Search results for: programme learning outcome

Authors: Jose Gay D. Gallego

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This study examined the learning performance in General Psychology of 297 freshmen of the CPSU-Main through the Pre and Post Tests. The instructional intervention via Universal Design for Learning (UDL) was applied to 33% (97 out of 297) of these freshmen as the Treatment Group while the 67% (200) belonged to the Control Group for traditional instructions. Statistical inferences utilized one-way Analysis of Variance for mean differences; Pearson R Correlations for bivariate relationships, and; Factor Analysis for significant components that contributed most to the Universal Design for Learning instructions. Findings showed very high levels of students’ acquired UDL skills. Results in the pre test in General Psychology, respectively, were low and average when grouped into low and high achievers. There was no significant mean difference in the acquired nine UDL components when categorized into seven colleges to generalize that between colleges they were on the same very high levels. Significant differences were found in three test areas in General Psychology in eight colleges whose students in College of teacher education taking the lead in the learning performance. Significant differences were also traced in the post test in favor of the students in the treatment group. This proved that UDL really impacted the learning performance of the low achieving students. Significant correlations were revealed between the components of UDL and General Psychology. There were twenty four significant itemized components that contributed most to UDL instructional interventions. Implications were emphasized to maximizing the principles of UDL with the contention of thoughtful planning related to the four curricular pillars of UDL: (a) instructional goals, (b) instructional delivery methods, (c) instructional materials, and (d) student assessments.

Keywords: universal design for learning, enhanced performance, teaching innovation, technology in education, social science area

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Authors: Emma Groenewald

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In 1994, numerous policies of a democratic dispensation envisage social justice and the transformation of the South Africa society. The drive for transformation and social justice resulted in an increasing number of university students from diverse backgrounds, which in turn, lead to the establishment of Sol Plaatje University (SPU) in 2014. A re-curriculated B. Ed. programme at SPU aims to equip students with knowledge and skills to realise the aim of social justice and to enhance the transformation of the South African society. The aim of this study is to explore the experiences and perceptions of students at a diverse university campus on a curriculum that aims to promote social justice. Four education modules, with the assumption that it reflects social justice content, were selected. Four students, representative of different ethnic and language groupings found at the SPU, were chosen as participants. Data were generated by the participants through four reflective exercises on each of the modules, spread over a period of four years. The module aims, linked with the narratives of the participants' perceptions and experiences of each module, provided an overview of the enacted curriculum. A qualitative research design with an interpretivist approach informed by Vygotsky's theory of learning was used. The participants' experiences of the four modules were analysed, and their views were interpreted. The students' narratives shed light on the strengths and weaknesses of how the B.Ed. Curriculum works towards social justice and revealed student's perceptions of otherness. From the narratives it became apparent that module did promote a social justice orientation in prospective teachers trained at a university.

Keywords: student diversity, social justice, transformation, teacher education

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Authors: Ramzi Rihane, Yassine Benayed

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Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Timely and accurate detection of these seizures is essential for improving patient care. In this study, we leverage the UK Bonn University open-source EEG dataset and employ advanced deep-learning techniques to automate the detection of epileptic seizures. By extracting key features from both time and frequency domains, as well as Spectrogram features, we enhance the performance of various deep learning models. Our investigation includes architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), 1D Convolutional Neural Networks (1D-CNN), and hybrid CNN-LSTM and CNN-BiLSTM models. The models achieved impressive accuracies: LSTM (98.52%), Bi-LSTM (98.61%), CNN-LSTM (98.91%), CNN-BiLSTM (98.83%), and CNN (98.73%). Additionally, we utilized a data augmentation technique called SMOTE, which yielded the following results: CNN (97.36%), LSTM (97.01%), Bi-LSTM (97.23%), CNN-LSTM (97.45%), and CNN-BiLSTM (97.34%). These findings demonstrate the effectiveness of deep learning in capturing complex patterns in EEG signals, providing a reliable and scalable solution for real-time seizure detection in clinical environments.

