Search results for: institutional learning outcomes

Authors: Ebtisam Alqahtani

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The purpose of this study is to investigate academic staff perspectives on using Augmented Reality in teaching practice to support students learning remotely during the COVID pandemic. the study adopted the DTPB theoretical model to guide the identification of key potential factors that could motivate academic staff to use or not use AR in teaching practices. A mixing method design was adopted for a better understanding of the study problem. A survey was completed by 851 academic staff, and this was followed by interviews with 20 academic staff. Statistical analyses were used to assess the survey data, and thematic analysis was used to assess the interview data. The study finding indicates that 75% of academic staff were aware of AR as a pedagogical tool, and they agreed on the potential benefits of AR in teaching and learning practices. However, 36% of academic staff use it in teaching and learning practice, and most of them agree with most of the potential barriers to adopting AR in educational environments. In addition, the study results indicate that 91% of them are planning to use it in the future. The most important factors that motivated them to use it in the future are the COVID pandemic factor, hedonic motivation factor, and academic staff attitude factor. The perceptions of academic staff differed according to the universities they attended, the faculties they worked in, and their gender. This study offers further empirical support for the DTPB model, as well as recommendations to help higher education implement technology in its educational environment based on the findings of the study. It is unprecedented the study the necessity of the use of AR technologies in the time of Covid-19. Therefore, the contribution is both theoretical and practice

Keywords: higher education, academic staff, AR technology as pedological tools, teaching and learning practice, benefits of AR, barriers of adopting AR, and motivating factors to adopt AR

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Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

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Authors: Omran Alharbi, Victor Lally

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The advantages of the adoption of new technology such as learning management systems (LMSs) in education and teaching methods have been widely recognised. This has led a large number of universities to integrate this type of technology into their daily learning and teaching activities in order to facilitate the education process for both learners and teachers. On the other hand, in some developing countries such as Saudi Arabia, educators have seldom used this technology. As a result, this study was conducted in order to investigate the factors that impede teachers’ use of technology (LMSs) in their teaching in Saudi Arabian institutions. This study used a qualitative approach. Eight participants were invited to take part in this study, and they were asked to give their opinions about the most significant factors that prevented them from integrating technology into their daily activities. The results revealed that a lack of LMS skills, interest in and knowledge about the LMS among teachers were the most significant factors impeding them from using technology in their lessons. The participants suggested that incentive training should be provided to reduce these challenges.

Keywords: LMS, factors, KSA, teachers

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Authors: Cesar Leines

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The social effects of the pandemic are significant and diverse, most of those effects have widened the gap of economic inequality. Without a doubt, each country faces difficulties associated with the strengths and weaknesses of its own institutions that can address these causes and consequences. Around the world, pricing practices that have no connection to production costs have been used extensively in numerous markets beyond those relating to the supply of essential goods and services, and although it is not unlawful to adjust pricing considering the increased demand of certain products, shortages and disruption of supply chains, illegitimate pricing practices may arise and these tend to transfer wealth from consumers to producers that affect the purchasing power of the former, making people worse off. High prices with no objective justification indicate a poor state of the competitive process in any market and the impact of those underlying competition issues leading to inefficiency is increased when national competition agencies are weak and ineffective in enforcing competition in law and policy. It has been observed that in those countries where competition authorities are perceived as weak or ineffective, price increases of a wide range of products and services were more significant during the pandemic than those price increases observed in countries where the perception of the effectiveness of the competition agency is high. When a perception is created of a highly effective competition authority, one which enforces competition law and its non-enforcement activities result in the fulfillment of its substantive functions of protecting competition as the means to create efficient markets, the price rise observed in markets under its jurisdiction is low. A case study focused on the effectiveness of the national competition agency in Mexico (COFECE) points to institutional weakness as one of the causes leading to excessive pricing. There are many factors that contribute to its low effectiveness and which, in turn, have led to a very significant price hike, potentiated by the pandemic. This paper contributes to the discussion of these factors and proposes different steps that overall help COFECE or any other competition agency to increase the perception of effectiveness for the benefit of the consumers.

