Search results for: learning management

Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

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Authors: Vuyayo Tsheleza, Simbarashe Ndhleve, Christopher Mpundu Musampa

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Mismanagement of waste is continuously emerging as a rising malpractice in most developing countries, especially in fast growing cities. Household solid waste in Mthatha has been reported to be one of the problems facing the city and is overwhelming local authorities, as it is beyond the environment and management capacity of the existing waste management system. This study estimates per capita waste generation, quantity of different waste types generated by inhabitants of formal and informal settlements in Mthatha as well as waste management practices in the aforementioned socio-economic stratums. A total of 206 households were systematically selected for the study using stratified random sampling categorized into formal and informal settlements. Data on household waste generation rate, composition, awareness, and household waste management behaviour and practices was gathered through mixed methods. Sampled households from both formal and informal settlements with a total of 684 people generated 1949kg per week. This translates to 2.84kg per capita per week. On average, the rate of solid waste generation per capita was 0.40 kg per day for a person living in informal settlement and 0.56 kg per day person living in formal settlement. When recorded in descending order, the proportion food waste accounted for the most generated waste at approximately 23.7%, followed by disposable nappies at 15%, papers and cardboards 13.34%, glass 13.03%, metals at 11.99%, plastics at 11.58%, residue at 5.17, textiles 3.93%, with leather and rubber at 2.28% as the least generated waste type. Different waste management practices were reported in both formal and informal settlements with formal settlements proving to be more concerned about environmental management as compared to their counterparts, informal settlement. Understanding attitudes and perceptions on waste management, waste types and per capita solid waste generation rate can help evolve appropriate waste management strategies based on the principle of reduce, re-use, recycle, environmental sound disposal and also assist in projecting future waste generation rate. These results can be utilized as input when designing growing cities’ waste management plans.

Keywords: awareness, characterisation, per capita, quantification

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Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

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Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

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Authors: Hans Schumann, Colin Domnauer, Louis Rosenberg

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In this study, machine learning techniques were deployed on real-time human swarm data to forecast the likelihood of outcomes for English Premier League matches in the 2020/21 season. These techniques included ensemble models in combination with neural networks and were tested against an industry standard of Vegas Oddsmakers. Predictions made from the collective intelligence of human swarm participants managed to achieve a positive return on investment over a full season on matches, empirically proving the usefulness of a new artificial intelligence valuing human instinct and intelligence.

Keywords: artificial intelligence, data science, English Premier League, human swarming, machine learning, sports betting, swarm intelligence

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Authors: Nor Azlina A. Rahman, Jamaliah Said, Salwana Hassan

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Prompt changes in global business environment, such as passionate competition, managerial/operational, changing governmental regulation and innovation in technology have significant impacts on the organizations. At present, global business environment demands for more proactive institutions on microfinance to provide an opportunity for the business success. Microfinance providers in Malaysia still accelerate its activities of funding by cash and cheque. These institutions are at high risk as the paper-based system is deemed to be slow and prone to human error, as well as requiring a major annual reconciliation process. The global transformation of financial services, growing involvement of technology, innovation and new business activities had progressively made risk management profile to be more subjective and diversified. The persistent, complex and dynamic nature of risk management activities in the institutions arise due to highly automated advancements of technology. This may thus manifest in a variety of ways throughout the financial services sector. This study seeks out to examine current operational risks management being experienced by microfinance providers in Malaysia; investigate the process of current practices on facilitator control factor mechanisms, and explore how the adoption of technology, innovation and use of management accounting practices would affect the risk management process of operation system in microfinance providers in Malaysia. A case study method was employed in this study. The case study also need to find that the vital past role of management accounting will be used for mitigation of risk management activities towards accountability as an information or guideline to microfinance provider. An empirical element obtainable with qualitative method is needed in this study, where multipart and in-depth information are essential to understand the issues of these institution phenomena. This study is expected to propose a theoretical model for implementation of technology, innovation and management accounting practices into the system of operation to improve internal control and subsequently lead to mitigation of risk management activities among microfinance providers to be more successful.

