Search results for: computer assisted learning

Authors: Olusegun Omoluwa, Kolawole Israel Anthony

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Pupils with learning disabilities are found in every classroom, but because learning disabilities cannot be seen, the condition is often too neglected. Unless these pupils are recognised and treated, they are likely to become educational discards. This study consequently attempted to determine effects of self-monitoring strategy on homework completion among pupils with learning disabilities. Ninety (90) participants were engaged in the study. Pre-test, post-test, control group quasi experimental design was adopted. Purposive sampling technique was used to select pupils with evidence of learning disabilities from three primary schools in Ondo State. Findings showed that self-monitoring strategy was significant in enhancing homework completion among pupils with learning disabilities. However, gender and self-esteem did not significantly contribute to homework completion. The study therefore recommended that measures such that would uncover unsettling academic, psychological and emotional deficiencies of these pupils through appropriate diagnosis should be undertaken by the parents and teachers, in order for them to have a sense of belonging in the society.

Keywords: self monitoring, home work completion, learning dissabilities, learning

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Authors: Wan Jen Chang

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Back ground: Taiwan's current adult lifelong learning services have expanded from vocational training to universal lifelong learning. However, both the professional knowledge training of learning guidance and consulting services and the provision of adult online learning consulting service systems still need to be established. Purpose: The purposes of this study are as follows: 1. Analyze the professional training mechanism for cultivating adult lifelong learning consultation and coaching; 2. Explore the feasibility of constructing a system that uses network technology to provide adult learning consultation services. Research design: This study conducts a literature analysis of counseling and coaching policy reports on lifelong learning in European countries and the United States. There are two focus discussions were conducted with 15 lifelong learning scholars, experts and practitioners as research subjects. The following two topics were discussed and suggested: 1. The current situation, needs and professional ability training mechanism of "Adult Lifelong Learning Consulting and Services"; 2. Strategies for establishing an "Adult Lifelong Learning Consulting and Service internet System". Conclusion: 1.Based on adult lifelong learning consulting and service needs, plan a professional knowledge training and certification system.2.Adult lifelong learning consulting and service professional knowledge and skills training should include the use of network technology to provide consulting service skills.3.To establish an adult lifelong learning consultation and service system, the Ministry of Education should promulgate policies and measures at the central level and entrust local governments or private organizations to implement them.4.The adult lifelong learning consulting and service system can combine the national qualifications framework, private sector and NPO to expand learning consulting service partners.

Keywords: adult lifelong learning, profesional knowledge, consulting and service, network system

Procedia PDF Downloads 68

Authors: Tingting Zou

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This paper firstly presents an overview of the situation of Xinjiang and Hotan, and describes the current status and features of Uyghur students’ English education. Then it summarizes the research on the theories of Third Language Acquisition and Foreign Language Learning Motivation at home and abroad. Further, through the data collected by the questionnaire, the paper points out the three main problems and causes of Uyghur students’ English learning in Hotan, Xinjiang. Finally, the paper draws a conclusion and puts forward some suggestions on how to improve their English learning quality based on the theory of Foreign Language Learning Motivation.

Keywords: countermeasures and difficulties, English learning, Hotan Xinjiang, Uyghur students

Procedia PDF Downloads 96

Authors: Zhidong Zhang, Yingchen Yang

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In this study, a diagnostic assessment model for Mastery Engineering Learning was established based on a group of undergraduate students who studied in an engineering course. A diagnostic assessment model can examine both students' learning process and report achievement results. One very unique characteristic is that the diagnostic assessment model can recognize the errors and anything blocking students in their learning processes. The feedback is provided to help students to know how to solve the learning problems with alternative strategies and help the instructor to find alternative pedagogical strategies in the instructional designs. Dynamics is a core course in which is a common course being shared by several engineering programs. This course is a very challenging for engineering students to solve the problems. Thus knowledge acquisition and problem-solving skills are crucial for student success. Therefore, developing an effective and valid assessment model for student learning are of great importance. Diagnostic assessment is such a model which can provide effective feedback for both students and instructor in the mastery of engineering learning.