Keywords: electroencephalogram, epileptic seizure, deep learning, LSTM, CNN, BI-LSTM, seizure detection

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Authors: Raimi, Kehinde Olawuyi, Omoare Ayodeji Motunrayo

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In the last two decades, there has been rising youth unemployment, restiveness, and social vices in Nigeria. The relevance of Vocational Education for skills acquisition, global learning, and national development to address these problems cannot be underestimated. Thus, the need to economically empower Nigerian youths to be able to develop the nation and meet up in the ever-changing global learning and economy led to the assessment of Vocational Education as Synergy for the Skills Acquisition and Global Learning in Ogun State, Nigeria. One hundred and twenty out of 1,500 students were randomly selected for this study. Data were obtained through a questionnaire and were analyzed with descriptive statistics and Chi-square. The results of the study showed that 59.2% of the respondents were between 20 – 24 years of age, 60.8% were male, and 65.8% had a keen interest in Vocational Education. Also, 90% of the respondents acquired skills in extension/advisory, 78.3% acquired skills in poultry production, and 69.1% acquired skills in fisheries/aquaculture. The major constraints to Vocational Education are inadequate resource personnel (χ² = 10.25, p = 0.02), inadequate training facilities (x̅ = 2.46) and unstable power supply (x̅ = 2.38). Results of Chi-square showed significance association between constraints and Skills Acquisition (χ² = 12.54, p = 0.00) at p < 0.05 level of significance. It was established that Vocational Education significantly contributed to students’ skills acquisition and global learning. This study, therefore, recommends that inadequate personnel should be looked into by the school authority in order not to over-stretch the available staff of the institution while the provision of alternative stable power supply (solar power) is also essential for effective teaching and learning process.

Keywords: vocational education, skills acquisition, national development, global learning

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Authors: Tonoyan Lusine

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The Communicative Grammar (CG) of a language deals with semantics and pragmatics in the first place as communication is a process of generating speech. As it is well known people can communicate with the help of limited word expressions and grammatical means. As to non-verbal communication, both vocabulary and grammar are not essential at all. However, the development of the communicative competence lies in verbal, non-verbal, grammatical, socio-cultural and intercultural awareness. There are several important issues and environment management strategies related to effective communication that one might need to consider for a positive learning experience. International students bring a broad range of cultural perspectives to the learning environment, and this diversity has the capacity to improve interaction and to enrich the teaching/learning process. Intercultural setting implies creative and thought-provoking work with different cultural worldviews and international perspectives. It is worth mentioning that the use of Communicative Grammar models creates a profound background for the effective intercultural communication.

Keywords: CG, cross-cultural communication, intercultural awareness, non-verbal behavior

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Authors: Nima Mahmoudi, Hamzeh Khazaei

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Serving machine learning inference workloads on the cloud is still a challenging task at the production level. The optimal configuration of the inference workload to meet SLA requirements while optimizing the infrastructure costs is highly complicated due to the complex interaction between batch configuration, resource configurations, and variable arrival process. Serverless computing has emerged in recent years to automate most infrastructure management tasks. Workload batching has revealed the potential to improve the response time and cost-effectiveness of machine learning serving workloads. However, it has not yet been supported out of the box by serverless computing platforms. Our experiments have shown that for various machine learning workloads, batching can hugely improve the system’s efficiency by reducing the processing overhead per request. In this work, we present MLProxy, an adaptive reverse proxy to support efficient machine learning serving workloads on serverless computing systems. MLProxy supports adaptive batching to ensure SLA compliance while optimizing serverless costs. We performed rigorous experiments on Knative to demonstrate the effectiveness of MLProxy. We showed that MLProxy could reduce the cost of serverless deployment by up to 92% while reducing SLA violations by up to 99% that can be generalized across state-of-the-art model serving frameworks.

Keywords: serverless computing, machine learning, inference serving, Knative, google cloud run, optimization

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Authors: Huda Al-Shuaily, E. M. Lacap

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Higher Education is one of the most essential fundamentals for the advancement and progress of a country. It demands to be as accessible as possible and as comprehensive as it can be reached. In this paper, we succeeded to expand the accessibility and delivery of higher education using an Open Educational Resources (OER), a freely accessible, openly licensed documents, and media for teaching and learning. This study creates a comparative design of student’s academic performance on the course Introduction to Database and student engagement to the virtual learning environment (VLE). The study was done in two successive semesters - one without using the OER and the other is using OER. In the study, we established that there is a significant increase in student’s engagement in VLE in the latter semester compared to the former. By using the latter semester’s data, we manage to show that the student’s engagement has a positive impact on students’ academic performance. Moreso, after clustering their academic performance, the impact is seen higher for students who are low performing. The results show that these engagements can be used to potentially predict the learning styles of the student with a high degree of precision.

