Computational thinking (CT) is an essential skill required for every individual in the digital era to become creative problem solvers. The purpose of this research is to investigate the relationships between computati...
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Computational thinking (CT) is an essential skill required for every individual in the digital era to become creative problem solvers. The purpose of this research is to investigate the relationships between computational thinking skills, the Big Five personality factors, and learning motivation using structural equation modeling (SEM). The research administered the computational thinking scale, NEO FFI scale, and Motivated Strategies for Learning Questionnaire to a sample of 92 students pursuing degrees in computerscience and Engineering. Based on the result analysis, it was determined that both personality and learning motivation had positive and significant impacts on computation thinking skills. Personality had a major contribution to the prediction of CT, with consciousness being the most influential predictor. The findings of this study suggest that educators and academics should focus on the significance of the psychological side of CT for the improvement of students' CT skills.
State-of-the-art large language models (LLMs) have demonstrated an extraordinary ability to write computer code. This ability can be quite beneficial when integrated into an IDE to assist a programmer with basic codin...
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ISBN:
(纸本)9798400705793
State-of-the-art large language models (LLMs) have demonstrated an extraordinary ability to write computer code. This ability can be quite beneficial when integrated into an IDE to assist a programmer with basic coding. On the other hand, it may be misused by computerscience students for cheating on coding tests or homework assignments. At present, knowledge about the exact capabilities and limitations of state-of-the-art LLMs is still inadequate. Furthermore, their capabilities have been changing quickly with each new release. In this paper, we present a dataset of 559 programming exercises in 10 programming languages collected from a system for evaluating coding assignments at our university. We have experimented with four well-known LLMs (GPT-3.5, GPT-4, Codey, Code Llama) and asked them to solve these assignments. The evaluation results are intriguing and provide insights into the strengths and weaknesses of the models. In particular, GPT-4 (which performed the best) is currently capable of solving 55% of all our exercises and achieved an average score of 86% on exercises from the introductory programming course (using the best of five generated solutions).
Block-based programming environments, widely used for teaching beginners, pose accessibility challenges for individuals with visual impairments due to limited support for screen readers and keyboard navigation. To add...
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The remanufacturing process, driven by human-robot collaboration technology, is becoming an important carrier for the circular economy, contributing to economic development while significantly reducing environmental p...
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The remanufacturing process, driven by human-robot collaboration technology, is becoming an important carrier for the circular economy, contributing to economic development while significantly reducing environmental pressure. However, existing researches on human-robot collaboration disassembly line have certain limitations and fails to address economic and environmental considerations adequately. Therefore, in this study, a techno-economic and environmental benefit-oriented human-robot collaborative disassembly line balancing problem (TEBHRC-DLBP) was formulated that requires the utilization of human and robot resources to improve disassembly efficiency and quality and to facilitate decision-making on recycling options for disassembled parts to maximize economic and environmental benefits. First, a mixed-integer programming (MIP) model was developed for the TEBHRC-DLBP to minimize the number of workstations, minimize the smoothing index, and maximize techno-economic and environmental benefits. Second, a multi-objective immune genetic algorithm (MIGA) was developed to solve the proposed TEBHRC-DLBP efficiently. A five-layer encoding method and a triple decoding strategy were constructed based on the problem characteristics, and immune operators were introduced into the well-known NSGA-II structure to interfere with the global search process with a certain degree of strength to avoid invalid work and to improve algorithm efficiency. In addition, the correctness and validity of the proposed MIP model and the MIGA were verified using the case of a small-scale personal computer. In 21 benchmark tests with scales ranging from 7 to 148, the proposed MIGA achieved significantly better results than the seven algorithms reported in the literature and obtained the best value for 16 benchmarks. Finally, the application of the MIGA to the disassembly case of a power battery module demonstrates its good stability, convergence, and diversity, as well as its excellent practical ap
As the demand for programming in STEM education continues to grow, computational thinking skills are becoming even more important for students. Research indicates Python is an effective language to teach computational...
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ISBN:
(数字)9783031345500
ISBN:
(纸本)9783031345494;9783031345500
As the demand for programming in STEM education continues to grow, computational thinking skills are becoming even more important for students. Research indicates Python is an effective language to teach computational thinking. In spite of its popularity, there are only a few schools that offer Python courses. This is often due to a lack of teaching resources especially in remote schools. This paper aims to integrate computational thinking methods utilizing Python, while bridging the gap between block-based and text-based programming. The effectiveness of the curriculum was evaluated using established computational thinking and blended learning surveys. The results of our study show that this newly developed course with the assessment tool identifies computationally talented students, creates an interest in computational thinking development, and encourages students to perform in a blended learning course. This paper lays out a comprehensive curriculum aligned with Ontario learning outcomes with the mission to remove geographical barriers to build sustainability in First Nation Schools in Northwestern communities of Canada.
Clustering is an unsupervised learning task that aims to partition data into a set of clusters. In many applications, these clusters correspond to real-world constructs (e.g., electoral districts, playlists, TV channe...
