This study introduces a novel genetic programming-based ensemble method for forecasting long-term electricity consumption in Ethiopia. The technique utilizes a two-stage ensemble approach to project Ethiopia's ele...
详细信息
This study introduces a novel genetic programming-based ensemble method for forecasting long-term electricity consumption in Ethiopia. The technique utilizes a two-stage ensemble approach to project Ethiopia's electricity consumption through 2031. In the initial stage, genetic algorithms, particle swarm optimization, and simulated annealing methods are applied to various regression models (linear, quadratic, and exponential). The preliminary forecast values generated in this stage were further refined in the second stage. Here, the genetic programming method was utilized to develop a formula based on the initial forecast values, which then provided the final forecast results. The most accurate predictions in the first stage were obtained using the GA_Quadratic, PSO_Quadratic, and SA_Quadratic methods, resulting in mean absolute percentage error (MAPE) values of 3.61, 3.63, and 4.68, respectively. In the second stage, the GP-based prediction achieved an even lower MAPE value of 2.83. Other error metrics, including MSE, root mean square error (RMSE), and R-2, were also evaluated, with the proposed model outperforming all methods from the first stage on these metrics. The study projected Ethiopia's total annual electricity consumption through 2031 under two different scenarios. Both scenarios indicate that by 2031, electricity consumption will have tripled compared to 2021 levels.
In this theoretical design paper, we describe using conjecture mapping to design an introductory computerscience course curriculum to facilitate horizontal interdisciplinary transfer between algebra and programming. ...
详细信息
ISBN:
(纸本)9798400710384
In this theoretical design paper, we describe using conjecture mapping to design an introductory computerscience course curriculum to facilitate horizontal interdisciplinary transfer between algebra and programming. The bulk of this paper is an extended discussion of the empirical and theoretical justifications for and implications of three major conjectures, that instruction designed to encourage horizontal interdisciplinary transfer between algebra and computerscience can (1) fulfill the requirements of horizontal inter-task transfer, increasing conceptual understanding in introductory programming, (2) decrease intrinsic cognitive load by drawing on students' latent prior knowledge in algebra, and (3) increase perceptions of experience in programming, preventing defensive classroom climates and increasing self-efficacy, particularly among students without conventional programming experience, who tend to be from underrepresented/minoritized groups. Next, we describe the design and fielding of this prototype course curriculum during a Summer 2022 synchronous remote introductory programming course at a large public research institution in the USA. Finally, we present a brief instructor experience report, with implications and future work.
This poster explores the potential of ChatGPT to replace the traditional approach of pair programming in introductory computerscience courses. Traditionally, two students collaborate as a driver and a navigator, peri...
详细信息
Live coding-a pedagogical technique in which an instructor plans, writes, and executes code in front of a class-is generally considered a best practice when teaching programming. However, only a few studies have evalu...
详细信息
ISBN:
(纸本)9798400701382
Live coding-a pedagogical technique in which an instructor plans, writes, and executes code in front of a class-is generally considered a best practice when teaching programming. However, only a few studies have evaluated the effect of live coding on student learning in a controlled experiment and most of the literature relating to live coding identifies students' perceived benefits of live-coding examples. In order to empirically evaluate the impact of live coding, we designed a controlled experiment in a CS1 course taught in Python at a large public university. In the two remote lecture sections for the course, one was taught using live-coding examples and the other was taught using static-code examples. Throughout the term, we collected code snapshots from students' programming assignments, students' grades, and the questions that they asked during the remote lectures. We then applied a set of process-oriented programming metrics to students' programming data to compare students' adherence to effective programming processes in the two learning groups and categorized each question asked in lectures following an open-coding approach. Our results revealed a general lack of difference between the two groups across programming processes, grades, and lecture questions asked. However, our experiment uncovered minimal effects in favor of the live-coding group indicating improved programming processes but lower performance on assignments and grades. Our results suggest an overall insignificant impact of the style of presenting code examples, though we reflect on the threats to validity in our study that should be addressed in future work.
Data leakage is a well-known problem in machine learning which occurs when the training and testing datasets are not independent. This phenomenon leads to unreliably overly optimistic accuracy estimates at training ti...
详细信息
Data leakage is a well-known problem in machine learning which occurs when the training and testing datasets are not independent. This phenomenon leads to unreliably overly optimistic accuracy estimates at training time, followed by a significant drop in performance when models are deployed in the real world. This can be dangerous, notably when models are used for risk prediction in high-stakes applications. In this paper, we propose an abstract interpretation-based static analysis to prove the absence of data leakage at development time, long before model deployment and even before model training. We implemented it in the NBLYzER framework and we demonstrate its performance and precision on 2111 Jupyter notebooks from the Kaggle competition platform.