Keywords: diagnostic assessment, mastery learning, engineering, bayesian network model, learning processes

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Authors: Duaa Al Maani, Andrew Roberts

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Students in higher education are expected to learn actively and independently. Whilst quite work has been done to understand the perceptions of students’ learning transition regarding independent learning, to author’s best knowledge, it seems relatively few published research on independent learning in studio-based subjects such as architecture. Another major issue in independent learning research concerned the inconsistency in terminology; there appears to be a paucity of research on its definition, challenges, and tools within the UK university sector. It is not always clear how independent learning works in practice, or what are the challenges that face students toward being independent learners. Accordingly, this paper seeks to highlight these problems by analyzing previous and current literature of independent learning, in addition, to measure students’ independence at the very begging of their first academic year and compare it with their level of learning independence at the end of the same year. Eighty-seven student enrolled in 2017/2018 at Cardiff University completed the Autonomous Learning Questionnaire in order to measure their level of learning independence. Students’ initial responses were very positive and showed high level of learning independence. Interestingly, these responses significantly decreased at the end of the year. Time management was the most obvious challenge facing students transition into higher education, and contrary to expectations, we found no effect of student maturity on their level of independence. Moreover, we found no significant differences among students’ gender, but we did find differences among nationalities.

Keywords: autonomous learning, first year, learning independence, transition

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Authors: Elana Mahboub, Lamar Bakhurji, Hind Alhindi, Sara Alesayi

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Duolingo is a revolutionary language learning platform that offers an interactive and accessible learning experience. Its gamified approach makes language learning engaging and enjoyable, with a diverse range of languages available. The platform's adaptive learning system tailors lessons to individual proficiency levels, ensuring a personalized and efficient learning journey. The incorporation of multimedia elements enhances the learning experience and promotes practical language application. Duolingo's success is attributed to its mobile accessibility, offering basic access to language courses for free, with optional premium features for those seeking additional resources. Research shows positive outcomes for users, and the app's global impact extends beyond individual learning to formal language education initiatives. Duolingo is a transformative force in language education, breaking down barriers and making language learning an attainable goal for millions worldwide.

Keywords: duolingo, artificial intelligence, artificial intelligence in duolingo, benefit of artificial intelligence

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

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

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

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Authors: Ferissa B. Ablola

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The two learning theories which were evidently used in medical education include cognitive and sociocultural frameworks. The interplay of different learning theories in education is vital since most of the existing theories have specific focus of development. In addition, a certain theory is best fit with a particular learning outcome and audience profile. The application of learning theories is education is said to be dynamic and becomes more complex with increasing educational level. This systematic review aims to describe the possible shift from integration of cognitive learning theory to employment of socio-cultural approach in medical and health-allied education over the years among students, educators and the learning institution through systematic review following the PRISMA guidelines. In addition, the changes in teaching modality and individual acceptance of the shift of learning framework among cognitive constructivist and social constructivist will also be documented. This present review may serve as baseline information on the connection of two widely used theories in medical education in different year levels. Further, this study emphasizes the significance of the alignment of different learning theories and combination of insights from several educational frameworks, would permit the creation of a teaching/learning design with real theoretical depth. A more inclusive systematic review is necessary to involve more related studies, and exploration of interaction among other learning theories in health and other fields of study is encouraged.

Keywords: learning theory, cognitive, sociocultural, medical education

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Authors: Wang Lin, Li Zhiqiang

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The purpose of this paper is to analyze the cooperative learning behavior pattern based on the data of students' movement. The study firstly reviewed the cooperative learning theory and its research status, and briefly introduced the k-means clustering algorithm. Then, it used clustering algorithm and mathematical statistics theory to analyze the activity rhythm of individual student and groups in different functional areas, according to the movement data provided by 10 first-year graduate students. It also focused on the analysis of students' behavior in the learning area and explored the law of cooperative learning behavior. The research result showed that the cooperative learning behavior analysis method based on movement data proposed in this paper is feasible. From the results of data analysis, the characteristics of behavior of students and their cooperative learning behavior patterns could be found.

