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Usability of Learning Management systems for Instructors - The Case of Canvas 8th

Usability of Learning Management Systems for Instructors - T...

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8th international conference on Learning and Collaboration technologies (LCT) Held as Part of the 23rd international conference on Human-computer Interaction (HCII)

作者： Chen, Weiqin Sanderson, Norun C. Nichshyk, Anna Bong, Way Kiat Kessel, Siri Oslo Metropolitan Univ OsloMet POB 4 N-0130 Oslo Norway

ISBN: (纸本)9783030778880;9783030778897

The past 30 years have seen increased adoption of learning management systems (LMSs) in education. Several studies have investigated the usability of LMSs for students. However, very few studies have assessed the usability from the instructors' perspective. Usability issues can pose challenges for instructors who use LMSs to create, manage and deliver courses. These challenges require instructors to spend extra time and energy on tackling the challenges rather on teaching-related tasks, which will have negative impacts on learners' experiences and learning outcomes. This paper aims to identify usability challenges in LMSs for instructors. We used Canvas as an example and conducted user testing with 35 university instructors in computer science and engineering disciplines. Pre- and post-interviews were transcribed and analyzed together with the observation data during their use of Canvas to carry out tasks. The results show that, although Canvas has made continuous efforts to improve its usability, instructors still face some usability challenges. Instructors are a diverse user group for LMSs. Further research should consider recruiting participants from other disciplines and investigating other LMSs to identify possibilities for improving general usability of digital tools for instructors.

关键词： Learning management system (LMS) Usability Instructor Canvas

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Effectiveness of System-Facilitated Monitoring Strategies on Learning in an Intelligent Tutoring System 8th

Effectiveness of System-Facilitated Monitoring Strategies on...

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8th international conference on Learning and Collaboration technologies (LCT) Held as Part of the 23rd international conference on Human-computer Interaction (HCII)

作者： Dever, Daryn A. Wortha, Franz Wiedbusch, Megan D. Azevedo, Roger Univ Cent Florida Orlando FL 32816 USA Univ Greifswald Greifswald Germany

ISBN: (纸本)9783030778880;9783030778897

To effectively process complex information within intelligent tutoring systems (ITSs), learners are required to engage in metacognitive monitoring micro-processes (content evaluations [CEs], judgments of learning [JOLs], feelings of knowing [FOKs], and monitoring progress towards goals [MPTGs]). Learners' average monitoring micro-process strategy frequencies were used to examine learning gains using a person-centered approach as they interacted with MetaTutor. Undergraduates (n = 94) engaged in self-initiated and system-facilitated self-regulated learning (SRL) strategies as they studied the human circulatory system with MetaTutor, a hypermedia-based ITS. Using hierarchical clustering, results showed a difference in learning between clusters differing in metacognitive monitoring process usage. Specifically, learners who used both CEs and FOKs for a greater proportion of monitoring strategy usage had significantly greater learning gains than learners who used MPTGs. Implications for monitoring strategy usage across different micro-processes and the development of ITSs to facilitate and scaffold learners' interactions with these micro-processes via prompting are discussed.

关键词： Monitoring Self-regulated learning Intelligent tutoring systems

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Exploiting Operator-Level Concurrency control to Guide Deployment for Real-Time Tasks in Edge AI Cluster 25

Exploiting Operator-Level Concurrency Control to Guide Deplo...