As the prominence of Large Language Models (LLMs) grows in various sectors, their potential in education warrants exploration. In this study, we investigate the feasibility of employing GPT-3.5 from OpenAI, as an LLM ...
详细信息
ISBN:
(纸本)9798400704239
As the prominence of Large Language Models (LLMs) grows in various sectors, their potential in education warrants exploration. In this study, we investigate the feasibility of employing GPT-3.5 from OpenAI, as an LLM teaching assistant (TA) or a virtual TA in computerscience (CS) courses. The objective is to enhance the accessibility of CS education while maintaining academic integrity by refraining from providing direct solutions to current-semester assignments. Targeting Foundations of programming (COMP202), an undergraduate course that introduces students to programming with Python, we have developed a virtual TA using the LangChain framework, known for integrating language models with diverse data sources and environments. The virtual TA assists students with their code and clarifies complex concepts. For homework questions, it is designed to guide students with hints rather than giving out direct solutions. We assessed its performance first through a qualitative evaluation, then a survey-based comparative analysis, using a mix of questions commonly asked on the COMP202 discussion board and questions created by the authors. Our preliminary results indicate that the virtual TA outperforms human TAs on clarity and engagement, matching them on accuracy when the question is non-assignment-specific, for which human TAs still proved more reliable. These findings suggest that while virtual TAs, leveraging the capabilities of LLMs, hold great promise towards making CS education experience more accessible and engaging, their optimal use necessitates human supervision. We conclude by identifying several directions that could be explored in future implementations.
Representations of early childhood children and their development of computational thinking skills while tangibly programming a robot are presented in this study. Data was taken from multiple case studies in preschool...
详细信息
ISBN:
(纸本)9783031552441;9783031552458
Representations of early childhood children and their development of computational thinking skills while tangibly programming a robot are presented in this study. Data was taken from multiple case studies in preschool settings. Research protocols recorded children's representations in individual basis semi-structured interviews in pre and post-sessions of an almost monthly educational intervention delivered by teachers in their classrooms. Some examples of depictions on children's drawings will also be presented, showing children's learning development in their computational thinking skills. The results show that while children attribute an animate identity to a robot, at the same time, they state and depict data for its properties and basic functional features.
The dismantling and recycling of aircrafts is one of the future challenges for the air transport industry in terms of sustainability. This problem is hard to solve and optimize as planning operations are highly constr...
详细信息
ISBN:
(数字)9783031605994
ISBN:
(纸本)9783031606014;9783031605994
The dismantling and recycling of aircrafts is one of the future challenges for the air transport industry in terms of sustainability. This problem is hard to solve and optimize as planning operations are highly constrained. Indeed, extracting each part requires technicians with the necessary qualifications and equipment. The parts to be extracted are constrained by precedence relations and the number of simultaneous technicians on specific zones is restricted. It is also essential to avoid unbalancing the aircraft during disassembly. Cost is a significant factor, influenced by the duration of ground mobilization and the choice of technicians for each operation. This paper presents a first constraint programming model for this problem using optional interval variables. This model is used to solve variations of a large instance involving up to 1500 tasks, based on real-life data provided by our industrial partner. The results show that the model can find feasible solutions for all variations of the instance and compares the solutions obtained to lower bounds.
Developing software for robot control often is tedious, difficult and error prone. This is more so for people without a computerscience background, such as artists. Functional Reactive programming (FRP) aims to make ...
详细信息
ISBN:
(纸本)9798400710995
Developing software for robot control often is tedious, difficult and error prone. This is more so for people without a computerscience background, such as artists. Functional Reactive programming (FRP) aims to make reactive programs, such as those used for robotics control, more modular, composable, and aesthetically pleasing. We present the results of a collaboration with the art studio Pors & Rao, specializing in robotic artwork. The aim of this collaboration was to explore the use of FRP in this application area to allow artists to express their intentions for robot control in a more effective manner. We demonstrate the artworks that we jointly worked on, as well as our impressions of the acceptance and potential of FRP for artists without a computerscience background.
Amid increasing calls for critical and anti-oppressive approaches to computerscience (CS) education, educators are exploring how to create justice-centered teaching material. Additionally, broadening participation in...
详细信息
暂无评论