Keywords: microfinance, accountability, operational risks, management accounting practices

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Authors: Marija Mihajlovic Pereira

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Teaching and learning a musical instrument is challenging for both teachers and students. Teachers generally use diverse strategies to resolve students' particular issues in a one-to-one context. Considering individual sessions as a supportive educational context, the teacher can play a decisive role in stimulating and promoting self-regulated learning strategies, especially with beginning learners. The teachers who promote self-controlling behaviors, strategic monitoring, and regulation of actions toward goals could expect their students to practice more qualitatively and consciously. When encouraged to adopt self-regulation habits, students' could benefit from greater productivity on a longer path. Founded on Bary Zimmerman's cyclical model that comprehends three phases - forethought, performance, and self-reflection, this work aims to articulate self-regulated and music learning. Self-regulated learning appeals to the individual's attitude in planning, controlling, and reflecting on their performance. Furthermore, this study aimed to present an observation grid for perceiving teaching instructions that encourage students' controlling cognitive behaviors in light of the belief that conscious promotion of self-regulation may motivate strategic actions toward goals in musical performance. The participants, two teachers, and two students have been involved in the social inclusion project in Lisbon (Portugal). The author and one independent inter-observer analyzed six video-recorded string instrument lessons. The data correspond to three sessions per teacher lectured to one (different) student. Violin (f) and violoncello (m) teachers hold a Master's degree in music education and approximately five years of experience. In their second year of learning an instrument, students have acquired reasonable skills in musical reading, posture, and sound quality until then. The students also manifest positive learning behaviors, interest in learning a musical instrument, although their study habits are still inconsistent. According to the grid's four categories (parent codes), in-class rehearsal frames were coded using MaxQda software, version 20, according to the grid's four categories (parent codes): self-regulated learning, teaching verbalizations, teaching strategies, and students' in-class performance. As a result, selected rehearsal frames qualitatively describe teaching instructions that might promote students' body and hearing awareness, such as "close the eyes while playing" or "sing to internalize the pitch." Another analysis type, coding the short video events according to the observation grid's subcategories (child codes), made it possible to perceive the time teachers dedicate to specific verbal or non-verbal strategies. Furthermore, a coding overlay analysis indicated that teachers tend to stimulate. (i) Forethought – explain tasks, offer feedback and ensure that students identify a goal, (ii) Performance – teach study strategies and encourage students to sing and use vocal abilities to ensure inner audition, (iii) Self-reflection – frequent inquiring and encouraging the student to verbalize their perception of performance. Although developed in the context of individual string instrument lessons, this classroom observation grid brings together essential variables in a one-to-one lesson. It may find utility in a broader context of music education due to the possibility to organize, observe and evaluate teaching practices. Besides that, this study contributes to cognitive development by suggesting a practical approach to fostering self-regulated learning.

Keywords: music education, observation grid, self-regulated learning, string instruments, teaching practices

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Authors: Vilma Zydziunaite, Vaida Jurgile, Roman Balandiuk

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Good, great, and leading teachers are experienced and respected role models, who are innovative, organized, collaborative, trustworthy, and confident facilitators of learning. They model integrity, have strong interpersonal and communication skills, display the highest level of professionalism, a commitment to students, and expertise, and demonstrate a passion for student learning while taking the initiative as influential change agents. Usually, we call them teacher(s) leaders by integrating three notions such as good, great, and leading in a one-teacher leader. Here are described essences of three concepts: ‘good teacher,’ ‘great teacher,’ and teacher leader’ as they are inseparable in teaching practices, teacher’s professional life, and educational interactions with students, fellow teachers, school administration, students’ families and school communities.

Keywords: great teacher, good teacher, leading teacher, school, student

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Authors: Juliana Osman, David Gilbert, Caroline Tan

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Innovation capability is recognized as a critical factor that contributes to promoting firm growth and wealth creation. While studies on innovation are in abundance, few empirical studies have been undertaken to examine the relationships of intellectual capital with innovation capability, and research investigating the combinations of strategic orientation dimensions is limited and virtually nothing in regard to the Malaysian context. This research investigates the impact of intellectual capital and three strategic orientations on the innovation capability and firm performance of Malaysian ICT SMEs. Data was collected from 213 firms relating to intellectual capital and the three strategic orientations; market orientation, learning orientation and technology orientation. Using partial least squares structural equation modelling (PLS-SEM) to analyse the data, results indicate that while market orientation has a direct negative relationship to firm performance, it is positively related to performance through the mediating effect of innovation capability. Learning orientation and technology orientation are mediated by innovation capability, while intellectual capital was found to be partially mediated by innovation capability. Findings indicate that firm performance is positively and significantly related to innovation capability and that market orientation, learning orientation, technology orientation and intellectual capital are all significant and positively related to innovation capability. The developed model indicates that Malaysian ICT SMEs would perform better with greater emphasis on developing innovation capability through enhancement of intellectual capital and the strategic orientations measured in this study.

