EFFECT OF DISTRACTOR WORD VARIABILITY IN CHILDREN'S PERFORMANCE ON A WORD IDENTIFICATION TEST∗

ABSTRACT

Second, third, and fourth grade children were administered one of three different versions of a word identification test. All versions tested the same target words but used a different combination of two different sets of distractor words. One set was deemed “highly confusing” (i.e., all dis‐tractors were graphically very similar to target words), the other “easily discriminable” (i.e., distractors were dissimilar to target words). One test version contained all highly confusing distractors, a second used all easily discriminable distractors, and a third contained a combination of both kinds. Results showed that at all grade levels children performed significantly better on the easily discritninable version than on the highly confusing version. Implications of this finding related to the content validity of extant word identification tests; that is, if test authors do not use a set of criteria for selecting distractor words and apply these criteria consistently, the content validity of a test may be in serious jeopardy, for one would not know whether the test measures target word difficulty of distractor word difficulty

     

Generation and evaluation of user tailored responses in multimodal dialogue

When people engage in conversation, they tailor their utterances to their conversational partners, whether these partners are other humans or computational systems. This tailoring, or adaptation to the partner takes place in all facets of human language use, and is based on a mental model or a user model of the conversational partner. Such adaptation has been shown to improve listeners’ comprehension, their satisfaction with an interactive system, the efficiency with which they execute conversational tasks, and the likelihood of achieving higher level goals such as changing the listener’s beliefs and attitudes. We focus on one aspect of adaptation, namely the tailoring of the content of dialogue system utterances for the higher level processes of persuasion, argumentation and advice-giving. Our hypothesis is that algorithms that adapt content for these processes, according to a user model, will improve the usability, efficiency, and effectiveness of dialogue systems. We describe a multimodal dialogue system and algorithms for adaptive content selection based on multi-attribute decision theory. We demonstrate experimentally the improved efficacy of system responses through the use of user models to both tailor the content of system utterances and to manipulate their conciseness.     

A versatile electronic switch with memory

This noise-conditioned switch provides a convenient interface between S response switches and logic circuitry and is adaptable to many experimental situations.