Computer Vision Based Virtual Sketch Using Detection

In todays world during this pandemic situation Online Learning is the only source where one could learn. Online learning makes students more curious about the knowledge and so they decide their learning path . But considering the academics as they have to pass the course or exam given, they need to take time to study, and have to be disciplined about their dedication. And there are many barriers for Online learning as well. Students are lowering their grasping power the reason for this is that each and every student was used to rely on their teacher and offline classes. Virtual writing and controlling system is challenging research areas in field of image processing and pattern recognition in the recent years. It contributes extremely to the advancement of an automation process and can improve the interface between man and machine in numerous applications. Several research works have been focusing on new techniques and methods that would reduce the processing time while providing higher recognition accuracy. Given the real time webcam data, this jambord like python application uses OpenCV library to track an object-of-interest (a human palm/finger in this case) and allows the user to draw bymoving the finger, which makes it both awesome and interesting to draw simple thing. Keyword: Detection, Handlandmark , Keypoints, Computer vision, OpenCV

Organizational and methodological support for students’ foreign language personalized learning pathway modeling in the context of digital challenges

В статье раскрывается организационно-методическое сопровождение построения персонализированных образовательных треков при обучении иностранному языку в контексте цифровизации образования, которое представляет собой пошаговый алгоритм проектирования гибких персонализированных моделей изучения английского языка студентами негуманитарных направлений в электронной образовательной среде. Предлагаемые модели изучения английского языка студентами негуманитарных направлений способствуют реализации активной адаптивности как универсального механизма управления образовательным процессом на основе динамических характеристик облучающегося, с разработанным комплексом вариативных аффективных технологий (повышение уровня мотивации и снижение уровня тревожности) как средства достижения более эффективного процесса обучения. Предложенные в исследовании методические советы могут быть применены при разработке персонализированных адаптивных обучающих систем, программы, методические материалы для преподавателей, инструкции по созданию адаптивных формирующих и диагностических контрольно-измерительных материалов, методические указания для студентов вузов. The article reveals organizational and methodological support for students’ foreign language personalized learning path design in the context of digitalization of education, which is a step-by-step algorithm for designing flexible personalized models for non-language-major students’ foreign language learning in the electronic educational environment. The proposed models of foreign language learning by non-linguistic university students contribute to the implementation of active adaptability as a universal mechanism of educational process management based on the dynamic characteristics of the student, with the developed complex of varied affective technologies (increase of motivation and reduction of anxiety levels) as a means of achieving a more effective learning process. The methodological tips proposed in the study can be applied in the development of personalized adaptive learning systems, programs, methodological materials for teachers, instructions for creating adaptive formative and diagnostic control and measurement materials, methodological guidelines for university students.

Reinforcement Learning Path Planning Method with Error Estimation

Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning.

Artificial Intelligence-Based Quality Management and Detection System for Personalized Learning

Recently, the teaching and learning method in the conventional engineering education system needs a group of learners with personalized learning paths. The introduction of technologies like Artificial Intelligence will aid the learners to identify and detect learning opportunities utilizing historical information, present student profile and success data from an institution, and recommend learning measures to enhance their performance. This study proposes an Artificial Intelligence-based Meta-Heuristic Approach (AIMHA) for personalized learning detection systems and quality management. The proposed model has been utilized to optimize learning effectiveness by considering the nature of the learning path and the number of simultaneous visits to every learning action. In addition, a quality resolution can be determined by a meta-heuristic approach. The simulation findings of the learning actions have been utilized to examine the efficiency of the suggested method. The proposed method is evaluated learning activities achieved an efficiency ratio of 92.3%, sensitivity analysis ratio of 88.4%, performance ratio of 92.3%, precision ratio of 94.3% compared to other existing models.

The Learning Path to Neural Network Industrial Application in Distributed Environments

Industrial companies focus on efficiency and cost reduction, which is very closely related to production process safety and secured environments enabling production with reduced risks and minimized cost on machines maintenance. Legacy systems are being replaced with new systems built into distributed production environments and equipped with machine learning algorithms that help to make this change more effective and efficient. A distributed control system consists of several subsystems distributed across areas and sites requiring application interfaces built across a control network. Data acquisition and data processing are challenging processes. This contribution aims to present an approach for the data collection based on features standardized in industry and for data classification processed with an applied machine learning algorithm for distinguishing exceptions in a dataset. Files with classified exceptions can be used to train prediction models to make forecasts in a large amount of data.

Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics. We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.

STE(A)M+: Transforming Higher Education through Collaborative Play

Our world is facing various wicked problems, such as climate change and extinction. These complex problems require an in-depth understanding. STEM disciplines in higher education play a crucial role in preparing students to solve such problems in their career. Yet it can be questioned whether STEM in higher education offers all the elements required to prepare students for a sustainable future. Additionally, a sole focus on STEM fields may not contribute to finding solutions to these problems. With STE(A)M in higher education, we explore what the missing element in higher education is and how higher education can be improved. We addressed this question within the Honours Programme Transdisciplinary Insights of the Institute for the Future at KU Leuven. Within this programme, a team of students, PhD researchers and coaches from various disciplines examined the educational system and explored how students can be better prepared to co-create a more sustainable future. This learning path was supported by reading books about systems thinking, watching documentaries, following co-creative workshops, and engaging in team discussions. In this process, we found that the following four key elements could be given a greater emphasis in education: transdisciplinarity, systems thinking, co-creation, and critical thinking. To promote this, we created a board game that aims to make learning about the importance of these elements engaging. While playing this game, we learned that we can bring students from different dis ciplines together and foster critical thinking and reflec tions. These insights illustrate how creative tools (e.g. board games) can be used in higher education to foster important skills that can prepare students for a sustainable future. Since this game, developed by students for students, is entirely learner-driven, it departs from the current educational system in which knowledge is mainly transferred by professors. An important advantage of such initiatives is that they foster co-creation and learning between students. Our findings have been summarised in a small video.

How to Become a Biostatistician: A study guide of Master's Degree in BioStatistics

Introduction: The aim of this study was to design of the study guide for the Master of BioStatistics program. Methods: In order to develop the study guide, the literature was reviewed and the first draft of the study guide was compiled using the opinions of experts. Then the face and content validity of the index was assessed from the perspective of the faculty members of Biostatistics across the country through electronic survey. Result: The present study guide includes a review of course titles, learning objectives and outcomes, educational prerequisites, a schedule of learning strategies, learning opportunities, assessment, and more resources for study. These titles are summarized in the present text. The face and content validity index were reported to be 87% and 92%, respectively. Conclusion: It seems that access to the study guide at the beginning of a master's degree in Biostatistics can put the learner on a better learning path, and the need to develop a unified study guide at the national level can be beneficial for the learners.