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ISBN:
(数字)9783031605994
ISBN:
(纸本)9783031606014;9783031605994
Clustering is an unsupervised learning task that aims to partition data into a set of clusters. In many applications, these clusters correspond to real-world constructs (e.g., electoral districts, playlists, TV channels) whose benefit can only be attained by groups when they reach a minimum level of representation (e.g., 50% to elect their desired candidate). In this paper, we study the k-means clustering problem with the additional constraint that each group (e.g., demographic group) must have a minumum level of representation in at least a given number of clusters. We formulate the problem through a mixed-integer optimization framework and present an alternating minimization algorithm, called MiniReL, that directly incorporates the fairness constraints. While incorporating the fairness criteria leads to an NP-Hard assignment problem within the algorithm, we present computational approaches that make the algorithm practical even for large datasets. Numerical results show that this approach can produce fair clusters with practically no increase in the clustering cost across standard benchmark datasets.
In the dynamic educational context of Malaysia, this study examines the impact of integrating Unplugged Activities (UA) with Block-Based programming (BBP) on improving the computational thinking (CT) skills of seconda...
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In the dynamic educational context of Malaysia, this study examines the impact of integrating Unplugged Activities (UA) with Block-Based programming (BBP) on improving the computational thinking (CT) skills of secondary students in full boarding schools in Northern Peninsular Malaysia. Using a quasi-experimental design and mixed-methods analysis, the research evaluates the impact of these teaching methods on students' CT skills and attitudes toward programming. This research compares the results between a group that uses only BBP and another that combines both UA and BBP. The results indicate that CT skills improved in both groups, while students in the UA + BBP group showed more significant gains in confidence and a more positive attitude toward programming. These results provide valuable insights into pedagogical strategies within digital education and highlight the benefits of an integrated approach that combines tactile learning experiences with digital technologies. By combining hands-on activities with technology-based instruction, this approach not only deepens students' understanding of CT concepts but also positively changes their perception and engagement with programming.
Language is a key topic of interest for students in the humanities - language is the way in which humans express themselves, communicate, and make art. Computing on language (e.g., recognizing language, generating lan...
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ISBN:
(纸本)9798400701399
Language is a key topic of interest for students in the humanities - language is the way in which humans express themselves, communicate, and make art. Computing on language (e.g., recognizing language, generating language, building bots) can be a pathway into using computing for humanities contexts. At the University of Michigan, we are developing a new program to support students in liberal arts and sciences to learn about computing, explicitly including programming. We have designed two courses for introducing computing (1) in terms of creative expression and (2) around the implications of computing on justice. In both classes, we use a scaffolded sequence of programming languages and activities to explore computing on language: (a) a teaspoon language for sentence generation and recognition, (b) a set of custom Snap blocks for sentence generation and recognition, (c) a set of custom Snap blocks for building Chatbots, and (d) an ebook activity for mapping from Snap to Python. Each language takes less than 10 minutes to introduce, with a wide variety of possible student activities (for in-class active learning or for later homework). While the tools build on each other, the earliest stage (the teaspoon language) could be used within a single class session in linguistics, communications, or other liberal arts courses.
This study investigates the implementation and impact of mastery learning in a computerscience course, particularly during the transition from traditional teaching methods to mastery learning amidst the COVID-19 pand...
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One of the most widely used smartphone operating systems,Android,is vulnerable to cutting-edge malware that employs sophisticated *** malware attacks could lead to the execution of unauthorized acts on the victims’de...
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One of the most widely used smartphone operating systems,Android,is vulnerable to cutting-edge malware that employs sophisticated *** malware attacks could lead to the execution of unauthorized acts on the victims’devices,stealing personal information and causing hardware *** previous studies,machine learning(ML)has shown its efficacy in detecting malware events and classifying their ***,attackers are continuously developing more sophisticated methods to bypass ***,up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile ***,this study employed ML algorithms to classify Android applications into malware or goodware using permission and application programming interface(API)-based features from a recent *** overcome the dataset imbalance issue,RandomOverSampler,synthetic minority oversampling with tomek links(SMOTETomek),and RandomUnderSampler were applied to the Dataset in different *** results indicated that the extra tree(ET)classifier achieved the highest accuracy of 99.53%within an elapsed time of 0.0198 s in the experiment that utilized the RandomOverSampler ***,the explainable Artificial Intelligence(EAI)technique has been applied to add transparency to the high-performance ET *** global explanation using the Shapely values indicated that the top three features contributing to the goodware class are:Ljava/net/URL;->openConnection,Landroid/location/LocationManager;->getLastKgoodwarewnLocation,and *** the other hand,the top three features contributing to themalware class are Receive_Boot_Completed,Get_Tasks,and Kill_Background_*** is believed that the proposedmodel can contribute to proactively detectingmalware events in Android devices to reduce the number of victims and increase users’trust.
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