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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Adam, Kelli Lightfoot, Robert Chowdhury, Mahjabin Phillip Donaldson, Jonan Center for Teaching Excellence Texas A&m University College Station United States Texas A&m University Department of Computer Science College Station United States School of Education University of Alabama at Birmingham Birmingham United States

ISBN: (纸本)9798350336429

Game-based learning (GBL) can motivate learners and enable them to engage in their learning experience. Additionally, the popularity of GBL has grown in computer science and programming courses, where it can be used to develop computational thinking (CT) skills. CT is critical thinking used to apply fundamental concepts and reasoning derived from computing and computer science. Research has shown that digital GBL can improve critical thinking skills and help learners understand programming concepts, structures, and problem-solving strategies. Despite the popularity of digital GBL, analog card games and bingo games are still valuable and effective tools for learning. However, most studies on GBL that aim to train critical thinking skills have focused on digital approaches. This indicates a gap in the body of knowledge because few studies have explored the effectiveness of analog games for developing CT skills through GBL. The theoretical framework of this study is grounded in two learning theories. Constructivist theory frames learning as the construction of knowledge through a process of action, reflection, and construction. Constructionist learning theory calls for construction of artifacts, learner agency, authentic purpose, or authentic audience during the process. To investigate the design of GBL for developing CT skills and to generate discourse for its widespread adoption, the current study implemented design-based research (DBR) in an undergraduate computer science course for non-computer science majors. By addressing the gap in the literature on GBL for CT skill development, the study aimed to investigate complex learner experiences, including learner struggles and successes, alignment with learning theory principles, and design moves for future iterations of the project. The research question posed is: how can we optimize CT learning through GBL, based on learner experiences and learning theories? Prior to this study, the instructor used GBL in which learne

关键词： analog computational thinking computer science constructionist constructivist design-based research (DBR) game-based learning (GBL) undergraduate

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Novel vision transformer and data augmentation technique for efficient detection of monkeypox disease

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Multimedia Tools and Applications 2024年 1-19页

作者： Alarfaj, Aisha Ahmed Ahmad, Salman Hakeem, Abeer M. Alabdulqader, Ebtisam Abdullah PERO, Chiara Alsubai, Shtwai Innab, Nisreen Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Post Graduate Resident Department of Urology Allama Iqbal Teaching Hospital Dera Ghazi Khan Pakistan Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University P. O. Box 800 Riyadh11421 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Via Giovanni Paolo II 132 Fisciano Salerno Italy Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh13713 Saudi Arabia Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Recent technological advancements have paved the way for the optimization of medical processes, particularly automated disease detection. Moreover, the adoption of machine learning (ML) has greatly helped in automating disease detection. Such approaches can detect various diseases early, enabling timely treatment to save countless lives. Early and accurate diagnosis is very important for diseases like monkeypox, to curb its spread. Monkeypox is a viral disease caused by double-stranded DNA and can be transmitted through close contact with infected humans or animals. It’s early identification and accurate lesion diagnosis are critical to contain the disease. This study proposes an automated approach to optimize the diagnosis of monkeypox disease using a novel vision transformer, which is utilized due to its effectiveness for feature extraction. The Proposed approach’s efficiency and accuracy are tested on a public benchmark dataset comprising a variety of skin lesions of different ages and genders. In addition, data augmentation involves rotation, scaling, and flipping thereby enhancing the density of the training data set for better generalization of ML models. Experiments involve binary, as well as, multi-class classification. For the binary class, the proposed model achieves an accuracy of 97.63%, outperforming traditional ML and deep learning (DL) techniques. In the case of multi-class classification with monkeypox, measles, normal, HFMD, cowpox, and chickenpox classes, the proposed model archives an accuracy of 90.61% while precision, recall, and F1 scores are 91.39%, 89.17%, and 90.28%, respectively. Furthermore, the proposed approach shows average accuracy, precision, recall, and F1 scores of 97.54%, 96.19%, 95.16%, and 95.67%, respectively for five-fold cross-validation. Experiments demonstrate that the combination of data augmentation techniques and the vision transformer model significantly optimizes diagnostic performance. In brief, advanced DL architectur

关键词： Medical image processing

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Brain Storm Optimization based directional sensors deployment for coverage of targets 3

Brain Storm Optimization based directional sensors deploymen...

