Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentation
The use of argumentation in education has been shown to improve critical thinking skills for end-users such as students, and computational models for argumentation have been developed to assist in this process. Although these models are useful for evaluating the quality of an argument, they oftentimes cannot explain why a particular argument is considered poor or not, which makes it difficult to provide constructive feedback to users to strengthen their critical thinking skills. In this survey, we aim to explore the different dimensions of feedback (Richness, Visualization, Interactivity, and Personalization) provided by the current computational models for argumentation, and the possibility of enhancing the power of explanations of such models, ultimately helping learners improve their critical thinking skills.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation
Providing feedback on the argumentation of the learner is essential for developing critical thinking skills, however, it requires a lot of time and effort. To mitigate the overload on teachers, we aim to automate a proce…
DiagnosticInformativenessslot-fillingSlot Filling"My Grade is Wrong!": A Contestable AI Framework for Interactive Feedback in Evaluating Student Essays
Interactive feedback, where feedback flows in both directions between teacher and student, is more effective than traditional one-way feedback. However, it is often too time-consuming for widespread use in educational pr…
ChatGPT in Research and Education: Exploring Benefits and Threats
In recent years, advanced artificial intelligence technologies, such as ChatGPT, have significantly impacted various fields, including education and research. Developed by OpenAI, ChatGPT is a powerful language model tha…
Language ModelingLanguage ModellingAutomated Evaluation for Student Argumentative Writing: A Survey
This paper surveys and organizes research works in an under-studied area, which we call automated evaluation for student argumentative writing. Unlike traditional automated writing evaluation that focuses on holistic ess…
Automated Writing EvaluationSurveyDataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
The process of creating training data to teach models is currently driven by humans, who manually analyze model weaknesses and plan how to create data that improves a student model. Approaches using LLMs as annotators re…
MathSequential Decision MakingVisual Question Answering (VQA)