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Proceedings of the 23rd ACM conference on Embedded Networked Sensor systems

作者： Changyao Lin Ziyang Zhang Jie Liu School of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang China NESLab Politecnico di Milano Milano Italy National Key Laboratory of Smart Farm Technologies and Systems Harbin Institute of Technology Harbin Heilongjiang China International Research Institute for Artificial Intelligence Harbin Institute of Technology Shenzhen Guangdong China

ISBN: (纸本)9798400714795

Existing task deployment frameworks for edge clusters optimize at the model-level, lacking fine-grained resource awareness and concurrency control, where the urgent tasks are frequently blocked and miss their deadlines. Therefore, we propose a multi-level collaborative deployment framework Coconut for real-time deep learning tasks in the typical heterogeneous edge GPU cluster. Coconut collaboratively optimizes model deployment and fine-grained concurrency control. To address the high complexity of multi-level collaborative optimization, we employ an efficient learning-based search algorithm. Based on the operator-level information, we also pre-train an accurate latency predictor for each device, enabling centralized optimization to further accelerate the search. We conduct a preliminary evaluation in an edge cluster and validate the effectiveness of Coconut.

关键词： edge computing

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Towards the Automation of Industrial Data science: A Meta-learning based Approach 23

Towards the Automation of Industrial Data Science: A Meta-le...

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23rd international conference on Enterprise Information systems (ICEIS)

作者： Garouani, Moncef Ahmad, Adeel Bouneffa, Mourad Lewandowski, Arnaud Bourguin, Gregory Hamlich, Mohamed Univ Littoral Cote dOpale UR 4491 Lab Informat Signal & Image Cote dOpale LISIC F-62100 Calais France Univ Hassan 2 ISSIEE Lab ENSAM Casablanca Morocco HESTIM Study & Res Ctr Engn & Management CERIM Casablanca Morocco

ISBN: (纸本)9789897585098

In context of the fourth industrial revolution (industry 4.0), the industrial big data is subject to grow rapidly to respond the agile industrial computing and manufacturing technologies. This data evolution can be captured using ubiquitous integrated sensors and multiple smart machines. We believe the use of data science methodologies, for the selection of models and configuration of hyper-parameters, may help to better control such data evolution. But, at the same time, the industrial practitioners and researchers often lack machine-learning expertise to directly retrieve the benefit from valuable manufacturing big data. Such a lack poses the major obstacle to yield value from even-though familiar data. In this case, a collaboration with data scientists may become an exigence along with the extensive machine learning knowledge which presumably may result to pursue further delays and effort. Multiple approaches for automating machine learning (AutoML) have been proposed for the past recent years in order to alleviate this deficiency. These approaches are expected to perform better along with accomplishment of computing resources which are mostly not readily accessible. To address this research challenge, in this paper, we propose a meta-learning based approach that may serve an effective decision support system for the AutoML process.

关键词： Automated Machine Learning Manufacturing Big Data Industry 4.0 Industrial Data science Meta-learning

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Human-computer Collaborative Interaction Design of Intelligent Vehicle-A Case Study of HMI of Adaptive Cruise control 3rd

Human-Computer Collaborative Interaction Design of Intellige...

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3rd international conference on HCI in Mobility, Transport and Automotive systems (MobiTAS) Held as Part of the 23rd international conference on Human-computer Interaction (HCII)

作者： Liu, Yujia Zhang, Jun Li, Yang Hansen, Preben Wang, Jianmin Tongji Univ Coll Design & Innovat Shanghai Peoples R China Tongji Univ Coll Art & Media Car Interact Design Lab Shanghai Peoples R China Karlsruher Inst Technol Baden Wurttemberg Germany Stockholm Univ Dept Comp & Syst Sci Stockholm Sweden

ISBN: (纸本)9783030783570;9783030783587

With the rapid development of the intelligent vehicle industry, in order to improve the efficiency and usability of systems, the interface design of the Human-computer Interaction of intelligent vehicles has received great attention. In this article, firstly, combined with the domestic and foreign taxonomy of automated driving systems and collaborative design concepts, a framework of human-computer collaborative interaction (Human-Engaged Automated Driving, HEAD) is proposed. Secondly, we discussed the engagement of people and intelligent vehicle at different levels. We analyzed the interaction flow chart under the Human-computer Collaboration, established the information architecture of Human-computer Engagement, and conducted the design practice on the Human-Machine Interface. Finally, a case study of the HMI design of Adaptive Cruise control was performed to validate the HEAD framework. SUS usability test was performed together with an experiment evaluating interface elements in two interface designs: Original UI design and Iterative UI design. This also including a Likert scale questionnaire. The results showthat HEADcan guide and improve the Human-Machine Interface design to enhance the efficiency of Human-computer Interaction and user experience.

