Throughout a programming course, students develop various source code tasks. Using these tasks to track students' progress can provide clues to the strengths and weaknesses found in each learning topic. This pract...
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Throughout a programming course, students develop various source code tasks. Using these tasks to track students' progress can provide clues to the strengths and weaknesses found in each learning topic. This practice allows the teacher to intervene in learning in the first few weeks of class and maximize student gains. However, the biggest challenge is to overcome the amount of work required of the teacher in the manual analysis of all tasks. In this context, our main research objective is to automatically group students with similar programming skills based on the analysis of their submitted source codes. Our research is applied and uses an experimental procedure. First, we prepared the database, with more than 700 real-world source code tasks written in C language, and distributed it in five different learning topics. Afterward, we define a set of features to be extracted from each learning topic. We defined and extracted 23 features from the source code for five learning topics. Then, we preprocess our database and extract the proposed features. Finally, we grouped the students. After performing the grouping, we obtained four groups of students, which were analyzed using a cluster midpoint calculation. Our results support the monitoring of students throughout the term, offering the teacher the freedom to create new exercises and waiving the obligation of any specific programming environment. We believe that these results can support the teacher in pedagogical decisions closer to the needs of each group of students.
computerscience Fundamentals (CSF) courses have become a popular study area from K-12 to higher education levels (i.e., community and technical colleges, and four-year institutions). Different educational approaches ...
computerscience Fundamentals (CSF) courses have become a popular study area from K-12 to higher education levels (i.e., community and technical colleges, and four-year institutions). Different educational approaches have been proposed to disseminate concepts in these areas (traditionally through books and online platforms including wikis, websites, forums). Although several resources are available to assist students in learning tricks or "how-to" for specific items, some lack curricular guidance to lead to a constructivist learning approach. Some of the other available resources rely on a solid mathematical background, which many potential computerscience students might not have, discouraging students from pursuing a computerscience or programming field, particularly from a K-12 environment and community colleges. In this paper, we report the experience of deploying an Open Educational Resource (OER) used for the CSF in a community college that hosts early college high school, workforce, and transfer students as part of the teaching community.
Imitation Learning (IL) is a promising paradigm for teaching robots to perform novel tasks using demonstrations. Most existing approaches for IL utilize neural networks (NN), however, these methods suffer from several...
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Imitation Learning (IL) is a promising paradigm for teaching robots to perform novel tasks using demonstrations. Most existing approaches for IL utilize neural networks (NN), however, these methods suffer from several well-known limitations: they 1) require large amounts of training data, 2) are hard to interpret, and 3) are hard to refine and adapt. There is an emerging interest in Programmatic Imitation Learning (PIL), which offers significant promise in addressing the above limitations. In PIL, the learned policy is represented in a programming language, making it amenable to interpretation and adaptation to novel settings. However, state-of-the-art PIL algorithms assume access to action labels and struggle to learn from noisy real-world demonstrations. In this paper, we propose Plunder, a novel PIL algorithm that addresses these shortcomings by synthesizing probabilistic programmatic policies that are particularly well-suited for modeling the uncertainties inherent in real-world demonstrations. Our approach leverages an EM loop to simultaneously infer the missing action labels and the most likely probabilistic policy. We benchmark Plunder against several established IL techniques, and demonstrate its superiority across five challenging imitation learning tasks under noise. Plunder policies outperform the next-best baseline by 19% and 17% in matching the given demonstrations and successfully completing the tasks, respectively.
In computer networks, swift recovery from failures requires prompt detection and diagnosis. Protocols such as Bidirectional Forwarding Detection (BFD) exists to probe the liveliness of a path and endpoint. These proto...
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ISBN:
(数字)9783031605970
ISBN:
(纸本)9783031605963;9783031605970
In computer networks, swift recovery from failures requires prompt detection and diagnosis. Protocols such as Bidirectional Forwarding Detection (BFD) exists to probe the liveliness of a path and endpoint. These protocols are run on specific nodes that are designated as network monitors. Monitors are responsible for continuously verifying the viability of communication paths. It is important to carefully select monitors as monitoring incurs a cost, necessitating finding a balance between the number of monitor nodes and the monitoring quality. Here, we examine two monitoring challenges from the Boolean network tomography research field: coverage, which involves detecting failures, and 1-identifiability, which additionally requires identifying the failing link or node. We show that minimizing the number of monitors while meeting these requirements constitutes NP-complete problems. We present integer linear programming (ILP), constraint programming (CP) and Maximum Satisfiability (MaxSAT) formulations for these problems and compare their performance. Using 625 network topologies, we demonstrate that employing such exact methods can reduce the number of monitors needed compared to the existing state-of-the-art greedy algorithm.
Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social *** researchers and industry experts show their attention to Twitter sentiment analysis to recog...
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Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social *** researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder *** sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning *** assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,*** addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral *** this work,we obligated Twitter Application programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account *** distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.
