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learning electricity with NIELS: Thinking with electrons and thinking in levels

International Journal of computers for Mathematical learning 2009年第1期14卷 21-50页

作者： Sengupta, Pratim Wilensky, Uri Department of Learning Sciences Northwestern University Evanston United States Northwestern University Evanston United States Department of Electrical Engineering and Computer Science Northwestern University Evanston United States

Electricity is regarded as one of the most challenging topics for students of all ages. Several researchers have suggested that naïve misconceptions about electricity stem from a deep incommensurability (Slotta and Chi 2006;Chi 2005) or incompatibility (Chi et al. 1994) between naïve and expert knowledge structures. In this paper we argue that adopting an emergent levels-based perspective as proposed by Wilensky and Resnick (1999), allows us to reconceive commonly noted misconceptions in electricity as behavioral evidences of "slippage between levels," i.e., these misconceptions appear when otherwise productive knowledge elements are sometimes activated inappropriately due to certain macro-level phenomenological cues only. We then introduce NIELS (NetLogo Investigations In Electromagnetism), a curriculum of emergent multi-agent-based computational models. NIELS models represent phenomena such as electric current and resistance as emergent from simple, body-syntonic interactions between electrons and other charges in a circuit. We discuss results from a pilot implementation of NIELS in an undergraduate physics course, that highlight the ability of an emergent levels-based approach to provide students with a deep, expert-like understanding of the relevant phenomena by bootstrapping, rather than discarding their existing repertoire of intuitive knowledge. © 2009 Springer science+Business Media B.V.

关键词： Knowledge representation

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The effect of the cyclic curricular design of modelling-based instruction with virtual labs

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International Journal of science Education 2024年第1期46卷 46-72页

作者： Chang, Hsin-Yi Liu, Chen-Chung Wen, Chi-Ting Chang, Ming-Hua Fan Chiang, Shih-Hsun Hwang, Fu-Kwun Program of Learning Sciences and Institute for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Department of Computer Science and Information Management Soochow University Taipei Taiwan Department of Physics National Taiwan Normal University Taipei Taiwan

One issue facing teachers who implement model-based instruction within the school system is the tight curricular schedule. The ‘lesson objective tension’ calls for investigations to address instructional issues such as how to organise modelling-based instruction at the curriculum level which is across multiple units. We investigated the effect of the cyclic curricular design spacing out three modelling-based instruction units with virtual labs over three semesters, each involving 73, 181, and 150 junior high school students, respectively. Results indicate that, overall, the approach of modelling with virtual labs better promoted students’ scientific literacy than the traditional instruction did. Specifically, students with low and moderate school science achievements benefitted more as shown in their scientific literacy scores than did the high achievement students. Furthermore, this study found that receiving one unit of modelling-based instruction may not be sufficient to promote students’ scientific literacy, whereas the cyclic approach that suggests students receive two or more units of modelling-based instruction with virtual labs spaced over consecutive semesters had significant effects on promoting students’ scientific literacy. The results provide insights into when and how modelling-based learning with virtual labs can promote scientific literacy at a curricular level. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： lesson objective tension Modelling virtual labs

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A comparison of gains between educational games and a traditional ITS

A comparison of gains between educational games and a tradit...

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25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25

作者： Jackson, G. Tanner Boonthum-Denecke, Chutima McNamara, Danielle S. Learning Sciences Institute Arizona State University United States Department of Computer Science Hampton University United States

ISBN: (纸本)9781577355588

Intelligent Tutoring Systems (ITSs) have begun to incorporate game-based components in an attempt to balance the learning benefits of ITSs with the motivational benefits of games. iSTART-ME (Motivationally Enhanced) is a new game-based learning environment that was developed on top of an existing ITS (iSTART). In a multi-session labbased efficacy study with 125 high school students, those students with a low prior reading ability who were trained by a game-based tutoring system (iSTART-ME) or a traditional intelligent tutoring system (iSTART-Regular) performed significantly better on posttest measures than students assigned to a time-delayed control condition. Additionally, the low reading ability students who interacted with the game-based system had a tendency to gain more than students in the traditional ITS system. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Students

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An atom is known by the company it keeps: A constructionist learning environment for materials science using agent-based modeling

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International Journal of computers for Mathematical learning 2009年第2期14卷 81-119页

作者： Blikstein, Paulo Wilensky, Uri Department of Computer Science School of Education Stanford University Stanford CA United States Department of Learning Sciences School of Education and Social Policy Northwestern University Evanston IL United States

