The Experts below are selected from a list of 1791 Experts worldwide ranked by ideXlab platform
Philip M. Sadler - One of the best experts on this subject based on the ideXlab platform.
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Psychometric Models of student conceptions in science reconciling qualitative studies and distractor driven assessment instruments
Journal of Research in Science Teaching, 1998Co-Authors: Philip M. SadlerAbstract:We stand poised to marry the fruits of qualitative research on children's conceptions with the machinery of Psychometrics. This merger allows us to build upon studies of limited groups of subjects to generalize to the larger population of learners. This is accomplished by reformulating multiple choice tests to reflect gains in understanding cognitive development. This study uses Psychometric modeling to rank the appeal of a variety of children's astronomical ideas on a single uniform scale. Alternative con- ceptions are captured in test items with highly attractive multiple choice distractors administered twice to 1250 8th through 12th-grade students at the start and end of their introductory astronomy courses. For such items, an unusual Psychometric profile is observed—instruction appears to strengthen support for alterna- tive conceptions before this preference eventually declines. This lends support to the view that such ideas may actually be markers of progress toward scientific understanding and are not impediments to learning. This method of analysis reveals the ages at which certain conceptions are more prevalent than others, aid- ing developers and practitioners in matching curriculum to student grade levels. This kind of instrument, in which distractors match common student ideas, has a profoundly different Psychometric profile from conventional tests and exposes the weakness evident in conventional standardized tests. Distractor-driven multiple choice tests combine the richness of qualitative research with the power of quantitative assess- ment, measuring conceptual change along a single uniform dimension. © 1998 John Wiley & Sons, Inc. J Res Sci Teach 35: 265-296, 1998.
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Psychometric Models of student conceptions in science: Reconciling qualitative studies and distractor‐driven assessment instruments
Journal of Research in Science Teaching, 1998Co-Authors: Philip M. SadlerAbstract:We stand poised to marry the fruits of qualitative research on children's conceptions with the machinery of Psychometrics. This merger allows us to build upon studies of limited groups of subjects to generalize to the larger population of learners. This is accomplished by reformulating multiple choice tests to reflect gains in understanding cognitive development. This study uses Psychometric modeling to rank the appeal of a variety of children's astronomical ideas on a single uniform scale. Alternative con- ceptions are captured in test items with highly attractive multiple choice distractors administered twice to 1250 8th through 12th-grade students at the start and end of their introductory astronomy courses. For such items, an unusual Psychometric profile is observed—instruction appears to strengthen support for alterna- tive conceptions before this preference eventually declines. This lends support to the view that such ideas may actually be markers of progress toward scientific understanding and are not impediments to learning. This method of analysis reveals the ages at which certain conceptions are more prevalent than others, aid- ing developers and practitioners in matching curriculum to student grade levels. This kind of instrument, in which distractors match common student ideas, has a profoundly different Psychometric profile from conventional tests and exposes the weakness evident in conventional standardized tests. Distractor-driven multiple choice tests combine the richness of qualitative research with the power of quantitative assess- ment, measuring conceptual change along a single uniform dimension. © 1998 John Wiley & Sons, Inc. J Res Sci Teach 35: 265-296, 1998.
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
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user preference representation based on Psychometric Models
Australasian Database Conference, 2011Co-Authors: Wenhan Chao, Jun WangAbstract:Neighbourhood-based collaborative filtering is one of the most popular recommendation techniques, and has been applied successfully in various fields. User ratings are often used by neighbourhood-based collaborative filtering to compute the similarity between two users or items, but, user ratings may not always be representatives of their true preferences, resulting in unreliable similarity information and poor recommendation. To solve these problems, this paper proposes to use latent preferences for neighbourhood-based collaborative filtering instead of user ratings. Latent preferences are based on user latent interest estimated from ratings through a Psychometric model. Experimental results show that latent preferences can improve the recommendation accuracy and coverage while lessening the prediction time of neighbourhood-based collaborative filtering by finding out reliable and effective neighbours; and latent preferences are better than user ratings for representing user preferences.
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ADC - User preference representation based on Psychometric Models
2011Co-Authors: Wenhan Chao, Jun WangAbstract:Neighbourhood-based collaborative filtering is one of the most popular recommendation techniques, and has been applied successfully in various fields. User ratings are often used by neighbourhood-based collaborative filtering to compute the similarity between two users or items, but, user ratings may not always be representatives of their true preferences, resulting in unreliable similarity information and poor recommendation. To solve these problems, this paper proposes to use latent preferences for neighbourhood-based collaborative filtering instead of user ratings. Latent preferences are based on user latent interest estimated from ratings through a Psychometric model. Experimental results show that latent preferences can improve the recommendation accuracy and coverage while lessening the prediction time of neighbourhood-based collaborative filtering by finding out reliable and effective neighbours; and latent preferences are better than user ratings for representing user preferences.
Carrie R Houts - One of the best experts on this subject based on the ideXlab platform.
