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A framework for inclusive AI learning design for diverse learners

computers and Education: Artificial Intelligence 2024年 6卷

作者： Song, Yukyeong Weisberg, Lauren R. Zhang, Shan Tian, Xiaoyi Boyer, Kristy Elizabeth Israel, Maya School of Teaching & Learning College of Education University of Florida GainesvilleFL32611 United States Department of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States

As artificial intelligence (AI) becomes more prominent in children's lives, an increasing number of researchers and practitioners underscored the importance of integrating AI as learning content in K-12. Despite the recent efforts in developing AI curricula and guiding frameworks in AI education, the educational opportunities often do not provide equally engaging and inclusive learning experiences for all learners. To promote equality and equity in society and increase competitiveness in the AI workforce, it is essential to broaden participation in AI education. However, the framework that guides teachers and learning designers into inclusive learning design tailored for AI education is lacking. Universal Design for Learning (UDL) provides guidelines for making learning more inclusive across disciplines. Based on the principles of UDL, this paper proposes a framework to guide the design of inclusive AI learning. We conducted a systematic literature review to identify AI learning design-related articles and synthesized them into our proposed framework. Our new framework includes the core component of AI learning content (i.e., five big ideas), anchored by the three UDL principles (the "why," "what," and "how" of learning), and six praxes with pedagogical examples of AI education. Alongside this, we present an illustrative example of the application of our proposed framework in the context of a middle school AI summer camp. We hope this paper will guide researchers and practitioners in designing more inclusive AI learning experiences. © 2024 The Author(s)

关键词： Curricula

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Managing Students in Hybrid Software Project Classes

Managing Students in Hybrid Software Project Classes

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Frontiers in Education (FIE) Conference

作者： Bruce R. Maxim Belen A. Garcia Department of Computer and Information Science University of Michigan-Dearborn Dearborn Michigan USA Hub for Teaching and Learning Resources University of Michigan-Dearborn Dearborn Michigan USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice paper describes the authors' experiences introducing active learning methodologies into a hybrid undergraduate software engineering project course. On our campus hybrid courses have both in-person and online students participating in a single course taught by the same instructor. The project team adapted the face-to-face class activities to accommodate the idiosyncrasies of an online course delivery environment. Using active learning and authentic assessment techniques, the authors sought to improve the levels of engagement exhibited by online students. The students in this course learn to use agile software engineering practices to deliver incremental software prototypes. The online and in-person students were surveyed at the conclusion of their course to measure their perceived levels of engagement with course activities. Data was collected from two recent offerings of this course. The results suggest that the neither the in-person or online students were not significantly different from one another on most survey responses or most performance measures.

关键词： Surveys Active learning Prototypes Agile software development Software Software engineering

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Contactless hand tremor amplitude measurement using smartphones: development and pilot evaluation

Contactless hand tremor amplitude measurement using smartpho...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： James Bungay Osasenaga Emokpae Samuel D. Relton Jane Alty Stefan Williams Hui Fang David C. Wong Department of Computer Science University of Manchester UK Leeds Institute of Health Sciences University of Leeds UK University of Tasmania Australia University of Leeds UK Leeds Teaching Hospitals Trust UK Department of Computer Science Loughborough University UK

Background - Physiological tremor is defined as an involuntary and rhythmic shaking. Tremor of the hand is a key symptom of multiple neurological diseases, and its frequency and amplitude differs according to both disease type and disease progression. In routine clinical practice, tremor frequency and amplitude are assessed by expert rating using a 0 to 4 integer scale. Such ratings are subjective and have poor inter-rater reliability. There is thus a clinical need for a practical and accurate method for objectively assessing hand *** - to develop a proof-of-principle method to measure hand tremor amplitude from smartphone *** - We created a computer vision pipeline that automatically extracts salient points on the hand and produces a 1-D time series of movement due to tremor, in pixels. Using the smartphones’ depth measurement, we convert this measure into real distance units. We assessed the accuracy of the method using 60 videos of simulated tremor of different amplitudes from two healthy adults. Videos were taken at distances of 50, 75 and 100 cm between hand and camera. The participants had skin tone II and VI on the Fitzpatrick scale. We compared our method to a gold-standard measurement from a slide rule. Bland-Altman methods agreement analysis indicated a bias of 0.04 cm and 95% limits of agreement from -1.27 to 1.20 cm. Furthermore, we qualitatively observed that the method was robust to limited *** relevance - We have demonstrated how tremor amplitude can be measured from smartphone videos. In conjunction with tremor frequency, this approach could be used to help diagnose and monitor neurological diseases.

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Remote Sensing-Based Agricultural Damage Assessment: A Semantic Segmentation Method for Analyzing Corn Field Changes 25

Remote Sensing-Based Agricultural Damage Assessment: A Seman...

