Search results for: teacher learning

Authors: Shimaa Nagro, Russell Campion

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Mobile devices are becoming ever more widely available, with growing functionality, and are increasingly used as an enabling technology to give students access to educational material anytime and anywhere. However, the design of educational material user interfaces for mobile devices is beset by many unresolved research issues such as those arising from emphasising the information concepts then mapping this information to appropriate media (modelling information then mapping media effectively). This report describes a multimedia user interface design method for mobile learning. The method covers specification of user requirements and information architecture, media selection to represent the information content, design for directing attention to important information, and interaction design to enhance user engagement based on Human-Computer Interaction design strategies (HCI). The method will be evaluated by three different case studies to prove the method is suitable for application to different areas / applications, these are; an application to teach about major computer networking concepts, an application to deliver a history-based topic; (after these case studies have been completed, the method will be revised to remove deficiencies and then used to develop a third case study), an application to teach mathematical principles. At this point, the method will again be revised into its final format. A usability evaluation will be carried out to measure the usefulness and effectiveness of the method. The investigation will combine qualitative and quantitative methods, including interviews and questionnaires for data collection and three case studies for validating the MDMLM method. The researcher has successfully produced the method at this point which is now under validation and testing procedures. From this point forward in the report, the researcher will refer to the method using the MDMLM abbreviation which means Multimedia Design Mobile Learning Method.

Keywords: human-computer interaction, interface design, mobile learning, education

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Authors: Shrabani Ghosh

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Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques.

Keywords: attention learning, language model, offensive language detection, self-supervised learning

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Authors: Isabel Medina, Mario Duran

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Although Problem-based learning provides students with multiple opportunities for rigorous instructional experiences in which students are challenged to address problems in the community; there are still gaps in connecting community leaders to the PBL process. At a south Texas high school, community participation serves as an integral component of the PBL process. Problem-based learning (PBL) has recently gained momentum due to the increase in global communities that value collaboration and critical thinking. As an instructional approach, PBL engages high school students in meaningful learning experiences. Furthermore, PBL focuses on providing students with a connection to real-world situations that require effective peer collaboration. For PBL leaders, providing students with a meaningful process is as important as the final PBL outcome. To achieve this goal, STEM high school strategically created a space for community involvement to be woven within the PBL fabric. This study examines the impact community members had on PBL students attending a STEM high school in South Texas. At STEM High School, community members represent a support system that works through the PBL process to ensure students receive real-life mentoring from business and industry leaders situated in the community. A phenomenological study using a semi-structured approach was used to collect data about students’ perception of community involvement within the PBL process for one South Texas high school. In our proposed presentation, we will discuss how community involvement in the PBL process academically impacted the educational experience of high school students at STEM high school. We address the instructional concerns PBL critics have with the lack of direct instruction, by providing a representation of how STEM high school utilizes community members to assist in impacting the academic experience of students.

Keywords: phenomenological, STEM education, student engagement, community involvement

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Authors: Natalia Efremova, Sergey Tarasenko

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We propose a novel neural network architecture for visual saliency detections, which utilizes neuro physiologically plausible mechanisms for extraction of salient regions. The model has been significantly inspired by recent findings from neuro physiology and aimed to simulate the bottom-up processes of human selective attention. Two types of features were analyzed: color and direction of maximum variance. The mechanism we employ for processing those features is PCA, implemented by means of normalized Hebbian learning and the waves of spikes. To evaluate performance of our model we have conducted psychological experiment. Comparison of simulation results with those of experiment indicates good performance of our model.

Keywords: neural network models, visual saliency detection, normalized Hebbian learning, Oja's rule, psychological experiment

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Authors: Michael Johnson, Vincenzo Oliveri

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This work considers an interdisciplinary Problem-Based Learning (PBL) project developed by lecturers from the Aeronautical and Electronic and Computer Engineering departments at the University of Limerick. This “CANSAT” project utilises the CanSat can-sized satellite platform in order to allow students from aeronautical and electronic engineering to engage in a mixed format (online/face-to-face), interdisciplinary PBL assignment using a real-world platform and application. The project introduces students to the design, development, and construction of the CanSat system over the course of a single semester, enabling student(s) to apply their aeronautical and technical skills/capabilities to the realisation of a working CanSat system. In this case study, the CanSat kits are used to pivot the real-world, discipline-relevant PBL goal of designing, building, and testing the CanSat system with payload(s) from a traditional module-based setting to an online PBL setting. Feedback, impressions, benefits, and challenges identified through the semester are presented. Students found the project to be interesting and rewarding, with the interdisciplinary nature of the project appealing to them. Challenges and difficulties encountered are also addressed, with solutions developed between the students and facilitators to overcoming these discussed.

