Search results for: enhancing learning experiences

Authors: Vani Singhal, Jitendra Parmar, Satyendra Singh Chouhan

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Unseen class discovery has now become an important part of a machine-learning algorithm to judge new classes. Unseen classes are the classes on which the machine learning model is not trained on. With the advancement in technology and AI replacing humans, the amount of data has increased to the next level. So while implementing a model on real-world examples, we come across unseen new classes. Our aim is to find the number of unseen classes by using a hierarchical-based active learning algorithm. The algorithm is based on hierarchical clustering as well as active sampling. The number of clusters that we will get in the end will give the number of unseen classes. The total clusters will also contain some clusters that have unseen classes. Instead of first discovering unseen classes and then finding their number, we directly calculated the number by applying the algorithm. The dataset used is for intent classification. The target data is the intent of the corresponding query. We conclude that when the machine learning model will encounter real-world data, it will automatically find the number of unseen classes. In the future, our next work would be to label these unseen classes correctly.

Keywords: active sampling, hierarchical clustering, open world learning, unseen class discovery

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Authors: Goudjil kamel, Boukhatem Ghania, Jlailia Djihene

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This paper delves into the development of a sophisticated desktop application designed to calculate soil bearing capacity and predict limit pressure. Drawing from an extensive review of existing methodologies, the study meticulously examines various approaches employed in soil bearing capacity calculations, elucidating their theoretical foundations and practical applications. Furthermore, the study explores the burgeoning intersection of artificial intelligence (AI) and geotechnical engineering, underscoring the transformative potential of AI- driven solutions in enhancing predictive accuracy and efficiency.Central to the research is the utilization of cutting-edge machine learning techniques, including Artificial Neural Networks (ANN), XGBoost, and Random Forest, for predictive modeling. Through comprehensive experimentation and rigorous analysis, the efficacy and performance of each method are rigorously evaluated, with XGBoost emerging as the preeminent algorithm, showcasing superior predictive capabilities compared to its counterparts. The study culminates in a nuanced understanding of the intricate dynamics at play in geotechnical analysis, offering valuable insights into optimizing soil bearing capacity calculations and limit pressure predictions. By harnessing the power of advanced computational techniques and AI-driven algorithms, the paper presents a paradigm shift in the realm of geotechnical engineering, promising enhanced precision and reliability in civil engineering projects.

Keywords: limit pressure of soil, xgboost, random forest, bearing capacity

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Authors: Margit Kastner

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To overcome the negative consequences associated with large class sizes and to support students in developing the necessary competences (e.g., critical thinking, problem-solving, or team-work skills) a marketing course has been redesigned by implementing project-based learning with peer assessment (PBL&PA). This means that students can voluntarily take advantage of this supplementary offer and explore -in addition to attending the lecture where clicker questions are asked- a real-world problem, find a solution, and assess the results of peers while working in small collaborative groups. In order to handle this with little further effort, the process is technically supported by the university’s e-learning system in such a way that students upload their solution in form of an assignment which is then automatically distributed to peer groups who have to assess the work of three other groups. Finally, students’ work is graded automatically considering both, students’ contribution to the project and the conformity of the peer assessment. The purpose of this study is to evaluate students’ perception of PBL&PA using an online-questionnaire to collect the data. More specifically, it aims to discover students’ motivations for (not) working on a project and the benefits and problems students encounter. In addition to the survey, students’ performance was analyzed by comparing the final grades of those who participated in PBL&PA with those who did not participate. Among the 260 students who filled out the questionnaire, 47% participated in PBL&PA. Besides extrinsic motivations (bonus credits), students’ participation was often motivated by learning and social benefits. Reasons for not working on a project were connected to students’ organization and management of their studies (e.g., time constraints, no/wrong information) and teamwork concerns (e.g., missing engagement of peers, prior negative experiences). In addition, high workload and insufficient extrinsic motivation (bonus credits) were mentioned. With regards to benefits and problems students encountered during the project, students provided more positive than negative comments. Positive aspects most often stated were learning and social benefits while negative ones were mainly attached to the technical implementation. Interestingly, bonus credits were hardly named as a positive aspect meaning that intrinsic motivations have become more important when working on the project. Team aspects generated mixed feelings. In addition, students who voluntarily participated in PBL&PA were, in general, more active and utilized further course offers such as clicker questions. Examining students’ performance at the final exam revealed that students without participating in any of the offered active learning tasks performed poorest in the exam while students who used all activities were best. In conclusion, the goals of the implementation were met in terms of students’ perceived benefits and the positive impact on students’ exam performance. Since the comparison of the automatic grading with faculty grading showed valid results, it is possible to rely only on automatic grading in the future. That way, the additional workload for faculty will be within limits. Thus, the implementation of project-based learning with peer assessment can be recommended for large classes.