Keywords: agency effectiveness, competition, institutional weakness, price gouging

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Authors: Mohammadreza Mohebbi, Masoumeh Sanagou

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The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed.

Keywords: clinical prediction models, clinical decision rule, prognosis, external validation, model calibration, biostatistics

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Authors: Yuxuan Tang

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Cognitive diagnostic assessment has received much attention in second language education, and assessment for it can provide pedagogically useful feedback for language learners. However, there is a lack of research on how students interpret and use cognitive diagnostic feedback. Thus the present study aims to adopt a mixed-method approach mainly to explore the relationship between the goal-orientation and students' response to cognitive diagnostic feedback. Almost 200 Chinese undergraduates from two universities in Xi'an, China, will be invited to do a cognitive diagnostic reading test, and each student will receive specialized cognitive diagnostic feedback, comprising of students' reading attributes mastery level generated by applying a well-selected cognitive diagnostic model, students' perceived reading ability assessed by a self-assessing questionnaire and students’ level position in the whole class. And a goal-orientation questionnaire and a self-generated questionnaire on the perception of feedback will be given to students the moment they receive feedback. In addition, interviews of students will be conducted on their future plans to see whether they have awareness of carrying out studying plans. The study aims to find a new perspective towards how students use and interpret cognitive diagnostic feedback in terms of their different goal-orientation (self-based, task-based, and other-based goals) by applying the newest goal orientation model, which is an important construct of motivation in psychology, seldom researched under language learning area. And the study is expected to provide evidence on how diagnostic feedback promotes students' learning under the educational belief of assessment for learning. Practically speaking, according to the personalized diagnostic feedback, students can take remedial self-learning more purposefully, and teachers can target students' weaknesses to adjust teaching methods and carry out tailored teaching.

Keywords: assessment for learning, cognitive diagnostic assessment, goal-orientation, personalized feedback

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Authors: Stephen McCarthy, Joanne Gray, Esther Carr, Gerard Danjoux, Paul Baker, Rhiannon Hackett

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Background: The Go Well Health (GWH) platform is a web-based programme that allows patients to access personalised care plans and resources, aimed at prehabilitation prior to surgery. The online digital platform delivers essential patient education and support for patients prior to undergoing total hip replacements (THR) and total knee replacements (TKR). This study evaluated the impact of an online digital platform (ODP) in terms of functional health outcomes, health related quality of life and hospital length of stay following surgery. Methods: A retrospective cohort study comparing a cohort of patients who used the online digital platform (ODP) to deliver patient education and support (PES) prior to undergoing THR and TKR surgery relative to a cohort of patients who did not access the ODP and received usual care. Routinely collected Patient Reported Outcome Measures (PROMs) data was obtained on 2,406 patients who underwent a knee replacement (n=1,160) or a hip replacement (n=1,246) between 2018 and 2019 in a single surgical centre in the United Kingdom. The Oxford Hip and Knee Score and the European Quality of Life Five-Dimensional tool (EQ5D-5L) was obtained both pre-and post-surgery (at 6 months) along with hospital LOS. Linear regression was used to compare the estimate the impact of GWH on both health outcomes and negative binomial regressions were used to impact on LOS. All analyses adjusted for age, sex, Charlson Comorbidity Score and either pre-operative Oxford Hip/Knee scores or pre-operative EQ-5D scores. Fractional polynomials were used to represent potential non-linear relationships between the factors included in the regression model. Findings: For patients who underwent a knee replacement, GWH had a statistically significant impact on Oxford Knee Scores and EQ5D-5L utility post-surgery (p=0.039 and p=0.002 respectively). GWH did not have a statistically significant impact on the hospital length of stay. For those patients who underwent a hip replacement, GWH had a statistically significant impact on Oxford Hip Scores and EQ5D-5L utility post (p=0.000 and p=0.009 respectively). GWH also had a statistically significant reduction in the hospital length of stay (p=0.000). Conclusion: Health Outcomes were higher for patients who used the GWH platform and underwent THR and TKR relative to those who received usual care prior to surgery. Patients who underwent a hip replacement and used GWH also had a reduced hospital LOS. These findings are important for health policy and or decision makers as they suggest that prehabilitation via an ODP can maximise health outcomes for patients following surgery whilst potentially making efficiency savings with reductions in LOS.