Keywords: innovation capability, intellectual capital, strategic orientations, PLS-SEM

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Authors: Girish Prayag, Wantanee Suntikul, Elizabeth Agyeiwaah

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Current research on dark tourism largely follows residents’ perspectives with limited evaluations of tourists’ experiences. Unravelling the case of a dark heritage site in Elmina, Ghana, this paper develops a theoretical model to understand the relationships among four constructs namely, motivation, tourism impacts, place attachment, and satisfaction. Based on a sample of 414 domestic tourists, PLS-SEM confirmed several relationships and inter-relationships among the four constructs. For example, motivation had a positive relationship with perceptions of positive and negative tourism impacts suggesting that the more tourists were motivated to visit the site for cultural/learning experiences, the more positive and negative tourism impacts they perceived. Implications for dark tourism and heritage site management are offered.

Keywords: dark tourism, motivation, place attachment, tourism impacts

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Authors: Shona Tritt, William Cunningham

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Our study examines whether teaching effectiveness is perceived differently in online versus traditional classroom contexts. To do so, we analyzed teaching evaluations from courses that were offered as web options and as in-person classes simultaneously at the University of [removed for blinding] (N=87). Although teaching evaluations were on average lower for larger classes, we found that learning context (traditional versus online) moderated this effect. Specifically, we found a crossover effect such that in relatively smaller classes, teaching was perceived to be more effective in-person versus online, whereas, in relatively larger classes, teaching was perceived to be more effective when engaged online versus in-person.

Keywords: teaching evaluations, teaching effectiveness, e-learning, web-option

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Authors: Pauline Calleja, Brooke Alexander

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In 2010 the College of Emergency Nursing Australasia (CENA) undertook sole administration of the Trauma Nursing Program (TNP) across Australia. The original TNP was developed from recommendations by the Review of Trauma and Emergency Services-Victoria. While participant and faculty feedback about the program was positive, issues were identified that were common for industry training programs in Australia. These issues included didactic approaches, with many lectures and little interaction/activity for participants. Participants were not necessarily encouraged to undertake deep learning due to the teaching and learning principles underpinning the course, and thus participants described having to learn by rote, and only gain a surface understanding of principles that were not always applied to their working context. In Australia, a trauma or emergency nurse may work in variable contexts that impact on practice, especially where resources influence scope and capacity of hospitals to provide trauma care. In 2011, a program review was undertaken resulting in major changes to the curriculum, teaching, learning and assessment approaches. The aim was to improve learning including a greater emphasis on pre-program preparation for participants, the learning environment and clinically applicable contextualized outcomes participants experienced. Previously if participants wished to undertake assessment, they were given a take home examination. The assessment had poor uptake and return, and provided no rigor since assessment was not invigilated. A new assessment structure was enacted with an invigilated examination during course hours. These changes were implemented in early 2012 with great improvement in both faculty and participant satisfaction. This presentation reports on a comparison of participant evaluations collected from courses post implementation in 2012 and in 2015 to evaluate if positive changes were sustained. Methods: Descriptive statistics were applied in analyzing evaluations. Since all questions had more than 20% of cells with a count of <5, Fisher’s Exact Test was used to identify significance (p = <0.05) between groups. Results: A total of fourteen group evaluations were included in this analysis, seven CENA TNP groups from 2012 and seven from 2015 (randomly chosen). A total of 173 participant evaluations were collated (n = 81 from 2012 and 92 from 2015). All course evaluations were anonymous, and nine of the original 14 questions were applicable for this evaluation. All questions were rated by participants on a five-point Likert scale. While all items showed improvement from 2012 to 2015, significant improvement was noted in two items. These were in regard to the content being delivered in a way that met participant learning needs and satisfaction with the length and pace of the program. Evaluation of written comments supports these results. Discussion: The aim of redeveloping the CENA TNP was to improve learning and satisfaction for participants. These results demonstrate that initial improvements in 2012 were able to be maintained and in two essential areas significantly improved. Changes that increased participant engagement, support and contextualization of course materials were essential for CENA TNP evolution.

Keywords: emergency nursing education, industry training programs, teaching and learning, trauma education

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Authors: Melissa C. Ingram

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This literature review sought to explore the dimensions of diversity that can affect classroom learning. This review is significant as it can aid educators in reaching more of their diverse student population and creating supportive classrooms for teachers and students. For this study, peer-reviewed articles were found and compiled using Google Scholar. Key terms used in the search include student individuality, classroom equality, student development, teacher development, and teacher individuality. Relevant educational standards such as Common Core and Partnership for the 21st Century were also included as part of this review. Student and teacher individuality and equality is discussed as well as methods to grow both within educational settings. Embracing student and teacher individuality was found to be key as it may affect how each person interacts with given information. One method to grow individuality and equality in educational settings included drafting and employing revised teaching standards which include various Common Core and U.S. State standards. Another was to use educational theories such as constructivism, cognitive learning, and Experiential Learning Theory. However, barriers to growing individuality, such as not acknowledging differences in a population’s dimensions of diversity, still exist. Studies found preserving the dimensions of diversity owned by both teachers and students yielded more positive and beneficial classroom experiences.