Keywords: automated grading, large classes, peer assessment, project-based learning

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Authors: Soofiyan Atar, Adil Shaikh, Sahil Rajpurkar, Pragnesh Bhalala, Aniket Desai, Irfan Siddavatam

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Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics.

Keywords: model-based reinforcement learning, gait stability, supervised learning, pneumatic quadruped

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Authors: Girish Choudhary

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Education is in a state of flux. Not only one need to acquire skills in order to cope with a fast changing global world, an explosive growth in technology, on the other hand is providing a new wave of teaching tools - computer aided video instruction, hypermedia, multimedia, CD-ROMs, Internet connections, and collaborative software environments. The emerging technology incorporates the group qualities of interactive, classroom-based learning while providing individual students the flexibility to participate in an educational programme at their own time and place. The technology facilitating self learning also seems to provide a cost effective solution to the dilemma of delivering education to masses. Online education is a unique learning domain that provides for many to many communications as well. The computer conferencing software defines the boundaries of the virtual classroom. The changing paradigm provides access of instruction to a large proportion of society, promises a qualitative change in the quality of learning and echoes a new way of thinking in educational theory that promotes active learning and open new learning approaches. Putting it to practice is challenging and may fundamentally alter the nature of educational institutions. The subsequent part of paper addresses such questions viz. 'Do we need to radically re-engineer the curriculum and foster an alternate set of skills in students?' in the onward journey.

Keywords: on-line education, self learning, energy and power engineering, future education

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Authors: Khalil Bachiri, Ali Yahyaouy, Nicoleta Rogovschi

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Improved user experience is a goal of contemporary recommender systems. Recommender systems are starting to incorporate reinforcement learning since it easily satisfies this goal of increasing a user’s reward every session. In this paper, we examine the most effective Reinforcement Learning agent tactics on the Movielens (1M) dataset, balancing precision and a variety of recommendations. The absence of variability in final predictions makes simplistic techniques, although able to optimize ranking quality criteria, worthless for consumers of the recommendation system. Utilizing the stochasticity of Leonard-Ornstein processes, our suggested strategy encourages the agent to investigate its surroundings. Research demonstrates that raising the NDCG (Discounted Cumulative Gain) and HR (HitRate) criterion without lowering the Ornstein-Uhlenbeck process drift coefficient enhances the diversity of suggestions.

Keywords: recommender systems, reinforcement learning, deep learning, DDPG, Leonard-Ornstein process

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Authors: Raunak Munjal, Mohammad Akif Ali, Prithiv Raj

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This study investigates the relative effectiveness of various flight controllers for drone obstacle avoidance. To assess ultrasonic sensors' performance in real-time obstacle detection, they are integrated with ESP32 and Arduino Nano controllers. The study determines which controller is most effective for this particular application by analyzing important parameters such as accuracy (mean absolute error), standard deviation, and mean distance range. Furthermore, the study explores the possibility of incorporating state-driven algorithms into the Arduino Nano configuration to potentially improve obstacle detection performance. The results offer significant perspectives for enhancing sensor integration, choosing the best flight controller for obstacle avoidance, and maybe enhancing drones' general environmental navigation ability.