Keywords: behavior pattern, cooperative learning, data analyze, k-means clustering algorithm

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Authors: Eva Laryea, Clement Yeboah Authors

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A pretest-posttest within subjects, experimental design was employed to examine the effects of a computerized basic statistics learning game on achievement and statistics-related anxiety of students enrolled in introductory graduate statistics course. Participants (N = 34) were graduate students in a variety of programs at state-funded research university in the Southeast United States. We analyzed pre-test posttest differences using paired samples t-tests for achievement and for statistics anxiety. The results of the t-test for knowledge in statistics were found to be statistically significant indicating significant mean gains for statistical knowledge as a function of the game-based intervention. Likewise, the results of the t-test for statistics-related anxiety were also statistically significant indicating a decrease in anxiety from pretest to posttest. The implications of the present study are significant for both teachers and students. For teachers, using computer games developed by the researchers can help to create a more dynamic and engaging classroom environment, as well as improve student learning outcomes. For students, playing these educational games can help to develop important skills such as problem solving, critical thinking, and collaboration. Students can develop interest in the subject matter and spend quality time to learn the course as they play the game without knowing that they are even learning the presupposed hard course. The future directions of the present study are promising, as technology continues to advance and become more widely available. Some potential future developments include the integration of virtual and augmented reality into educational games, the use of machine learning and artificial intelligence to create personalized learning experiences, and the development of new and innovative game-based assessment tools. It is also important to consider the ethical implications of computer game-based pedagogy, such as the potential for games to perpetuate harmful stereotypes and biases. As the field continues to evolve, it will be crucial to address these issues and work towards creating inclusive and equitable learning experiences for all students. This study has the potential to revolutionize the way basic statistics graduate students learn and offers exciting opportunities for future development and research. It is an important area of inquiry for educators, researchers, and policymakers, and will continue to be a dynamic and rapidly evolving field for years to come.

Keywords: pretest-posttest within subjects, experimental design, achievement, statistics-related anxiety

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Authors: John Pomerat, Aviv Segev

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Recently, deep learning has had many theoretical breakthroughs. For deep learning to be successful in the industry, however, there need to be practical algorithms capable of handling many real-world hiccups preventing the immediate application of a learning algorithm. Although AI promises to revolutionize the healthcare industry, getting access to patient data in order to train learning algorithms has not been easy. One proposed solution to this is data- sharing. In this paper, we propose an alternative protocol, based on multi-party computation, to train deep learning models while maintaining both the privacy and security of training data. We examine three methods of training neural networks in this way: Transfer learning, average ensemble learning, and series network learning. We compare these methods to the equivalent model obtained through data-sharing across two different experiments. Additionally, we address the security concerns of this protocol. While the motivating example is healthcare, our findings regarding multi-party computation of neural network training are purely theoretical and have use-cases outside the domain of healthcare.

Keywords: neural network aggregation, multi-party computation, transfer learning, average ensemble learning

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Authors: Omar Elharrouss, Youssef Hmamouche, Assia Kamal Idrissi, Btissam El Khamlichi, Amal El Fallah-Seghrouchni

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Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.