Keywords: EDM, learning analytics, moodle, OER, student-engagement

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Authors: Meduranda Marco

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This paper aimed to discuss the Integrative Assessment (IA) initiative of the Schools Division Office - Navotas City. The introduction provided a brief landscape analysis of the current state of education, the context of SDO Navotas, and the rationale for the administration of Integrative Assessment (IA) in schools. The IA methodology, procedure, and implementation activities were also shared. Feedback and reports on IA showed positive results as all schools in the Division were able to operationalize IA and consequently foster academic ease for learners and parents. Challenges met after compliance were also documented and strategies to continuously improve the Integrative Assessment process were proposed.

Keywords: distance learning assessment, integrative assessment, academic ease, learning outcomes evaluation

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Authors: Abdullahi Bn Umar

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Over the years curriculum planners and researchers in education have continued to seek for ways to improve teaching and learning by way of varying approaches to curriculum and instruction in line with dynamic nature of knowledge. In this regards various innovative strategies to teaching and learning have been adopted to match with the technological advancement in education particularly in the aspect of instructional delivery through Information Communication Technology (ICT) as a tools. This paper reviews some innovative strategies and how they impact on learner’s achievement and educational development in Nigeria. The paper concludes by recommending innovative approach appropriate for use in Nigerian context.

Keywords: innovation, instructional delivery, virtual laboratory, educational design

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Authors: Hang T. T. Tran, Ha T. Le, Hanh T. P. Tran, Hung V. Cao, Giang T. H. Nguyen, Dien M. Tran, Tobias Alfvén, Linus Olson

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Background: Hypoxic Ischemic Encephalopathy is one of the major causes of neonatal death and those who survive with severe encephalopathy are more likely to develop adverse long-term outcomes such as neurocognitive impairment and cerebral palsy, which is a huge burden, especially in low-middle income countries. It is important to have a long-term follow-up for early detection and promote early intervention for these groups of high-risk infants. Aim: To determine the neurological outcome of cooling infants at 18 months and identify an optimized neurological examination scale for Hypoxic Ischemic Encephalopathy infants in Vietnam. Method: Descriptive study of neurodevelopmental outcomes at 18 months of HIE infants who underwent therapeutic hypothermia treatment in Vietnam. All survived cooling infants were assessed at discharge and at 6, 12, and 18 months by a pediatric physical therapist and a neurologist using two assessment tools: Ages and Stages Questionnaires and the Hammersmith Infant Neurological Examination scale to detect impairments and promote early intervention for those who require it. Results: During a 3-year period, a total of 130 neonates with moderate to severe HIE underwent therapeutic hypothermia treatment using Phase change material mattress (65% moderate, 35% severe – Sarnat). 43 (33%) died during hospitalization and infancy; among survivors, 69 (79%) completed 3 follow-ups at 18 months. At 18 months, 25 had cerebral palsy, 11 had mild delayed neurodevelopment. At each time-point, infants with a normal/mildly delayed neurodevelopment had significantly higher Ages and Stages Questionnaires and Hammersmith Infant Neurological Examination scores (p<0.05) than those with cerebral palsy. Conclusion: The study showed that the Ages and Stages Questionnaires and Hammersmith Infant Neurological Examination is a helpful tool in the process of early diagnosis of infants at low and high neurological risk and identifying those infants needing specific rehabilitation programme.

Keywords: encephalopathy, phase-change-material, neurodevelopment, cerebral palsy

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Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

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Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

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Authors: Bahia Braktia, Ana Marcela Montenegro Sanchez

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Social media, such as Facebook, Twitter, and YouTube, has been used for teaching and learning for quite some time. These platforms have been proven to be a good tool to improve various language skills, students’ performance of the English language, motivation as well as trigger the authentic language interaction. However, little is known about the potential effects of social media usage on the learning performance of Arabic language learners. The present study explores the potential role that the social media technologies play in learning Arabic as a foreign language at a university in Southeast of United States. In order to investigate this issue, an online survey was administered to examine the perceptions and attitudes of American students learning Arabic. The research questions were: How does social media, specifically Facebook and Twitter, impact the students' Arabic language skills, and what is their attitude toward it? The preliminary findings of the study showed that students had a positive attitude toward the use of social media to enhance their Arabic language skills, and that they used a range of social media features to expose themselves to the Arabic language and communicate in Arabic with native Arabic speaking friends. More detailed findings will be shared in the light data analysis with the audience during the presentation.