Keywords: ultrasonic distance measurement, accuracy and consistency, flight controller comparisons, ESP32 vs arduino nano

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Authors: Kaycee Bills

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Action research is a method of inquiry useful in solving social problems in social work. This study seeks to address a significant problem: higher education’s use of traditional instructional methods in social work education. Ineffective techniques, such as lecturing, fail to account for students’ variable learning needs. In contrast to traditional pedagogy, universal design for learning (UDL) is a robust framework that '[improves] and [optimizes] teaching and learning for all people' (CAST, 2018), including students with disabilities. For this project, the research team interviewed the UDL and Accessibility Specialist at their institution for two reasons: (1) to learn how to implement UDL practices in their classrooms, and in turn, (2) to motivate other faculty members at their institution to consider enacting UDL principles. A thematic analysis of the interview’s transcript reveals themes relevant to practicing UDL. Implications for future practice, as well as the researcher’s reflections on the research process, are shared in the discussion section.

Keywords: disabilities, higher education, inclusive education, universal design for learning

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Authors: Tiffany Wicks

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Queer patients can face issues with self-advocating due to the factors of implicit provider bias, lack of tools and resources to self-advocate, and lack of comfortability in self-advocating based on prior experiences. In this study, five participants who identify as queer discussed their interactions with their healthcare providers. This exploratory study revealed that there is a need for healthcare provider education to reduce implicit bias and judgments about queer patients. There is also an important need for peer advocates in order to further inform healthcare promotion and decision-making before and during provider visits in an effort for a better outcome. Through this exploration, queer patients voiced their experiences and concerns to inform a need for change in healthcare collaboration between providers and patients in the queer community.

Keywords: queer, LGBT, patient, self-advocacy, healthcare

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Authors: B. Ramesh Naik, T. Venugopal

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This paper presents a novel approach which improves the high-level semantics of images based on machine learning approach. The contemporary approaches for image retrieval and object recognition includes Fourier transforms, Wavelets, SIFT and HoG. Though these descriptors helpful in a wide range of applications, they exploit zero order statistics, and this lacks high descriptiveness of image features. These descriptors usually take benefit of primitive visual features such as shape, color, texture and spatial locations to describe images. These features do not adequate to describe high-level semantics of the images. This leads to a gap in semantic content caused to unacceptable performance in image retrieval system. A novel method has been proposed referred as discriminative learning which is derived from machine learning approach that efficiently discriminates image features. The analysis and results of proposed approach were validated thoroughly on WANG and Caltech-101 Databases. The results proved that this approach is very competitive in content-based image retrieval.

Keywords: CBIR, discriminative learning, region weight learning, scale invariant feature transforms

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Authors: Dawn Tsang

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This paper aims at synthesizing the difficulties in cognitive processes faced by translation majors in mainland China. The purpose is to develop possible solutions and innovation in terms of translation pedagogy, curriculum reform, and syllabus design. This research will base its analysis on students’ instant feedback and interview after training in translation and interpreting courses, and translation faculty’s teaching experiences. This research will take our translation majors as the starting point, who will be one of the focus groups. At present, our Applied Translation Studies Programme is offering translation courses in the following areas: practical translation and interpreting, translation theories, culture and translation, and internship. It is a four-year translation programme, and our students would start their introductory courses since Semester 1 of Year 1. The medium of instruction of our College is solely in English. In general, our students’ competency in English is strong. Yet in translation and especially interpreting classes, no matter it is students’ first attempt or students who have taken university English courses, students find class practices very challenging, if not mission impossible. Their biggest learning problem seems to be weakening cognitive processes in terms of lack of intercultural competence, incomprehension of English language and foreign cultures, inadequate aptitude and slow reaction, and inapt to utilize one’s vocabulary bank etc. This being so, the research questions include: (1) What specific and common cognitive difficulties are students facing while learning translation and interpreting? (2) How to deal with such difficulties, and what implications can be drawn on curriculum reform and syllabus design in translation? (3) How significant should cognitive study be placed on translation curriculum, i.e., the proportion of cognitive study in translation/interpreting courses and in translation major curriculum? and (4) What can we as translation educators do to maximize teaching and learning effectiveness by incorporating the latest development of cognitive study?. We have collected translation students’ instant feedback and conduct interviews with both students and teaching staff, in order to draw parallels as well as distinguishing from our own current teaching practices at United International College (UIC). We have collected 500 questionnaires for now. The main learning difficulties include: poor vocabulary bank, lack of listening and reading comprehension skills in terms of not fully understanding the subtext, aptitude in translation and interpreting etc. This being so, we propose to reform and revitalize translation curriculum and syllabi to address to these difficulties. The aim is to maximize teaching effectiveness in translation by addressing the above-mentioned questions with a special focus on cognitive difficulties faced by translation majors.