Keywords: problem-based learning, interdisciplinary, engineering, CanSATs

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Authors: Loren Clarke, Katie Reed

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The integration of artificial intelligence (AI) in high school education assessment offers transformative potential, providing more personalized, timely, and accurate evaluations of student performance. However, the successful adoption of AI-driven assessment systems requires robust change management strategies to navigate the complexities and resistance that often accompany such technological shifts. This presentation explores effective methods for implementing AI in high school assessment, emphasizing the need for strategic planning and stakeholder engagement. Focusing on a case study of a Victorian high school, it will examine the practical steps taken to integrate AI into teaching and learning. This school has developed innovative processes to support academic integrity and foster authentic cogeneration with AI, ensuring that the technology is used ethically and effectively. By creating comprehensive professional development programs for teachers and maintaining transparent communication with students and parents, the school has successfully aligned AI technologies with their existing curricula and assessment frameworks. The session will highlight how AI has enhanced both formative and summative assessments, providing real-time feedback that supports differentiated instruction and fosters a more personalized learning experience. Participants will learn about best practices for managing the integration of AI in high school settings while maintaining a focus on equity and student-centered learning. This presentation aims to equip high school educators with the insights and tools needed to effectively manage the integration of AI in assessment, ultimately improving educational outcomes and preparing students for future success. Methodologies: The research is a case study of a Victorian high school to examine AI integration in assessments, focusing on practical implementation steps, ethical practices, and change management strategies to enhance personalized learning and assessment. Outcomes: This research explores AI integration in high school assessments, focusing on personalized evaluations, ethical use, and change management. A Victorian school case study highlights best practices to enhance assessments and improve student outcomes. Main Contributions: This research contributes by outlining effective AI integration in assessments, showcasing a Victorian school's implementation, and providing best practices for ethical use, change management, and enhancing personalized learning outcomes.

Keywords: artificial intelligence, assessment, curriculum design, teaching and learning, ai in education

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Authors: Georges Bou Ghantous

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This literature review examines the transformative impact of Artificial Intelligence (AI) on higher education, highlighting both the potential benefits and challenges associated with its adoption. The review reveals that AI significantly enhances personalized learning by tailoring educational experiences to individual student needs, thereby boosting engagement and learning outcomes. Automated grading systems streamline assessment processes, allowing educators to focus on improving instructional quality and student interaction. AI's data-driven insights provide valuable analytics, helping educators identify trends in at-risk students and refine teaching strategies. Moreover, AI promotes enhanced instructional innovation through the adoption of advanced teaching methods and technologies, enriching the educational environment. Administrative efficiency is also improved as AI automates routine tasks, freeing up time for educators to engage in research and curriculum development. However, the review also addresses the challenges that accompany AI integration, such as data privacy concerns, algorithmic bias, dependency on technology, reduced human interaction, and ethical dilemmas. This balanced exploration underscores the need for careful consideration of both the advantages and potential hurdles in the implementation of AI in higher education.

Keywords: administrative efficiency, data-driven insights, data privacy, ethical dilemmas, higher education, personalized learning

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Authors: Leorisyl D. Siarot

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It is conventional to see and hear Filipinos, in general, speak two or more languages. This phenomenon brings us to a closer look on how our minds process the input and produce an output with a specific chosen language. This study aimed to generate a theoretical model which explained the interaction of the first and the second languages in the human mind. After a careful analysis of the gathered data, a theoretical prototype called Language Activation Model was generated. For every string, there are three specialized banks: lexico-semantics, morphono-syntax, and pragmatics. These banks are interrelated to other banks of other language strings. As the bilingual learns more languages, a new string is replicated and is filled up with the information of the new language learned. The principles of the first and second languages' interaction are drawn; these are expressed in laws, namely: law of dominance, law of availability, law of usuality and law of preference. Furthermore, difficulties encountered in the learning of second languages were also determined.