Keywords: foreign language learning, social media, students’ perceptions, survey

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Authors: Beng Yew Low, Cher Liang Cha, Cheng Yong Teoh

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Due to a lack of continual assessment or grade related data, identifying first-year engineering students in a polytechnic education at risk of failing is challenging. Our experience over the years tells us that there is no strong correlation between having good entry grades in Mathematics and the Sciences and excelling in hardcore engineering subjects. Hence, identifying students at risk of failure cannot be on the basis of entry grades in Mathematics and the Sciences alone. These factors compound the difficulty of early identification and intervention. This paper describes the development of a predictive analytics model in the early detection of students at risk of failing and evaluates its effectiveness. Data from continual assessments conducted in term one, supplemented by data of student psychological profiles such as interests and study habits, were used. Three classification techniques, namely Logistic Regression, K Nearest Neighbour, and Random Forest, were used in our predictive model. Based on our findings, Random Forest was determined to be the strongest predictor with an Area Under the Curve (AUC) value of 0.994. Correspondingly, the Accuracy, Precision, Recall, and F-Score were also highest among these three classifiers. Using this Random Forest Classification technique, students at risk of failure could be identified at the end of term one. They could then be assigned to a Learning Support Programme at the beginning of term two. This paper gathers the results of our findings. It also proposes further improvements that can be made to the model.

Keywords: continual assessment, predictive analytics, random forest, student psychological profile

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Authors: Robert John Godby

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To be responsive now and in the future, workplaces must address the demands of multicultural teams as they become more common elements of the global labor force. This is especially the case for aged care due to the aging population, industry growth and migrant recruitment. This research identifies influences on and improvements for learning in these environments. Its unique contribution is to illuminate how culturally diverse workplaces can work and learn together more effectively. A mixed-methods approach was used to gather data about this topic in two phases. Firstly, the research methods included a survey of 102 aged care workers around Australia from two multi-site aged care organisations. The questionnaire elicited both quantitative and qualitative data about worker characteristics and perspectives on working and learning in aged care. Secondly, a case study of one aged care worksite was formulated drawing on worksite information and interviews with workers. A review of the literature suggests that learning in multicultural work environments is influenced by three main factors: 1) the individual workers themselves, 2) their interaction with each other and 3) the environment in which they work. There are various accounts of these three factors, how they are manifested and how they lead to a change in workers’ disposition, knowledge, or expertise when confronted with new circumstances. The study has found that a key individual factor influencing learning is cultural background. Their unique view of the world was shown to affect their approach to both their work and co-working. Interactional factors suggest that the high requirement for collaboration in aged care positively supports learning in this context; however, it can be hindered by cultural bias and spoken accent. The study also found that environmental factors, such as disruptions caused by the pandemic, were another key influence. For example, the need to wear face masks hindered the communication needed for workplace learning. This was especially challenging due to the diverse language backgrounds and abilities within the teams. Potential improvements for learning in multicultural aged care work environments were identified. These include more frequent and structured inter-peer learning (e.g. buddying), communication training (e.g. English language usage for both native and non-native speaking workers) and support for cross-cultural habitude (e.g. recognizing and adapting to cultural differences). Workplace learning in cross-cultural aged care environments is an area that is not extensively dealt with in the literature. This study addresses this gap and holds the potential to contribute practical insights to aged care and other diverse industries.

