Factors Affecting Difficulty in the Generating Examples Item Type

Katz, Irvin R.; Lipps, Audrey W.; Trafton, J. Gregory

doi:10.1002/j.2333-8504.2002.tb01874.x

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Research supports the importance of multiplestrategy use and representations in mathematics in terms of skill acquisition, understanding, and transfer (e.g., Katz, Lipps, & Trafton, 2002;Tabachneck, Koedinger, & Nathan, 1994). The requirement for integrating different types of response formats, and hence representations, is also consistent with the research-based expectation in state and national standards that students should be flexible in moving across representations (tables, graphs, expressions).…”

Section: Multiple Representations Of Contentmentioning

confidence: 93%

Tensions, Trends, Tools, and Technologies: Time for an Educational Sea Change

Shute¹

2006

ETS Research Report Series

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This report outlines three educational approaches: (a) traditional, the currently dominant approach, a largely lecture-oriented, authoritarian style that makes heavy use of assessments of learning, which are useful for accountability purposes but only marginally useful for guiding day-to-day instruction; (b) progressive, a highly student-centered approach that relies on assessments for learning, which can be very useful in guiding day-to-day instruction; and (c) unified, a new, integrated approach that uses the best of both kinds of assessments-for and of learning-and which leverages computer technology, educational measurement, and cognitive science to address factors that undermined earlier attempts to implement the progressive approach. This report examines some of the research, trends, and factors that should be considered, understood, and, in some cases, leveraged, in order to move toward the unified approach. Further, this report presents examples of how ETS projects are moving toward the new approach to harness assessment in the service of learning.

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Section: Multiple Representations Of Contentmentioning

confidence: 93%

Tensions, Trends, Tools, and Technologies: Time for an Educational Sea Change

Shute¹

2006

ETS Research Report Series

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“…Jusqu'à présent, une grande partie des recherches effectuées sur ces modèles ont porté sur des sujets bien précis comme des problèmes d'analogie (par exemple Mulholland et al, 1980 ;Sternberg, 1977) ou des problèmes algébriques (par exemple Tout et al, 2020 ;Keehner et al, 2016 ;Katz et al, 2002) et se sont limitées aux composantes cognitives nécessaires à la résolution. Ainsi les modèles élaborés prenaient en compte différents niveaux de difficulté au sein d'ensembles très homogènes de types d'items.…”

Section: Modèle Prédictif De Difficultés D'une Questionunclassified

Élaboration d’un modèle de difficulté de questions évaluant la culture scientifique des élèves

Duclos¹,

Hebel²,

Tiberghien³

et al. 2021

educationdidactique

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Les difficultés des questions posées dans le cadre des évaluations ne sont pas toujours faciles à anticiper par des enseignants ou des concepteurs de tests même si cette expertise est pourtant cruciale. Cet article se concentre sur l'établissement a priori des caractéristiques des questions qui expliqueraient les difficultés des élèves, difficultés dont certaines possiblement spécifiques du milieu socio-économico-culturel et du niveau de performance des élèves. Notre étude a donc consisté à élaborer un modèle prédictif des difficultés de questions relevant de la culture scientifique. Pour cela, nous prenons l'exemple des tâches de culture scientifique de l'enquête internationale PISA 2015. Cela nous permet de discuter la construction de notre modèle, les attentes de résultats qu'il peut induire et l'implication de ce type d'études pour l'enseignement et la conception d'évaluations en sciences. Elaboration of a question difficulty model assessing students' scientific literacyThe difficulties of the questions asked in the framework of the assessments are not always easy to anticipate by teachers or test designers even if this expertise is nevertheless crucial. This article focuses on establishing a priori of the characteristics of the questions that would explain the students' difficulties, some of which may be specific to the socioeconomic-cultural environment and the level of performance of the students. Our study therefore consisted in developing a predictive model of the difficulties of questions related to scientific literacy. For this, we choose the example of the scientific literacy tasks of the international PISA 2015 survey. This allows us to discuss the construction of our model, the expectations of outcomes it might induce and the implication of this type of studies for teaching and designing assessments in science.Mots-clés : culture scientifique, modèle de difficulté de question, statut socio-économico-culturel des élèves, performance PISA Science 2015.

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“…Many researchers in cognitive psychology and mathematics education have identified features that influence item difficulty in many quantitative areas. For example, the determinants of difficulty have been explored for algebra problems (e.g., Embretson, 1995;Heffernan & Koedinger, 1998;Koedinger & MacLaren, 2002;Koedinger & Nathan, 2004;Mayer, Larkin, & Kadane, 1984;Sebrechts, Enright, Bennett, & Martin, 1996), proportional reasoning problems (e.g., Kaput & West, 1994 ;Karplus, Pulos, & Stage, 1983;Noelting, 1980;Vergnaud, 1980), arithmetic word problems (e.g., Kintsch & Greeno, 1985;Riley & Greeno, 1988), and quantitative reasoning items with more than one correct response (Bennett et al, 1999;Katz, Lipps, & Trafton, 2002).…”

Section: Incorporating Item Difficulty Predictions Into Item Model Dementioning

confidence: 99%

Psychometric and Cognitive Analysis as a Basis for the Design and Revision of Quantitative Item Models

Graf

Peterson

Steffen³

et al. 2005

ETS Research Report Series

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We describe the item modeling development and evaluation process as applied to a quantitative assessment with high‐stakes outcomes. In addition to expediting the item‐creation process, a model‐based approach may reduce pretesting costs, if the difficulty and discrimination of model‐generated items may be predicted to a predefined level of accuracy. The development and evaluation of item models represents a collaborative effort among content specialists, statisticians, and cognitive scientists. A cycle for developing and revising item models that generate items with more predictable statistics is described. We review the goals of item modeling from different perspectives and recommend a method for structuring families of models that span content and generate items with more predictable psychometric parameters.

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Factors Affecting Difficulty in the Generating Examples Item Type

Cited by 6 publications

References 12 publications

Tensions, Trends, Tools, and Technologies: Time for an Educational Sea Change

Tensions, Trends, Tools, and Technologies: Time for an Educational Sea Change

Élaboration d’un modèle de difficulté de questions évaluant la culture scientifique des élèves

Psychometric and Cognitive Analysis as a Basis for the Design and Revision of Quantitative Item Models

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