Core Concepts
Hint generation is a critical component of intelligent tutoring systems that can facilitate self-learning. This survey article presents a comprehensive review of prior research on hint generation, aiming to bridge the gap between research in education and cognitive science, and research in AI and Natural Language Processing.
Abstract
The article starts by highlighting the importance of intelligent tutoring systems (ITSs) in addressing educational challenges, particularly the need for personalized learning. Hints play a critical role in the ability of ITSs to provide step-by-step guidance to students.
The authors first discuss the key characteristics of effective hints based on research from education and cognitive sciences. They identify two important pragmatic aspects of hints: scaffolding support and personalization/learner feedback. They also discuss the semantic (relevance to learning objectives, link to prior knowledge, conceptual depth) and stylistic (clarity, simplicity, encouragement, and creative/multimodal elements) aspects of effective hints.
The authors then provide a comprehensive survey of computational approaches for automatic hint generation. They first review the extensive work on hint generation for computer programming, discussing the datasets, approaches, and evaluation metrics used in this domain. They then explore question answering-based hint generation for diverse domains like mathematics, language acquisition, and factual questions.
Based on the findings from the literature review, the authors propose a roadmap for future research in hint generation. They provide a refined formal definition of the hint generation task, incorporating the key principles from education and cognitive sciences. They then discuss various research areas that can inform the design of effective hint generation systems, including question answering, answer assessment, user modeling, question generation, and dialogue modeling.
The authors also outline several open challenges and future directions for hint generation systems, such as privacy-preserving self-evolving frameworks, diverse domain exploration, multilingual and multicultural aspects, multimodal elements, affective systems, accessible systems, and improved evaluation metrics. Finally, they discuss the ethical considerations surrounding the integration of NLP technologies in educational settings, including data privacy, bias and fairness, and effects on language variation.
Stats
The article does not contain any specific data or statistics. It is a survey paper that reviews the existing literature on hint generation.
Quotes
"The hallmark of intelligent tutoring systems is their ability to provide step-by-step guidance to students while they work on problems, and hints play a critical role in their ability to provide this help."
"Hints are a tool to provide scaffolded support to the learners, and can be traced back to the socio-cultural theory of Vygotsky's Zone of Proximal Development (ZPD), referring to 'the gap between what a learner can do without assistance and what a learner can do with adult guidance or in collaboration with more capable peers'."