Keywords: cognitive difficulties, teaching and learning effectiveness, translation curriculum reform, translation pedagogy

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Authors: Abdulkadir Mustapha Gana, Musa Baba Adamu, Edimeh Augustine Jr

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There are growing concerns over the astronomical decline inquality of teaching and learning amongst youths especially in developing countries, and handful research have been conducted in this regard. However, results from many of these studies revealed similar findings which all pointed to the steady decline in quality of teaching and learning across the globe. One common factor attributed for this drawback was the new media due to the evolution and advancement of technology as studies have revealed. In the beginning, what was then the new media (broadcast media of radio and television) was singled out as being responsible for diverting people’s attention from reading; particularly television. At present times, it was revealed that the social media and internet connectivity were responsible for diverting the attention of many, thus distracting attentions from reading. However, it is pertinent to note that the devastating effects, social media platforms have a couple of tools that could improve reading by extension teaching and learning amongst students. Therefore, this study reviewed the literature on the advantageous aspect of social media to reading and learning; whilst laying emphasis on how youths can utilize social media to improve their reading habits.

Keywords: ebook, reading, learning, students

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Authors: M. Aherrahrou

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Moroccan education is currently facing many difficulties and problems due to traditional methods of teaching. Neuro -Linguistic Programming (NLP) appears to hold much potential for education at all levels. In this paper, the major aim is to explore the effect of certain Neuro -Linguistic Programming techniques in one educational institution in Morocco. Quantitative and Qualitative methods are used. The findings prove the effectiveness of this new approach regarding Moroccan education, and it is a promising tool to improve the quality of learning.

Keywords: learning and teaching environment, Neuro- Linguistic Programming, education, quality of learning

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Authors: Dian Guan

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With the wide application of multimedia and the Internet, the development of teaching theories, and the implementation of teaching reforms, many different university English classroom teaching modes have emerged. The university English teaching mode is changing from the traditional teaching mode based on conversation and text to the multimodal English teaching mode containing discussion, pictures, audio, film, etc. Applying university English teaching models is conducive to cultivating lifelong learning skills. In addition, lifelong learning skills can also be called learners' autonomous learning skills. Learners' independent learning ability has a significant impact on English learning. However, many university students, especially art and design students, don't know how to learn individually. When they become university students, their English foundation is a relative deficiency because they always remember the language in a traditional way, which, to a certain extent, neglects the cultivation of English learners' independent ability. As a result, the autonomous learning ability of most university students is not satisfactory. The participants in this study were 60 students and one teacher in their first year at a university in western China. Two observations and interviews were conducted inside and outside the classroom to understand the impact of a multimodal teaching model of university English on students' autonomous learning ability. The results were analyzed, and it was found that the multimodal teaching model of university English significantly affected learners' autonomy. Incorporating classroom presentations and poster exhibitions into multimodal teaching can increase learners' interest in learning and enhance their learning ability outside the classroom. However, further exploration is needed to develop multimodal teaching materials and evaluate multimodal teaching outcomes. Despite the limitations of this study, the study adopts a scientific research method to analyze the impact of the multimodal teaching mode of university English on students' independent learning ability. It puts forward a different outlook for further research on this topic.

Keywords: art university, EFL education, learner autonomy, multimodal pedagogy

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Authors: C. Mayr, J. Periya, A. Kariminezhad

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In this paper, we developed a learning framework for the classification of vulnerable road users (VRU) by their range-Doppler signatures. The frequency-modulated continuous-wave (FMCW) radar raw data is first pre-processed to obtain robust object range-Doppler maps per coherent time interval. The complex-valued range-Doppler maps captured from our outdoor measurements are further fed into a convolutional neural network (CNN) to learn the classification. This CNN has gone through a hyperparameter optimization process for improved learning. By learning VRU range-Doppler signatures, the three classes 'pedestrian', 'dog', and 'noise' are classified with an average accuracy of almost 95%. Interestingly, this classification accuracy holds for a combined longitudinal and lateral object trajectories.

