Question Asking During Learning wit a Point and Query Interface

Educational software would benefit from question asking facilities that are theoretically grounded in psychology, education, and artificial intelligence. Our previous research has investigated the psychological mechanisms of question asking and has developed a computationally tractable model of human question answering. We have recently developed a Point and Query (P&Q) human-computer interface based on this research. With the P & Q software, the student asks a question by simply pointing to a word or picture element and then to a question chosen from a menu of "good" questions associated with the element. This study examined students' question asking over time, using the P & Q software, while learning about woodwind instruments. While learning, the students were expected to solve tasks that required either deep-level causal knowledge or superficial knowledge. The frequency of questions asked with the P & Q interface was approximately 800 limes the number of questions asked per student per hour in a classroom. The learning goals directly affected the ordering of questions over time. For example, students did not ask deep-level causal questions unless that knowledge was necessary to achieve the learning goal

Probing Supersymmetry With Third-Generation Cascade Decays

The chiral structure of supersymmetric particle couplings involving third generation Standard Model fermions depends on left-right squark and slepton mixings as well as gaugino-higgsino mixings. The shapes and intercorrelations of invariant mass distributions of a first or second generation lepton with bottoms and taus arising from adjacent branches of SUSY cascade decays are shown to be a sensitive probe of this chiral structure. All possible cascade decays that can give rise to such correlations within the MSSM are considered. For bottom-lepton correlations the distinctive structure of the invariant mass distributions distinguishes between decays originating from stop or sbottom squarks through either an intermediate chargino or neutralino. For decay through a chargino the spins of the stop and chargino are established by the form of the distribution. When the bottom charge is signed through soft muon tagging, the structure of the same-sign and opposite-sign invariant mass distributions depends on a set function of left-right and gaugino-higgsino mixings, as well as establishes the spins of all the superpartners in the sequential two-body cascade decay. Tau-lepton and tau-tau invariant mass distributions arising from MSSM cascade decays are likewise systematically considered with particular attention to their dependence on tau polarization. All possible tau-lepton and tau-tau distributions are plotted using a semi-analytic model for hadronic one-prong taus. Algorithms for fitting tau-tau and tau-lepton distributions to data are suggested.Comment: 35 pages, 17 .eps figure

Contextual Representation of Abstract Nouns: A Neural Network Approach

This paper explores the use of an artificial neural network to investigate the mental representation of abstract noun meanings. Unlike concrete nouns, abstract nouns refer to entities that cannot be pointed to. Cues to their meaning must therefore be in their context of use. It has frequently been shown that the meaning of a word varies with its contexts of use. It is more difficult, however, to identify which elements of context are relevant to a word's meaning. The present study demonstrates that a connectionist network can be used to examine this problem. A feedforward network learned to distinguish among seven abstract nouns based on characteristics of their verbal contexts in a corpus of randomly selected sentences. The results suggest that, for our sample, the contextual representation of abstract nouns is in principle sufficient to identify and distinguish abstract nouns and thus meets the functional requirements of concept representation

Question Answering in the Context of Illustrated Expository Text

We investigated how college students answer questions about the content of illustrated expository text. Subjects studied illustrated texts describing causal event chains that unfold during the operation of everyday machines. Subjects subsequently provided written answers to questions about events occurring in each machine. Four types of questions were asked: why did event X occur?. how did X occur?, what are the consequences pf X occurring?, and what if X didn't occur?. In our analysis of the answer protocols, we adopted the theoretical framework of the QUES T model of human question answering (Graesser & Franklin, 1990). The present study supported predictions generated from three components of the QUEST model: question categorization, utilization of information sources, and convergence principles. Our results also revealed two novel findings. First, subjects had a bias toward sampling information from the text more than from the picture. Second, subjects tended to sample infontiation depicted in both the text and the picture

Question Answering in the Context of Causal Mechanisma

model of human question answering (called QUEST) accounts for the answers that adults produce when they answer different categories of open-class questions (such as why, how, when, what-enabled, and what-are-the-consequences). This project investigated the answers that adults generate when events are queried in the context of biological, technological, and physical mechanisms. According to QUEST, an event sequence in a scientific mechanism is represented as a causal network of events and states; a teleological goal hierarchy may also be superimposed on the causal network in biological and technological domains, but not in physical systems (e.g., rainfall, earthquake). When questions are answered, QUEST systematically operates on the causal networks and goal hierarchies that underlie a causal mechanism. Answers to how and enablement questions sample causal antecedents of the queried event in the causal network; consequence questions sample causal consequents. Answers to when questions sample antecedents to a greater extent than consequents even though events from both directions fumish sensible answers. Answers to why questions sample both causal antecedents in the causal network and superordinate goals from goal hierarchies that exist in technological and biological knowledge structures

Inferring the Meaning of Verbs from Context

This paper describes a cross-disciplinary extension of previous work on infeiring the meanings of unknown verbs from context. In earlier work, a computational model was developed to incrementally infer meanings while processing texts in an information extraction task setting. In order to explore the space of possible predictors that the system could use to infer verb meanings, we performed a statistical analysis of the corpus that had been used to test the computational system. There were various syntactic and semantic features of the verbs that were significantly diagnostic in detemiining verb meaning. We also evaluated human performance at inferring the verb in the same set of sentences. The overall number of correct predictions for humans was quite similar to that of the computational system, but humans had higher precision scores. The paper concludes with a discussion of the implications of these statistical and experimental findings for future computational work

Question Generation from Concept Maps

In this paper we present a question generation approach suitable for tutorial dialogues. The approach is based on previous psychological theories that hypothesize questions are generated from a knowledge representation modeled as a concept map. Our model automatically extracts concept maps from a textbook and uses them to generate questions. The purpose of the study is to generate and evaluate pedagogically-appropriate questions at varying levels of specificity across one or more sentences. The evaluation metrics include scales from the Question Generation Shared Task and Evaluation Challenge and a new scale specific to the pedagogical nature of questions in tutoring