Recent advances in AI, machine learning, and NLP have led to the development of a new generation of Large Language Models (LLMs) that are trained on massive amounts of data and often have trillions of parameters. Comm...
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ISBN:
(数字)9783031627002
ISBN:
(纸本)9783031626999;9783031627002
Recent advances in AI, machine learning, and NLP have led to the development of a new generation of Large Language Models (LLMs) that are trained on massive amounts of data and often have trillions of parameters. Commercial applications (e.g., ChatGPT) have made this technology available to the general public, thus making it possible to use LLMs to produce high-quality texts for academic and professional purposes. Schools and universities are aware of the increasing use of AI-generated content by students and they have been researching the impact of this new technology and its potential misuse. Educational programs in computerscience (CS) and related fields are particularly affected because LLMs are also capable of generating programming code in various programming languages. To help understand the potential impact of publicly available LLMs in CS education, we introduce CSEPrompts (https://***/mraihan-gmu/CSEPrompts), a framework with hundreds of programming exercise prompts and multiple-choice questions retrieved from introductory CS and programming courses. We also provide experimental results on CSEPrompts to evaluate the performance of several LLMs with respect to generating Python code and answering basic computerscience and programming questions.
There have been attempts to connect machine learning and symbolic reasoning, providing interfaces between them. This work focuses on our original approach to integrate machine learning and symbolic reasoning, in the c...
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ISBN:
(纸本)9789819722990;9789819723003
There have been attempts to connect machine learning and symbolic reasoning, providing interfaces between them. This work focuses on our original approach to integrate machine learning and symbolic reasoning, in the context of algebraic approaches to logic programming. We here realize logical reasoning using algebraic methods, in which algebraic data structures such as matrices and tensors are used to represent logical formulas. These reasoning methods are robust against noise, while allowing for high parallelism and scalable computation. Algebraic logic programming has been applied to fixponit computation, abduction, answer set programming and inductive logic programming.
Nowadays, educators face the challenge of students developing their own work and acquiring problem-solving skills, especially with the widespread use of AI tools. This includes issues like excessive dependence on AI, ...
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ISBN:
(纸本)9798400706035
Nowadays, educators face the challenge of students developing their own work and acquiring problem-solving skills, especially with the widespread use of AI tools. This includes issues like excessive dependence on AI, creativity stagnation, and prioritizing comprehension over rote learning, all while navigating ethical concerns in academic submissions. Higher education in computerscience encounters difficulties as traditional methods become less relevant for new generations (e.g., early college students taking dual enrollment courses) and emerging computerscience programs such as CS + X, data science/analytics, and cybersecurity. Some computerscience programs integrate labs into lectures, while others keep them as a separate course component. The modalities and resources in higher education vary, including teacher assistance, Peer-Led Team Learning (PLTL) with tutors, and more, e.g., see: [4]. In addition, some nations find themselves in the position of needing to provide professional development for new computerscience teachers. These educators may not have a background in computerscience but instead, come from fields related to education or STEM. They require training to effectively teach the material or, at the very least, a guide on what to teach. To address this, there is a need for a guided inquiry learning tool to help disseminate course material. The workbook tool we introduce is designed for the first year on computerscience courses such as CS 1 and CS 2, allowing students to work independently or in teams guided by multimedia access through a QR code. Each activity is structured to align with the topics outlined in the CS2023 [2] knowledge areas while also incorporating Bloom's for Computing verbs [1].
The recent emergence of LLM-based code generation models can potentially transform programming education. To pinpoint the current state of research on using LLM-based code generators to support the teaching and learni...
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ISBN:
(纸本)9798400704239
The recent emergence of LLM-based code generation models can potentially transform programming education. To pinpoint the current state of research on using LLM-based code generators to support the teaching and learning of programming, we conducted a systematic literature review of 21 papers published since 2018. The review focuses on (1) the teaching and learning practices in programming education that utilized LLM-based code generation models, (2) characteristics and (3) performance indicators of the models, and (4) aspects to consider when utilizing the models in programming education, including the risks and challenges. We found that the most commonly reported uses of LLM-based code generation models for teachers are generating assignments and evaluating student work, while for students, the models function as virtual tutors. We identified that the models exhibit accuracy limitations;generated content often contains minor errors that are manageable by instructors but pose risks for novice learners. Moreover, risks such as academic misconduct and over-reliance on the models are critical when considering integrating these models into education. Overall, LLM-based code generation models can be an assistive tool for both learners and instructors if the risks are mitigated.
We explored how undergraduate introductory programming students naturalistically used generative AI to solve programming problems. We focused on the relationship between their use of AI to their self-regulation strate...