Keywords: edge detection, convolutional neural networks, deep learning, scale-representation, backbone

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Authors: Kamal Heidari

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Covid-19 has borne drastic effects on different areas of society, including the education area, in that it brought virtual education to the center of attention, as an alternative to in-person education. In virtual education, the importance of flipped learning doubles, as students are supposed to take the main responsibility of teaching/learning process; and teachers play merely a facilitative/monitoring role. Given the students’ responsibility in virtual flipped learning, students’ motivation plays a pivotal role in the effectiveness of this learning method. The L2 Motivational Self-System (L2MSS) model is a currently proposed model elaborating on students’ motivation based on three sub-components: ideal L2 self, ought-to L2 self, and L2 learning experience. Drawing on an exploratory sequential mixed-methods research design, this study probed the effect of virtual flipped learning (via SHAD platform) on 112 gifted and non-gifted students’ motivation based on the L2 MSS. This study uncovered that notwithstanding the point that virtual flipped learning improved both gifted and non-gifted students’ motivation, it differentially affected their motivation. In other words, gifted students mostly referred to ideal L2 self, while non-gifted ones referred to ought-to L2 self and L2 learning experience aspects of motivation.

Keywords: virtual flipped learning, giftedness, motivation, L2MSS

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Authors: Riaan Kleyn

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Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

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Authors: Vijayan Narayananayar

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Identifying learning gaps is crucial in ensuring learners have the necessary knowledge and skills to succeed. The Learning and Teaching (L&T) approach requires tutors to identify gaps in knowledge and improvise learning activities to close them. One approach to identifying learning gaps is through diagnostic assessment, which uses well-structured questions and answer options. The paper focuses on the use of diagnostic assessment as a learning and teaching approach in a foundational module at a polytechnic. The study used diagnostic assessment over two semesters, including the COVID and post-COVID semesters, to identify gaps in learning. The design of the diagnostic activity, pedagogical intervention, and survey responses completed by learners were analyzed. Results showed that diagnostic assessment can be an effective tool for identifying learning gaps and designing interventions to address them. Additionally, the use of diagnostic assessment provides an opportunity for tutors to engage with learners on a one-to-one basis, tailoring teaching to individual needs. The paper also discusses the design of diagnostic questions and answer options, including characteristics that need to be considered in achieving the target of identifying learning gaps. The implications of using diagnostic assessment as a learning and teaching approach include bridging the gap between theory and practice, and ensuring learners are equipped with skills necessary for their future careers. This paper can be useful in helping educators and practitioners to incorporate diagnostic assessment into their L&T approach.

Keywords: assessment, learning & teaching, diagnostic assessment, analytics

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Authors: Annette McArthur

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The traditional model of teaching and learning, where ICT sits as a separate entity is not a model for a 21st century school. It is imperative that teaching and learning embraces technological advancements. The challenge in schools lies in shifting the mindset of teachers so they see ICT as integral to their teaching, learning and curriculum rather than a separate E-Learning curriculum stream. This research project investigates how the effective, planned, intentional integration of ICT into a STEM curriculum, can enable the shift in the teacher mindset. The project incorporated: • Developing a professional coaching relationship with key STEM teachers. • Facilitating staff professional development involving student centered project based learning pedagogy in the context of a STEM curriculum. • Facilitating staff professional development involving digital literacy. • Establishing a professional community where collaboration; sharing and reflection were part of the culture of the STEM community. • Facilitating classroom support for the effective delivery innovative STEM curriculum. • Developing STEM learning spaces where technologies were used to empower and engage learners to participate in student-centered, project-based learning.

Keywords: e-learning, ICT, project based learning, STEM

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Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