Keywords: machine learning, radar, signal processing, autonomous driving

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Authors: Andreas Johansson

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Conceptual understanding needs to be grounded on observation of physical phenomena, experiences or metaphors. Observation of physical phenomena using demonstration experiments has a long tradition within physics education and students need to develop mental models to relate the observations to concepts from scientific theories. This study investigates how live and video experiments involving an acoustic trap to visualize particle-field interaction, field properties and particle properties can help develop students' mental models and how they can be used differently to realize their potential as teaching tools. Initially, they were treated as analogs and the lesson designs were kept identical. With a design-based approach, the experimental and video designs, as well as best practices for a respective teaching tool, were then developed in iterations. Variation theory was used as a theoretical framework to analyze the planned respective realized pattern of variation and invariance in order to explain learning outcomes as measured by a pre-posttest consisting of conceptual multiple-choice questions inspired by the Force Concept Inventory and the Force and Motion Conceptual Evaluation. Interviews with students and teachers were used to inform the design of experiments and videos in each iteration. The lesson designs and the live and video experiments has been developed to help teachers improve student learning and make school physics more interesting by involving experimental setups that usually are out of reach and to bridge the gap between what happens in classrooms and in science research. As students’ conceptual knowledge also rises their interest in physics the aim is to increase their chances of pursuing careers within science, technology, engineering or mathematics.

Keywords: acoustic trap, design-based research, experiments, variation theory

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Authors: Valerie Yocco Rossi

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Objective: The author will share why differentiation is necessary for all classrooms as well as strategies for differentiating content, process, and product. Through learning how to differentiate, teachers will be able to create activities and assessments to meet the abilities, readiness levels, and interests of all learners. Content and Purpose: This work will focus on how to create a learning experience for students that recognizes their different interests, abilities, and readiness levels by differentiating content, process, and product. Likewise, the best learning environments allow for choice. Choice boards allow students to select tasks based on interests. There can be challenging and basic tasks to meet the needs of various abilities. Equally, rubrics allow for personalized and differentiated assessments based on readiness levels and cognitive abilities. The principals of DI help to create a classroom where all students are learning to the best of their abilities. Outcomes: After reviewing the work, readers will be able to (1) identify the benefits of differentiated instruction; (2) convert traditional learning activities to differentiated ones; (3) differentiate, writing-based assessments.

Keywords: differentiation, personalized learning, design, instructional strategies

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: Shuqi Zhang

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Federated learning allows users to participate in collaborative model training without sending data to third-party servers, reducing the risk of user data privacy leakage, and is widely used in smart finance and smart healthcare. However, the distributed architecture design of federation learning itself and the existence of secure aggregation protocols make it inherently vulnerable to backdoor attacks. To solve this problem, the federated learning backdoor defense framework FLEX based on group aggregation, cluster analysis, and neuron pruning is proposed, and inter-compatibility with secure aggregation protocols is achieved. The good performance of FLEX is verified by building a horizontal federated learning framework on the CIFAR-10 dataset for experiments, which achieves 98% success rate of backdoor detection and reduces the success rate of backdoor tasks to 0% ~ 10%.

Keywords: federated learning, secure aggregation, backdoor attack, cluster analysis, neuron pruning

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Authors: Somchai Buaroong

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This research aims to investigating strong points and challenges in application of an online learning management system to an English course. Data were collected from observation, learners’ oral and written reports, and the teacher’s journals. A questionnaire was utilized as a tool to collect data. Statistics utilized in this research included frequency, percentage, mean, standard deviation, and multiple regression analysis. The findings show that the system was an additional channel to enhance English language learning through written class assignments that were digitally accessible by any group members, and through communication between the teacher and learners and among learners themselves. Thus, the learning management system could be a promising tool for foreign language teachers. Also revealed in the study were difficulties in its use. The article ends with discussions of findings of the system for foreign language classes in association to pedagogy are also included and in the level of signification.

