time series and visual analytics techniques that allow to evaluate the complete picture of the situation and the evolution of the diet assigned for specific pathologies.

Keywords: medical decision support, physiological data extraction, data driven diagnosis, human centered AI, symbiotic AI paradigm

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Authors: Hoorieh Darvishi, Rezaei

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Introduction: This study investigates the efficacy of computer-based interventions for children with Autism Spectrum Disorder , specifically targeting communication deficits and repetitive behaviors. The research evaluates novel software applications designed to enhance narrative capabilities and sensory integration through structured, progressive intervention protocols Method: The study evaluated two intervention software programs designed for children with autism, focusing on narrative speech and sensory integration. Twelve children aged 5-11 participated in the two-month intervention, attending three 45-minute weekly sessions, with pre- and post-tests measuring speech, communication, and behavioral outcomes. The narrative speech software incorporated 14 stories using the Cohen model. It progressively reduced software assistance as children improved their storytelling abilities, ultimately enabling independent narration. The process involved story comprehension questions and guided story completion exercises. The sensory integration software featured approximately 100 exercises progressing from basic classification to complex cognitive tasks. The program included attention exercises, auditory memory training (advancing from single to four-syllable words), problem-solving, decision-making, reasoning, working memory, and emotion recognition activities. Each module was accompanied by frequency and pitch-adjusted music that child enjoys it to enhance learning through multiple sensory channels (visual, auditory, and tactile). Conclusion: The results indicated that the use of these software programs significantly improved communication and narrative speech scores in children, while also reducing scores related to repetitive behaviors. Findings: These findings highlight the positive impact of computer-based interventions on enhancing communication skills and reducing repetitive behaviors in children with autism.

Keywords: autism, communication_skills, repetitive_behaviors, sensory_integration

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Authors: Hoorieh Darvishi, Rezaei

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Introduction: This study investigates the efficacy of computer-based interventions for children with Autism Spectrum Disorder , specifically targeting communication deficits and repetitive behaviors. The research evaluates novel software applications designed to enhance narrative capabilities and sensory integration through structured, progressive intervention protocols Method: The study evaluated two intervention software programs designed for children with autism, focusing on narrative speech and sensory integration. Twelve children aged 5-11 participated in the two-month intervention, attending three 45-minute weekly sessions, with pre- and post-tests measuring speech, communication, and behavioral outcomes. The narrative speech software incorporated 14 stories using the Cohen model. It progressively reduced software assistance as children improved their storytelling abilities, ultimately enabling independent narration. The process involved story comprehension questions and guided story completion exercises. The sensory integration software featured approximately 100 exercises progressing from basic classification to complex cognitive tasks. The program included attention exercises, auditory memory training (advancing from single to four-syllable words), problem-solving, decision-making, reasoning, working memory, and emotion recognition activities. Each module was accompanied by frequency and pitch-adjusted music that child enjoys it to enhance learning through multiple sensory channels (visual, auditory, and tactile). Conclusion: The results indicated that the use of these software programs significantly improved communication and narrative speech scores in children, while also reducing scores related to repetitive behaviors. Findings: These findings highlight the positive impact of computer-based interventions on enhancing communication skills and reducing repetitive behaviors in children with autism.

Keywords: autism, narrative speech, persian, SI, repetitive behaviors, communication

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Authors: Marie Sams, Kate Barnett-Richards, Jacqui Speculand, Gemma Tombs

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Business education in the United Kingdom has seen a number of improvements over the years in moving from delivering traditional chalk and talk lectures to using digital technologies and inviting guest lectures from industry to deliver sessions for students. Engaging topical industry talks to enhance course delivery is generally seen as a positive aspect of enhancing curriculum, however it is acknowledged that perhaps there are better ways in which industry can contribute to the quality of business programmes. Additionally, there is a consensus amongst UK industry managers that a bigger involvement in designing and inputting into business curriculum will have a greater impact on the quality of business ready graduates. Funded by the Disruptive Media Learning Lab at Coventry University in the UK, a project (SOPI - Student Online Projects with Industry) was initiated to enable students to work in project teams to respond and engage with real problems and challenges faced by five managers in various industries including retail, events and manufacturing. Over a semester, approximately 200 students were given the opportunity to develop their management, facilitation, problem solving and reflective skills, whilst having some exposure to real challenges in industry with a focus on supply chain and project management. Face to face seminars were re-designed to enable students to work on live issues in a competitive environment, and were guided to consider the theoretical aspects of their module delivery to underpin the solutions that they were generating. Dialogue between student groups and managers took place using Google+ community; an online social media tool which enables private discussions to take place and can be accessed on mobile devices. Results of the project will be shared in how this development has added value to students experience and understanding of the two subject areas. Student reflections will be analysed and evaluated to assess how the project has contributed to their perception of how the theoretical nature of these two business subjects are applied in practical situations.