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Authors: Nurul Yusnita

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Many studies have been developed to help the students to get good achievement in English learning. They can be from the teaching method or psychological ones. One of the psychological studies in educational research is learning style. In some ways, learning style can affect the achievement of the students. This study aimed to examine 4 (four) learning styles and their relations to English achievement among the students learning English in Language Training Center of Universitas Muhammadiyah Yogyakarta (LTC UMY). The method of this study was descriptive analytical. The sample consisted of 39 Accounting students in LTC UMY. The data was collected through questionnaires with Likert-scale. The achievement was obtained from the grade of the students. To analyze the questionnaires and to see the relation between the learning styles and the student achievement, SPSS statistical software of correlational analysis was used. The result showed that both visual and auditory had the same percentage of 35.9% (14 students). 3 students (7.7%) had kinaesthetic learning style and 8 students (20.5%) had visual and auditory ones. Meanwhile, there were 5 students (12.8%) who had visual learning style could increase their grades. Only 1 student (2.5%) who had visual and auditory could improve his grade. Besides grade increase, there were also grade decrease. Students with visual, auditory, visual and auditory, and kinaesthetic learning styles were 3 students (7.7%), 5 students (12%), 4 students (10.2%) and 1 student (2.5%) respectively. In conclusion, there was no significant relationship between learning style and English achievement. Most of the good achievers were the students with visual and auditory learning styles and most of them preferred visual method. The implication is the teachers and material designers could improve their method through visual things to achieve effective English teaching learning.

Keywords: accounting students, English achievement, language training centre, learning styles

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Authors: Raana Babadi Fathipour

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Tea, a beverage widely cherished across the globe, has captured the interest of scholars with its recent acknowledgement for possessing noteworthy health advantages. Of particular significance is its proven ability to ward off ailments such as cancer and cardiovascular afflictions. Moreover, within the realm of culinary creations, lipid oxidation poses a significant challenge for food product development. In light of these aforementioned concerns, this present discourse turns its attention towards exploring diverse methodologies employed in extracting polyphenols from various types of tea leaves and examining their utility within the vast landscape of the ever-evolving food industry. Based on the discoveries unearthed in this comprehensive investigation, it has been determined that the fundamental constituents of tea are polyphenols possessed of intrinsic health-enhancing properties. This includes an assortment of catechins, namely epicatechin, epigallocatechin, epicatechin gallate, and epigallocatechin gallate. Moreover, gallic acid, flavonoids, flavonols and theaphlavins have also been detected within this aromatic beverage. Of these myriad components examined vigorously in this study's analysis, catechin emerges as particularly beneficial. Multiple techniques have emerged over time to successfully extract key compounds from tea plants, including solvent-based extraction methodologies, microwave-assisted water extraction approaches and ultrasound-assisted extraction techniques. In particular, consideration is given to microwave-assisted water extraction method as a viable scheme which effectively procures valuable polyphenols from tea extracts. This methodology appears adaptable for implementation within sectors such as dairy production along with meat and oil industries alike.

Keywords: camellia sinensis, extraction, food application, shelf life, tea

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Authors: Denise Levy, Helen J. Khoury

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Nuclear technology is a controversial issue among a great share of the Brazilian population. Misinformation and common wrong beliefs confuse public’s perceptions and the scientific community is expected to offer a wider perspective on the benefits and risks resulting from ionizing radiation in everyday life. Attentive to the need of new approaches between science and society, the Nuclear Energy Museum, in northeast Brazil, is an initiative created to communicate the growing impact of the beneficial applications of nuclear technology in medicine, industry, agriculture and electric power generation. Providing accessible scientific information, the museum offers a rich learning environment, making use of different educational strategies, such as films, interactive panels and multimedia learning tools, which not only increase the enjoyment of visitors, but also maximize their learning potential. Developed according to modern active learning instructional strategies, multimedia materials are designed to present the increasingly role of nuclear science in modern life, transforming science education into a meaningful learning experience. In year 2016, nine different interactive computer-based activities were developed, presenting curiosities about ionizing radiation in different landmarks around the world, such as radiocarbon dating works in Egypt, nuclear power generation in France and X-radiography of famous paintings in Italy. Feedback surveys have reported a high level of visitors’ satisfaction, proving the high quality experience in learning nuclear science at the museum. The Nuclear Energy Museum is the first and, up to the present time, the only permanent museum in Brazil devoted entirely to nuclear science.