Keywords: english course, foreign language system, online learning management system, teacher’s journals

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Authors: Nji Clement Bang, Rosemary Shafack M., Kum Henry Asei, Yaro Loveline Y

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Scholars perceive learner-centered teaching-learning reform as roles and resources in teacher education (TE) and professional outcome with transformative learning (TL) continuum dimensions. But, teaching-learning reform is fast proliferating amidst debilitating stakeholder systemic dichotomies, resources, commitment, resistance and poor quality outcome that necessitate stronger TE and professional continuums. Scholars keep seeking greater understanding of themes in teaching-learning reform, TE and professional outcome as continuums and how policymakers, student-teachers, teacher trainers and local communities concerned with initial TE can promote continuous holistic quality performance. To sustain the debate continuum and answer the overarching question, we use mixed-methods research-design with diverse literature and 409 sample-data. Onset text, interview and questionnaire analyses reveal debilitating teaching-learning reform in TE continuums that need TL revival. Follow-up focus group discussion and teaching considering TL insights reinforce holistic teaching-learning in TE. Therefore, significant increase in diverse prior-experience articulation1; critical reflection-discourse engagement2; teaching-practice interaction3; complex-activity constrain control4 and formative outcome- reintegration5 reinforce teaching-learning in learning-to-teach role-resource pathways and outcomes. Themes reiterate complex teaching-learning in TE programs that suits TL journeys and student-teachers and students cum teachers, workers/citizens become akin, transformative-learners who evolve personal and collective roles-resources towards holistic-lifelong-learning outcomes. The article could assist debate about quality teaching-learning reform through TL dimensions as TE and professional role-resource continuums.

Keywords: transformative learning perspectives, teacher education, initial teacher education, learner-centered pedagogical reform, life-long learning

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Authors: Fazilet Alachaher (Benzerdjeb)

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From the chalkboard to the abacus and beyond, technology has always played an important role in education. Educational technology refers to any teaching tool that helps supports learning, and given the rapid advancements in Information Technology and multimedia applications, the potential to support the teaching of foreign languages in our universities is ever greater. In language teaching and learning, we have a lot of to choose from the world of technology: TV, CDs, DVDs, Computers, the Internet, Email, and Blogs. The use of modern technologies can enrich the experience of learning a foreign language because they provide features that are not present in traditional technology. They can offer a wide range of multimedia resources, opportunities for intensive one-to-one learning in language labs and resources for authentic materials, which can be motivating to both students and teachers. The advent of Information and Communication Technology (ICT) and online interaction can also open up new range of self-access and distance learning opportunities The two last decades have witnessed a revolution due to the onset of technology, and has changed the dynamics of various industries, and has also influenced the way people live and work in society. That is why using the multimedia to create a certain context to teach English has its unique advantages. This paper tries then to analyse the necessity of multimedia technology to language teaching and brings out the problems faced by using these technologies. It also aims at making English teachers aware of the strategies to use it in an effective manner.

Keywords: strategies English teaching, multimedia technology, advantages, disadvantages, English learning

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Authors: Ora Geiger

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Reading is a cognitive process of deciphering visual signs to produce meaning. During the reading process, written information of symbols and signs is received in the person’s eye and processed in the brain. This definition is relevant to both the reading of letters and the reading of musical notation. But while the letters of the alphabet are signs determined arbitrarily, notes are recorded systematically on a staff, with the location of each note on the staff indicating its relative pitch. In this paper, the researcher specifies the characteristics of alphabet reading in comparison to musical notation reading, and discusses the question whether a person diagnosed with dyslexia will necessarily have difficulty in reading musical notes. Dyslexia is a learning disorder that makes it difficult to acquire alphabet-reading skills due to difficulties expressed in the identification of letters, spelling, and other language deciphering skills. In order to read, one must be able to connect a symbol with a sound and to join the sounds into words. A person who has dyslexia finds it difficult to translate a graphic symbol into the sound that it represents. When teaching reading to children diagnosed with dyslexia, the multi-sensory approach, supporting the activation and involvement of most of the senses in the learning process, has been found to be particularly effective. According to this approach, when most senses participate in the reading learning process, it becomes more effective. During years of experience, the researcher, who is a music specialist, has been following the music reading learning process of elementary school age students, some of them diagnosed with Dyslexia, while studying to play soprano (descant) recorder. She argues that learning music reading while studying to play a musical instrument is a multi-sensory experience by its nature. The senses involved are: sight, hearing, touch, and the kinesthetic sense (motion), which provides the brain with information on the relative positions of the body. In this way, the learner experiences simultaneously visual, auditory, tactile, and kinesthetic impressions. The researcher concludes that there should be no contra-indication for teaching standard music reading to children with dyslexia if an appropriate process is offered. This conclusion is based on two main characteristics of music reading: (1) musical notation system is a systematic, logical, relative set of symbols written on a staff; and (2) music reading learning connected with playing a musical instrument is by its nature a multi-sensory activity since it combines sight, hearing, touch, and movement. This paper describes music reading teaching procedures and provides unique teaching methods that have been found to be effective for students who were diagnosed with Dyslexia. It provides theoretical explanations in addition to guidelines for music education practices.