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ISBN:
(纸本)9798400706004
We explored how undergraduate introductory programming students naturalistically used generative AI to solve programming problems. We focused on the relationship between their use of AI to their self-regulation strategies, self-efficacy, and fear of failure in programming. In this repeated-measures, mixed-methods research, we examined students' patterns of using generative AI with qualitative student reflections and their self-regulation, self-efficacy, and fear of failure with quantitative instruments at multiple times throughout the semester. We also explored the relationships among these variables to learner characteristics, perceived usefulness of AI, and performance. Overall, our results suggest that student factors affect their baseline use of AI. In particular, students with higher self-efficacy, lower fear of failure, or higher prior grades tended to use AI less or later in the problem-solving process and rated it as less useful than others. Interestingly, we found no relationship between students' self-regulation strategies and their use of AI. Students who used AI less or later in problem-solving also had higher grades in the course, but this is most likely due to prior characteristics as our data do not suggest that this is a causal relationship.
Probability theory has extensive applications across various domains, such as statistics, computerscience, and finance. In probability education, students are introduced to fundamental principles which may include ma...
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Probability theory has extensive applications across various domains, such as statistics, computerscience, and finance. In probability education, students are introduced to fundamental principles which may include mathematical topics such as combinatorics and symmetric sample spaces. Students pursuing degrees in computerscience possess a robust foundation in programming, software engineering, and algorithmic thinking. Despite entering probability courses with a unique perspective and learning potential, these students encounter challenges in grasping combinatorial concepts. In this experiment, we challenged first-year postsecondary computerscience students to program a simulation of a practical combinatorics problem. Students commented on whether and how this task helped them internalize the basic concepts of combinatorics. We aim to show how utilizing programming tasks may empower students with a deeper grasp of combinatorics.
This article offers perspective on quantum computing programming languages, as well as their emerging runtimes and algorithmic modalities. With the scientific high-performance computing (HPC) community as a target aud...
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This article offers perspective on quantum computing programming languages, as well as their emerging runtimes and algorithmic modalities. With the scientific high-performance computing (HPC) community as a target audience, we describe the current state of the art in the field, and outline programming paradigms for scientific workflows. One take-home message is that there is significant work required to first refine the notion of the quantum processing unit in order to integrate in the HPC environments. programming for today's quantum computers is making significant strides toward modern HPC-compatible workflows, but key challenges still face the field.
The difficulty of acquiring computerprogramming skills is a plausible cause for the elevated attrition rates in computerscience (CS). Music and robotics integrated with computerprogramming are approaches to engage ...
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ISBN:
(纸本)9783031358968;9783031358975
The difficulty of acquiring computerprogramming skills is a plausible cause for the elevated attrition rates in computerscience (CS). Music and robotics integrated with computerprogramming are approaches to engage students in CS by prioritizing personal expression, creativity, and aesthetics. This paper conducts a systematic review of using music and robotics to teach CS concepts to elementary school students. The authors aim to identify the existing problem in the literature to make CS more interactive, accessible, and engaging for students to improve their motivation in enhancing CS concepts and develop creative interpersonal skills. This paper also identifies a mixed-methods study to determine ways to promote CS among elementary school students. The mixed methods describe an adaptation of Blockly, Xylophone, and Dash robots for use in an introductory elementary school-level programming course that will be implemented in an open-access camp at Auburn University where American grades 3-5 will participate in pre-, post-, and follow-up surveys while attending the CS camp. The authors want to demonstrate how music and robotics programming can contribute to STEAM (science, Technology, Engineering, Arts, and Mathematics) education regarding technology and engineering integration and include existing research studies resulting from the search and review processes. These studies were synthesized according to some common characteristics, including their use of educational robotics, preliminary results, subjects, and potential contributions to STEAM education. A few educational implications of educational robotics are also discussed as possible contributions to technology and engineering education. As a result of this systematic review, using robotics and music in early childhood STEAM education is a promising tool and application for integrating technology and engineering. The study concludes with a summary of the findings on the effectiveness of this approach.
Ever since Large Language Models (LLMs) and related applications have become broadly available, several studies investigated their potential for assisting educators and supporting students in higher education. LLMs su...
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ISBN:
(纸本)9798400706004
Ever since Large Language Models (LLMs) and related applications have become broadly available, several studies investigated their potential for assisting educators and supporting students in higher education. LLMs such as Codex, GPT-3.5, and GPT 4 have shown promising results in the context of large programming courses, where students can benefit from feedback and hints if provided timely and at scale. This paper explores the quality of GPT-4 Turbo's generated output for prompts containing both the programming task specification and a student's submission as input. Two assignments from an introductory programming course were selected, and GPT-4 was asked to generate feedback for 55 randomly chosen, authentic student programming submissions. The output was qualitatively analyzed regarding correctness, personalization, fault localization, and other features identified in the material. Compared to prior work and analyses of GPT-3.5, GPT-4 Turbo shows notable improvements. For example, the output is more structured and consistent. GPT-4 Turbo can also accurately identify invalid casing in student programs' output. In some cases, the feedback also includes the output of the student program. At the same time, inconsistent feedback was noted such as stating that the submission is correct but an error needs to be fixed. The present work increases our understanding of LLMs' potential, limitations, and how to integrate them into e-assessment systems, pedagogical scenarios, and instructing students who are using applications based on GPT-4.
Our goal is to provide integrated lessons where computerprogramming concepts are introduced based on mathematics. We consider the development of lessons that would be interesting to our students. At the middle school...
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