Keywords: nuclear technology, multimedia learning tools, science museum, society and education

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Authors: Yen-Mei Lee

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Mobile learning (m-learning) has been applied in the education field not only because it is an alternative to web-based learning but also it possesses the ‘anytime, anywhere’ learning features. However, most studies focus on the technology-related issue, such as usability and functionality instead of addressing m-learning from the motivational perspective. Accordingly, the main purpose of the current paper is to integrate critical factors from different motivational theories and related findings to have a better understand the catalysts of an individual’s learning motivation toward m-learning. The main research question for this study is stated as follows: based on different motivational perspectives, what factors of applying mobile devices as medium can facilitate people’s learning motivations? Self-Determination Theory (SDT), Uses and Gratification Theory (UGT), Malone and Lepper’s taxonomy of intrinsic motivation theory, and different types of motivation concepts were discussed in the current paper. In line with the review of relevant studies, three motivating factors with five essential elements are proposed. The first key factor is autonomy. Learning on one’s own path and applying personalized format are two critical elements involved in the factor of autonomy. The second key factor is to apply a build-in instant feedback system during m-learning. The third factor is creating an interaction system, including communication and collaboration spaces. These three factors can enhance people’s learning motivations when applying mobile devices as medium toward learning. To sum up, in the currently proposed paper, with different motivational perspectives to discuss the m-learning is different from previous studies which are simply focused on the technical or functional design. Supported by different motivation theories, researchers can clearly understand how the mobile devices influence people’s leaning motivation. Moreover, instructional designers and educators can base on the proposed factors to build up their unique and efficient m-learning environments.

Keywords: autonomy, learning motivation, mobile learning (m-learning), motivational perspective

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Authors: Kornchanok Chidchaisuwan

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This project is a study of learning activities through experience to enhance career skills and technical abilities on the creative strategies for advertising course of undergraduate students. This instructional model consisted of study learning approaches: 1) Simulation-based learning: used to create virtual learning activities plans for work like working at advertising companies. 2) Project-based learning: Actual work based on the processed creating and focus on producing creative works to present on new media channels. The results of learning management found that there were effects on the students in various areas, including 1) The learners have experienced in the step by step of advertising work process. 2) The learner has the skills to work from the actual work (Learning by Doing), allowing the ability to create, present, and produce the campaign accomplished achievements and published on online media at a better level.

Keywords: technical, advertising, presentation, career skills, experience, simulation based learning

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Authors: Asmaa El Harat, Toumi Hicham, Youssef Baddi

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This survey delves into the complex realm of Internet of Things (IoT) security, highlighting the urgent need for effective cybersecurity measures as IoT devices become increasingly common. It explores a wide array of cyber threats targeting IoT devices and focuses on mitigating these attacks through the combined use of deep learning and machine learning algorithms, as well as edge and cloud computing paradigms. The survey starts with an overview of the IoT landscape and the various types of attacks that IoT devices face. It then reviews key machine learning and deep learning algorithms employed in IoT cybersecurity, providing a detailed comparison to assist in selecting the most suitable algorithms. Finally, the survey provides valuable insights for cybersecurity professionals and researchers aiming to enhance security in the intricate world of IoT.

Keywords: internet of things (IoT), cybersecurity, machine learning, deep learning

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Authors: Jatuphum Ketchatturat

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The objective of this research was to study the learning and innovation skills of undergraduate students after Research-Based Learning had been applied. Eighty research participants were selected from undergraduate students enrolled in Educational Research Program using the Purposive Sampling Method. Research Methodology was Descriptive Research, the research took one semester to complete. The research instruments consisted of (1) Research Skill Assessment Form, (2) Research Quality Assessment Form, (3) Scale of learning and innovation skills 25 items. The quantitative data were analysed using descriptive statistics including, frequency, percentage, average and standard deviation. The qualitative data were analyzed using content analysis. The research results were (1) The students were able to conduct research that focused on educational research, which has a fair to the excellent level of standards of a research learning outcome, research skills, and research quality. The student’s learning and innovation skills have relating to research skills and research quality. (2) The findings found that the students have been developed to be learning and innovation skills such as systematic thinking, analytical thinking, critical thinking, creative problem solving, collaborative, research-creation, communication, and knowledge and experience sharing to friends, community and society.