Keywords: cross-cultural learning, learning in aged care, migrant learning, workplace learning

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Authors: Oluwaseun Ijiwade, Chris Davison, Kelvin Gregory

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In Australia, like other parts of the world, the debate on how to enhance teachers using assessment data to inform teaching and learning of English as an Additional Language (EAL, Australia) or English as a Foreign Language (EFL, United States) have occupied the centre of academic scholarship. Traditionally, this approach was conceptualised as ‘Formative Assessment’ and, in recent times, ‘Assessment for Learning (AfL)’. The central problem is that teacher-made tests are limited in providing data that can inform teaching and learning due to variability of classroom assessments, which are hindered by teachers’ characteristics and assessment literacy. To address this concern, scholars in language education and testing have proposed a uniformed large-scale computer-based assessment program to meet the needs of teachers and promote AfL in language education. In Australia, for instance, the Victoria state government commissioned a large-scale project called 'Tools to Enhance Assessment Literacy (TEAL) for Teachers of English as an additional language'. As part of the TEAL project, a tool called ‘Reading and Vocabulary assessment for English as an Additional Language (RVEAL)’, as a diagnostic language assessment (DLA), was developed by language experts at the University of New South Wales for teachers in Victorian schools to guide EAL pedagogy in the classroom. Therefore, this study aims to provide qualitative evidence for understanding beliefs about the diagnostic language assessment (DLA) among EAL teachers in primary and secondary schools in Victoria, Australia. To realize this goal, this study raises the following questions: (a) How do teachers use large-scale assessment data for diagnostic purposes? (b) What skills do language teachers think are necessary for using assessment data for instruction in the classroom? and (c) What factors, if any, contribute to teachers’ beliefs about diagnostic assessment in a large-scale assessment? Semi-structured interview method was used to collect data from at least 15 professional teachers who were selected through a purposeful sampling. The findings from the resulting data analysis (thematic analysis) provide an understanding of teachers’ beliefs about DLA in a classroom context and identify how these beliefs are crystallised in language teachers. The discussion shows how the findings can be used to inform professional development processes for language teachers as well as informing important factor of teacher cognition in the pedagogic processes of language assessment. This, hopefully, will help test developers and testing organisations to align the outcome of this study with their test development processes to design assessment that can enhance AfL in language education.

Keywords: beliefs, diagnostic language assessment, English as an additional language, teacher cognition

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Authors: Kwok W. Mui, Ling T. Wong, Tsz W. Tsang

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Many Hong Kong people nowadays spend most of their lifetime working indoor. Since poor Indoor Air Quality (IAQ) potentially leads to discomfort, ill health, low productivity and even absenteeism in workplaces, a call for establishing statutory IAQ control to safeguard the well-being of residents is urgently required. Although policies, strategies, and guidelines for workplace IAQ diagnosis have been developed elsewhere and followed with remedial works, some of those workplaces or buildings have relatively late stage of the IAQ problems when the investigation or remedial work started. Screening for IAQ problems should be initiated as it will provide information as a minimum provision of IAQ baseline requisite to the resolution of the problems. It is not practical to sample all air pollutants that exit. Nevertheless, as a statutory control, reliable, rapid screening is essential in accordance with a compromise strategy, which balances costs against detection of key pollutants. This study investigates the feasibility of using an IAQ index as a parameter of IAQ control in Hong Kong. The index is a screening parameter to identify the unsatisfactory workplace IAQ and will highlight where a fully effective IAQ monitoring and assessment is needed for an intensive diagnosis. There already exist a number of representative common indoor pollutants based on some extensive IAQ assessments. The selection of pollutants is surrogate to IAQ control consists of dilution, mitigation, and emission control. The IAQ Index and assessment will look at high fractional quantities of these common measurement parameters. With the support of the existing comprehensive regional IAQ database and the IAQ Index by the research team as the pre-assessment probability, and the unsatisfactory IAQ prevalence as the post-assessment probability from this study, thresholds of maintaining the current measures and performing a further IAQ test or IAQ remedial measures will be proposed. With justified resources, the proposed IAQ Index and assessment protocol might be a useful tool for setting up a practical public IAQ surveillance programme and policy in Hong Kong.