With the COVID-19 pandemic causing universities to close, online learning became a popular solution for educators and students. This study explored the factors that determine the success of video-based online learning...
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ISBN:
(纸本)9783031485350;9783031485367
With the COVID-19 pandemic causing universities to close, online learning became a popular solution for educators and students. This study explored the factors that determine the success of video-based online learning for a programming course. The programming course was designed based on principles from a Problem-Solving Learning Environment (PSLE) that develops computational thinking, with video lectures forming part of the scaffolding and information processing components. To conduct the research, a mixed methods approach was used, using a survey for quantitative data collection and open-ended questions to collect qualitative data from 509 survey respondents taking a C# programming course. The researchers used the Unified Theory of Acceptance and Use of Technology (UTAUT) as a lens in order to make sense of and obtain a deeper understanding of the factors, including performance expectancy, effort expectancy, attitude towards using technology, facilitating conditions, and behavioural intention. The use of online concept videos was found to be beneficial for learning programming concepts, with participants reporting improvements in academic achievement, increased efficiency, low effort, enjoyment, and compatibility with learning styles. Key factors for success include ease of use, short duration, relevance, thoroughness, engagement, and availability of necessary resources. The study provides insights for lecturers of programming courses to create effective online learning videos.
We developed a programing education tool "P-CUBE3", which uses blocks with tangible information as its language. P-CUBE3 can control audio output by placing HIRAGANA (Japanese character) blocks on a "pr...
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ISBN:
(纸本)9783031628450;9783031628467
We developed a programing education tool "P-CUBE3", which uses blocks with tangible information as its language. P-CUBE3 can control audio output by placing HIRAGANA (Japanese character) blocks on a "program mat", and also implements an operation history acquisition system that can record the type of block, its position, and operation time. We conducted a hands-on programming lesson for visually impaired and sighted people, and analyzed the operation history data when they worked on a task program in the lesson. Participants in the lesson were classified into four groups according to whether they had programming experience or not and whether they had visual impairment or not. We classified the programming process into several patterns, and analyzed the relationship between the patterns using a co-occurrence matrix. We report on the characteristics of the programming process for the visually impaired and sighted.
With the increasing demand for programming skills comes a trend towards more online programming courses and assessments. While this allows educators to teach larger groups of students, it also opens the door to dishon...
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ISBN:
(纸本)9798400701399
With the increasing demand for programming skills comes a trend towards more online programming courses and assessments. While this allows educators to teach larger groups of students, it also opens the door to dishonest student behaviour, such as copying code from other students. When teachers use assignments where all students write code for the same problem, source code similarity tools can help to combat plagiarism. Unfortunately, teachers often do not use these tools to prevent such behaviour [1]. In response to this challenge, we have developed a new source code plagiarism detection tool named Dolos. Dolos is open-source(1), supports a wide range of programming languages, and is designed to be user-friendly. It enables teachers to detect, prove and prevent plagiarism in programming courses by using fast algorithms and powerful visualisations, as shown in Fig. 1. We present further enhancements to Dolos and discuss how it can be integrated into modern computing education courses to meet the challenges of online learning and assessment. By lowering the barriers for teachers to detect, prove and prevent plagiarism in programming courses, Dolos can help protect academic integrity and ensure that students earn their grades honestly.
Online coding platforms (OCPs) often offer a limited selection of exercises, which can restrict the scope of computerscience (CS) education. This study investigates the capabilities of Large Language Models (LLMs), p...
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ISBN:
(纸本)9798350364941;9798350364958
Online coding platforms (OCPs) often offer a limited selection of exercises, which can restrict the scope of computerscience (CS) education. This study investigates the capabilities of Large Language Models (LLMs), particularly GPT-4 Turbo, in broadening this scope by autonomously generating Python programming exercises. These exercises are tailored to the CS1 curriculum-an introductory course in computerscience. Utilizing curriculum-driven prompt engineering, we developed a dataset of 11,700 exercises, characterized by a variety of categories, types, and difficulty levels. These exercises are distributed across 78 unique topics, which were derived from the CS1 course catalogs of leading universities and supplemented with online educational resources. To evaluate the effectiveness of GPT-4 Turbo in generating CS1 Python programming exercises, we conducted a user study involving both students and instructors. The study focused on several metrics: exercise quality, curriculum relevance, understandability, appropriate difficulty level, and the generation of useful hints. Our findings indicate that GPT-4 Turbo can produce high-quality, educationally effective programming exercises at scale, provided that the prompts are systematically crafted. Based on insights from the user study, adjustments to prompt design are recommended to optimize exercise generation. Our research concludes that GPT-4 Turbo can be seamlessly integrated into AI-driven OCPs, offering a scalable, cost and time-effective method to enhance CS education. This is achieved through targeted prompt engineering and thorough data preprocessing to mitigate inconsistencies. The code is available online: https://***/DSAatUSU/ GPT_CS1400_Exercise_Generation
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