Keywords: learning and innovation skills, research based learning, research skills, undergraduate students

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Authors: Preshanthan Moodley, E. B. Gueguim-Kana

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The objective of this study was to develop a method to pretreat sugarcane leaf waste using microwave-assisted (MA) inorganic salt. The effects of process parameters of salt concentration, microwave power intensity and pretreatment time on reducing sugar yield from enzymatically hydrolysed sugarcane leaf waste were investigated. Pretreatment models based on MA-NaCl, MA-ZnCl2 and MA-FeCl3 were developed. Maximum reducing sugar yield of 0.406 g/g was obtained with 2 M FeCl3 at 700W for 3.5 min. Scanning electron microscopy (SEM) and Fourier Transform Infrared analysis (FTIR) showed major changes in lignocellulosic structure after MA-FeCl3 pretreatment with 71.5 % hemicellulose solubilization. This pretreatment was further assessed on sorghum leaves and Napier grass under optimal MA-FeCl3 conditions. A 2 fold and 3.1-fold increase in sugar yield respectively were observed compared to previous reports. This pretreatment was highly effective for enhancing enzymatic saccharification of lignocellulosic biomass.

Keywords: acid, pretreatment, salt, sugarcane leaves

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Authors: Natcha Mahapoonyanont

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This study examines the intersection of Assessment for Learning (AfL) and visible learning principles, focusing on their combined impact on educational outcomes. This study aligns with recent findings on implementing this approach in Thailand, underscoring its potential to transform classrooms into active, student-centered learning spaces. The research highlights key AfL strategies—feedback loops, peer assessment, and goal-setting—as essential for engaging students and fostering self-regulation. Data collected from model schools in Songkhla, Thailand, reveal that integrating visible learning into AfL, alongside professional learning communities (PLCs) for teachers, has led to substantial improvements in student achievement, critical thinking, and engagement. Teachers who underwent targeted training based on visible learning principles demonstrated enhanced skills in providing effective feedback and supporting student autonomy, with post-training assessments showing significant improvements. This study supports the need for continuous teacher development programs that emphasize visible learning concepts alongside strong administrative support and modern educational resources. The findings advocate for AfL and visible learning integration as a model for empowering teachers, enriching student learning, and enhancing educational equity across diverse school settings.

Keywords: visible learning, assessment for learning (AfL), feedback loops, professional learning communities (PLCs), teacher development, student autonomy

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Authors: Jenny Hall, Alison Jaquet

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Universities’ push toward the production of high quality research is not limited to academic staff and experienced researchers. In this environment of research rich agendas, Higher Degree Research (HDR) students are increasingly expected to engage in the publishing of good quality papers in high impact journals. IFN001: Advanced Information Research Skills (AIRS) is a credit bearing mandatory coursework requirement for Queensland University of Technology (QUT) doctorates. Since its inception in 1989, this unique blended learning program has provided the foundations for new researchers to produce original and innovative research. AIRS was redeveloped in 2012, and has now been evaluated with reference to the university’s strategic research priorities. Our research is the first comprehensive evaluation of the program from the learner perspective. We measured whether the program develops essential transferrable skills and graduate capabilities to ensure best practice in the areas of publishing and data management. In particular, we explored whether AIRS prepares students to be agile researchers with the skills to adapt to different research contexts both within and outside academia. The target group for our study consisted of HDR students and supervisors at QUT. Both quantitative and qualitative research methods were used for data collection. Gathering data was by survey and focus groups with qualitative responses analyzed using NVivo. The results of the survey show that 82% of students surveyed believe that AIRS assisted their research process and helped them learn skills they need as a researcher. The 18% of respondents who expressed reservation about the benefits of AIRS were also examined to determine the key areas of concern. These included trends related to the timing of the program early in the candidature and a belief among some students that their previous research experience was sufficient for postgraduate study. New insights have been gained into how to better support HDR learners in partnership with supervisors and how to enhance learning experiences of specific cohorts, including international students and mature learners.