Keywords: business, education, industry, projects

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Authors: Jigg Pelayo, Ricardo Villar

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The Philippines is long considered as a valuable producer of high value crops globally. The country’s employment and economy have been dependent on agriculture, thus increasing its demand for the efficient agricultural mechanism. Remote sensing and geographic information technology have proven to effectively provide applications for precision agriculture through image-processing technique considering the development of the aerial scanning technology in the country. Accurate information concerning the spatial correlation within the field is very important for precision farming of high value crops, especially. The availability of height information and high spatial resolution images obtained from aerial scanning together with the development of new image analysis methods are offering relevant influence to precision agriculture techniques and applications. In this study, an algorithm was developed and implemented to detect and count high value crops simultaneously through adaptive scaling of support vector machine (SVM) algorithm subjected to object-oriented approach combining watershed transformation and local maxima filter in enhancing tree counting and detection. The methodology is compared to cutting-edge template matching algorithm procedures to demonstrate its effectiveness on a demanding tree is counting recognition and delineation problem. Since common data and image processing techniques are utilized, thus can be easily implemented in production processes to cover large agricultural areas. The algorithm is tested on high value crops like Palm, Mango and Coconut located in Misamis Oriental, Philippines - showing a good performance in particular for young adult and adult trees, significantly 90% above. The s inventories or database updating, allowing for the reduction of field work and manual interpretation tasks.

Keywords: high value crop, LiDAR, OBIA, precision agriculture

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Authors: Wen Zhen

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Compared to content words, function words have been relatively overlooked by English learners especially articles. The article system, to a certain extent, becomes a resistance to know English better, driven by different elements. Three principal factors can be summarized in term of the nature of the articles when referring to the difficulty of the English article system. However, making the article system more complex are difficulties in the second acquisition process, for [-ART] learners have to create another category, causing even most non-native speakers at proficiency level to make errors. According to the sequences of acquisition of the English article, it is showed that the zero article is first acquired and in high inaccuracy. The zero article is often overused in the early stages of L2 acquisition. Although learners at the intermediate level move to underuse the zero article for they realize that the zero article does not cover any case, overproduction of the zero article even occurs among advanced L2 learners. The aim of the study is to investigate noun-phrase factors which give rise to incorrect usage or overuse of the zero article, thus providing suggestions for L2 English acquisition. Moreover, it enables teachers to carry out effective instruction that activate conscious learning of students. The research question will be answered through a corpus-based, data- driven approach to analyze the noun-phrase elements from the semantic context and countability of noun-phrases. Based on the analysis of the International Thurber Thesis corpus, the results show that: (1) Although context of [-definite,-specific] favored the zero article, both[-definite,+specific] and [+definite,-specific] showed less influence. When we reflect on the frequency order of the zero article , prototypicality plays a vital role in it .(2)EFL learners in this study have trouble classifying abstract nouns as countable. We can find that it will bring about overuse of the zero article when learners can not make clear judgements on countability altered from (+definite ) to (-definite).Once a noun is perceived as uncountable by learners, the choice would fall back on the zero article. These findings suggest that learners should be engaged in recognition of the countability of new vocabulary by explaining nouns in lexical phrases and explore more complex aspects such as analysis dependent on discourse.

Keywords: noun phrase, zero article, corpus, second language acquisition

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Authors: Manish Kumar Pillai, Madhavi Pathak Pillai, Rajender Lal, Dinesh P. Sharma

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India has not yet been able to produce many weightlifters in the past years. Karnam Malleshwari is the only woman to win a medal for India in Olympics. When we try to introspect, there seem to be different reasons. One of the probable cause could be the lack of biomechanical analysis for technique improvements. The analysis of motion in sports has gained prime importance for technical improvement. It helps an athlete to develop a better understanding of his own skills and increasing the rate of technical learning process. Kinematics is concerned with describing and quantifying both the linear and angular position of bodies and their time derivatives. The techniques analysis of barbell movement is very important in weightlifting. But women weightlifting has a shorter history than men’s. Research on women weightlifting based on video analysis is less; there is a lack of scientific evidence based on kinematic analysis of especially on Indian weightlifters at national level are limited. Hence, the present investigation was aimed to analyze the barbell kinematics of women weightlifters in India. The study was delimited to the medal winners of 69-kilogram weight category in the All India Inter-University Competition, age ranging between 18 and 28 years. The variables selected for the mechanical analysis of Barbell kinematics included barbell trajectory, velocity, acceleration, potential energy, kinetic energy, mechanical energy, and average power output. The performance was captured during the competition by two DV PC-60 Digital cameras (Panasonic Company, Ltd). Two cameras were placed 6-meters perpendicular to the plane of the motion, 130 cm. above the ground to record/capture the frontal and lateral view of the lifters simultaneously. Video recordings were analyzed by using Dartfish software, and barbell kinematics were analyzed with the information derived with the help of software. The result documented on the basis of the finding of the study clearly states that there are differences in the selected kinematic variables in all three lifters in respect to their technique in five phases during snatch technique using by them.