关键词： Human-computer Collaboration Interaction Design Human-Machine Interface Adaptive Cruise control Intelligent vehicle

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Edge Deep Learning Applied to Granulometric Analysis on Quasi-particles from the Hybrid Pelletized Sinter (HPS) Process 23

Edge Deep Learning Applied to Granulometric Analysis on Quas...

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23rd international conference on Enterprise Information systems, ICEIS 2021

作者： de Meira, Natália F.C. Silva, Mateus C. Oliveira, Ricardo A.R. Souza, Aline D’Angelo, Thiago Vieira, Cláudio B. Metallurgical Engineering Department Federal University of Ouro Preto Ouro Preto Brazil Department of Computer Science Federal University of Ouro Preto Ouro Preto Brazil ArcelorMittal João Monlevade Brazil

ISBN: (纸本)9789897585098

The mining and metallurgical industry seeks to adapt to Industry 4.0 with the implementation of Artificial Intelligence in the processes. The purpose of this paper is to develop the first steps of an Artificial Intelligence in Deep Learning with Edge Computing to recognize the quasi-particles from the Hybrid Pelletized Sinter (HPS) process and provide its particle size distribution. We trained our model with the aXeleRate tool using the Keras-Tensorflow framework and the MobileNet architecture and tested it with an embedded system using the SiPEED MaiX Dock board. Our model obtained 98.60% accuracy in training validation using real and synthetic images and 100% accuracy in tests with synthetic images and 70% recall. The tests’ results indicate the feasibility of the proposed system, but with probable overfitting in the training stage. Copyright © 2021 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： computer vision

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Exploring Differential Privacy in Practice 23

Exploring Differential Privacy in Practice

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23rd international conference on Enterprise Information systems, ICEIS 2021

作者： Hasuda, Davi Grossi de Melo Bezerra, Juliana Computer Science Department ITA São José dos Campos Brazil

ISBN: (纸本)9789897585098

Every day an unimaginable amount of data is collected from Internet users. All this data is essential for designing, improving and suggesting products and services. In this frenzy of capturing data, privacy is often put at risk. Therefore, there is a need for considering together capturing relevant data and preserving the privacy of each person. Differential Privacy is a method that adds noise in data in a way to keep privacy. Here we investigate Differential Privacy in practice, aiming to understand how to apply it and how it can affect data analysis. We conduct experiments with four classification techniques (including Decision Tree, Näive Bayes, MLP and SVM) by varying privacy degree in order to analyze their accuracy. Our initial results show that low noise guarantees high accuracy;larger data size is not always better in the presence of noise;and noise in the target does not necessary disrupt accuracy. Copyright © 2021 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Decision trees

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On Active-Set Methods for Quadratic Problems with Positive Semidefinite Matrices 23rd

On Active-Set Methods for Quadratic Problems with Positive...

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23rd international conference on Mathematical Optimization Theory and Operations Research, MOTOR 2024

作者： Vasiliev, Igor Gruzdeva, Tatiana Barkova, Mariia Boyarkin, Denis Iakubovskii, Dmitrii Matrosov Institute for System Dynamics and Control Theory of SB RAS 134 Lermontov str. Irkutsk664033 Russia Melentiev Energy Systems Research Institute of SB RAS 130 Lermontov str. Irkutsk664033 Russia