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Proceedings of the 2025 3rd International Conference on Communication Networks and Machine Learning

作者： Meng Wu Zeyu Li Yu Wang Yi Wei Ying Chen Xiaoyue Tu School of Information Science and Technology Sanda University Shanghai China Computer Science and Artificial Intelligence Department Leiden University Leiden Holland Teaching Department Kede Future (Shanghai) Education Technology Co. Ltd. Shanghai China

ISBN: (纸本)9798400713231

The increasing impact of natural disasters on agricultural production highlights the vital need for accurate estimation of changes in crop cultivation areas as pertains to agricultural recovery and formulation of food security strategies. In this study, the authors apply an integrated approach of remote sensing data and semantic segmentation technology by taking the June 2023 event in Illinois, termed the derecho event, as a case study. DeepCBAMNet is applied to analyze the pre-and post-disaster remote sensing images, yielding an overall model accuracy of 93.39% and a mean Intersection over Union (mIoU) of 81.88% on the training dataset. The study reports an area affected by the disaster of 69,352.01 acres with a 91.98% accuracy rate when compared with that reported by the United States department of Agriculture (75,401.60 acres). The methodology is a key advancement in the assessment of agricultural disasters, as it gives a new technical approach to the rapid and accurate assessment of disaster impact, thus allowing government authorities to put in place more productive post-disaster recovery strategies and agricultural policies.

关键词： Agricultural Disaster Monitoring

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The Design of a USB Interface Temperature and Humidity Detector 5

The Design of a USB Interface Temperature and Humidity Detec...

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5th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Zhao, Fengyi Hu, Junmin He, Renyu Nankai University Binhai College Department of Computer Science Tianjin China College of Information Science and Engineering Wuchang Shouyi University Wuhan China Nankai University Binhai College Department of Public Computer Teaching Tianjin China

This paper introduces the design of a USB interface temperature and humidity detector. System firmware is implemented by RTL logic based on CPLD, such as interface timing, data processing, communication and so on. USB to UART communication interface realizes the data transmission between the detector and the host computer. The detector is powered by USB, supporting plug and play. This new product design can solve the problems such as poor adaptability of traditional temperature and humidity detector and difficulty in feeding power supply, so as to meet the high requirements of reliability and convenience in industrial field. It has certain practical value and is worthy of promotion. © 2021 IEEE.

关键词： Firmware

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Evaluating Anatomy Learning of First-Year University Students: A Pilot Study 1

Evaluating Anatomy Learning of First-Year University Student...

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1st International Conference on Education Research, ICER 2024

作者： Fritz, Kaylyn Isafiade, Omowunmi Elizabeth Conana, Honjiswa Ekpo, Okobi Kotze, Retha Christina Department of Medical Bioscience University of the Western Cape Cape Town South Africa Department of Computer Science University of the Western Cape Cape Town South Africa Department of Teaching and Learning University of the Western Cape Cape Town South Africa Department of Biological Sciences Khalifa University Abu Dhabi United Arab Emirates Department of Dietetics and Nutrition University of the Western Cape Cape Town South Africa

ISBN: (纸本)9781917204262

Many students find anatomy concepts overwhelming and difficult to grasp, therefore, understanding how anatomy is learned, and the influences thereon is important to ensure academic performance. To evaluate student diversity and anatomy learning, this study presents a pilot study on how first-year students at the University of the Western Cape (South Africa) learn anatomy. A mixed-methods approach was implemented using a Google Form questionnaire. Data was obtained from 20 first-year anatomy undergraduate students in 2022. The findings revealed participants preferred face-to-face practicals (60%) and specimens (55%). Face-to-face practicals, teaching slides and 3D models were identified as effective learning measures, with 3D models showing significant differences across academic degrees. Students perceived greater benefits from face-to-face practicals, 3D models and specimens. Students strongly preferred the visual modality and spaced practice strategy. Differences in aural and writing modalities were observed across degrees, where BSc in Medical Bioscience and BSc in Sports and Exercise science had higher scores, respectively. Learning barriers included content overload, loadshedding, and less student-lecturer interaction. Overall, face-to-face teaching with teaching slides remains important, with online methods playing a supplementary role. Further research on anatomy learning in different degrees and the impact thereof on academic performance is needed. © 2024 Proceedings of the International Conference on Education Research, ICER 2024. All rights reserved.

关键词： Students

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Extracting Eye Models from MRI Scans Using U-Net-Based Deep Learning Framework

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Journal of computer and Communications 2024年第11期12卷 95-107页

作者： Waqar Ahmad Tahir Opeyemi Sheu Alamu Dip Sarker Md Tafhimul Haque Sadi Abdullah Al Hasib Tonmoy Kumar Sarker Md Rakibul Islam Dipankar Roy Dip Ashish Sharma Syed Wajeeh Abbas Rizvi Department of Internal Medicine/Casualty Khalifa Gull Nawaz Teaching Hospital Bannu Pakistan Department of Statistics Federal College of Animal Health and Production Technology Ibadan Nigeria Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Department of Internal Medicine Arogya Hospital Pvt. Ltd. Baglung Nepal Department of Data Science National University of Computer & Emerging Sciences (FAST City Campus) Karachi Pakistan