Keywords: digital prehabilitation, online digital platform, orthopaedics, surgery

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Authors: Tharaki S. Hettiarachchi

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Sustainable construction has gained massive attention in the present world as the construction industry is highly responsible for carbon emissions and other types of unsustainable practices. Yet, the construction industry has not been able to completely attain sustainable goals. Therefore, the present study aims to identify the extent to which sustainability has been considered within the scope of construction project management and to analyze the challenges, gaps, and constraints associated. Accordingly, this study develops a sustainable framework to integrate in construction project management. In accomplishing the research aim, this research integrates a qualitative approach while relying on secondary data sources. The data shall be then analyzed with the use of a systematic literature review (SLR) method while following the PRISMA (2020) guideline and represented in a statistical form. The outcomes of this study may become highly significant in identifying the nature of the existing sustainable frameworks associated with construction project management scopes and to develop a new framework to integrate in order to enhance the effectiveness of sustainable applications in construction management. The outcomes of this research may benefit present and future construction professionals and academicians to organize sustainable construction-related knowledge in a useful way to apply in practical implementation for effective project management. Overall, this study directs present and future construction professionals toward an advanced construction project management mechanism.

Keywords: construction, framework development, project management, sustainability

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Authors: R. M. Krishna Sureddi, V. Kamakshi Prasad, R. Santosh

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Machine learning techniques are applied for the analysis of the impact of mental illness on the burden of disease. It is calculated using the disability-adjusted life year (DALY). One DALY represents the loss of the equivalent of one year of full health. DALYs for a disease or health condition are the sum of years of life lost due to premature mortality (YLLs) and years of healthy life lost due to disability (YLDs) due to prevalent cases of the disease or health condition in a population. The critical analysis is done based on the Data sources, machine learning techniques and feature extraction method. The reviewing is done based on major databases. The extracted data is examined using statistical analysis and machine learning techniques were applied. The prediction of the impact of mental illness on the population using machine learning techniques is an alternative approach to the old traditional strategies, which are time-consuming and may not be reliable. The approach makes it necessary for a comprehensive adoption, innovative algorithms, and an understanding of the limitations and challenges. The obtained prediction is a way of understanding the underlying impact of mental illness on the health of the people and it enables us to get a healthy life expectancy. The growing impact of mental illness and the challenges associated with the detection and treatment of mental disorders make it necessary for us to understand the complete effect of it on the majority of the population.

Keywords: ML, DALY, BD, DL

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Authors: Larasati Zuhro, Ida F Priyanto

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Background: In March 2020, Indonesia started to be affected by Covid-19, and the number of victims increased slowly but surely until finally, the highest number of victims reached the highest—about 50,000 persons—for the daily cases in the middle of 2021. Like other institutions, schools and universities were suddenly closed in March 2020, and students had to change their ways of studying from face-to-face to online. This sudden changed affected students and faculty, including LIS students and faculty because they never experienced online classes in Indonesia due to the previous regulation that academic and school activities were all conducted onsite. For almost two years, school and academic activities were held online. This indeed has affected the way students learned and faculty delivered their courses. This raises the question of whether students are now ready for their new learning activities due to the covid-19 disruption. Objectives: this study was conducted to find out the impact of covid-19 pandemic on the LIS learning process and the effectiveness of online classes for students of LIS in Indonesia. Methodology: This was qualitative research conducted among LIS students at UIN Sunan Kalijaga, Yogyakarta, Indonesia. The population are students who were studying for masters’program during covid-19 pandemic. Results: The study showed that students were ready with the online classes because they are familiar with the technology. However, the Internet and technology infrastructure do not always support the process of learning. Students mention slow WIFI is one factor that causes them not being able to study optimally. They usually compensate themselves by visiting a public library, a café, or any other places to get WIFI network. Noises come from the people surrounding them while they are studying online.Some students could not concentrate well when attending the online classes as they studied at home, and their families sometimes talk to other family members, or they asked the students while they are attending the online classes. The noise also came when they studied in a café. Another issue is that the classes were held in shorter time than that in the face-to-face. Students said they still enjoyed the onsite classes instead of online, although they do not mind to have hybrid model of learning. Conclusion: Pandemic of Covid-19 has changed the way students of LIS in Indonesia learn. They have experienced a process of migrating the way they learn from onsite to online. They also adapted their learning with the condition of internet access speed, infrastructure, and the environment. They expect to have hybrid classes in the future.

