Background and contextDespite the growing importance of computerscience (CS) education, high-quality CS curricula for students in kindergarten to lower elementary grades are lacking. It is also unclear how students f...
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Background and contextDespite the growing importance of computerscience (CS) education, high-quality CS curricula for students in kindergarten to lower elementary grades are lacking. It is also unclear how students from underrepresented groups such as female students, students from low socioeconomic status, and students with disability respond to CS curriculum at this age *** study is aimed to examine the effectiveness of a novel CS curriculum (Coding as Another Language - ScratchJr) on the development of programming and computational thinking skills for students in kindergarten to second grade *** conducted a randomized controlled trial with 1057 students and used multilevel regression analysis to examine the impact of the CAL curriculum on students' coding and computational thinking skills, as well as moderation analysis to investigate how students' demographic characteristics including gender, socioeconomic status, English learners and disability interacted with the intervention *** CAL curriculum was effective in improving students' programming skills, but no significant differences were detected for students' computational thinking skills. Students with limited English proficiency and students from low socioeconomic backgrounds achieved similar gain in coding skills but students with disabilities and female students did not demonstrate the same improvements as their *** findings shed light on the educational efficacy of CAL as a promising CS curriculum for young children and underscore the importance of understanding how underrepresented minority students respond to the curriculum in order to better guide the development and design of the CS programs.
Artificial intelligence (AI) is revolutionizing the way programming is taught and learnt and is redefining t he way programming skills are assessed in the continuously changing field of computerscience education. Thi...
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In multiagent systems,agents usually do not have complete information of the whole system,which makes the analysis of such systems *** incompleteness of information is normally modelled by means of accessibility relat...
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In multiagent systems,agents usually do not have complete information of the whole system,which makes the analysis of such systems *** incompleteness of information is normally modelled by means of accessibility relations,and the schedulers consistent with such relations are called *** this paper,we consider probabilistic multiagent systems with accessibility relations and focus on the model checking problem with respect to the probabilistic epistemic temporal logic,which can specify both temporal and epistemic ***,the problem is undecidable in *** show that it becomes decidable when restricted to memoryless uniform ***,we present two algorithms for this case:one reduces the model checking problem into a mixed integer non-linear programming(MINLP)problem,which can then be solved by Satisfiability Modulo Theories(SMT)solvers,and the other is an approximate algorithm based on the upper confidence bounds applied to trees(UCT)algorithm,which can return a result whenever *** algorithms have been implemented in an existing model checker and then validated on *** experimental results show the efficiency and extendability of these algorithms,and the algorithm based on UCT outperforms the one based on MINLP in most cases.
Many practical optimization problems require models that are able to reflect uncertainty or inexactness inherently present in the data. Interval linear programming provides a model for handling uncertain optimization ...
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Governing algorithms and artificial intelligence (AI) involves multidisciplinary work across different fields to ensure human control over technology. This article aims to show how computerscience and social sciences...
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Governing algorithms and artificial intelligence (AI) involves multidisciplinary work across different fields to ensure human control over technology. This article aims to show how computerscience and social sciences must work together to rethink how we oversee algorithms and AI in a more democratic way.
Based on the background of the big data era, personalised adaptive learning has become the new normal of digital learning. The change of learners' access to learning resources from "learner-initiated search&q...
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ISBN:
(纸本)9798350354560
Based on the background of the big data era, personalised adaptive learning has become the new normal of digital learning. The change of learners' access to learning resources from "learner-initiated search"to "learning system automatically providing learners with personalised learning resources"is the inherent requirement of personalised and adaptive learning. The development and application of educational big data provide scientific basis for the realisation of personalised and precise learning support services. Learning resource recommendation, as an important application direction of personalised adaptive learning system, was initially used to solve the problems of "information overload"and "information disorientation"caused by massive learning resources. In recent years, C programming, which is popular both at home and abroad, is a basic course for computer majors and related majors, a prerequisite course for other computer courses such as data structure and Java programming, and also a foundation for indepth study of programming. Therefore, improving the teaching quality of C language is crucial to cultivate the computer skills of college students. However, on the one hand, due to the complexity of C language concepts, rules and flexible use, it is difficult for students to master every knowledge point taught by the teacher in 45 minutes under the traditional classroom teaching environment. However, the small amount of data analysed by the traditional learning resources recommendation system and the lack of learning resources recommendation algorithm design have become the main reasons hindering the improvement of learning resources recommendation effect. Aiming at the above problems, this paper makes use of educational big data to mine, store, analyse and apply the data generated by each learner in a more scientific and comprehensive way. Based on the research background of big data, this paper researches and explores the teaching and learning of computers, a
The probabilistic programming paradigm is gaining popularity due to the possibility of easily representing probabilistic systems and running a number of off-the-shelf inference algorithms on them. This paper explores ...