Keywords: bilingualism, psycholinguistics, second language learning, languages

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Authors: Newton Muhury, Armando A. Apan, Tek N. Marasani, Gebiaw T. Ayele

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The changing climate has degraded freshwater availability in Australia that influencing vegetation growth to a great extent. This study assessed the vegetation responses to groundwater using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). A hydrological model, SWAT, has been set up in a southeast Australian river catchment for groundwater analysis. The model was calibrated and validated against monthly streamflow from 2001 to 2006 and 2007 to 2010, respectively. The SWAT simulated soil water content for 43 sub-basins and monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) were applied in the machine learning tool, Waikato Environment for Knowledge Analysis (WEKA), using two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The assessment shows that different types of vegetation response and soil water content vary in the dry and wet seasons. The WEKA model generated high positive relationships (r = 0.76, 0.73, and 0.81) between NDVI values of all vegetation in the sub-basins against soil water content (SWC), the groundwater flow (GW), and the combination of these two variables, respectively, during the dry season. However, these responses were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: Arian Hosseini, Mahmudul Hasan

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To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based on a combination of simple visual features, designed for high-accuracy classification of violent content with low false positives. Our ensemble architecture utilizes a set of lightweight models with narrowed-down color features, and we apply it to both images and videos. We evaluated our approach using a large dataset of explosion and blast contents and compared its performance to popular deep learning models such as ResNet-50. Our evaluation results demonstrate significant improvements in prediction accuracy, while benefiting from 7.64x faster inference and lower computation cost. While our approach is tailored to explosion detection, it can be applied to other similar content moderation and violence detection use cases as well. Based on our experiments, we propose a "think small, think many" philosophy in classification scenarios. We argue that transforming a single, large, monolithic deep model into a verification-based step model ensemble of multiple small, simple, and lightweight models with narrowed-down visual features can possibly lead to predictions with higher accuracy.

Keywords: deep classification, content moderation, ensemble learning, explosion detection, video processing

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Authors: Aguiar-Castillo Lidia, Perez-Jimenez Rafael

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The integration of gamification in educational settings aims to enhance student engagement and motivation through game design elements in learning activities. This paper introduces "Gamipulation," the subtle manipulation of students via gamification techniques serving hidden agendas without explicit consent. It highlights the need to distinguish between beneficial and exploitative uses of gamification in education, focusing on its potential to psychologically manipulate students for purposes misaligned with their best interests. Through a literature review and expert interviews, this study presents a conceptual framework outlining gamipulation's features. It examines ethical concerns like gradually introducing desired behaviors, using distraction to divert attention from significant learning objectives, immediacy of rewards fostering short-term engagement over long-term learning, infantilization of students, and exploitation of emotional responses over reflective thinking. Additionally, it discusses ethical issues in collecting and utilizing student data within gamified environments.  Key findings suggest that while gamification can enhance motivation and engagement, there's a fine line between ethical motivation and unethical manipulation. The study emphasizes the importance of transparency, respect for student autonomy, and alignment with educational values in gamified systems. It calls for educators and designers to be aware of gamification's manipulative potential and strive for ethical implementation that benefits students. In conclusion, this paper provides a framework for educators and researchers to understand and address gamipulation's ethical challenges. It encourages developing ethical guidelines and practices to ensure gamification in education remains a tool for positive engagement and learning rather than covert manipulation.

Keywords: gradualness, distraction, immediacy, infantilization, emotion

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Authors: Hamed Zolfaghari, Mojtaba Kord

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After feasibility study the design phase is started and the rest of other phases are highly dependent on this phase. forecasting the duration of design phase could do a miracle and would save a lot of time. This study provides a fast and accurate Machine learning (ML) and optimization framework, which allows a quick duration estimation of project design phase, hence improving operational efficiency and competitiveness of a design construction company. 3 data sets of three years composed of daily time spent for different design projects are used to train and validate the ML models to perform multiple projects. Our study concluded that Artificial Neural Network (ANN) performed an accuracy of 0.94.

Keywords: time estimation, machine learning, Artificial neural network, project design phase

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Authors: Vladimira Hornackova, Jana Kottasova, Zuzana Vanova, Anna Jungrova

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Present study deals with drama education in preschool education. The research made in this field brings a qualitative comparative survey with the aim to find out the use of methods and techniques of drama education in preschool education at university or secondary school graduate preschool teachers. The research uses a content analysis and an unstandardized questionnaire for preschool teachers and obtained data are processed with the help of descriptive methods and correlations. The results allow a comparison of aspects applied through drama in preschool education. The research brings impulses for education improvement in kindergartens and inspiration for university study programs of drama education in the professional training of preschool teachers.

