Principles of Asking Effective Questions During Student Problem Solving

ABSTRACT Using effective teaching practices is a high priority for educators. One important pedagogical skill for computer science instructors is asking effective questions. This paper presents a set of instructional principles for effective question asking during guided problem solving. We illustrate these principles with results from classifying the questions that untrained human tutors asked while working with students solving an introductory programming problem. We contextualize the findings from the question classification study with principles found within the relevant literature. The results highlight ways that instructors can ask questions to 1) facilitate students' comprehension and decomposition of a problem, 2) encourage planning a solution before implementation, 3) promote self-explanations, and 4) reveal gaps or misconceptions in knowledge. These principles can help computer science educators ask more effective questions in a variety of instructional settings

Principles of Asking Effective Questions During Student Problem Solving

ABSTRACT Using effective teaching practices is a high priority for educators. One important pedagogical skill for computer science instructors is asking effective questions. This paper presents a set of instructional principles for effective question asking during guided problem solving. We illustrate these principles with results from classifying the questions that untrained human tutors asked while working with students solving an introductory programming problem. We contextualize the findings from the question classification study with principles found within the relevant literature. The results highlight ways that instructors can ask questions to 1) facilitate students' comprehension and decomposition of a problem, 2) encourage planning a solution before implementation, 3) promote self-explanations, and 4) reveal gaps or misconceptions in knowledge. These principles can help computer science educators ask more effective questions in a variety of instructional settings

How Domain Differences Impact the Mode Structure of Expert Tutoring Dialogue

Whitney Layne Cade granted permission for the digitization of her paper. It was submitted by CDWhile human-to-human dialogue in tutoring sessions has received considerable attention in the last 25 years, there exists a paucity of work examining the pedagogical and motivational strategies of expert human tutors. An established trend in the tutorial dialogue community is to study tutorial dialogues in a very fine-grained manner, at the level of the speech act or dialogue move. The present work offers a coding scheme that examines larger, pedagogically distinct phases as the unit of analysis, referred to as “modes”, which exist in expert tutoring and provide the context needed to understand patterns of dialogue moves. The eight modes identified by this coding scheme are the Introduction, Lecture, Modeling, Scaffolding, Fading, Highlighting, Off Topic, and Conclusion mode, and each mode was reliably identified at or above the .8 kappa level. After determining how often modes occur and the amount of dialogue devoted to them in expert tutoring sessions, differences between the domains of math and science were investigated. Significant variance between the domains was revealed using this largergrained coding scheme, particularly in how Lecture and Scaffolding are used in expert tutoring. While these two modes tend to dominate most tutorial dialogue in this sample regardless of domain, the differences in their frequency and the amount of dialogue devoted to each mode suggest diverse tutoring goals associated with each domain. Other subtle differences in mode distributions draw attention both to the complexities of expert tutoring and the danger of generalizing tutorial structures across domains.This honors paper was approved by Dr. Natalie Person, Dr. Chris Wetzel, and Dr. Andrew Olne

Dialogue-learning correlations in spoken dialogue tutoring

Abstract. We examine correlations between dialogue characteristics and learning in two corpora of spoken tutoring dialogues: a human-human corpus and a humancomputer corpus, both of which have been manually annotated with dialogue acts relative to the tutoring domain. The results from our human-computer corpus sho