Keywords: learning, LIS students, pandemic, covid-19

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Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

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Authors: Sania Bashir

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A promising approach to healthcare delivery, telemedicine makes use of communication technology to reach out to remote regions of the world, allowing for beneficial interactions between diabetic patients and healthcare professionals as well as the provision of affordable and easily accessible medical care. The emergence of contemporary care models, fueled by the pervasiveness of mobile devices, provides better information, offers low cost with the best possible outcomes, and is known as digital health. It involves the integration of collected data using software and apps, as well as low-cost, high-quality outcomes. The goal of this study is to assess how well telemedicine works for diabetic patients and how it impacts their HbA1c levels. A questionnaire-based survey of 300 diabetics included 150 patients in each of the groups receiving usual care and via telemedicine. A descriptive and observational study that lasted from September 2021 to May 2022 was conducted. HbA1c has been gathered for both categories every three months. A remote monitoring tool has been used to assess the efficacy of telemedicine and continuing therapy instead of the customary three monthly meetings like in-person consultations. The patients were (42.3) 18.3 years old on average. 128 men were outnumbered by 172 women (57.3% of the total). 200 patients (66.6%) have type 2 diabetes, compared to over 100 (33.3%) candidates for type 1. Despite the average baseline BMI being within normal ranges at 23.4 kg/m², the mean baseline HbA1c (9.45 1.20) indicates that glycemic treatment is not well-controlled at the time of registration. While patients who use telemedicine experienced a mean percentage change of 10.5, those who visit the clinic experienced a mean percentage change of 3.9. Changes in HbA1c are dependent on several factors, including improvements in BMI (61%) after 9 months of research and compliance with healthy lifestyle recommendations for diet and activity. More compliance was achieved by the telemedicine group. It is an undeniable reality that patient-physician communication is crucial for enhancing health outcomes and avoiding long-term issues. Telemedicine has shown its value in the management of diabetes and holds promise as a novel technique for improved clinical-patient communication in the twenty-first century.

Keywords: diabetes, digital health, mobile app, telemedicine

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: Dercan Gencbas, Hatice Bebis, Sue Moorhead