Keywords: classroom equality, student development, student individuality, teacher development, teacher individuality

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Authors: Mirzaii Khadigeh

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Background and objectives: Sexual health education is an important part of health promotion services. The major role of sex education is on mothers’ shoulders. So, they have to be equipped with enough knowledge, attitude and self-efficacy for teens’ education. The present study aimed to determine the effect of team-learning and multimedia software on mothers’ knowledge, attitudes and self-efficacy in sexual health education to 12-14-year-old sons in Mashhad in 1395. Materials and methods: In this research, two experimental and one control group were employed using random sampling, which was done on 132 mothers of high school pupils. They were randomly assigned into experimental and control groups. The data were collected using demographic information and a researcher-constructed questionnaire to investigate the mothers’ knowledge, attitude, and self-efficacy and DASS21(The Depression, Anxiety and Stress Scale). They were run after confirming their reliability and validity. Intervention for the multimedia group was in the form of four CDs- each for 45 minutes- that were given to the mothers each week. At the end of the fourth week, a question-and-answer session was administered for 60 minutes. The team-learning group received intervention once a week (totally four weeks). Two weeks later, the data were collected and analyzed via Chi-square, Fisher, Kruskal-Wallis and ANOVA. Findings: Knowledge, attitude and self-efficacy of mothers in sexual health before the intervention did not have any significant differences (p >0.05). At the end of the study, the difference between the scores of the knowledge, attitude and self-efficacy in the three groups was meaningfully different (p < 0/001), but the difference between the two groups of multimedia and team-learning was not significant (p> 0.05 ). Discussion and conclusion: The result reported the efficacy of both team-leaning and multimedia software, which implies that the multimedia software training method was as effective as team-learning training one on the knowledge, attitude and self-efficacy of mothers. But, the multimedia training method is highly advised due to its availability, flexibility, and interest.

Keywords: training one on the knowledge, attitude, self-efficacy of mothers, boys

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Authors: Salome Schulze

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The delay of students in completing their dissertations is a worldwide problem. At the University of South Africa where this research was done, only about a third of the students complete their studies within the required period of time. This study explored the reasons why the students interrupted their studies, and why they resumed their research at a later stage. If this knowledge could be utilised to improve the throughput of doctoral students, it could have significant economic benefits for institutions of higher education while at the same time enhancing their academic prestige. To inform the investigation, attention was given to key theories concerning the learning of doctoral students, namely the situated learning theory, the social capital theory and the self-regulated learning theory, based on the social cognitive theory of learning. Ten students in the faculty of Education were purposefully selected on the grounds of their poor progress, or of having been in the system for too long. The collection of the data was in accordance with a Finnish study, since the two studies had the same aims, namely to investigate student engagement and disengagement. Graphic elicitation interviews, based on visualisations were considered appropriate to collect the data. This method could stimulate the reflection and recall of the participants’ ‘stories’ with very little input from the interviewer. The interviewees were requested to visualise, on paper, their journeys as doctoral students from the time when they first registered. They were to indicate the significant events that occurred and which facilitated their engagement or disengagement. In the interviews that followed, they were requested to elaborate on these motivating or challenging events by explaining when and why they occurred, and what prompted them to resume their studies. The interviews were tape-recorded and transcribed verbatim. Information-rich data were obtained containing visual metaphors. The data indicated that when the students suffered a period of disengagement, it was sometimes related to a lack of self-regulated learning, in particular, a lack of autonomy, and the inability to manage their time effectively. When the students felt isolated from the academic community of practice disengagement also occurred. This included poor guidance by their supervisors, which accordingly deprived them of significant social capital. The study also revealed that situational factors at home or at work were often the main reasons for the students’ procrastinating behaviour. The students, however, remained in the system. They were motivated towards a renewed engagement with their studies if they were self-regulated learners, and if they felt a connectedness with the academic community of practice because of positive relationships with their supervisors and of participation in the activities of the community (e.g., in workshops or conferences). In support of their learning, networking with significant others who were sources of information provided the students with the necessary social capital. Generally, institutions of higher education cannot address the students’ personal issues directly, but they can deal with key institutional factors in order to improve the throughput of doctoral students. It is also suggested that graphic elicitation interviews be used more often in social research that investigates the learning and development of the students.