Keywords: assessment, index, indoor air quality, surveillance programme

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Authors: Inje Cho

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The leading health risks threatening the world today are associated with a modern lifestyle characterized by sedentary behavior, stress, anxiety, and an obesogenic food environment. To counter this alarming trend, the Centers for Disease Control and Prevention have proffered Physical Activity guidelines to bolster physical engagement. Concurrently, the burgeon of smartphones and mobile applications has witnessed a proliferation of fitness applications aimed at invigorating exercise adherence and real-time activity monitoring. Grounded in the Uses and gratification theory, this study delves into the technological affordances of mobile fitness applications, discerning the mediating influences of escapism and social outcome expectations on attitudes and exercise intention. The theory explains how individuals employ distinct communication mediums to satiate their exigencies and desires. Technological affordances manifest as attributes of emerging technologies that galvanize personal engagement in physical activities. Several features of mobile fitness applications include affordances for goal setting, virtual rewards, peer support, and exercise information. Escapism, denoting the inclination to disengage from normal routines, has emerged as a salient motivator for the consumption of new media. This study postulates that individual’s perceptions technological affordances within mobile fitness applications, can affect escapism and social outcome expectations, potentially influencing attitude, and behavior formation. Thus, the integrated model has been developed to empirically examine the interrelationships between technological affordances, escapism, social outcome expectations, and exercise intention. Structural Equation Modelling serves as the methodological tool, and a cohort of 400 Fitbit users shall be enlisted from the Prolific, data collection platform. A sequence of multivariate data analyses will scrutinize both the measurement and hypothesized structural models. By delving into the effects of mobile fitness applications, this study contributes to the growing of new media studies in sport management. Moreover, the novel integration of the uses and gratification theory, technological affordances, via the prism of escapism, illustrates the dynamics that underlies mobile fitness user’s attitudes and behavioral intentions. Therefore, the findings from this study contribute to theoretical understanding and provide pragmatic insights to developers and practitioners in optimizing the impact of mobile fitness applications.

Keywords: technological affordances, uses and gratification, mobile fitness apps, escapism, physical activity

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Authors: C.W. Kan

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Softening applied on textile products based on several reasons. First, the synthetic detergent removes natural oils and waxes, thus lose the softness. Second, compensate the harsh handle of resin finishing. Also, imitate natural fibres and improve the comfort of fabric are the reasons to apply softening. There are different types of softeners for softening finishing of textiles, nonionic softener, anionic softener, cationic softener and silicone softener. The aim of this study is to illustrate the proper application of different softeners and their final softening effect in textiles. The results could also provide guidance note to the students in learning this topic. Acknowledgment: Authors would like to thank the financial support from the Hong Kong Polytechnic University for this work.

Keywords: learning materials, softening, textiles, effect

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Authors: Imen Elleuch, Wael Ouarda, Gargouri Bilel

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We proposes in this paper a Deep Learning based approach to classify text in order to enrich an Arabic ontology based on the objects classes of Gaston Gross. Those object classes are defined by taking into account the syntactic and semantic features of the treated language. Thus, our proposed approach is a hybrid one. In fact, it is based on the one hand on the object classes that represents a knowledge based-approach on classification of text and in the other hand it uses the deep learning approach that use the word embedding-based-approach to classify text. We have applied our proposed approach on a corpus constructed from an Arabic dictionary. The obtained semantic classification of text will enrich the Arabic objects classes ontology. In fact, new classes can be added to the ontology or an expansion of the features that characterizes each object class can be updated. The obtained results are compared to a similar work that treats the same object with a classical linguistic approach for the semantic classification of text. This comparison highlight our hybrid proposed approach that can be ameliorated by broaden the dataset used in the deep learning process.

Keywords: deep-learning approach, object-classes, semantic classification, Arabic

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Authors: Maiya Smith, Tyler Thorne

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Background: The Pacific faces multiple healthcare crises, including high rates of noncommunicable diseases, infectious disease outbreaks, and susceptibility to natural disasters. These issues are expected to worsen in the coming decades, increasing the burden on an already understaffed healthcare system. Telehealth is not new to the Pacific, but improvements in technology and accessibility have increased its utility and have already proven to reduce costs and increase access to care in remote areas. Telehealth includes distance learning; a form of education that can help alleviate many healthcare issues by providing continuing education to healthcare professionals and upskilling staff, while decreasing costs. This study examined distance learning programs at the Ministry of Health in the Pacific nation of Palau and identified key elements to their successful distance learning programs. Methods: Staff at the Belau National Hospital in Koror, Palau as well as private practitioners were interviewed to assess distance learning programs utilized. This included physicians, IT personnel, public health members, and department managers of allied health. In total, 36 people were interviewed. Standardized questions and surveys were conducted in person throughout the month of July 2019. Results: Two examples of successful distance learning programs were identified. Looking at the factors that made these programs successful, as well as consulting with staff who undertook other distance learning programs, four factors for success were determined: having a cohort, having a facilitator, dedicated study time off from work, and motivation. Discussion: In countries as geographically isolated as the Pacific, with poor access to specialists and resources, telehealth has the potential to radically change how healthcare is delivered. Palau shares similar resources and issues as other countries in the Pacific and the lessons learned from their successful programs can be adapted to help other Pacific nations develop their own distance learning programs.

