APex, un système d'aide à la préparation d'examens

Dessus, Phillipe; Lemaire, Benoı̂t

doi:10.3406/stice.1999.1637

Cited by 8 publications

(4 citation statements)

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“…1). This method is similar to other previous methods for content-detection tasks such as partial content similarity or partial golden summaries (Dessus & Lemaire, 1999;Franzke et al, 2005;Kintsch et al, 2007;Magliano & Graesser, 2012;Martínez-Huertas et al, 2021). This procedure results in a vector having different numbers of nouns for each concept to be evaluated.…”

Section: Methodsmentioning

confidence: 93%

“…of such text fragments. The essence of this procedure is similar to other previous methods performing content-detection tasks such as partial content similarity or partial golden summaries (explained previously) that select relevant fragments of the original text stimuli (Dessus & Lemaire, 1999;Franzke et al, 2005;Kintsch et al, 2007;Magliano & Graesser, 2012;Martínez-Huertas et al, 2021), but it only uses nouns embedded in those fragments of the instructional text. In this version, a β matrix is created using different k vectors that are compounded by different number of nouns for each concept to be evaluated.…”

Section: Mapping Assessment Rubrics Into Vector Spaces Using the Inbuilt Rubric Methodsmentioning

confidence: 94%

“…In psychoeducational assessments, distances between the vectors of student summaries and gold summaries (ideal summaries written by experts) are computed. Golden summaries can be different depending on the method used, as they can be whole gold summaries (ideal summaries to compare with) or partial summaries (paragraphs or sentences extracted from whole gold summaries describing different topics; Dessus & Lemaire, 1999 ; Franzke et al, 2005 ; Kintsch et al, 2007 ; Magliano & Graesser, 2012 ; Martínez-Huertas et al, 2021 ), or a set of gold summaries pre-rated with scores from different expert (Olmos et al, 2009b ). In the field of automated summary evaluation, León et al ( 2006 ) analyzed six different LSA methods.…”

Section: Introductionmentioning

confidence: 99%

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Distilling vector space model scores for the assessment of constructed responses with bifactor Inbuilt Rubric method and latent variables

et al. 2022

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In this paper, we highlight the importance of distilling the computational assessments of constructed responses to validate the indicators/proxies of constructs/trins using an empirical illustration in automated summary evaluation. We present the validation of the Inbuilt Rubric (IR) method that maps rubrics into vector spaces for concepts’ assessment. Specifically, we improved and validated its scores’ performance using latent variables, a common approach in psychometrics. We also validated a new hierarchical vector space, namely a bifactor IR. 205 Spanish undergraduate students produced 615 summaries of three different texts that were evaluated by human raters and different versions of the IR method using latent semantic analysis (LSA). The computational scores were validated using multiple linear regressions and different latent variable models like CFAs or SEMs. Convergent and discriminant validity was found for the IR scores using human rater scores as validity criteria. While this study was conducted in the Spanish language, the proposed scheme is language-independent and applicable to any language. We highlight four main conclusions: (1) Accurate performance can be observed in topic-detection tasks without hundreds/thousands of pre-scored samples required in supervised models. (2) Convergent/discriminant validity can be improved using measurement models for computational scores as they adjust for measurement errors. (3) Nouns embedded in fragments of instructional text can be an affordable alternative to use the IR method. (4) Hierarchical models, like the bifactor IR, can increase the validity of computational assessments evaluating general and specific knowledge in vector space models. R code is provided to apply the classic and bifactor IR method.

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Section: Methodsmentioning

confidence: 93%

Section: Mapping Assessment Rubrics Into Vector Spaces Using the Inbuilt Rubric Methodsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Distilling vector space model scores for the assessment of constructed responses with bifactor Inbuilt Rubric method and latent variables

et al. 2022

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“…Apex (Dessus & Lemaire, 1999;Dessus, Lemaire, & Vernier, 2000;Lemaire & Dessus, 2001) is an interactive learning environment developed at the Université Pierre Mendès France in Grenoble. Available as a Web-based application, it uses LSA to assess student essays on topic coverage, discourse structure, and coherence.…”

Section: Apexmentioning

confidence: 99%

Essay Assessment with Latent Semantic Analysis

Miller

2003

Journal of Educational Computing Research

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Latent semantic analysis (LSA) is an automated, statistical technique for comparing the semantic similarity of words or documents. In this article, I examine the application of LSA to automated essay scoring. I compare LSA methods to earlier statistical methods for assessing essay quality, and critically review contemporary essay-scoring systems built on LSA, including the Intelligent Essay Assessor, Summary Street, State the Essence, Apex, and Select-a-Kibitzer. Finally, I discuss current avenues of research, including LSA's application to computer-measured readability assessment and to automatic summarization of student essays.

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Representing and Analysing Meaning with LSA

Wild

2016

Learning Analytics in R With SNA, LSA, and MPIA

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APex, un système d'aide à la préparation d'examens

Abstract: To cite this version:Philippe Dessus, Benoit Lemaire. Apex, un système d'aide à la préparation d'examens.

Cited by 8 publications

References 0 publications

Distilling vector space model scores for the assessment of constructed responses with bifactor Inbuilt Rubric method and latent variables

Distilling vector space model scores for the assessment of constructed responses with bifactor Inbuilt Rubric method and latent variables

Essay Assessment with Latent Semantic Analysis

Representing and Analysing Meaning with LSA

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