Keywords: doctoral students, engaging and disengaging experiences, graphic elicitation interviews, student procrastination

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Authors: Iqra Yaseen

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One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

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Authors: Sarvada Nand

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Teacher quality is directly proportional to the achievement level of students. Recent researches reveal that the teacher learning communities enhance the quality of teacher. It is a proven fact that community does help in enhancing teachers’ self-esteem as professionals, their teaching skills and enhancing classroom transaction that results in the higher achievement of students. The purpose of this study is to develop TLC and provide them platform where they share their views and ideas on various academic issues. The study examines how teachers conceptualize TLCs, up to what extent TLC help in developing professionalism among teachers and how they prepare themselves for the days to come. In this study, pre-test in five subjects, Hindi, English, Mathematics, Science and Social Studies was conducted and a questionnaire was designed to judge the teachers' attitude towards teaching practice. After completion of the project duration of three and a half-month, an exercise of post-test was conducted in all the above subjects. The post tests show tremendous improvements in achievement level of those students who were regular in their classes and were attended through this new method. A visible shift in teacher’s attitude is seen for the better. They were able to realize their own potentials. There was a group of Facilitators formed to perform continuously supervision and monitor in regular intervals so that they could easily handle the challenges, and factors much important for the attainment towards the fulfillment of the objectives.

Keywords: teacher learning communities, best practice, teacher professionalism, student achievement

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Authors: Muhammad Younas, Sidra Sawati, M. Razzaq Athar

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Organizational development history reveals that it has ever been a challenge to identify and fathom the role of talent management, career development and compensation management towards employees’ retention and organizational performance. Organizations strive hard to measure the impact of all those factors which affect employee retention and organizational performance. Researchers have worked in great deal in order to know the relationship of independent variables i.e. Talent Management, Career Development and Compensation Management on dependent variables i.e. Employee Retention and Organizational Performance. Employees adorned with latest skills with long lasting loyalty play a significant role towards successful achievement of short term as well as long term goals of the organizations. Retention of valuable and resourceful employees for a longer time is equally essential for meeting the set goals. The organizations which spend reasonable chunk of their resources for taking such measures that help to retain their employees through talent management and satisfactory career development always enjoy a competitive edge over their competitors. Human resource is regarded as one of the most precious and difficult resource to management. It has its own needs and requirement. It becomes an easy prey to monotony when lacks career development. Wants and aspirations of this resource are seldom met completely but can be managed through career development and compensation management. In this era of competition, organizations have to take viable steps to management their resources especially human resource. Top management and Managers keep on working for an amenable solution in order to address the challenges relating career development and compensation management as their ultimate goal is to ensure the organizational performance on optimum level. The current study was conducted to examine the impact of Talent Management, Career Development and Compensation Management towards Employees Retention and Organizational Performance with mediating effect of Employees Motivation in Service Sector of Pakistan. The current study is based on Resource Based View (RBV) and Ability Motivation Opportunity (AMO) theories. It explains that by increasing internal resources we can manage employee talent, career development through compensation management and employee motivation more effectively. It will result in effective execution of HRM practices for employee retention enabling an organization to achieve and sustain competitive advantage through optimal performance. Data collection was made through a structured questionnaire which was based upon adopted instruments after testing reliability and validity. A total 300 employees of 30 firms in service sector of Pakistan were sampled through non-probability sampling technique. Regression analysis revealed that talent management, career development and compensation management have significant positive impact on employee retention and perceived organizational performance. The results further showed that employee motivation have a significant mediating effect on employee retention and organizational performance. The interpretation of the findings and limitations, theoretical and managerial implications are also discussed.

Keywords: career development, compensation management, employee retention, organizational performance, talent management

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Authors: Gaya Tridinanti

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Enhanching English vocabulary in early childhood is the main problem often faced by teachers. Thus, the purpose of this study was to determine the enhancement of children’s English vocabulary acquisition by using digital storytelling. This type of research was an action research. It consisted of a series of four activities done in repeated cycles: planning, implementation, observation, and reflection. The subject of the study consisted of 30 students of B group (5-6 years old) attending Happy Kids Kindergarten Palembang, Indonesia. This research was conducted in three cycles. The methods used for data collection were observation and documentation. Descriptive qualitative and quantitative methods were also used to analyse the data. The research showed that the digital storytelling learning activities could enhance the children’s English vocabulary acquisition. It is based on the data in which the enhancement in pre-cycle was 37% and 51% in Cycle I. In Cycle II it was 71% and in Cycle III it was 89.3%. The results showed an enhancement of about 14% from the pre-cycle to Cycle I, 20% from Cycle I to Cycle II, and enhancement of about 18.3% from Cycle II to Cycle III. The conclusion of this study suggests that digital storytelling learning method could enhance the English vocabulary acquisition of B group children at the Happy Kids Kindergarten Palembang. Therefore, digital storytelling can be considered as an alternative to improve English language learning in the classroom.