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In the nursing process, many of the nursing classification systems were created to be used in international. From these, NANDA-I, Nursing Outcomes Classification (NOC) and Nursing Interventions Classification (NIC). In this direction, the main objective of this study is to establish a model for caregivers in hospitals and communities in Turkey and to ensure that nursing outputs are assessed by NOC-based measures. There are many scales to measure Urinary Incontinence (UI), which is very common in children, in old age, vaginal birth, NOC scales are ideal for use in the nursing process for comprehensive and holistic assessment, with surveys available. For this reason, the purpose of this study is to evaluate the validity of the NOC outputs and indicators used for UI NANDA-I. This research is a methodological study. In addition to the validity of scale indicators in the study, how much they will contribute to recovery after the nursing intervention was assessed by experts. Scope validations have been applied and calculated according to Fehring 1987 work model. According to this, nursing inclusion criteria and scores were determined. For example, if experts have at least four years of clinical experience, their score was 4 points or have at least one year of the nursing classification system, their score was 1 point. The experts were a publication experience about nursing classification, their score was 1 point, or have a doctoral degree in nursing, their score was 2 points. If the expert has a master degree, their score was 1 point. Total of 55 experts rated Fehring as a “senior degree” with a score of 90 according to the expert scoring. The nursing interventions to be applied were asked to what extent these indicators would contribute to recovery. For coverage validity tailored to Fehring's model, each NOC and NOC indicator from specialists was asked to score between 1-5. Score for the significance of indicators was from 1=no precaution to 5=very important. After the expert opinion, these weighted scores obtained for each NOC and NOC indicator were classified as 0.8 critical, 0.8 > 0.5 complements, > 0.5 are excluded. In the NANDA-I / NOC / NIC system (guideline), 5 NOCs proposed for nursing diagnoses for UI were proposed. These outputs are; Urinary Continence, Urinary Elimination, Tissue Integrity, Self CareToileting, Medication Response. After the scales are translated into Turkish, the weighted average of the scores obtained from specialists for the coverage of all 5 NOCs and the contribution of nursing initiatives exceeded 0.8. After the opinions of the experts, 79 of the 82 indicators were calculated as critical, 3 of the indicators were calculated as supplemental. Because of 0.5 > was not obtained, no substance was removed. All NOC outputs were identified as valid and usable scales in Turkey. In this study, five NOC outcomes were verified for the evaluation of the output of individuals who have received nursing knowledge of UI and variant types. Nurses in Turkey can benefit from the outputs of the NOC scale to perform the care of the elderly incontinence.

Keywords: nursing outcomes, content validity, nursing diagnosis, urinary incontinence

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Authors: Faisal Kalota

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Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: big data, learning analytics, analytics, big data in education, Hadoop

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Authors: Promothes Saha

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Recently, transportation engineering industry members at the study university showed concern that students lacked the skills needed to solve real-world engineering problems using spreadsheet data analysis. In response to the concerns shown by industry members, this study investigated how to engage students in a better way by incorporating spreadsheet analysis during class - also, help them learn the course topics. Helping students link theoretical knowledge to real-world problems can be a challenge. In this effort, in-class activities and worksheets were redesigned to integrate with Excel to solve example problems using built-in tools including cell referencing, equations, data analysis tool pack, solver tool, conditional formatting, charts, etc. The effectiveness of this technique was investigated using students’ evaluations of the course, enrollment data, and students’ comments. Based on the data of those criteria, it is evident that the spreadsheet activities may increase student learning.

Keywords: civil, engineering, active learning, transportation

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Authors: Muncharee Phaobthip

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In this research, the researcher re-examine the mediating effect of employee engagement between its antecedents and consequences for investigates the relation of leadership practices, employment branding and employee engagement based on social exchange theory. As such the researcher has four objectives as follows: First, to study the effects of leadership practices on employment branding, employee engagement and work intention; second, to examine the effects of employer brand perception on employee engagement and work intention; third, to examine the effects of employee engagement on work intention; and last, forth, the researcher inquires into the respondence of work intention. The researcher constituted a sample population of 535 employees of a Thai hotel chain located in four regions of the Kingdom of Thailand (Thailand). The researcher utilized a mixed-methods approach divided into quantitative and qualitative research investigatory phases, respectively. In the quantitative phase of research investigation, the researcher collected germane data from the 535 members of the sample population through the use of a questionnaire as a research instrument. In the qualitative phase of research investigation, relevant data were obtained through carrying out in-depth interviews with three subgroups of members of the sample population. These three subgroups consisted of twelve hotelier experts, six employees at the administrator level, and operational level employees. Focus group discussions were held with discussants from these three subgroups. Findings are as follows: Leadership practices showed positive effects on employment branding, employee engagement, and work intention. Employment branding displayed positive effects on employee engagement and work intention. Employee engagement had positive effects on work intention. However, in the analysis of the equation, the researcher confirmed that the important role of employee engagement is mediator factor between its antecedent and consequence factors. This provides benefits, in that it augments the body of knowledge devoted to the fostering of employee engagement in respect to psychological conditions. In conclusion, the researcher found that the value co-creation between leaders, employers and employees had positive effects on employee outcomes for lead to business outcomes according to reciprocal rule.