.Abstracting eye models from MRI images is critical in advancing medical imaging, particularly for clinical diagnostics. Current methods often struggle with accuracy and efficiency, highlighting a gap this research aims to fill. This study investigates the application of machine learning methods, focusing on the U-net-based deep learning framework, to improve the accuracy of eye model extraction. The objectives include fitting measured eye data to models such as the Ellipsoid model, evaluating automated segmentation tools, and assessing the usability of machine learning-based extractions in clinical scenarios. We employed point cloud data of 202,872 points to fit eye models using ellipsoid, non-linear, and spherical fitting techniques. The fitting processes were optimized to ensure precision and reliability. We compared the performance of these models using mean squared error (MSE) as the primary metric. The non-linear model emerged as the most accurate, with a significantly lower MSE (1.186562) compared to the ellipsoid (781.0542) and spherical models. This finding indicates that the non-linear model provides a more detailed and precise representation of the eye’s geometry. These results suggest that machine learning methods, particularly non-linear models, can significantly enhance the accuracy and usability of eye model extraction in clinical diagnostics, offering a robust framework for future advancements in medical imaging.

关键词： Eye Model Extraction U-Net Deep Learning MRI Segmentation Ellipsoid and Non-Linear Models Mean Squared Error(MSE)

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Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

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computer Systems science & Engineering 2023年第9期46卷 3321-3338页

作者： Abdelwahed Motwakel Badriyya B.Al-onazi Jaber S.Alzahrani Sana Alazwari Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Amgad Atta Abdelmageed Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Arabic is the world’s first language,categorized by its rich and complicated grammatical ***,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and *** Arabic language consists of distinct variations utilized in a community and particular *** media sites are a medium for expressing opinions and social phenomena like racism,hatred,offensive language,and all kinds of verbal *** conduct does not impact particular nations,communities,or groups only,extending beyond such areas into people’s everyday *** study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic *** presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social *** the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and ***,data pre-processing is performed to transform the Arabic social media text into a useful *** addition,the word2vec word embedding process is utilized to produce word *** attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification ***,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier *** illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were *** extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.

关键词： Hate speech offensive speech Arabic corpora natural language processing social networks

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Intelligent Collision Danger Assessment of Autonomous Unmanned Sea-Going Vessels

Intelligent Collision Danger Assessment of Autonomous Unmann...

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2022 International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2022

作者： Sedova, Nelly A. Sedov, Viktor A. Bazhenov, Ruslan I. Alutina, Elena F. Arkhipova, Zoya V. Mendel, Viktor V. Maritime State University Named after G.I. Nevelskoy Automatic and Information Systems Vladivostok Russia Maritime State University Named after G.I. Nevelskoy Electrician Theoretical Bases Vladivostok Russia Sholom-Aleichem Priamursky State University Information Systems and Mathematics Birobidzhan Russia Blagoveschensk State Pedagogical University Computer Science and Methods of Teaching Computer Science Blagoveschensk Russia Baikal State University Mathematical Methods and Digital Technologies Irkutsk Russia Pacific National University Pedagogical Institute Khabarovsk Russia

ISBN: (数字)9781665488709

ISBN: (纸本)9781665488709

Navigation safety of both sea-going crewed ships and sea-going autonomous unmanned ships is possible to improve by enhancing navigation engineering facilities, including software and soft hardware (firmware), providing control over navigation tools on such ships. The article dwells on the technique of an intelligent assessment of collision danger for autonomous unmanned vessels with the surface crafts facing them along their seaway. The intelligent assessment is carried out by synthesizing three values given by three modules which are different in input data. The first module uses the information on bearing and distance, the second one is based on computing distance and time of approach of ships, and the third renders the information on bearing, speed and a target ship heading got from the radar system. Each module actively processes the information got from sensors for transferring it to the intelligent unit, which decides on the necessity of maneuvering of collision avoidance to the dangerous objects found on a seaway of an autonomous unmanned vessel. Each module uses a model explored in fuzzy and neuro-fuzzy bases. Upon that, quality assessment calculations show it is the neuro-fuzzy approach that allows for a uniquely determined danger level. © 2022 IEEE.

关键词： Fuzzy inference

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The role of convection in the existence of wavefronts for biased movements

arXiv

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arXiv 2023年

作者： Berti, Diego Corli, Andrea Malaguti, Luisa Department of Mathematics University of Pisa Italy Department of Mathematics and Computer Science University of Ferrara Italy Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Italy

We investigate a model, inspired by Johnson et al. (2017), see [8], to describe the movement of a biological population which consists of isolated and grouped organisms. We introduce biases in the movements and then obtain a scalar reaction-diffusion equation which includes a convective term as a consequence of the biases. We focus on the case the diffusivity makes the parabolic equation of forward-backward-forward type and the reaction term models a strong Allee effect, with the Allee parameter lying between the two internal zeros of the diffusion. In such a case, the unbiased equation (i.e., without convection) possesses no smooth traveling-wave solutions;on the contrary, in the presence of convection, we show that traveling-wave solutions do exist for some significant choices of the parameters. We also study the sign of their speeds, which provides information on the long term behavior of the population, namely, its survival or *** Codes 35K65, 35C07, 35K57, 92D25 © 2023, CC BY.

关键词： Diffusion

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