Keywords: alphabet reading, dyslexia, multisensory teaching method, music reading, recorder playing

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Authors: Sima Najafzadehkhoei, George Vega Yon

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This paper presents an approach for calibrating Agent-Based Models (ABMs) efficiently, utilizing Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. These machine learning techniques are applied to Susceptible-Infected-Recovered (SIR) models, which are a core framework in the study of epidemiology. Our method replicates parameter values from observed trajectory curves, enhancing the accuracy of predictions when compared to traditional calibration techniques. Through the use of simulated data, we train the models to predict epidemiological parameters more accurately. Two primary approaches were explored: one where the number of susceptible, infected, and recovered individuals is fully known, and another using only the number of infected individuals. Our method shows promise for application in other ABMs where calibration is computationally intensive and expensive.

Keywords: ABM, calibration, CNN, LSTM, epidemiology

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Authors: Jun Wang, Ge Zhang

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Machine learning has been used in many areas today. It thrives at reviewing large volumes of data and identifying patterns and trends that might not be apparent to a human. Given the huge potential benefit and the amount of data available in the financial market, it is not surprising to see machine learning applied to various financial products. While future prices of financial securities are extremely difficult to forecast, we study them from a different angle. Instead of trying to forecast future prices, we apply machine learning algorithms to predict the direction of future price movement, in particular, whether a sector Exchange Traded Fund (ETF) would outperform or underperform the market in the next week or in the next month. We apply several machine learning algorithms for this prediction. The algorithms are Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Neural Networks (NN). We show that these machine learning algorithms, most notably GNB and NN, have some predictive power in forecasting out-performance and under-performance out of sample. We also try to explore whether it is possible to utilize the predictions from these algorithms to outperform the buy-and-hold strategy of the S&P 500 index. The trading strategy to explore out-performance predictions does not perform very well, but the trading strategy to explore under-performance predictions can earn higher returns than simply holding the S&P 500 index out of sample.

Keywords: machine learning, ETF prediction, dynamic trading, asset allocation

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Authors: Nilki Upathissa, Torin Wirasinghe

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This solution addresses the challenges faced by traditional bookstores and the limitations of digital media, striking a balance between the tactile experience of printed books and the convenience of modern technology. The book exchange system offers a sustainable alternative, empowering users to access a diverse range of books while promoting community engagement. The user-friendly interfaces incorporated into the book exchange system ensure a seamless and enjoyable experience for users. Intuitive features for book management, search, and messaging facilitate effortless exchanges and interactions between users. By streamlining the process, the system encourages readers to explore new books aligned with their interests, enhancing the overall reading experience. Central to the system's success is the hybrid recommendation engine, which leverages advanced technologies such as Long Short-Term Memory (LSTM) models. By analyzing user input, the engine accurately predicts genre preferences, enabling personalized book recommendations. The hybrid approach integrates multiple technologies, including user interfaces, machine learning models, and recommendation algorithms, to ensure the accuracy and diversity of the recommendations. The evaluation of the book exchange system with the hybrid recommendation engine demonstrated exceptional performance across key metrics. The high accuracy score of 0.97 highlights the system's ability to provide relevant recommendations, enhancing users' chances of discovering books that resonate with their interests. The commendable precision, recall, and F1score scores further validate the system's efficacy in offering appropriate book suggestions. Additionally, the curve classifications substantiate the system's effectiveness in distinguishing positive and negative recommendations. This metric provides confidence in the system's ability to navigate the vast landscape of book choices and deliver recommendations that align with users' preferences. Furthermore, the implementation of this book exchange system with a hybrid recommendation engine has the potential to revolutionize the way readers interact with printed books. By facilitating book exchanges and providing personalized recommendations, the system encourages a sense of community and exploration within the reading community. Moreover, the emphasis on sustainability aligns with the growing global consciousness towards eco-friendly practices. With its robust technical approach and promising evaluation results, this solution paves the way for a more inclusive, accessible, and enjoyable reading experience for book lovers worldwide. In conclusion, the developed book exchange system with a hybrid recommendation engine represents a progressive solution to the challenges faced by traditional bookstores and the limitations of digital media. By promoting sustainability, widening access to printed books, and fostering engagement with reading, this system addresses the evolving needs of book enthusiasts. The integration of user-friendly interfaces, advanced machine learning models, and recommendation algorithms ensure accurate and diverse book recommendations, enriching the reading experience for users.