ISBN: (纸本)9783031733642

Quadratic programming problems (QP) arise in a vast variety of real-world applications in finance, engineering, operational research, and many other fields. A huge number of methods and their various modifications have been developed for solving convex QPs. One of the first algorithms popularized as the solution method for QPs was the active-set method. The main idea of the method is selection of a set of binding constraints (an active set) and then iteratively adaptation it by adding and dropping constraints from the index set. The dual active-set (DAS) method of Goldfarb and Idnani is still well-known to be efficient and numerically stable. Taking advantage of the fact that the unconstrained minimum of the objective function can be used as a starting point of the method, its implementation utilizes the Cholesky and QR factorizations and procedures for updating them. At the same time, it suffers from the restriction that it cannot be applied to problems with positive semidefinite objective matrices, whereas in practice optimization problems may often be of such structure. We propose an add-in to the method of Goldfarb and Idnani that allows solving problems with positive semidefinite matrices directly by the DAS algorithm with a warm-start where a good estimate of the optimal active set is used to start the algorithm. The proposed technique is based on the well-known possibility of representing any matrix, as a difference of two, positive definite ones and solving then a sequence of so-called partially linearized problems. It can be viewed as a particular case of the special local search method for DC minimization problems. A computational experiment to testing the proposed approach was carried out on QPs with positive semidefinite objective matrices from the Maros and Meszaros test set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Iterative methods

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Optimization to Automated Phonetic Transcription Grading Tool (APTgt) - Automatic Exam Generator 8th

Optimization to Automated Phonetic Transcription Grading Too...

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8th international conference on Learning and Collaboration technologies (LCT) Held as Part of the 23rd international conference on Human-computer Interaction (HCII)

作者： Liu, Jueting Speights, Marisha Bailey, Dallin Li, Sicheng Luan, Yaoxuan Mishra, Ishaan Cao, Yang Seals, Cheryl Auburn Univ Auburn AL 36930 USA

ISBN: (纸本)9783030778880;9783030778897

The Automated Phonetic Transcription Grading Tool (APTgt) is an online exam system developed by Auburn University HCI group. This application aims to support faculty in communications disorders to improve their pedagogy and timely feedback for students. This article discusses an attempt to improve teacher's experience by providing an automated method for exam generation, which can significantly save time while creating exams. The exam entry is created with the AU IPA (international Phonetic Alphabet) keyboard and system that grades the exams upon student completion of the exam. The exam is composed of several linguistics words with their pronunciation. Students need to answer the questions by inputting the correct phonetic spells during the exam. The core part in the auto-exam generator is the classification module, which can classify the input words into different difficulty levels. In this paper, we proposed two classification algorithms in the classification module: Rule-based algorithm and Classification and Regression Trees (CART).

关键词： E-learning Linguistics words international phonetic alphabet Phonetic transcription Decision tree classifier Multiclass classification

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Querying Brazilian Educational Open Data using a Hybrid NLP-based Approach 23

Querying Brazilian Educational Open Data using a Hybrid NLP-...

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23rd international conference on Enterprise Information systems, ICEIS 2021

作者： Antoni, Marco Charão, Andrea Schwertner Franciscatto, Maria Helena Department of Languages and Computer Systems Federal University of Santa Maria Santa Maria Brazil Department of Computer Science Federal University of Paraná Curitiba Brazil

ISBN: (纸本)9789897585098

The need for capturing information suitable to the user has favored the development of Question Answering (QA) systems, whose main goal is retrieving a precise answer to a question expressed in Natural Language. Thus, these systems have been adopted in many domains to make data accessible, including Open Data. Although there are many QA approaches that access Open Data sources, querying Brazilian Open Data is still a research gap, possibly motivated by the complexity that Portuguese language presents to Natural Language Processing (NLP) approaches. For this reason, this paper proposes a hybrid NLP-based approach for querying Open Data of Brazilian Educational Census. The proposed solution is based on a combination of linguistic and rule-based NLP approaches, that are applied in two main processing stages (Text Preprocessing and Question Mapping) to identify the meaning of an input question and optimize the querying process. Our approach was evaluated through a QA prototype developed as a Web interface and showed feasible results, since concise and accurate answers were presented to the user. Copyright © 2021 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Open Data

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