Keywords: acquisition, enhancing, digital storytelling, English vocabulary

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Authors: Chien-kuo Chang, Bo-wei Wu, Yi-yun Tang, Min-chiu Wu

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This paper proposes a power equipment diagnosis system based on partial discharge (PD), which is characterized by increasing the readability of experimental data and the convenience of operation. This system integrates a variety of analysis programs of different data formats and different programming languages and then establishes a set of interfaces that can follow and expand the structure, which is also helpful for subsequent maintenance and innovation. This study shows a case of using the developed Convolutional Neural Networks (CNN) to integrate with this system, using the designed model architecture to simplify the complex training process. It is expected that the simplified training process can be used to establish an adaptive deep learning experimental structure. By selecting different test data for repeated training, the accuracy of the identification system can be enhanced. On this platform, the measurement status and partial discharge pattern of each equipment can be checked in real time, and the function of real-time identification can be set, and various training models can be used to carry out real-time partial discharge insulation defect identification and insulation state diagnosis. When the electric power equipment entering the dangerous period, replace equipment early to avoid unexpected electrical accidents.

Keywords: partial discharge, convolutional neural network, partial discharge analysis platform, adaptive deep learning

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Authors: Hamid Reza Nikkhah, Abbas Toloei Eshlaghi, Hossein Ali Momeni

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One of the most important services of Knowledge Management Systems (KMS) is collaboration service which plays a decisive role in organization efficiency. Cloud computing as one of the latest IT technologies has brought a new paradigm in delivering services and communications. In this research, we evaluate the impact of cloud computing on the collaboration service of KMS and for doing so, four variables of cloud computing and three variables of the collaboration service were detected to be assessed.It was found that cloud computing has a far-fetching direct impact on the collaboration service.

Keywords: cloud computing, collaboration service, knowledge management systems, cloud computing

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Authors: Ana Mouta, Ana Paulino

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The following text presents the ik.model, a theoretical framework that guided the pedagogical implementation of meaningful educational technology-based projects in formal education worldwide. In this paper, we will focus on how this framework has enabled the development of media literacy projects for early childhood education during the last three years. The methodology that guided educators through the challenge of systematically merging analogic and digital means in dialogic high-quality opportunities of world exploration is explained throughout these lines. The effects of this methodology on early age media literacy development are considered. Also considered is the relevance of this skill in terms of post-contemporary challenges posed to learning.

Keywords: early learning, ik.model, media literacy, pedagogy

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Authors: Rik van Leeuwen, Ger Koole

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Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

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Authors: Lifar M. S., Tereshchenko A. A., Bulgakov A. N., Guda S. A., Guda A. A., Soldatov A. V.

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Metal nanoparticles are widely used as heterogeneous catalysts to activate adsorbed molecules and reduce the energy barrier of the reaction. Reaction product yield depends on the interplay between elementary processes - adsorption, activation, reaction, and desorption. These processes, in turn, depend on the inlet feed concentrations, temperature, and pressure. At stationary conditions, the active surface sites may be poisoned by reaction byproducts or blocked by thermodynamically adsorbed gaseous reagents. Thus, the yield of reaction products can significantly drop. On the contrary, the dynamic control accounts for the changes in the surface properties and adjusts reaction parameters accordingly. Therefore dynamic control may be more efficient than stationary control. In this work, a reinforcement learning algorithm has been applied to control the simulation of CO oxidation on a catalyst. The policy gradient algorithm is learned to maximize the CO₂ production rate based on the CO and O₂ flows at a given time step. Nonstationary solutions were found for the regime with surface deactivation. The maximal product yield was achieved for periodic variations of the gas flows, ensuring a balance between available adsorption sites and the concentration of activated intermediates. This methodology opens a perspective for the optimization of catalytic reactions under nonstationary conditions.

Keywords: artificial intelligence, catalyst, co oxidation, reinforcement learning, dynamic control

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Authors: Anshu Radha Aggarwal