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Balaji, S. Rajkumar, S. Sharma, Shekhar Yadav, Aditi Vellore Institute of Technology Division of Mathematics Sas Chennai India Boston University Department of Computer Science BostonMA United States IIM Ahmedabad Department of Production and Quantitative Methods India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Sensors are used in various areas and have numerous applications including military surveillance, weather monitoring, maritime communication etc. Most of the applications need the multimedia sensors with directional sensing range of the sensors to perform advance task. Additionally, there are some constraints on the targets namely Q-constraints, such that each target must be covered by different number of sensors. The crucial issue in this case is deployment of minimum number of multimedia sensors to satisfy the Q-constraints. The optimization of the placement of the sensors can result in reduced number of sensors needed for the coverage of the specified targets. In this paper, the placement of the sensors is optimized using the Brain Storm Optimization Algorithm (BSO) which is inspired by the human brainstorming process. BSO algorithm efficiently designed for the considered problem to identify the optimal position of the sensors. The performance of the proposed algorithm is verified by simulation results, which performs better than the random deployment of the sensors. © 2022 IEEE.

关键词： Storms

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Creating an Appropriate computer science and Computational Thinking Graduate Curriculum for K-12 Teachers: Context and Initial Results 12

Creating an Appropriate Computer Science and Computational T...

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12th IEEE Integrated STEM Education Conference, ISEC 2022

作者： Herbert-Berger, Katherine G. Hagiwara, Sumi Rogacki, Elizabeth M. Marlowe, Thomas J. Department of Computer Science Montclair State University Herbertk United States Department of Teaching and Learning Acting Associate Dean for Academic Affairs College of Education and Human Services Montclair State University Herbertk United States Department of Science Mount St Dominic Academy CaldwellNJ United States Department of Mathematics and Computer Science Seton Hall University United States

ISBN: (纸本)9781665484299

K-12 computer science education has challenges related to content and to teacher expertise and comfort. This is further made difficult with inconsistent standards and teacher preparation from state-to-state. We describe a K-12 computer science teaching certificate program, located at Montclair State University, aimed at providing current teachers in northern New Jersey with enhanced understanding of computer science concepts, capabilities, and skills, plus scaffolding of equitable and inclusive teacher practices for applied CS pedagogy. We discuss a brief history of the field, our curriculum and approach and then our first graduating cohort's experiences and challenges. Finally, we discuss our future work. © 2022 IEEE.

关键词： Curricula

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Project-Based Learning with historic buildings: immersion in real training environments for the Degree in Technical Architecture

Project-Based Learning with historic buildings: immersion in...

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2021 International Conference of Innovation, Learning and Cooperation, CINAIC 2021

作者： Valentín-Gamazo, David Villanueva Durango, Nelson Tuesta García-Herrero, Francisco Blanco, Ma Isabel Mansilla de las Moras, Ma Cruz Rey Technical Teaching Department European University Miguel de Cervantes Valladolid Spain Department of Computer Architecture and Automatics Computer Science Faculty Complutense University of Madrid Madrid Spain Humanities Department European University Miguel de Cervantes Valladolid Spain

University education in the knowledge branches of Engineering and Architecture, has usually been based on the system of assignments and projects as a practical dynamic of training and assessment in many subjects. With the EHEA arrival, the search for new complementary systems more focused on learning than on teaching or, in other words, on the student than on the teacher, boosted the commitment to the methodology known as Project Based Learning (PBL), oriented to the development of technical skills in contexts similar to those produced in professional environments. In general, these pedagogical scenarios are built on the basis of theoretical models inspired by real cases to a greater or lesser extent. This paper analyzes the method implemented since the 2017-2018 academic year to innovate in the compulsory subject of Rehabilitation, Restoration and Pathology of the Technical Architecture Degree at the European University Miguel de Cervantes, when the PBL applied in case studies of existing historic buildings;providing students with heritage constructions to visit, recognize, measure, diagnose, draw, rehabilitate, restore, etc., and allowing their immersion in situations that introduce them to their future work practice reality. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Students

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Bi-LSTM-attention Based on ACNN Model for Disfluency Detection 2

Bi-LSTM-attention Based on ACNN Model for Disfluency Detecti...