Keywords: recommendation systems, hybrid recommendation systems, machine learning, data science, long short-term memory, recurrent neural network

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Authors: Prachyanun Nilsook, Panita Wannapiroon

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The purpose of this research is to study a Digital Learning Repository System (DLRS) on vocational teachers and teaching in Thailand. The innobpcd.net is a DLRS being utilized by the Office of Vocational Education Commission and operationalized by the Bureau of Personnel Competency Development for vocational education teachers. The aim of the system is to support and enhance the process of vocational teaching and to improve staff development by providing teachers with a variety of network connections and information. The system provides centralized hosting and access to content, and the ability to share digital objects or files, to set permissions and controls for access to content that can be used vocational education teachers for their teaching and for their own development. The elements of DLRS include; Digital learning system, Media Library, Knowledge-based system and Mobile Application. The system aims to link vocational teachers to the most effective emerging technologies available for learning, so they are better resourced to support their vocational students. The initial results from this evaluation indicate that there is a range of services provided by the system being used by vocational teachers and this paper indicates which facilities have the greatest usage and impact on vocational teaching in Thailand.

Keywords: digital learning repositories, vocational education, knowledge sharing, learning objects

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: data estimation, link data, machine learning, road network

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Authors: B. Remya Mohan, Kamal Bijlani, R. Jayakrishnan

Abstract:

Visualization in an educational context provides the learner with visual means of information. Conceptualizing certain circumstances such as consequences of smoking can be done more effectively with the help of the technology, Augmented Reality (AR). It is a new methodology for effective learning. This paper proposes an approach on how AR based on Marker Technology simulates the harmful effects of smoking and its consequences using Unity 3D game engine. The study also illustrates the impact of AR technology on students for better learning. AR technology can be used as a method to improve learning.

Keywords: augmented reality, marker technology, multi-platform, virtual buttons

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Authors: Mohammed Belalia Douma

Abstract:

Interactive learning is considered as the most effective strategy of learning, it is an educational philosophy based on the learner's contribution and involvement mainly in classroom and how he interacts toward his small society “classroom”, and the level of his collaboration into challenge, discovering, games, participation, all these can be provided through the interactive learning, which aims to activate the learner's role in the operation of learning, which focuses on research and experimentation, and the learner's self-reliance in obtaining information, acquiring skills, and forming values and attitudes. Whereas not based on memorization only, but rather on developing thinking and the ability to solve problems, on teamwork and collaborative learning. With the exchange or roles - teacher to student- , when the student will be more active and performing operations more than the student under the interactive learning method; we might face a several issues dealing with class controlling management, noise, and stability of learning… etc. This research paper is observing the application of the interactive learning on reality “classroom” and answers several assumptions and analyzes the issues coming up of these strategies mainly: noise, class control…etc The research sample was about 150 student of the 3rd year primary school in “Chlef” district, Algeria, level: beginners in the range of age 08 to 10 years old . We provided a questionnaire of confidential fifteen questions and also analyzing the attitudes of learners during three months. it have witnessed as teachers a variety of strategies dealing with applying the interactive learning but with a different issues; time management, noise, uncontrolled classes, overcrowded classes. Finally, it summed up that although the active education is an inevitably effective method of teaching, however, there are drawbacks to this, in addition to the fact that not all theoretical strategies can be applied and we conclude with solutions of this case study.

Keywords: interactive learning, student, learners, strategies.

Procedia PDF Downloads 59