This article reports on "MaterialSim", an undergraduate-level computational materials science set of constructionist activities which we have developed and tested in classrooms. We investigate: (a) the cognition of students engaging in scientific inquiry through interacting with simulations;(b) the effects of students programming simulations as opposed to only interacting with ready-made simulations;(c) the characteristics, advantages, and trajectories of scientific content knowledge that is articulated in epistemic forms and representational infrastructures unique to computational materials science, and (d) the principles which govern the design of computational agent-based learning environments in general and for materials science in particular. Data sources for the evaluation of these studies include classroom observations, interviews with students, videotaped sessions of model-building, questionnaires, and analysis of artifacts. Results suggest that by becoming "model builders," students develop deeper understanding of core concepts in materials science, and learn how to better identify unifying principles and behaviors within the content matter. © 2009 Springer science+Business Media B.V.

关键词： Students

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Invasion of the Energy Monsters: A family board game about energy consumption

Invasion of the Energy Monsters: A family board game about e...

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34th Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2016

作者： Banerjee, Amartya Horn, Michael S. Davis, Pryce Computer Science Northwestern University EvanstonIL60208 United States Computer Sci. and Learning Sciences Northwestern University EvanstonIL60208 United States Learning Sciences Northwestern University EvanstonIL60208 United States

ISBN: (纸本)9781450340823

We present Invasion of the Energy Monsters, a board game that attempts to highlight the role of family practices in household energy consumption. While household energy management activities - such as controlling heating and cooling systems - tend to be adult centric, we believe that children and adolescents have a meaningful role to play in helping their families move toward more sustainable ways of living. To that end, Energy Monsters has been designed to scaffold interactions between family members and encourage conversations about electricity consumption and waste. In this paper we provide an overview of our current design and a brief summary of results from our playtesting sessions with five families, and a description of versions that are under development. © 2016 Authors.

关键词： Energy utilization

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STRUCTURE RECOGNITION BY CONNECTIONIST RELAXATION: FORMAL ANALYSIS

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Computational Intelligence 1992年第1期8卷 25-40页

作者： Cooper, Paul R. The Institute for the Learning Sciences Northwestern University Department of Electrical Engineering and Computer Science Evanston Illinois 60201 1890 Maple Street United States

A formal description is given of a connectionist implementation of discrete relaxation for labelled graph matching. The network is shown to converge. The desired behavior of the algorithm is formally specified;then it is proved that the result of the relaxation meets the formal goal. The network is limited by complexity considerations to the detection and propagation of unary and binary consistency constraints. The application is fast parallel indexing into a memory of object models, based on a visually derived junction/link structure description. Implementation experiments are presented, and explicit and exact space and time requirements are developed. Copyright © 1992, Wiley Blackwell. All rights reserved

关键词： attributed graph matching connectionist network constraint propagation discrete relaxation structure mat

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Sustainable Crop Yield Forecasting with the Dual-Branch Geometric Progressive Feedback Cosine Convolutional Network Model

Sustainable Crop Yield Forecasting with the Dual-Branch Geom...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： M. Siva Ramkumar Nivetha R Gokul Gopan Mohammad Kanan Rahmath Ulla Baig Jayant Giri Dept of ECE SNS College of Technology Computer Science and Engineering Kumaraguru College of Technology Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan Department of Artificial Intelligence and Machine Learning Cambridge Institute of Technology Bangalore Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Using deep learning algorithms to extract important agricultural traits has made crop production prediction based on environmental, soil, water, and crop parameters an important area of research. But conventional approaches have trouble creating a straight linear or non-linear mapping between yield values and raw data, and the quality of the characteristics that are extracted has a significant impact on how well they work. Deep reinforcement learning builds a strong prediction framework by combining the intelligence of deep learning with reinforcement learning to get beyond these drawbacks. In order to anticipate agricultural yields accurately and sustainably, this work aims to create a Dual-Branch Geometric Progressive Feedback Cosine Convolutional Network with Skill Optimization Algorithm (DB-GPFCNet-SOA). Robust Double-Weighted Guided Filtering (RDWGF) is used for data preprocessing in order to minimize noise and improve important components. To ensure a complete prediction model, the N-Branch retrieves local neighbor-based information while the C-Branch records global geometric properties. Moreover, the accuracy is enhanced by the SOA, which enhances loss parameters. The suggested model has RMSE of 0.25 and MAE of 0.15 with exceptional accuracy of 99.2%, precision of 99.1%, recall of 99%, and F1-score of 99%. These results validate the performance of the model, which renders it a suitable approach for real-time agricultural applications.