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Higher Education (HE) in the India has experienced a series of remarkable changes over the last fifteen years as successive governments have sought to make the sector more efficient and more accountable for investment of public funds. Rapid expansion in student numbers and pressures to widen Participation amongst non-traditional students are key challenges facing HE. Learning outcomes can act as a benchmark for assuring quality and efficiency in HE and they also enable universities to describe courses in an unambiguous way so as to demystify (and open up) education to a wider audience. This paper examines how learning outcomes are used in HE and evaluates the implications for curriculum design and student learning. There has been huge expansion in the field of higher education, both technical and non-technical, in India during the last two decades, and this trend is continuing. It is expected that another about 400 colleges and 300 universities will be created by the end of the 13th Plan Period. This has lead to many concerns about the quality of education and training of our students. Many studies have brought the issues ailing our curricula, delivery, monitoring and assessment. Govt. of India, (via MHRD, UGC, NBA,…) has initiated several steps to bring improvement in quality of higher education and training, such as National Skills Qualification Framework, making accreditation of institutions mandatory in order to receive Govt. grants, and so on. Moreover, Outcome-based Education and Training (OBET) has also been mandated and encouraged in the teaching/learning institutions. MHRD, UGC and NBAhas made accreditation of schools, colleges and universities mandatory w.e.f Jan 2014. Outcome-based Education and Training (OBET) approach is learner-centric, whereas the traditional approach has been teacher-centric. OBET is a process which involves the re-orientation/restructuring the curriculum, implementation, assessment/measurements of educational goals, and achievement of higher order learning, rather than merely clearing/passing the university examinations. OBET aims to bring about these desired changes within the students, by increasing knowledge, developing skills, influencing attitudes and creating social-connect mind-set. This approach has been adopted by several leading universities and institutions around the world in advanced countries. Objectives of this paper is to highlight the issues concerning quality in higher education and quality frameworks, to deliberate on the various education and training models, to explain the outcome-based education and assessment processes, to provide an understanding of the NAAC and outcome-based accreditation criteria and processes and to share best-practice outcomes-based accreditation system and process.

Keywords: learning outcomes, curriculum development, pedagogy, outcome based education

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Authors: Deborah L. Smith

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To review our program and determine the best redesign options, department members gathered feedback and input through focus groups, analysis of data, and a review of the current research to ensure that the changes proposed were not based solely on the state’s new professional standards. In designing course assignments and assessments, we listened to a variety of constituents, including students, other institutions of higher learning, MDE webinars, host teachers, literacy clinic personnel, and other disciplinary experts. As a result, we are designing a program that is more inclusive of a variety of field experiences for growth. We have determined ways to improve our program by connecting academic disciplinary knowledge, educational psychology, and community building both inside and outside the classroom for professional learning communities. The state’s release of new professional standards led my department members to question what is working and what needs improvement in our program. One aspect of our program that continues to be supported by research and data analysis is the function of supervised field experiences with meaningful feedback. We seek to expand in this area. Other data indicate that we have strengths in modeling a variety of approaches such as cooperative learning, discussions, literacy strategies, and workshops. In the new program, field assignments will be connected to multiple courses, and efforts to scaffold student learning to guide them toward best evidence-based practices will be continuous. Despite running a program that meets multiple sets of standards, there are areas of need that we directly address in our redesign proposal. Technology is ever-changing, so it’s inevitable that improving digital skills is a focus. In addition, scaffolding procedures for English Language Learners (ELL) or other students who struggle is imperative. Diversity, equity, and inclusion (DEI) has been an integral part of our curriculum, but the research indicates that more self-reflection and a deeper understanding of culturally relevant practices would help the program improve. Connections with professional learning communities will be expanded, as will leadership components, so that teacher candidates understand their role in changing the face of education. A pilot program will run in academic year 22/23, and additional data will be collected each semester through evaluations and continued program review.

Keywords: DEI, field experiences, program redesign, teacher preparation

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Authors: D. I. George Amalarethinam, A. Emima

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Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.

Keywords: classification technique, data mining, EDM methods, prediction methods

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Authors: Syed Atif Hassan, Luv Mehta, Syed Asif Hassan

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Hit song science is a field of investigation wherein machine learning techniques are applied to music tracks in order to extract such features from audio signals which can capture information that could explain the popularity of respective tracks. Record companies invest huge amounts of money into recruiting fresh talents and churning out new music each year. Gaining insight into the basis of why a song becomes popular will result in tremendous benefits for the music industry. This paper aims to extract basic musical and more advanced, acoustic features from songs while also taking into account external factors that play a role in making a particular song popular. We use a dataset derived from popular Spotify playlists divided by genre. We use ten genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock), chosen on the basis of clear to ambiguous delineation in the typical sound of their genres. We feed these features into three different classifiers, namely, SVM with RBF kernel, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model at the end. Predicting song popularity is particularly important for the music industry as it would allow record companies to produce better content for the masses resulting in a more competitive market.