Keywords: distance learning, Pacific, Palau, telehealth

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Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

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In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

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Authors: Slava Kalyuga

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There are two opposing major points of view on the optimal degree of initial instructional guidance that is usually discussed in the literature by the advocates of the corresponding learning approaches. Using unguided or minimally guided problem-solving tasks prior to explicit instruction has been suggested by productive failure and several other instructional theories, whereas an alternative approach - using fully guided worked examples followed by problem solving - has been demonstrated as the most effective strategy within the framework of cognitive load theory. An integrated approach discussed in this paper could combine the above frameworks within a broader theoretical perspective which would allow bringing together their best features and advantages in the design of learning tasks for STEM education. This paper represents a systematic review of the available empirical studies comparing the above alternative sequences of instructional methods to explore effects of several possible moderating factors. The paper concludes that different approaches and instructional sequences should coexist within complex learning environments. Selecting optimal sequences depends on such factors as specific goals of learner activities, types of knowledge to learn, levels of element interactivity (task complexity), and levels of learner prior knowledge. This paper offers an outline of a theoretical framework for the design of complex learning tasks in STEM education that would integrate explicit instruction and inquiry (exploratory, discovery) learning approaches in ways that depend on a set of defined specific factors.

Keywords: cognitive load, explicit instruction, exploratory learning, worked examples

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Authors: Jonathan Frank, Michelle Salmona

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We conducted quantitative and qualitative analyses of nurses’ perceived ease of use of electronic medical records and telemedicine in the Cook Islands. We examined antecedents of perceived ease of use through the lens of social construction of learning, and cultural diffusion. Our findings confirmed expected linkages between PEOU, attitudes and intentions. Interviews with nurses suggested infrastructural barriers to engaged learning. We discussed managerial implications of our findings, and areas of interest for future research.

Keywords: health information technology, ICT4D, TAM, developing countries

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Authors: Fiseha M. Guangul, Annissa Muhammed, Aja O. Chikere

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Group task is one method to create the conducive environment for the active teaching-learning process. Performing group task with active involvement of students will benefit the students in many ways. However, in most cases all students do not participate actively in the group task, and hence the intended benefits are not acquired. This paper presents the improvements of students’ participation in the group task and learning from the group task by introducing different techniques to enhance students’ participation. For the purpose of this research Carpentry and Joinery II (WT-392) course from Wood Technology Department at Adama Science and Technology University was selected, and five groups were formed. Ten group tasks were prepared and the first five group tasks were distributed to the five groups in the first day without introducing the techniques that are used to enhance participation of students in the group task. On another day, the other five group tasks were distributed to the same groups and various techniques were introduced to enhance students’ participation in the group task. The improvements of students’ learning from the group task after the implementation of the techniques. After implementing the techniques the evaluation showed that significant improvements were obtained in the students’ participation and learning from the group task.

Keywords: group task, students participation, active learning, the evaluation method

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Authors: Beulah Rani Inbanathan

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In the present era, it is vivid from the numerous outcomes that data privacy is being compromised in various ways. Machine learning is one technology that uses the centralized server, and then data is given as input which is being analyzed by the algorithms present on this mentioned server, and hence outputs are predicted. However, each time the data must be sent by the user as the algorithm will analyze the input data in order to predict the output, which is prone to threats. The solution to overcome this issue is federated learning, where the models alone get updated while the data resides on the local machine and does not get exchanged with the other local models. Nevertheless, even on these local models, there are chances of data poisoning, and it is crystal clear from various experiments done by many people. This paper delves into many ways where data poisoning occurs and the many methods through which it is prevalent that data poisoning still exists. It includes the poisoning attacks on IoT devices, Edge devices, Autoregressive model, and also, on Industrial IoT systems and also, few points on how these could be evadible in order to protect our data which is personal, or sensitive, or harmful when exposed.