Keywords: antecedents, employee engagement, psychological conditions, work intention

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Authors: I. Pelizzoni, C. Cavallini, I. Salvaderi, F. Cavallini

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We present the improvement in writing skills obtained by 94 participants (aged between six and 10 years) with special educational needs through a writing enhancement program based on fluency principles. The study was planned and conducted with a single-subject experimental plan for each of the participants, in order to confirm the results in the literature. These results were obtained using precision teaching (PT) methodology to increase the number of written graphemes per minute in the pre- and post-test, by curriculum based measurement (CBM). Results indicated an increase in the number of written graphemes for all participants. The average overall duration of the intervention is 144 minutes in five months of treatment. These considerations have been analyzed taking account of the complexity of the implementation of measurement systems in real operational contexts (an Italian learning center) and important aspects of replicability and cost-effectiveness of such interventions.

Keywords: curriculum based measurement, precision teaching, writing skill, Italian learning center

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Authors: Yukiko Fujiwara

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As a language learning methodology, Content and Language Integrated Learning (CLIL) has become increasingly prevalent in Japan. Most CLIL classroom practice and its research are conducted in EFL fields. However, much less research has been done in the Japanese language learning setting. Therefore, there are still many issues to work out using CLIL in the Japanese language teaching (JLT) setting. it is expected that more research will be conducted on both authentically and academically. Under such circumstances, this is one of the few classroom-based CLIL researches experiments in JLT and aims to find an effective course design for a class with students at different proficiency levels. The class was called ‘Japanese culture A’. This class was offered as one of the elective classes for International exchange students at a Japanese university. The Japanese proficiency level of the class was above the Japanese Language Proficiency Test Level N3. Since the CLIL approach places importance on ‘authenticity’, the class was designed with materials and activities; such as books, magazines, a film and TV show and a field trip to Kyoto. On the field trip, students experienced making traditional Japanese desserts, by receiving guidance directly from a Japanese artisan. Through the course, designated task sheets were used so the teacher could get feedback from each student to grasp what the class proficiency gap was. After reading an article on Japanese culture, students were asked to write down the words they did not understand and what they thought they needed to learn. It helped both students and teachers to set learning goals and work together for it. Using questionnaires and interviews with students, this research examined whether the attempt was effective or not. Essays they wrote in class were also analyzed. The results from the students were positive. They were motivated by learning authentic, natural Japanese, and they thrived setting their own personal goals. Some students were motivated to learn Japanese by studying the language and others were motivated by studying the cultural context. Most of them said they learned better this way; by setting their own Japanese language and culture goals. These results will provide teachers with new insight towards designing class materials and activities that support students in a multilevel CLIL class.

Keywords: authenticity, CLIL, Japanese language and culture, multilevel class

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Authors: Naziema B. Jappie

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Internationalization is very complex and multifaceted and has implications for the entire university sector, and the larger community which it serves. Higher education strategic plans require sustainability on all levels of academic engagement and internationalization contributes to the sustainability because of the global competition but, at the same time, ensures diversity on campuses. Universities all over the world are increasingly recognizing the challenges of globalization and the pressures towards internationalization. The past 25 years of internationalization has faded away, and new challenges have emerged. Although internationalization remains a central strategic objective for all universities, for many leaders and education practitioners it has remained a confused concept. It has various interpretations, and it intersects with numerous other national agendas in higher education domain; it often builds upon narrow notions limited to one of its facets –attracting international student fees for financial sustainability or for ensuring a diverse campus culture. It is essential to have clear institutional views, but it is imperative that everyone reflects on the values and beliefs that underpin the internationalization of higher education and have a global focus. This paper draws together the international experience locally and globally to explore the emerging patterns of strategy and practice in internationalizing higher education. This will highlight some critical notions of how the concepts of internationalization and globalization in the context of higher education is understood by those who lead universities and what new challenges are being created as universities seek to become more international. Institutions cannot simply have bullet points in the strategic plan about recruitment of international students; there has to be a complete commitment to an international strategy of inclusivity. This paper will further examine the leadership styles that ensure transformation together with the goals set out for internationalization. The interviews with the senior leadership are in-depth semi-structured recorded interviews of approximately one-hour to learn about their institutional experiences, promotion, and enhancement of the value of internationalisation to the tertiary education sector and initiating discussions around adding the international relations dimension to the curriculum. This paper will address the issues relevant to the cross-border delivery of higher education. To ensure anonymity throughout this study, the interviewees are identified only by their institutions.

