The Prediction of Gre Reading Comprehension Item Difficulty for Expository Prose Passages for Each of Three Item Types: Main Ideas, Inferences and Explicit Statements

Freedle, Roy; Kostin, Irene

doi:10.1002/j.2333-8504.1991.tb01426.x

Cited by 17 publications

(42 citation statements)

References 21 publications

(48 reference statements)

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“…1 Harrington and Sawyer (1992) 2 Vandergrift et al (2006) 3 Tsui and Fullilove (1998) 4 Field (2004) 5 Goh (2000) 6 Tyler (2001) 7 Nation (2001) 8 e.g., Rupp, Garcia, & Jamieson (2001) 9 e.g., Nissan, DeVincenzi, & Tang (1996) 10 Moyer (2006) 11 Rupp et al (2001) 12 Rupp et al (2001) 13 Chang & Dunkel (1992) 14 Chaudron (1983) 15 Nissan et al (1996); Kostin (2004) 16 Kostin (2004) 17 e.g., Pica, Young, & Doughty (1987) 18 Taguchi (2008) 19 Taguchi (2005) 20 Nissan et al (1996) 21 Freedle & Kostin (1992 22 Sasaki (2000) 23 Kostin (2004) 24 Tannen (1982) 25 e.g., Kiany & Jalali (2006) 26 Odlin (1989) 27 Freedle & Kostin (1992) 28 Ying-hui (2006) 29 e.g., Jung (2003) 30 Chaudron & Richards (1986) 31 Freedle & Kostin (1996 32 Major et al (200...…”

Section: Endnotesmentioning

confidence: 99%

“…In the existing literature, a passage has been considered to be concrete if there is a concrete object in the passage (Nissan et al, 1996) or if the main idea of the text and its development are concerned with concrete entities (Freedle & Kostin, 1992.…”

Section: Concretenessmentioning

confidence: 99%

“…55 Other factors that impact difficulty (e.g., speech rate) Rupp et al (2001) 12 Rupp et al (2001) 13 Chang & Dunkel (1992) 14 Chaudron (1983) 15 Nissan et al (1996); Kostin (2004) 16 Kostin (2004) 17 e.g., Pica, Young, & Doughty (1987) 18 Taguchi (2008) 19 Taguchi (2005) 20 Nissan et al (1996) 21 Freedle & Kostin (1992 22 Sasaki (2000) 23 Kostin (2004) 24 Tannen (1982) 25 e.g., Kiany & Jalali (2006) 26 Odlin (1989) 27 Freedle & Kostin (1992) 28 Ying-hui (2006) 29 e.g., Jung (2003) 30 Chaudron & Richards (1986) 31 Freedle & Kostin (1996 …”

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What makes listening difficult? Factors affecting second language listening comprehension

Bloomfield¹,

Wayland²,

Rhoades³

et al. 2010

111

123

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Factors affecting second language listening comprehension gies-that is, those who are aware of and use effective strategies, such as avoiding mental translation-demonstrate better L2 listening comprehension. 2 In addition to these general cognitive abilities, a number of factors pertaining to experience with the L2 influence listening skill. These factors include the amount of prior exposure to the language; familiarity with and an ability to understand the non-native language's phonology; vocabulary size; and background knowledge about the topic, text, structure, schema, and culture.Familiarity with the L2 changes the extent to which the L2 listener uses top-down or bottom-up strategies in listening. For example, expert listeners use both types of strategies: They are able to accurately make sense of the speech signal (bottom-up information) 3 and integrate this information with PurPose-To establish what is currently known about factors that affect foreign language listening comprehension, with a focus on characteristics of the listener, passage, and testing conditions.ConClusions-Research on second language (L2) listening comprehension strongly supports the importance of a number of factors, for example, a listener's working memory capacity and the number of ideas in a passage. Much of the research, however, reports weak or inconclusive results, leaving many factors and complex interactions among factors unresolved and in need of further investigation.relevanCe-Identifying the factors that affect L2 listening comprehension will help Defense Language Institute Proficiency Test (DLPT) designers anticipate how qualities of selected authentic materials will impact listening comprehension. Executive Summary PurPoseThe U.S. Government administers the Defense Language Proficiency Test (DLPT) to military linguists and other government personnel to assess their listening and reading comprehension in a number of foreign languages, including critical languages such as Mandarin, Modern Standard Arabic, Egyptian Arabic, and Persian Farsi. The DLPT is updated every 10 to 15 years, and the most recent transition-from DLPT IV to DLPT5-included a greater emphasis on testing listening comprehension with authentic materials. In turn, this has led to a growing interest in the factors that make second language (L2) listening difficult.To examine these factors, CASL reviewed the current scientific literature and summarized the characteristics of listeners, passages, and testing conditions. The review targeted features of particular interest to stakeholders at the Defense Language Institute (DLI). The long-term goal of the project is to support the selection of authentic listening materials that accurately reflect different proficiency levels. ConClusionsAlthough the available research on L2 listening comprehension is limited, CASL's literature review identified several factors that affect listening comprehension. These factors are summarized below and in Tables 1, 2, and 3. 1 Characteristics of the listenerUnderstanding a fo...