Keywords: drama education, preschool education, preschool teacher, research

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Authors: Sebastian Leal-Arenas

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TikTok’s influence on contemporary society is undeniable. The impact of the mobile app transcends entertainment, as shown by the growing presence of specialized accounts dedicated to providing educational content, particularly as it pertains to language learning. However, the prevailing trend on the platform is vocabulary and grammar acquisition, neglecting a critical component: pronunciation. This study examines English pronunciation materials available on TikTok by taking a comprehensive approach that incorporates established assessment tools, such as the Learning Object Review Instrument and the Framework for Language Learning App Evaluation. Furthermore, novel evaluation categories are introduced to provide a more holistic assessment of these educational resources. 60 English pronunciation videos were part of the analysis. The findings reveal that these audio-visual materials present clear audio bolstered by high-quality video content and automatically generated closed captions. These three components enhance the comprehensibility of the input, making these concise videos valuable assets for language learners. Nevertheless, certain deficiencies are observed, such as the lack of emphasis on specific segments and their relationship with articulators. Improvements and refinements are discussed, as well as their potential utility within the language classroom. This study contributes to the ongoing investigation of multimedia materials used for language teaching and emphasizes the need to adapt pronunciation instruction methods to today’s technology.

Keywords: pronunciation, segments, teaching materials, technology

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Authors: Felicia Darling

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In Yucatec Maya middle schools in the Yucatán, mathematics scores are low and high school dropout rates are high. While addressing larger social and economic causes is crucial, improving mathematics instruction is a feasible approach. This paper draws from a six-month ethnographic, mixed-method study documenting two cultural approaches to problem solving. It explores the extent to which middle school mathematics instruction capitalizes upon these cultural assets and pilots two real-life mathematics tasks that incorporate them. Findings add details to the school/community culture gap around mathematics knowledge and have implications for mathematics education for marginalized students in México and the US.

Keywords: math education, indigenous, Maya, cultural assets, secondary school, teacher education

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Authors: Tiptunya Duangsri, Panwilai Chomchid, Natchanok Jansawang

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This research study was to investigate of education decisions must be made which a part of it should be passed on to future generations as obligatory for all members of a chemistry class for students who will prepare themselves for a special position. The descriptions of instructional design were provided and the recent criticisms are discussed. This research study to an outline of an integrative framework for the description of information and the instructional design model give structure to negotiate a semblance of conscious understanding. The aims of this study are to describe the instructional design model for comparisons between students’ genders of their effects on STEM educational learning achievement motives to their science attitudes and logical thinking abilities with a sample size of 18 students at the 11th grade level with the cluster random sampling technique in Mahawichanukul School were designed. The chemistry learning environment was administered with the STEM education method. To build up the 5-instrument lesson instructional plan issues were instructed innovations, the 30-item Logical Thinking Test (LTT) on 5 scales, namely; Inference, Recognition of Assumptions, Deduction, Interpretation and Evaluation scales was used. Students’ responses of their perceptions with the Test Of Chemistry-Related Attitude (TOCRA) were assessed of their attitude in science toward chemistry. The validity from Index Objective Congruence value (IOC) checked by five expert specialist educator in two chemistry classroom targets in STEM education, the E1/E2 process were equaled evidence of 84.05/81.42 which results based on criteria are higher than of 80/80 standard level with the IOC from the expert educators. Comparisons between students’ learning achievement motives with STEM educational model on stoichiometry issue in chemistry classes with different genders were differentiated at evidence level of .05, significantly. Associations between students’ learning achievement motives on their posttest outcomes and logical thinking abilities, the predictive efficiency (R2) values indicate that 69% and 70% of the variances in different male and female student groups of their logical thinking abilities. The predictive efficiency (R2) values indicate that 73%; and 74% of the variances in different male and female student groups of their science attitudes toward chemistry were associated. Statistically significant on students’ perceptions of their chemistry learning classroom environment and their science attitude toward chemistry when using the MCI and TOCRA, the predictive efficiency (R2) values indicated that 72% and 74% of the variances in different male and female student groups of their chemistry classroom climate, consequently. Suggestions that supporting chemistry or science teachers from science, technology, engineering and mathematics (STEM) in addressing complex teaching and learning issues related instructional design to develop, teach, and assess traditional are important strategies with a focus on STEM education instructional method.

Keywords: development, the instructional design model, students learning achievement motives, science attitudes with STEM educational model, stoichiometry issue, chemistry classes, genders

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Authors: Hyo Jung Lee

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The purpose of this study is to develop a literature appreciation education program for classic literatures and apply them to the field, and to derive the achievements and improvement points. Classic literature is a work of value recognized in the context of literature history and culture history, and learners can develop interest in literature and inherit tradition through appreciation of classic literature. However, in Korean educational environment, classic literature is a means for college entrance examination, and many learners analyze contents and language in textbooks and concentrate on memorizing the whole plot. This study is one of the reasons that classic literature appreciation education is not done properly and it is not able to give an opportunity to appreciate the whole work in the early learning stage. In Korean primary education, classic literature is used as a means to achieve the goals of reading, writing, speaking and listening, rather than being used as a material for its own appreciation. It is problematic to make the piece appreciation experience fragmentary. This study proposes a program to experience classic literatures by linking school education and school library with primary school students in grades 4-6. We work with local primary schools (siheung-si, gyeonggi-do, Korea) to provide appropriate activities and rewards to learners, observe their participation, and introduce student learning outcomes. Through this, we are able to systematically improve the learner 's ability to appreciate the literature and to diversify primary education.