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2022 2nd International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2022

作者： Tian, Xin Fang, Bei He, Juhou He, Xiuqing Key Laboratory of Modern Teaching Technology Shaanxi Normal University Shaanxi Xi'an710062 China School of Computer Science Shaanxi Normal University Shaanxi Xi'an710119 China

Disfluencies are self-corrections in spontaneous speech, including filled pauses, repetitions, repairs and false starts. The task of disfluency detection is to identify these disfluencies phenomena in spoken speech and make them consistent with written text. Recent research has applied machine learning and deep learning approaches to disfluency recognition and classification. In addition, long distance dependence is one of the core issues in disfluency detection. In order to solve this problem, most existing approaches had combined plenty of hand-crafted features and words as input. However, hand-crafted model features need lots of time and energy. Some studies reduced the dependence on hand-crafted features, but they ignored the dependence between long sentences. In this article, we consider disfluency detection as a problem of sequence labeling, then apply Bi-LSTM and attention mechanisms to disfluency detection. In particular, on the basis of obtaining the dependency of rough copy by Auto-correlational neural network (ACNN), we improve the ACNN model to handle the long-term dependency, so that we can better capture dependency between words and words. In other words, our method can not only find the rough copy relationship between sentences without additional hand-crafted features, but also obtain the dependency relationship between long sentences. Experiments on commonly used English Switchboard test sets show that our approach achieves good performance compared to previous models that used texts only without other hand-crafted features. © Published under licence by IOP Publishing Ltd.

关键词： Neural network models

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Can gamification help in software testing education? Findings from an empirical study

arXiv

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arXiv 2023年

作者： Blanco, Raquel Trinidad, Manuel Suárez-Cabal, María José Calderón, Alejandro Ruiz, Mercedes Tuya, Javier Software Engineering Research Group University of Oviedo Department of Computer Science Gijón Spain Software Process Improvement and Formal Methods Research Group University of Cadiz Department of Computer Science and Engineering Cádiz Spain

Software testing is an essential knowledge area required by industry for software engineers. However, software engineering students often consider testing less appealing than designing or coding. Consequently, it is difficult to engage students to create effective tests. To encourage students, we explored the use of gamification and investigated whether this technique can help to improve the engagement and performance of software testing students. We conducted a controlled experiment to compare the engagement and performance of two groups of students that took an undergraduate software testing course in different academic years. The experimental group is formed by 135 students from the gamified course whereas the control group is formed by 100 students from the non-gamified course. The data collected were statistically analyzed to answer the research questions of this study. The results show that the students that participated in the gamification experience were more engaged and achieved a better performance. As an additional finding, the analysis of the results reveals that a key aspect to succeed is the gamification experience design. It is important to distribute the motivating stimulus provided by the gamification throughout the whole experience to engage students until the end. Given these results, we plan to readjust the gamification experience design to increase student engagement in the last stage of the experience, as well as to conduct a longitudinal study to evaluate the effects of gamification. © 2023, CC BY-NC-ND.