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ISBN:
(纸本)9783031737084;9783031737091
The probabilistic programming paradigm is gaining popularity due to the possibility of easily representing probabilistic systems and running a number of off-the-shelf inference algorithms on them. This paper explores how this paradigm can be used to analyse collective systems, in the form of Markov Population Processes (MPPs). MPPs have been extensively used to represent systems of interacting agents, but their analysis is challenging due to the high computational cost required to perform exact simulations of the systems. We represent MPPs as runs of the approximate variant of the Stochastic Simulation Algorithm (SSA), known as tau-leaping, which can be seen as a probabilistic program. We apply Gaussian Semantics, a recently proposed inference method for probabilistic programs, to analyse it. We show that tau-leaping runs can be effectively analysed using a tailored version of Second Order Gaussian Approximation in which we use a Gaussian Mixture encoding of Poisson distributions. In the resulting analysis, the state of the system is approximated by a multivariate Gaussian Mixture generalizing other common Gaussian approximations such as the Linear Noise Approximation and the Langevin Method. Preliminary numerical experiments show that this approach is able to analyse MPPs with reasonable accuracy on the significant statistics while avoiding expensive numerical simulations.
computerized exams have benefits for large enrollment courses and computerscience classes, specifically. In this research paper, we compare student self-reported test anxiety between two modes of administering comput...
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ISBN:
(纸本)9798400705311
computerized exams have benefits for large enrollment courses and computerscience classes, specifically. In this research paper, we compare student self-reported test anxiety between two modes of administering computerized exams: a computer-based testing facility (CBTF) and a bring-your-own-device (BYOD) setup. We conducted crossover design experiments in two computerscience courses, measuring trait anxiety, as well as students' test anxiety and their test performance after each exam. We found no statistically significant differences between testing modes on either students' test anxiety or their performance. We did replicate prior findings showing positive correlations between trait anxiety and test anxiety (statistically significant in one course) and inverse correlations between test anxiety and exam score. From a test anxiety perspective, our findings indicate that either a CBTF or BYOD configuration for computerized exams is an acceptable means for addressing large classes and/or a desire to permit computational elements (e.g., IDEs, testing/debugging code) on exams.
In a typical introductory programming course, grading student-submitted programs involves an autograder which compiles and runs the programs and tests their functionality with predefined test cases, with no attention ...
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ISBN:
(纸本)9798400705328
In a typical introductory programming course, grading student-submitted programs involves an autograder which compiles and runs the programs and tests their functionality with predefined test cases, with no attention to the source code. However, in an educational setting, grading based on inspection of the source code is required for two main reasons (1) awarding partial marks to 'partially correct' code that may be failing the testcase check (2) awarding marks (or penalties) based on source code quality or specific criteria that the instructor may have laid out in the problem statement (e.g. 'implement sorting using bubble-sort'). However, grading based on studying the source code can be highly time consuming when the course has a large enrollment. In this paper we present the design and evaluation of an AI Assistant for source code grading, which we have named TA Buddy. TA Buddy is powered by Code Llama, a large language model especially trained for code related tasks, which we fine-tuned using a graded programs dataset. Given a problem statement, student code submissions and a grading rubric, TA Buddy can be asked to generate suggested grades, i.e. ratings for the various rubric criteria, for each submission. The human teaching assistant (TA) can then accept or overrule these grades. We evaluated the TA Buddy-assisted manual grading against 'pure' manual grading and found that the time taken to grade reduced by 24% while maintaining grade agreement in the two cases at 90%.
In the Internet of Things (IoT) perceived applications of monitoring the states of the environment, a technology is to use fog radio access networks (F-RANs) to alleviate the problems of long response time cloud serve...
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In the Internet of Things (IoT) perceived applications of monitoring the states of the environment, a technology is to use fog radio access networks (F-RANs) to alleviate the problems of long response time cloud server bottlenecks in cloud computing. In response to the above problems, this work investigates problem of minimizing the retrieval delay of IoT contents in F-RANs under the constraints of system resources. The problem is formulated as an integer linear programming (ILP) model. Then, a polynomial-time with linear programming (LP) relaxation and rounding is proposed to approximate the optimal solution problem. Through proof, the method can obtain a feasible solution with abounded approximation polynomial time. The conducted simulations validate that the obtained feasible solution is very close optimal one. On the other hand, when the system resources are not enough to meet the continuous growth content retrieval and need to be expanded, this work further establishes an association relation between contents and system resources. Based on the above relation, the second method of expanding system resources with performance sensitivity is proposed to provide the service provider with an effective and economical expansion of system resources. It utilizes a predefined system parameter in balancing the trade-off the approximation ratio to the optimal solution of the problem and the extended system resources. The obtained by the second method is also proved to have abounded approximation ratio.
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