Keywords: classic literature education, primary education, out-of-school program, learning by appreciation experience

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Vishnu Pratap Singh Kirar

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The main aim of the communication system is to achieve maximum performance. In cognitive radio, any user or transceiver have the ability to sense best suitable channel, while the channel is not in use. It means an unlicensed user can share the spectrum of licensed user without any interference. Though the spectrum sensing consumes a large amount of energy and it can reduce by applying various artificial intelligent methods for determining proper spectrum holes. It also increases the efficiency of Cognitive Radio Network (CRN). In this survey paper, we discuss the use of different learning models and implementation of Artificial Neural Network (ANN) to increase the learning and decision-making capacity of CRN without affecting bandwidth, cost and signal rate.

Keywords: artificial neural network, cognitive radio, cognitive radio networks, back propagation, spectrum sensing

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Authors: Wong Seng Yue

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Game dynamics and game mechanics are the two main components that used in gamification to engage and encourage students to learn. The advantages of gamified classroom are engaging students, increasing students interest, preserving students focus and remain a positive behaviour. However, the empirical studies on gamification are still at early stage, especially the effectiveness of various gamification components have not been evaluated. Thus, this study is aimed to conduct a user experience (UX) evaluation on gamified classroom through Prezi, which focused on learning experience, gaming experience, adaptivity, and gameplay experience. This study is a further study extended from the previous exploratory study to explore more on UX of gamified classroom via Prezi by interview. A focus group study, which involves 22 students from a foundation course has been conducted for the study. Besides the empirical data from the previous study, this focus group study has significantly found that 90.9% respondents show their positive perceptions on gaming experience via Prezi. They are interested, feel fresh, good, and highly motivated of the contents of Prezi. 95.5% participants have had a positive learning experience from the gamified classroom via Prezi, which can engage them, made them concentrate on learning and easy to remember what they have learned if compared to the traditional classroom slides. The adaptivity of the gamified classroom also high due to its zooming user interface, narrative, rewards and engagement features. This study has uncovered on how far the impact of gamification components in the classroom, especially UX that implemented in gamified classroom.

Keywords: user experience (UX), gamification, gamified classroom, Prezi

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Authors: Kehabtimer Shiferaw Kotiso, Getachew Hailemariam, Abiy Seifu Estifanos

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Introduction: Despite the highest share of low birth weight (LBW) for neonatal mortality and morbidity, predicting births with LBW for better intervention preparation is challenging. This study aims to predict LBW using a dataset encompassing 53,637 birth cohorts collected from 36 primary hospitals across seven regions in Ethiopia from February 2022 to June 2024. Methods: We identified ten explanatory variables related to maternal and neonatal characteristics, including maternal education, age, residence, history of miscarriage or abortion, history of preterm birth, type of pregnancy, number of livebirths, number of stillbirths, antenatal care frequency, and sex of the fetus to predict LBW. Using WEKA 3.8.2, we developed and compared seven machine learning algorithms. Data preprocessing included handling missing values, outlier detection, and ensuring data integrity in birth weight records. Model performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the Receiver Operating Characteristic curve (ROC AUC) using 10-fold cross-validation. Results: The results demonstrated that the decision tree, J48, logistic regression, and gradient boosted trees model achieved the highest accuracy (94.5% to 94.6%) with a precision of 93.1% to 93.3%, F1-score of 92.7% to 93.1%, and ROC AUC of 71.8% to 76.6%. Conclusion: This study demonstrates the effectiveness of machine learning models in predicting LBW. The high accuracy and recall rates achieved indicate that these models can serve as valuable tools for healthcare policymakers and providers in identifying at-risk newborns and implementing timely interventions to achieve the sustainable developmental goal (SDG) related to neonatal mortality.