Keywords: dartfish, digital camera, kinematic, snatch, weightlifting

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Authors: Warnakulasuriya Arachichige Malisha Ann Rosary Fernando, Udalamatta Gamage Omila Chalanka Jinadasa, Bihini Pabasara Amandi Amarasinghe, Manul Thisuraka Mandalawatta, Uthpala Samarakoon, Manori Gamage

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The surge in sexually transmitted diseases (STDs) has become a critical public health crisis demanding urgent attention and action. Like many other nations, Sri Lanka is grappling with a significant increase in STDs due to a lack of education and awareness regarding their dangers. Presently, the available applications for tracking and managing STDs cover only a limited number of easily detectable infections, resulting in a significant gap in effectively controlling their spread. To address this gap and combat the rising STD rates, it is essential to leverage technology and data. Employing technology to enhance the tracking and management of STDs is vital to prevent their further propagation and to enable early intervention and treatment. This requires adopting a comprehensive approach that involves raising public awareness about the perils of STDs, improving access to affordable healthcare services for early detection and treatment, and utilizing advanced technology and data analysis. The proposed mobile application aims to cater to a broad range of users, including STD patients, recovered individuals, and those unaware of their STD status. By harnessing cutting-edge technologies like image detection, symptom-based identification, prevention methods, doctor and clinic recommendations, and virtual counselor chat, the application offers a holistic approach to STD management. In conclusion, the escalating STD rates in Sri Lanka and across the globe require immediate action. The integration of technology-driven solutions, along with comprehensive education and healthcare accessibility, is the key to curbing the spread of STDs and promoting better overall public health.

Keywords: STD, machine learning, NLP, artificial intelligence

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Authors: Carmen Silvia Gabriel, Dsniele Bernardi da Costa, Andrea Bernardes, Sabrina Elias Mikael, Daniele da Silva Ramos

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The goal of this quantitative study is to investigate patient safety culture from the perspective of professional from the hospital nursing team.It was conducted in two Brazilian hospitals,.The sample included 282 nurses Data collection occurred in 2013, through the questionnaire Hospital Survey on Patient Safety Culture.Based on the assessment of the dimensions is stressed that, in the dimension teamwork across hospital units, 69.4% of professionals agree that when a lot of work needs to be done quickly, they work together as a team; about the dimension supervisor/ manager expectations and actions promoting safety, 70.2% agree that their supervisor overlooks patient safety problems.Related to organizational learning and continuous improvement, 56.5% agree that there is evaluation of the effectiveness of the changes after its implementation.On hospital management support for patient safety, 52.8% refer that the actions of hospital management show that patient safety is a top priority.On the overall perception of patient safety, 57.2% disagree that patient safety is never compromised due to higher amount of work to be completed.In what refers to feedback and communication about error, 57.7% refer that always and usually receive such information. Relative to communication openness, 42.9% said they never or rarely feel free to question the decisions / actions of their superiors.On frequency of event reporting, 64.7% said often and always notify events with no damages to patients..About teamwork across hospital units is noted similarity between the percentages of agreement and disagreement, as on the item there is a good cooperation among hospital units that need to work together, that indicates 41.4% and 40.5% respectively.Related to adequacy of professionals, 77.8 % disagree on the existence of sufficient amount of employees to do the job, 52.4% agree that shift changes are problematic for patients. On nonpunitive response to errors, 71.7% indicate that when an event is reported it seems that the focus is on the person.On the patient safety grade of the institution, 41.6 % classified it as very good. it is concluded that there are positive points in the safety culture, and some weaknesses as a punitive culture and impaired patient safety due to work overload .

Keywords: quality of health care, health services evaluation, safety culture, patient safety, nursing team

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Authors: Dan Frederick Orcherton

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This research explores the fusion of "Critical Pedagogy of Place" and "Indigenous Education Philosophy" to create a holistic pedagogical framework within Instructional Theory, focusing on its application in Distance Education, specifically within two Pacific Island cultures. The study's objectives included investigating culturally relevant instructional techniques, strategies, and technologies for the Itaukei (Indigenous Fijian) and ni-Vanuatu cultures, enhancing appreciation for culturally sensitive pedagogical methods. Methodologically, a qualitative inquiry phenomenological approach was employed within a constructivist paradigm, utilizing a comprehensive qualitative scoping review and online literature search. Key findings include the prioritization of cultural inclusivity and indigenous knowledge integration in both indigenous education philosophies and various instructional approaches. Learner-centered methods like constructivist andragogy and the learning cycle are applicable and effective in distance education within these cultures, aligning with indigenous learners' values and preferences. Place-based education and critical pedagogy of place are particularly pertinent, fostering a deeper connection between education, local environments, and social justice. Integrating digital technologies in culturally responsive education bridges geographical gaps and preserves cultural knowledge. Lastly, blending Western and Indigenous Science, influenced by a Two-Eyed Seeing approach, informs pedagogy by combining Western and Indigenous Science. This research underscores the importance of acknowledging cultural diversity and respecting indigenous knowledge in distance education. It highlights the value of learner-centered approaches, place-based education, and technology integration. The study enriches the educational experience within the Itaukei and niVanuatu cultures and provides insights for educators and policymakers aiming to bridge cultural gaps in distance education.

Keywords: critical pedegory of place, Itaukei (indigenous Fijian) and ni-Vanuatu cultures, placed-based education, indigenous knowledge, distance education

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