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a diagnostic procedure to detect departures from local independence in item response theory Models
Psychological Methods, 2017Co-Authors: Michael C. Edwards, Carrie R HoutsAbstract:: Item response theory (IRT) is a widely used measurement model. When considering its use in education, health outcomes, and psychology, it is likely to be one of the most impactful Psychometric Models in existence. IRT has many advantages over classical test theory-based measurement Models. For these advantages to hold in practice, strong assumptions must be satisfied. One of these assumptions, local independence, is the focus of the work described here. Local independence is the assumption that, conditional on the latent variable(s), item responses are unrelated to one another (i.e., independent). Stated another way, local independence implies that the only thing causing items to covary is the modeled latent variable(s). Violations of this assumption, quite aptly titled local dependence, can have serious consequences for the estimated parameters. A new diagnostic is proposed, based on parameter stability in an item-level jackknife resampling procedure. We review the ideas underlying the new diagnostic and how it is computed before covering some simulated and real examples demonstrating its effectiveness. (PsycINFO Database Record
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Comparing Surface and Underlying Local Dependence Levels via Polychoric Correlations.
Applied Psychological Measurement, 2014Co-Authors: Carrie R Houts, Michael C. EdwardsAbstract:Item response theory (IRT) is a set of Psychometric Models used in the social and behavioral sciences. As part of applying these Models in practice, a number of assumptions are made. A large literature exists assessing the extent to which these assumptions are satisfied in a given data set. One of these assumptions, local independence, is the focus of the research described here. When the local independence assumption is violated, there is said to be local dependence (LD). Several different Models of LD have been proposed, and a number of studies have been conducted examining the performance of different methods at detecting LD. Underlying LD (ULD) and surface LD (SLD) were proposed as two possible mechanisms underlying observed LD in an early exploration of detection procedures. In a number of previous studies, it appears as though ULD is more difficult to detect than SLD. In this article, the authors demonstrate a procedure to examine comparability of induced LD and present results, which suggest a re-i...
Wenhan Chao - One of the best experts on this subject based on the ideXlab platform.
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user preference representation based on Psychometric Models
Australasian Database Conference, 2011Co-Authors: Wenhan Chao, Jun WangAbstract:Neighbourhood-based collaborative filtering is one of the most popular recommendation techniques, and has been applied successfully in various fields. User ratings are often used by neighbourhood-based collaborative filtering to compute the similarity between two users or items, but, user ratings may not always be representatives of their true preferences, resulting in unreliable similarity information and poor recommendation. To solve these problems, this paper proposes to use latent preferences for neighbourhood-based collaborative filtering instead of user ratings. Latent preferences are based on user latent interest estimated from ratings through a Psychometric model. Experimental results show that latent preferences can improve the recommendation accuracy and coverage while lessening the prediction time of neighbourhood-based collaborative filtering by finding out reliable and effective neighbours; and latent preferences are better than user ratings for representing user preferences.
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ADC - User preference representation based on Psychometric Models
2011Co-Authors: Wenhan Chao, Jun WangAbstract:Neighbourhood-based collaborative filtering is one of the most popular recommendation techniques, and has been applied successfully in various fields. User ratings are often used by neighbourhood-based collaborative filtering to compute the similarity between two users or items, but, user ratings may not always be representatives of their true preferences, resulting in unreliable similarity information and poor recommendation. To solve these problems, this paper proposes to use latent preferences for neighbourhood-based collaborative filtering instead of user ratings. Latent preferences are based on user latent interest estimated from ratings through a Psychometric model. Experimental results show that latent preferences can improve the recommendation accuracy and coverage while lessening the prediction time of neighbourhood-based collaborative filtering by finding out reliable and effective neighbours; and latent preferences are better than user ratings for representing user preferences.
Michael C. Edwards - One of the best experts on this subject based on the ideXlab platform.
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a diagnostic procedure to detect departures from local independence in item response theory Models
Psychological Methods, 2017Co-Authors: Michael C. Edwards, Carrie R HoutsAbstract:: Item response theory (IRT) is a widely used measurement model. When considering its use in education, health outcomes, and psychology, it is likely to be one of the most impactful Psychometric Models in existence. IRT has many advantages over classical test theory-based measurement Models. For these advantages to hold in practice, strong assumptions must be satisfied. One of these assumptions, local independence, is the focus of the work described here. Local independence is the assumption that, conditional on the latent variable(s), item responses are unrelated to one another (i.e., independent). Stated another way, local independence implies that the only thing causing items to covary is the modeled latent variable(s). Violations of this assumption, quite aptly titled local dependence, can have serious consequences for the estimated parameters. A new diagnostic is proposed, based on parameter stability in an item-level jackknife resampling procedure. We review the ideas underlying the new diagnostic and how it is computed before covering some simulated and real examples demonstrating its effectiveness. (PsycINFO Database Record
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Comparing Surface and Underlying Local Dependence Levels via Polychoric Correlations.
Applied Psychological Measurement, 2014Co-Authors: Carrie R Houts, Michael C. EdwardsAbstract:Item response theory (IRT) is a set of Psychometric Models used in the social and behavioral sciences. As part of applying these Models in practice, a number of assumptions are made. A large literature exists assessing the extent to which these assumptions are satisfied in a given data set. One of these assumptions, local independence, is the focus of the research described here. When the local independence assumption is violated, there is said to be local dependence (LD). Several different Models of LD have been proposed, and a number of studies have been conducted examining the performance of different methods at detecting LD. Underlying LD (ULD) and surface LD (SLD) were proposed as two possible mechanisms underlying observed LD in an early exploration of detection procedures. In a number of previous studies, it appears as though ULD is more difficult to detect than SLD. In this article, the authors demonstrate a procedure to examine comparability of induced LD and present results, which suggest a re-i...