Keywords: low birth weight, machine learning, classification, neonatal mortality, Ethiopia

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Authors: Weidong Xia, Malgorzata Kolotylo, Xuan Tan

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This study examines the key factors that influence general practitioners’ learning and transfer of specialized arthritis knowledge and self-care techniques to patients during normal patient visits. Drawing on the theory of planed behavior and using matched survey data collected from general practitioners before and after training sessions provided by specialized orthopedic physicians, the study suggests that the general practitioner’s intention to use and transfer learned knowledge was influenced mainly by intrinsic motivation, organizational learning culture and absorptive capacity, but was not influenced by extrinsic motivation. The results provide both theoretical and practical implications.

Keywords: empirical study, healthcare knowledge management, patient self-care, physician knowledge transfer

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Authors: Ramona-Niculina Jurcau, Ioana-Marieta Jurcau

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Background: Preparing in the stress field (SF) is, increasingly, a concern for doctors of different specialties. Aims: The aim was to evaluate the importance of an e-learning program for doctors postgraduate courses, in SF. Methods: Doctors (n= 40 male, 40 female) of different specialties and ages (31-71 years), who attended postgraduate courses in SF, voluntarily responded to a questionnaire that included the following themes: Importance of SF courses for specialty practiced by each respondent doctor (using visual analogue scale, VAS); What SF themes would be indicated as e-learning (EL); Preferred form of SF information assimilation: Classical lectures (CL), EL or a combination of these methods (CL+EL); Which information on the SF course are facilitated by EL model versus CL; In their view which are the first four advantages and the first four disadvantages of EL compared to CL, for SF. Results: To most respondents, the SF courses are important for the specialty they practiced (VAS by an average of 4). The SF themes suggested to be done as EL were: Stress mechanisms; stress factor models for different medical specialties; stress assessment methods; primary stress management methods for different specialties. Preferred form of information assimilation was CL+EL. Aspects of the course facilitated by EL versus CL model: Active reading of theoretical information, with fast access to keywords details; watching documentaries in everyone's favorite order; practice through tests and the rapid control of results. The first four EL advantages, mentioned for SF were: Autonomy in managing the time allocated to the study; saving time for traveling to the venue; the ability to read information in various contexts of time and space; communication with colleagues, in good times for everyone. The first three EL disadvantages, mentioned for SF were: It decreases capabilities for group discussion and mobilization for active participation; EL information accession may depend on electrical source or/and Internet; learning slowdown can appear, by temptation of postponing the implementation. Answering questions was partially influenced by the respondent's age and genre. Conclusions: 1) Post-graduate courses in SF are of interest to doctors of different specialties. 2) The majority of participating doctors preferred EL, but combined with CL (CL+EL). 3) Preference for EL was manifested mainly by young or middle age men doctors. 4) It is important to balance the proper formula for chosen EL, to be the most efficient, interesting, useful and agreeable.

Keywords: stress field, doctors’ postgraduate courses, classical lectures, e-learning lecture

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Authors: Jacinta Ifeoma Obidile

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Teachers (vocational and technical educators inclusive) use Information and Communications Technology (ICT) for tasks outside of their normal working hours. This expansion of work duties to non-work time challenges their work-life balance. However, there has been inconsistency in the results on how these relationships correlate. This, therefore, calls for further research studies to examine the moderating mechanisms of such relationships. The present study, therefore, ascertained how vocational and technical educators’ technology acceptance relates to their work-related ICT use beyond their working hours and work-life balance, as well as how their integration affects these relationships. The population of the study comprised 320 Vocational and Technical Educators from the Southeast geopolitical zone of Nigeria. Data were collected from the respondents using the structured questionnaire. The questionnaire was validated by three experts. The reliability of the study was conducted using 20 vocational and technical educators from the South who were not part of the population. The overall reliability coefficient of 0.81 was established using Cronbach’s alpha method. The data collected was analyzed using Structural equation modeling. Findings, among others, revealed that vocational and technical educators’ work-life balance was mediated by increased digital learning environment use after work hours, although reduced by social influence.

Keywords: vocational and technical educators, digital learning environment, working hours, work-life balance, integration preference

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Authors: Roula Kitsiou, Sofia Tsioli, Eleni Gana