Keywords: data poisoning, federated learning, Internet of Things, edge computing

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Authors: Vasilis C. Hristidis, Ricardo Caceda, Ji Soo Kim, Brian Bronson, Emily A. Hill

Abstract:

Objective: Leave passes to provide authorized leave for hospitalized patients from a psychiatric inpatient unit. Though providing day passes was once a relatively common practice, there is relatively little data describing their safety and efficacy. Methods: This descriptive study examines the use of leave passes in an adult inpatient unit at a university hospital between 2017 and 2021, with attention to reasons for granting the day pass, duration, and outcome of the pass. Results: During the study period, ten patients with primary psychotic or mood disorders received 12 passes for either housing coordination, COVID-19 vaccination, or major family events. There were no fatalities or elopements. One patient experienced severe agitation and engaged in non-suicidal self-injurious behavior. A second patient showed mild, redirectable psychomotor agitation upon return to the unit. The remaining 10 passes were uneventful. Conclusions: Our findings support the view that patients with diverse diagnoses can safely be provided leave from an inpatient setting with adequate planning and support, yielding a low incidence of adverse events.

Keywords: passes, inpatient, psychiatry, inpatient leave, outcome

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Authors: Golnush Masghati-Amoli, Paul Chin

Abstract:

Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

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Authors: Om Viroje

Abstract:

Quantum machine learning represents a frontier in computational technology, promising significant advancements in data processing capabilities. This study explores the significance of data encoding techniques, specifically amplitude and phase encoding, in this emerging field. By employing a comparative analysis methodology, the research evaluates how these encoding techniques affect the accuracy, efficiency, and noise resilience of quantum algorithms. Our findings reveal that amplitude encoding enhances algorithmic accuracy and noise tolerance, whereas phase encoding significantly boosts computational efficiency. These insights are crucial for developing robust quantum frameworks that can be effectively applied in real-world scenarios. In conclusion, optimizing encoding strategies is essential for advancing quantum machine learning, potentially transforming various industries through improved data processing and analysis.

Keywords: quantum machine learning, data encoding, amplitude encoding, phase encoding, noise resilience

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Authors: Yi-Cheng Tian, Miin-Shen Yang

Abstract:

The mixture likelihood approach to clustering is a popular clustering method where the expectation and maximization (EM) algorithm is the most used mixture likelihood method. In the literature, the EM algorithm had been used for mixture regression models. However, these EM mixture regression algorithms are sensitive to initial values with a priori number of clusters. In this paper, to resolve these drawbacks, we construct a learning-based schema for the EM mixture regression algorithm such that it is free of initializations and can automatically obtain an approximately optimal number of clusters. Some numerical examples and comparisons demonstrate the superiority and usefulness of the proposed learning-based EM mixture regression algorithm.

Keywords: clustering, EM algorithm, Gaussian mixture model, mixture regression model

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Authors: Samar Alharbi, Yota Dimitriadi

Abstract:

The literature on educational technology in Saudi Arabia reveals female learners’ unwillingness to study fully online courses in higher education despite the fact that Saudi universities have offered a variety of online degree programmes to undergraduate students in many regions of the country. The root causes keeping female students from successfully learning in online environments are limited social interaction, lack of motivation and difficulty with the use of e-learning platforms. E-immediacy remains an important method of online teaching to enhance students’ interaction and support their online learning. This study explored Saudi female students’ perceptions, as well as the experiences of lecturers’ immediacy behaviours in online environments, who participate in fully online courses using Blackboard at a Saudi university. Data were collected through interviews with focus groups. The three focus groups included five to seven students each. The female participants were asked about lecturers’ e-immediacy behaviours and which e-immediacy behaviours were important for an effective learning environment. A thematic analysis of the data revealed three main themes: the encouragement of student interaction, the incorporation of social media and addressing the needs of students. These findings provide lecturers with insights into instructional designs and strategies that can be adopted in using e-immediacy in effective ways, thus improving female learners’ interactions as well as their online learning experiences.

Keywords: e-learning, female students, higher education, immediacy

Procedia PDF Downloads 346