关键词： Software testing

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Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects

arXiv

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arXiv 2024年

作者： Wang, Yingrong Wu, Anpeng Li, Haoxuan Liu, Weiming Miao, Qiaowei Xiong, Ruoxuan Wu, Fei Kuang, Kun The College of Computer Science and Technology Zhejiang University Zhejiang Province Hangzhou310027 China The Center for Data Science Peking University Beijing China The Department of Quantitative Theory & Methods Emory University United States

This paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes the total reward from both short-term and long-term effects, which might conflict with each other. For example, a higher dosage of medication might increase the speed of a patient's recovery (short-term) but could also result in severe long-term side effects. Although recent works have investigated the problems about short-term or long-term effects or the both, how to trade-off between them to achieve optimal treatment remains an open challenge. Moreover, when multiple objectives are directly estimated using conventional causal representation learning, the optimization directions among various tasks can conflict as well. In this paper, we systematically investigate these issues and introduce a Pareto-Efficient algorithm, comprising Pareto-Optimal Estimation (POE) and Pareto-Optimal Policy Learning (POPL), to tackle them. POE incorporates a continuous Pareto module with representation balancing, enhancing estimation efficiency across multiple tasks. As for POPL, it involves deriving short-term and long-term outcomes linked with various treatment levels, facilitating an exploration of the Pareto frontier emanating from these outcomes. Results on both the synthetic and real-world datasets demonstrate the superiority of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Pareto principle

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Design and Implementation of Course Articulation Information System 1

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5th International Conference on Innovative Computing, IC 2022

作者： Dai, Yanmei Xie, Yongsheng Fang, Jiajue Li, Shuwu Office of Academic Affairs Guangxi Science and Technology Normal University Guangxi Laibin545004 China Faculty of Mathematics and Computer Science Guangxi Science and Technology Normal University Guangxi Laibin545004 China Department of Teaching Quality Control and Evaluation Guangxi Science and Technology Normal University Guangxi Laibin545004 China

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

The course articulation information system construction is required by the educational modernization and high-quality education. Given this, such a system was designed by following the principle of the main leader in charge, the people-oriented principle, the principle of authority consistency, the systematic and dynamic principle, the stability principle, and the principle of easy development and maintenance. Lenovo R4900d was selected as the physical platform, J2EE as the integrated development environment and NetBeans 8.0 as the development tool to develop and apply the course articulation information system. Next, the talent training programs, curriculum syllabuses and teaching plans of secondary vocational schools and undergraduate colleges were shared, inquired and analyzed, in an effort to solve the articulation information asymmetry between secondary vocational education and undergraduate education, provide information reference for teachers to improve their teaching and enhance the educational quality. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Curricula

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The Impact of Online STEM teaching and Learning During COVID-19 on Underrepresented Students’ Self-Efficacy and Motivation

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Journal of College science teaching 2022年第6期51卷 6-15页

作者： Kahn, Sami Vertesi, Janet Adriaenssens, Sigrid Byeon, Julia Fixdal, Mona Godfrey, Kelly Lumbroso, Jérémie Wagoner, Kasey Council on Science and Technology United States Department of Sociology United States Department of Civil and Environmental Engineering United States McGraw Center for Teaching and Learning United States Department of Computer Science United States Department of Physics Princeton University Princeton NJ United States

Female students, students of color, first-generation students, and low-income students face considerable barriers in access to STEM education, leading to their underrepresentation in STEM fields. Ensuring that these students develop strong self-efficacy and motivation in STEM during the college years is key to addressing the “leaky” STEM pipeline. To determine whether the rapid shift to online teaching and learning during the COVID-19 pandemic exacerbated or mitigated inequities for college-level STEM students, we examined correlations between demographic and sociocultural factors and students’ self-assessments on indicators of self-efficacy and motivation. Our findings suggest that students from underrepresented groups were differentially negatively impacted by the shift to online teaching and learning, particularly with regard to access to study spaces, the internet, and peers. However, we found that the loss of traditional laboratories was not particularly impactful on any students’ motivation or self-efficacy, regardless of a course’s levels of dependence on such labs, as students were generally more impacted by concerns about family members’ health and loss of social and structural supports than academic experiences. We discuss these results in light of psychosocial theory and suggest pedagogical and structural changes that can support more equitable outcomes in online and in-person college-level STEM education. © 2022 National science teaching Association.

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