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The current sociopolitical realities (displacement, encampment, and resettlement) refugees experience in Greece are a quite complex issue. Their educational and social ‘integration’ is characterized by transition, insecurity, and constantly changing needs. Based on the current research data, technology and more specifically mobile phones are one of the most important resources for refugees, regardless of their levels of conventional literacy. The proposed paper discusses the challenges encountered during the design and implementation of the educational Action 16 ‘Language Education for Adult Refugees’. Action 16 is one of the 24 Actions of the Project PRESS (Provision of Refugee Education and Support Scheme), funded by the Hellenic Open University (2016-2017). Project PRESS had two main objectives: a) to address the educational and integration needs of refugees in transit, who currently reside in Greece, and b) implement research-based educational interventions in online and offline sites. In the present paper, the focus is on reflection and discussion about the challenges and the potential of integrating technology in language learning for a target-group with many specific needs, which have been recorded in field notes among other research tools (ethnographic data) used in the context of PRESS. Action 16, explores if and how technology enhanced language activities in real-time and place mediated through teachers, as well as an autonomous computer-mediated learning space (moodle platform and application) builds on and expands the linguistic, cultural and digital resources and repertoires of the students by creating collaborative face-to-face and digital learning spaces. A broader view on language as a dynamic puzzle of semiotic resources and processes based on the concept of translanguaging is adopted. Specifically, designing the blended learning environment we draw on the construct of translanguaging a) as a symbolic means to valorize students’ repertoires and practices, b) as a method to reach to specific applications of a target-language that the context brings forward (Greek useful to them), and c) as a means to expand refugees’ repertoires. This has led to the creation of a learning space where students' linguistic and cultural resources can find paths to expression. In this context, communication and learning are realized by mutually investing multiple aspects of the team members' identities as educational material designers, teachers, and students on the teaching and learning processes. Therefore, creativity, humour, code-switching, translation, transference etc. are all possible means that can be employed in order to promote multilingual communication and language learning towards raising intercultural awareness in a critical digital pedagogy context. The qualitative analysis includes critical reflection on the developed educational material, team-based reflexive discussions, teachers’ reports data, and photographs from the interventions. The endeavor to involve women and men with a refugee background into a blended learning experience was quite innovative especially for the Greek context. It reflects a pragmatist ethos of the choices made in order to respond to the here-and-now needs of the refugees, and finally it was a very challenging task that has led all actors involved into Action 16 to (re)negotiations of subjectivities and products in a creative and hopeful way.

Keywords: blended learning, integration, language education, refugees

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Authors: Valerie Triggs, Michele Sorensen

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As two university professors in postsecondary faculties of social work and education, we have observed that students often recognize the importance of learning facts about colonization but have difficulty grappling with how they themselves might be implicated in reconciliation or how they might respond to these facts in meaningful ways. The detachment observed between students and factual information results in the initiation of a research study centered around an approach to teaching the course. This involved transitioning its pedagogical format to embrace a/r/tographic methods of teaching, learning, and inquiry. By taking seriously the arguments of various Indigenous scholars for learning from the land and by working alongside traditional Indigenous knowledge, we chose to engage a speculative approach to course design and teaching, which actually used the land as one of the course texts. We incorporated art practices that involved connecting bodies with land as well as using land materials in various creative and aesthetic projects while being informed by Medicine Keepers, Indigenous and settler artists, and knowledge-keeper helpers. In this study, we share some of the unanticipated themes that arose when students began to allow land and artmaking, both aesthetically and intuitively, through both joy and sorrow, to affect a reimagining and repositioning of selves and relations. We found that time and engagement with land and art began to build more empathic understanding and foster personal and professional practices grounded in respect, relevance, reciprocity, and responsibility.

Keywords: reconciliation, decolonization, artmaking, respect

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Authors: Sweta Sinha

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Rapid evolution of technology has changed language pedagogy as well as perspectives on language use, leading to strategic changes in discourse studies. We are now firmly embedded in a time when digital technologies have become an integral part of our daily lives. This has led to generalized approaches to English Language Teaching (ELT) which has raised two-pronged concerns in linguistically diverse settings: a) the diverse linguistic background of the learner might interfere/ intervene with the learning process and b) the differential level of already acquired knowledge of target language might make the classroom practices too easy or too difficult for the target group of learners. ELT needs a more systematic and differential pedagogical approach for greater efficiency and accuracy. The present research analyses the need of identifying learner groups based on different levels of target language proficiency based on a longitudinal study done on 150 undergraduate students. The learners were divided into five groups based on their performance on a twenty point scale in Listening Speaking Reading and Writing (LSRW). The groups were then subjected to varying durations of technology aided language learning sessions and their performance was recorded again on the same scale. Identifying groups and introducing differential teaching and learning strategies led to better results compared to generalized teaching strategies. Language teaching includes different aspects: the organizational, the technological, the sociological, the psychological, the pedagogical and the linguistic. And a facilitator must account for all these aspects in a carefully devised differential approach meeting the challenge of learner diversity. Apart from the justification of the formation of differential groups the paper attempts to devise framework to account for all these aspects in order to make ELT in multilingual setting much more effective.