Keywords: challenges, globalization, higher education, internationalization, strategic focus

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Authors: Gaurav Kumar Sinha

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The volatility of the oil market, influenced by geopolitical, economic, and environmental factors, presents significant challenges for stakeholders in predicting trends and demand. This article explores the application of artificial intelligence (AI) in developing robust forecasting models to anticipate changes in the oil market more accurately. We delve into various AI techniques, including machine learning, deep learning, and time series analysis, that have been adapted to analyze historical data and current market conditions to forecast future trends. The study evaluates the effectiveness of these models in capturing complex patterns and dependencies in market data, which traditional forecasting methods often miss. Additionally, the paper discusses the integration of external variables such as political events, economic policies, and technological advancements that influence oil prices and demand. By leveraging AI, stakeholders can achieve a more nuanced understanding of market dynamics, enabling better strategic planning and risk management. The article concludes with a discussion on the potential of AI-driven models in enhancing the predictive accuracy of oil market forecasts and their implications for global economic planning and strategic resource allocation.

Keywords: AI forecasting, oil market trends, machine learning, deep learning, time series analysis, predictive analytics, economic factors, geopolitical influence, technological advancements, strategic planning

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Authors: Monika Benedek

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This paper intends to present a pilot study of blended ear-training course at a Finnish university. The course ran for ten weeks and included both traditional (offline) group lessons for 90 minutes each week and an online learning platform. Twelve students majored in musicology and music education participated in the course. The aims of pilot research were to develop a new blended ear-training course at university level, to determine the ideal amount of workload in each part of the blended instruction (offline and online) and to develop the course material. The course material was selected from the Classical period in order to develop students’ aural skills together with their stylistic knowledge. Students were asked to provide written feedback of the course content and learning approaches of face-to-face group lessons and online learning platform each week during the course. Therefore, the teaching material is continuously planned for each week. This qualitative data collection and weekly analysis of data are on progress. However, based on the teacher-researcher’s experiences and the students’ feedback already collected, it could be seen that the blended instruction would be an ideal teaching strategy for ear-trainging at the music programmes of universities to develop students’ aural skills and stylistic knowledge. It is also presumed that such blended instruction with less workload would already improve university students’ aural skills and related musicianship skills. The preliminary findings of research also indicated that students generally found those ear-training tasks the most useful to learn online that combined listening, singing, singing and playing an instrument. This paper intends to summarise the final results of the pilot study.

Keywords: blended-learning, ear-training, higher music education, online-learning, pilot study

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Authors: Mulu G. Gencha, Gebremedhin Simon, Menna Olango

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This study was conducted at Hawassa University (HwU) in the Southern Nation Nationalities Peoples Regional State (SNNPRS) of Ethiopia. The prime concern of this study was to examine the writing performances of experimental and control group students, perception of experimental group students, and subject instructors. The course was blended learning (BL). Blended learning is a hybrid of classroom and on-line learning. Participants were eighty students from the School of Computer Science. Forty students attended the BL delivery involved using Face-to-Face (FTF) and campus-based online instruction. All instructors, fifty, of School of Language and Communication Studies along with 10 FGD members participated in the study. The experimental group went to the computer lab two times a week for four months, March-June, 2012, using the local area network (LAN), and software (MOODLE) writing program. On the other hand, the control group, forty students, took the FTF writing course five times a week for four months in similar academic calendar. The three instruments, the attitude questionnaire, tests and FGD were designed to identify views of students, instructors, and FGD participants on BL. At the end of the study, students’ final course scores were evaluated. Data were analyzed using independent samples t-tests. A statistically, significant difference was found between the FTF and BL (p<0.05). The analysis showed that the BL group was more successful than the conventional group. Besides, both instructors and students had positive attitude towards BL. The final section of the thesis showed the potential benefits and challenges, considering the pedagogical implications for the BL, and recommended possible avenues for further works.