Keywords: classifier, machine learning, music tracks, popularity, prediction

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Authors: Nguyen Thi Kim Thoa

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Vietnam currently has about two million five hundred deaf/hard of hearing people. Although the issue of Vietnamese Sign Language (VSL) education has received attention from the State, there are still many issues that need to be resolved, such as policies, teacher training in both knowledge and teaching methods, education programs, and textbook compilation. Furthermore, the issue of research on VSL has not yet attracted the attention of linguists. Using the quantitative description method, the article will analyze, synthesize, and compare to find methods to create signed words in VSL, such as based on external shape characteristics, operational characteristics, operating methods, and basic meanings, from which we can see the special nature of signed words, the division of word types and the morphological meaning of creating new words through sign methods. From the results of this research, the aspect of ‘visual culture’ will be clarified in Vietnamese Deaf Culture. Through that, we also develop a number of vocabulary teaching methods (such as teaching vocabulary through a group of methods of forming signed words, teaching vocabulary using mind maps, and teaching vocabulary through culture...), with the aim of further improving the effectiveness of teaching and learning VSL in Vietnam. The research results also provide deaf people in Vietnam with a scientific and effective method of learning vocabulary, helping them quickly integrate into the community. The article will be a useful reference for linguists who want to research VSL.

Keywords: Vietnamese sign language (VSL), signed word, teaching, method

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Authors: Ken Hampshire, Thomas Mazzuchi, Shahram Sarkani

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As a subset of metaheuristics, nature-inspired optimization algorithms such as particle swarm optimization (PSO) have shown promise both in solving intractable problems and in their extensibility to novel problem formulations due to their general approach requiring few assumptions. Unfortunately, single instantiations of algorithms require detailed tuning of parameters and cannot be proven to be best suited to a particular illustrative problem on account of the “no free lunch” (NFL) theorem. Using these algorithms in real-world problems requires exquisite knowledge of the many techniques and is not conducive to reconciling the various approaches to given classes of problems. This research aims to present a unified view of PSO-based approaches from the perspective of relevant systems engineering problems, with the express purpose of then eliciting the best solution for any problem formulation in an ensemble learning bucket of models approach. The central hypothesis of the research is that extending the PSO algorithms found in the literature to real-world optimization problems requires a general ensemble-based method for all problem formulations but a specific implementation and solution for any instance. The main results are a problem-based literature survey and a general method to find more globally optimal solutions for any systems engineering optimization problem.

Keywords: particle swarm optimization, nature-inspired optimization, metaheuristics, systems engineering, ensemble learning

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Authors: F. Slaven, M. Uys, Y. Erasmus

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The South African National Mental Health Education Programme (SANMHEP) was a National Department of Health (NDoH) initiative to strengthen mental health services in South Africa in collaboration with the Foundation for Professional Development (FPD), SANOFI and the various provincial departments of health. The programme was implemented against the backdrop of a number of challenges in the management of mental health in the country related to staff shortages and infrastructure, the intersection of mental health with the growing burden of non-communicable diseases and various forms of violence, and challenges around substance abuse and its relationship with mental health. The Mental Health Care Act (No. 17 of 2002) prescribes that mental health should be integrated into general health services including primary, secondary and tertiary levels to improve access to services and reduce stigma associated with mental illness. In order for the provisions of the Act to become a reality, and for the journey of mental health patients through the system to improve, sufficient and skilled health care providers are critical. SANMHEP specifically targeted Medical Doctors and Professional Nurses working within the facilities that are listed to conduct 72-hour assessments, as well as District Hospitals. The aim of the programme was to improve the clinical diagnosis and management of mental disorders/conditions and the understanding of and compliance with the Mental Health Care Act and related Regulations and Guidelines in the care, treatment and rehabilitation of mental health care users. The course used a blended-learning approach and trained 1 120 health care providers through 36 workshops between February and November 2019. Of those trained, 689 (61.52%) were Professional Nurses, 337 (30.09%) were Medical Doctors, and 91 (8.13%) indicated their occupation as ‘other’ (of these more than half were psychologists). The pre- and post-evaluation of the face-to-face training sessions indicated a marked improvement in knowledge and confidence level scores (both clinical and legislative) in the care, treatment and rehabilitation of mental health care users by participants in all the training sessions. There was a marked improvement in the knowledge and confidence of participants in performing certain mental health activities (on average the ratings increased by 2.72; or 27%) and in managing certain mental health conditions (on average the ratings increased by 2.55; or 25%). The course also required that participants obtain 70% or higher in their formal assessments as part of the online component. The 337 participants who completed and passed the course scored 90% on average. This illustrates that when participants attempted and completed the course, they did very well. To further assess the effect of the course on the knowledge and behaviour of the trained mental health care practitioners a mixed-method outcome evaluation is currently underway consisting of a survey with participants three months after completion, follow-up interviews with participants, and key informant interviews with department of health officials and course facilitators. This will enable a more detailed assessment of the impact of the training on participants' perceived ability to manage and treat mental health patients.

Keywords: mental health, public health facilities, South Africa, training

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