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Found 19 papers, 3 papers with code
Semiautomatic Speech Alignment for Under-Resourced Languages
Cross-language forced alignment is a solution for linguists who create speech corpora for very low-resource languages.
Grapheme-Based Cross-Language Forced Alignment: Results with Uralic Languages
Forced alignment is an effective process to speed up linguistic research.
Service registration chatbot: collecting and comparing dialogues from AMT workers and service’s users
In this article, we compare two crowdsourcing sources on a dialogue paraphrasing task revolving around a chatbot service.
"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models
Grasping complex computing concepts often poses a challenge for students who struggle to anchor these new ideas to familiar experiences and understandings.
Interactions with Prompt Problems: A New Way to Teach Programming with Large Language Models
In this paper, we propose a new way to teach programming with Prompt Problems.
Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models
Unlike syntax errors, for which a compiler or interpreter can issue a message, logic errors can be subtle.
The Robots are Here: Navigating the Generative AI Revolution in Computing Education
Second, we report the findings of a survey of computing students and instructors from across 20 countries, capturing prevailing attitudes towards LLMs and their use in computing education contexts.
Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
When learnersourcing multiple-choice questions, creating explanations for the solution of a question is a crucial step; it helps other students understand the solution and promotes a deeper understanding of related concepts.
Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generators
In parallel with this shift, a new essential skill is emerging -- the ability to construct good prompts for code-generating models.
Can We Trust AI-Generated Educational Content? Comparative Analysis of Human and AI-Generated Learning Resources
In this study, we investigated the potential for LLMs to produce learning resources in an introductory programming context, by comparing the quality of the resources generated by an LLM with those created by students as part of a learnersourcing activity.
Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests
At the same time, the results highlight the unreliability of LLMs: LLMs make some of the same mistakes that students do, perhaps especially when formatting output as required by automated assessment systems.
Computing Education in the Era of Generative AI
The computing education community has a rich history of pedagogical innovation designed to support students in introductory courses, and to support teachers in facilitating student learning.
Comparing Code Explanations Created by Students and Large Language Models
In this paper, we explore the potential of LLMs in generating explanations that can serve as examples to scaffold students' ability to understand and explain code.
"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers
Recent developments in deep learning have resulted in code-generation models that produce source code from natural language and code-based prompts with high accuracy.
Using Large Language Models to Enhance Programming Error Messages
Large language models can be used to create useful and novice-friendly enhancements to programming error messages that sometimes surpass the original programming error messages in interpretability and actionability.
Automatic Generation of Programming Exercises and Code Explanations using Large Language Models
Our analysis suggests that there is significant value in massive generative machine learning models as a tool for instructors, although there remains a need for some oversight to ensure the quality of the generated content before it is delivered to students.
Empirical Evaluation of Deep Learning Models for Knowledge Tracing: Of Hyperparameters and Metrics on Performance and Replicability
To evaluate how different aspects of DLKT models influence model performance, we test input and output layer variations found in the compared models that are independent of the main architectures.
FinChat: Corpus and evaluation setup for Finnish chat conversations on everyday topics
Using this corpus, we also construct a retrieval-based evaluation task for Finnish chatbot development.