关键词： Accuracy Filtering Computational modeling Noise Predictive models Prediction algorithms Real-time systems Convolutional neural networks Optimization Crop yield

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"Of script:" Design opportunities emerging from long-term social robot interactions in-the-wild 18

"Of script:" Design opportunities emerging from long-term so...

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18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023

作者： Michaelis, Joseph E. Cagiltay, Bengisu Ibtasar, Rabia Mutlu, Bilge Learning Sciences Research Institute and Computer Science University of Illinois Chicago ChicagoIL United States Department of Computer Sciences University of Wisconsin-Madison MadisonWI United States Learning Sciences Research Institute University of Illinois Chicago ChicagoIL United States

ISBN: (纸本)9781450399647

Social robots are becoming increasingly prevalent in the real world. Unsupervised user interactions in a natural and familiar setting, such as the home, can reveal novel design insights and opportunities. This paper presents an analysis and key design insights from family-robot interactions, captured via on-robot recordings during an unsupervised four-week in-home deployment of an autonomous reading companion robot for children. We analyzed interviews and 160 interaction videos involving six families who regularly interacted with a robot for four weeks. Throughout these interactions, we observed how the robot's expressions facilitated unique interactions with the child, as well as how family members interacted with the robot. In conclusion, we discuss fve design opportunities derived from our analysis of natural interactions in the wild. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Machine design

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A Comprehensive Food Identification and Waste Reduction Solution with Built-in Nutritional Tracking using Machine learning 8

A Comprehensive Food Identification and Waste Reduction Solu...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Jonathan, J. Benjamin, Ruth Moly Prasad, Gunji Prem Karunya Institute of Technology and Sciences Computer Science Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350386578

Food Solutions and Nutrition Checker is an innovative and comprehensive project aimed at providing users with a reliable tool to identify various foods and dishes, while also offering a custom-built database for detecting fresh and wasted food items. With this system, users can easily determine the nutritional value of different foods, making informed decisions about their dietary choices. This study incorporates advanced technology that enables the recognition of a wide range of food items. By simply capturing an image or specifying the dish, users can obtain accurate information about the food, including its ingredients and origin. This feature is particularly helpful for individuals with specific dietary needs or restrictions, allowing them to easily identify suitable menu options. Additionally, this study includes a custom-built data system that helps to identify fresh and wasted food. By analyzing factors such as expiration dates, storage conditions, and quality indicators, the proposed system can determine the freshness status of various food items. This capability promotes food waste reduction by enabling users to make informed decisions regarding the consumption or disposal of perishable goods. Moreover, this study incorporates a nutritional tracker that provides users with comprehensive information regarding the nutritional composition of different foods. This feature includes details such as calorie count, macronutrient breakdown, vitamins, and minerals, empowering users to make healthier food choices aligned with their dietary goals and requirements. Overall, the proposed model offers a holistic approach for food identification, waste reduction, and nutritional tracking. By incorporating advanced technology and a robust database, this study aims to facilitate informed decision-making and promote healthier dietary practices. © 2024 IEEE.

关键词： Food ingredients

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DETECTION OF PULMONARY EMBOLISM WITH IMPROVED XCEPTION ARCHITECTURE 10

DETECTION OF PULMONARY EMBOLISM WITH IMPROVED XCEPTION ARCHI...

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10th International Conference on AI and the Digital Economy, CADE 2024

作者： Paulraj, Getzi Jeba Leelipushpam Princess, P. Joyce Beryl Jebadurai, Immanuel Johnraja Celshia, S. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences India

ISBN: (纸本)9781837241880

The blocking of arteries in the lungs through blood clots is known as pulmonary embolism. This blood vessel block usually originates in the legs and travels to the lungs causing fatal damage. An imaging test called Computed Tomography (CT) pulmonary angiography scans the lungs for blood clots. The preferred method for identifying pulmonary embolism is CT pulmonary angiography. Manual examination by radiologists increases the workload, consumes more time and also requires experience for correct examination. The objective of this paper is to propose an improved Xception architecture based on Convolutional Neural Networks. The existing architecture is improvised by replacing the Relu activation function with the Swish activation function. This improves the feature extraction of the algorithm thereby improving efficiency in terms of loss and accuracy. The algorithm has been trained and tested using the standard RSNA-STR Pulmonary Embolism CT Dataset. It is evident from the experimental results that our model achieves an improvement of 3.4% and 1.4% in terms of accuracy and loss respectively. © The Institution of Engineering & Technology 2024.

关键词： Blood vessels

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