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Section: Endnotesmentioning

confidence: 99%

Section: Concretenessmentioning

confidence: 99%

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confidence: 99%

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What makes listening difficult? Factors affecting second language listening comprehension

Bloomfield¹,

Wayland²,

Rhoades³

et al. 2010

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“…For example, Freedle and Kostin (1992) studied the prediction of item statistics for reading comprehension, and Chalifour and Powers (1989) predicted analytical reasoning item statistics. In these classical prediction studies, coded characteristics of items were used as predictors of item statistics.…”

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confidence: 99%

“…For example, a validated set of characteristics could be used to manipulate item difficulty distributions and to set test or item specifications. Freedle and Kostin (1992) and Chalifour and Powers (1989) used four steps to establish prediction formulas:…”

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Gre Analytical Reasoning Item Statistics Prediction Study

Boldt¹

1998

ETS Research Report Series

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Chalifour and Powers (1989) noted that the ability to predict item statistics might be used to reduce the volume of item pretesting (Mislevy, Sheehan, & Wingersky, 1993). It could also lead to better control of test statistics, such as item difficulty distributions, and to improved test specifications. Research, such as the previously cited study, is needed to examine the prospects for attaining these benefits. That study examined prediction of GRE analytical reasoning item statistics. Linear regression was used by these authors, but they surmised that non-linear techniques might provide somewhat better prediction of item statistics.Chalifour and Powers had amassed item statistics on a very large sample of analytical reasoning items. That sample was used in the present study.For the present study, predictions were generated using a type of neural net, a technique that can accommodate a wide variety of non-linear relationships, though at a cost of requiring the calculation of many constants in the prediction function. This technique did indeed provide more accurate predictions of item difficulty and item discrimination in an estimation sample. However, when the functions developed in the estimation sample were cross validated in a fresh sample, the advantages noted in the estimation sample disappeared. Only by including expert judgements of items difficulty with other predictors was the accuracy of prediction improved.The variables that produced the above results had been selected by Chalifour and Powers (1989) for their efficacy in linear prediction. The study reported here used a "genetic algorithm", which provides a quasi-random search of the predictor set to find an optimal set of predictors. The search used the validity of neural nets computed during operation of the algorithm to evaluate the efficacy of prediction. Hence, this technique sought variables sets that were optimal when a neural net was to be used.The genetic algorithm accomplished some improved prediction of item difficulties in the estimation, but no improvement with regard to discrimination. When the variable sets and the related nets were evaluated in the validation sample, any advantage gained by the search was lost. The validities of predictions in the validation sample were approximately equal whether or not the genetic algorithm had been used in developing the prediction functions.Examination of the root-mean squares of the discrepancies between predicted and actual values for item difficulties (and discriminations) revealed no advantage for the neural net. This examination used the validation sample.In sum/ application of the complex and computer-intensive neural nets and genetic algorithms revealed no advantage over linear methods for predicting item difficulty and item discrimination statistics for GRE analytical reasoning items.

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Creating Vocabulary Item Types That Measure Students' Depth of Semantic Knowledge

Deane

Lawless

et al. 2014

ETS Research Report Series

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We expect that word knowledge accumulates gradually. This article draws on earlier approaches to assessing depth, but focuses on one dimension: richness of semantic knowledge. We present results from a study in which three distinct item types were developed at three levels of depth: knowledge of common usage patterns, knowledge of broad topical associations, and knowledge of specific conceptual relationships. We attempted to avoid common sources of variance across items (such as attractive distracters) and hypothesized that the item types that required greater depth of semantic knowledge would tend to show greater difficulty and discrimination after other sources of variance were accounted for. Our results, while still exploratory, support the conclusion that the item types measure different aspects of lexical knowledge, consistent with the hypothesis of increasing semantic depth.

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The Prediction of Gre Reading Comprehension Item Difficulty for Expository Prose Passages for Each of Three Item Types: Main Ideas, Inferences and Explicit Statements

Cited by 17 publications

References 21 publications

What makes listening difficult? Factors affecting second language listening comprehension

What makes listening difficult? Factors affecting second language listening comprehension

Gre Analytical Reasoning Item Statistics Prediction Study

Creating Vocabulary Item Types That Measure Students' Depth of Semantic Knowledge

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