Keywords: data management, enhancing learning experience, publishing, research higher degree students, doctoral students

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Authors: Gaya Bin Noon, Thoko Hanjahanja-Phiri, Laura Xavier Fadrique, Plinio Pelegrini Morita, Hélène Vaillancourt, Jennifer Teague, Tania Donovska

Abstract:

Active Assisted Living (AAL) refers to systems designed to improve the quality of life, aid in independence, and create healthier lifestyles for care recipients. As the population ages, there is a pressing need for non-intrusive, continuous, adaptable, and reliable health monitoring tools to support aging in place. AAL has great potential to support these efforts with the wide variety of solutions currently available, but insufficient efforts have been made to address concerns arising from the integration of AAL into care. The purpose of this research was to (1) explore the integration of AAL technologies and data into the clinical pathway, and (2) map data access and governance for AAL technology in order to develop standards for use by policy-makers, technology manufacturers, and developers of smart communities for seniors. This was done through four successive research phases: (1) literature search to explore existing work in this area and identify lessons learned; (2) modeling of the continuum of care; (3) adapting a framework for data governance into the AAL context; and (4) interviews with stakeholders to explore the applicability of previous work. Opportunities for standards found in these research phases included a need for greater consistency in language and technology requirements, better role definition regarding who can access and who is responsible for taking action based on the gathered data, and understanding of the privacy-utility tradeoff inherent in using AAL technologies in care settings.

Keywords: active assisted living, aging in place, internet of things, standards

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Authors: Jinfeng Zhang, Chun-Sing Lee

Abstract:

Pure nanodrugs (PNDs) – nanoparticles consisting entirely of drug molecules, have been considered as promising candidates for the next-generation nanodrugs. However, the traditional preparation method via reprecipitation faces critical challenges including low production rates, relatively large particle sizes and batch-to-batch variations. Here, for the first time, we successfully developed a novel, versatile and controllable strategy for preparing PNDs via an anodized aluminium oxide (AAO) template-assisted method. With this approach, we prepared PNDs of an anti-cancer drug (VM-26) with precisely controlled sizes reaching the sub-20 nm range. This template-assisted approach has much higher feasibility for mass production comparing to the conventional reprecipitation method and is beneficial for future clinical translation. The present method is further demonstrated to be easily applicable for a wide range of hydrophobic biomolecules without the need of custom molecular modifications and can be extended for preparing all-in-one nanostructures with different functional agents.

Keywords: drug delivery, pure nanodrugs, size control, template

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Authors: Muhammad Afzal Malik

Abstract:

Traditional Learning & Distance based learning are the two systems prevailing in Pakistan. These systems affect the level of education standard. The purpose of this study was to compare the traditional classroom learning and distance learning in Pakistan: (a) To explore the effectiveness of the traditional to Distance learning in Pakistan; (b) To identify the factors that affect traditional and distance learning. This review found that, on average, students in traditional classroom conditions performed better than those receiving education in and distance learning. The difference between student outcomes for traditional Classroom and distance learning classes —measured as the difference between treatment and control means, divided by the pooled standard deviation— was larger in those studies contrasting conditions that blended elements of online and face-to-face instruction with conditions taught entirely face-to-face. This research was conducted to highlight the impact of distance learning education system on education standard. The education standards were institutional support, course development, learning process, student support, faculty support, evaluation and assessment. A well developed questionnaire was administered and distributed among 26 faculty members of GCET, H-9 and Virtual University of Pakistan from each. Data was analyzed through correlation and regression analysis. Results confirmed that there is a significant relationship and impact of DLE system on education standards. This will also provide baseline for future research. It will add value to the existing body of knowledge.

Keywords: distance learning education, higher education, education standards, student performance

Procedia PDF Downloads 280