Keywords: differential groups, English language teaching, language pedagogy, multilingualism, technology aided language learning

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Authors: Sebastián Olivares Lizana, Marianna Oyanedel González

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This study takes part of a major project based on implementing a CLIL program (Content and Language Integrated Learning) at Universidad Técnica Federico Santa María, a leading Chilean tertiary Institution. It aims at examining the relationship between the development of Reflective Processes (RP) and Cognitive Academic Language Proficiency (CALP) in weekly learning logs written by faculty members, participants of an initial professional development online course on English for Academic Purposes (EAP). Such course was designed with a genre-based approach, and consists of multiple tasks directed to academic writing proficiency. The results of this analysis will be described and classified in a scale of key indicators that represent both the Reflective Processes and the advances in CALP, and that also consider linguistic proficiency and task progression. Such indicators will evidence affordances and constrains of using a genre-based approach in an EFL Engineering CLIL program implementation at tertiary level in Chile, and will serve as the starting point to the design of a professional development course directed to teaching methodologies in a CLIL EFL environment in Engineering education at Universidad Técnica Federico Santa María.

Keywords: EFL, EAL, genre, CLIL, engineering

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Authors: Ann Dashwood

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Using Standard Australian English with confidence is a cultural expectation of parents of primary school aged children who want to engage effectively with their children’s teachers and school administration. That confidence in support of their children’s learning at school is seldom experienced by parents whose first language is not English. Sharing language with competence in an intercultural environment is the common denominator for meaningful communication and engagement to occur in a school community. Experience in relevant, interactive sessions is known to enhance engagement and participation. The purpose of this paper is to identify a pedagogy for parents otherwise isolated from daily use of functional Australian cultural language learned to engage effectively in their children’s learning at school. The outcomes measure parents’ intercultural engagement with classroom teachers and attention to the school’s administrative procedures using quantitative and qualitative methods. A principled communicative task-based language learning approach, combined with intercultural communication strategies provide the theoretical base for intensive English inquiry-based learning and engagement. The quantitative analysis examines data samples collected by classroom teachers and administrators and parents’ writing samples. Interviews and observations qualitatively inform the study. Currently, significant numbers of projects are active in community centers and schools to enhance English language knowledge of parents from Language Backgrounds Other Than English (LBOTE). The study is significant to explore the effects of an intensive English pedagogy with parents of varied English language backgrounds, by targeting inquiry-based language use for social interactions in the school and wider community, specific engagement and cultural interaction with teachers and school activities and procedures.

Keywords: engagement, intercultural communication, language teaching pedagogy, LBOTE, school community

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Authors: Kate Duggan

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Introduction: Functional Communication Training (FCT) is an approach which aims to reduce behaviours of concern by teaching more effective ways to communicate. It requires identification of the function of the behaviour of concern, through gathering information from key stakeholders and completing observations of the individual’s behaviour including antecedents to, and consequences of the behaviour. Appropriate communicative alternatives are then identified and taught to the individual using systematic instruction techniques. Behaviours of concern demonstrated by individuals with autism spectrum conditions (ASC) frequently have a communication function. When contributing to positive behavior support plans, speech and language therapists and other professionals working with individuals with ASC need to identify alternative communicative behaviours which are equally reinforcing as the existing behaviours of concern. Successful implementation of FCT is dependent on an effective ‘response match’. The new way of communicating must be equally as effective as the behaviour previously used and require the same amount or less effort from the individual. It must also be understood by the communication partners the individual encounters and be appropriate to their communicative contexts. Method: Four case studies within a non-maintained special school environment were described and analysed. A response match framework was used to identify the effectiveness of functional communication training delivered by the student’s speech and language therapist, teacher and learning support assistants. The success of systematic instruction techniques used to develop new communicative behaviours was evaluated using the CODES framework. Findings: Functional communication training can be used as part of a positive behaviour support approach for students within this setting. All case studies reviewed demonstrated ‘response success’, in that the desired response was gained from the new communicative behaviour. Barriers to the successful embedding of new communicative behaviours were encountered. In some instances, the new communicative behaviour could not be consistently understood across all communication partners which reduced ‘response recognisability’. There was also evidence of increased physical or cognitive difficulty in employing the new communicative behaviour which reduced the ‘response effectivity’. Successful use of ‘thinning schedules of reinforcement’, taught students to tolerate a delay to reinforcement once the new communication behaviour was learned.

Keywords: augmentative and alternative communication, autism spectrum conditions, behaviours of concern, functional communication training

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