Keywords: blended learning, computer attitudes, computer usefulness, computer liking, computer confidence, computer phobia

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Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

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Authors: Atiqa Marium

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In 2015, all 193 UN Member countries adopted the 2030 Agenda for Sustainable Development, which is an ambitious 15- year plan to address some of the most pressing issues the world faces. Goal 4 is about Quality Education which highlights the importance of inclusive and quality education for sustainable development. Sustainable Development Goal 10 focuses on reducing inequalities within and among countries. In June 2019, Federal Government in Canada released “Towards Canada’s 2030 Agenda National Strategy”, which was an important step to move the 2030 Agenda forward. In April 2019, the Ontario government announced the performance-based funding model for publically assisted colleges and universities in Ontario, which is now part of the universities’ budget 2024-2025. The literature review has shown that the funding model has been implemented by different governments to achieve objectives. However, this model has also resulted in conflicting consequences like reducing university autonomy, education quality/ academic standards, and increased equity concerns. The primary focus of this paper will be to analyze the tensions and coherence between the proposed funding model for education for sustainable development goals and targets set by Canada’s 2030 Agenda National Strategy. Considering that the literature review has provided evidence that the performance-based funding model has resulted in reducing quality of education and increased equity issues in other countries, it will be interesting to see how this proposed funding will align with the SDGs of “Quality Education” and “Reduced Inequalities”. This paper will be well-suited for Volume 4, with the theme of re-visioning institutional impact and sustainability. This paper will underscore the importance of policy coherence between federal and provincial policies for higher education institutions in Ontario for better institutional impact and helping universities in the attainment of goals set in 2030 Agenda towards education for sustainable development.

Keywords: performance-based funding model, education for sustainable development, policy coherence, sustainable development gaols

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Authors: Yumeng He

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The purpose of this paper is to establish the “extenics” of children music education, the “extenics” thought and methods are introduced into the children music education field. Discussions are made from the perspective of children music education on how to generate new music cognitive from music cognitive, how to generate new music education from music education and how to generate music learning from music learning. The research methods including the extensibility of music art, extensibility of music education, extensibility of music capability and extensibility of music learning. Results of this study indicate that the thought and research methods of children’s extended music education not only have developed the “extenics” concept and ideological methods, meanwhile, the brand-new thought and innovative research perspective have been employed in discussing the children music education. As indicated in research, the children’s extended music education has extended the horizon of children music education, and has endowed the children music education field with a new thought and research method.

Keywords: comprehensive evaluations, extension thought, extension cognition music education, extensibility

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Authors: Stefan Papastefanou

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Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

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Authors: Matan Cohen, Maxim Shoshany

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Climatic, topographic and geological diversity, together with frequent disturbance and recovery cycles, produce highly complex spatial patterns of trees, shrubs, dwarf shrubs and bare ground patches. Assessment of spatial and temporal variations of these life-forms patterns under climate change is of high ecological priority. Here we report on one of the first attempts to discriminate between images of three Mediterranean life-forms patterns at three densities. The development of an extensive database of orthophoto images representing these 9 pattern categories was instrumental for training and testing pre-trained and newly-trained DL models utilizing DenseNet architecture. Both models demonstrated the advantages of using Deep Learning approaches over existing spectral and spatial (pattern or texture) algorithmic methods in differentiation 9 life-form spatial mixtures categories.

Keywords: texture classification, deep learning, desert fringe ecosystems, climate change

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Authors: Niklas Panten, Eberhard Abele

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This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

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Authors: Norah Mohammed Alshahrani, Abdulaziz Almaleh

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Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD and they are Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then Feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by the Support Vector Machine (SVM), achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: autism spectrum disorder, machine learning, feature selection, support vector machine

Procedia PDF Downloads 142