Persona: Martínez Huertas, José Ángel
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Martínez Huertas
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José Ángel
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Publicación Strong versus weak embodiment: Spatial iconicity in physical, abstract, and social semantic categories(Wiley, 2023-10) León, José A.; Moreno, D.; Martín, L. A; Martínez Huertas, José ÁngelBackground Perceptual and action systems seem to be related to complex cognitive processes, but the scope of grounded or embodied cognition has been questioned. Zwaan and Yaxley (2003) proposed that cognitive processes of making semantic relatedness judgments can be facilitated when word pairs are presented in ways that their referents maintain their iconic configuration rather than their reverse-iconic configuration (the spatial iconicity effect). This effect has been observed in different semantic categories using specific experiments, but it is known that embodiment is highly dependent on task demands. Method The present study analyzed the spatial iconicity effect in three semantic categories (physical, abstract, and social) using the same experimental criteria to determine the scope of embodied cognition. In this reaction-time experiment, 75 participants judged the semantic relatedness of 384 word pairs whose experimental items were presented in their iconic or reverse-iconic configurations. Results Two mixed-effects models with crossed random effects revealed that the interaction between word meaning and spatial position was present only for physical concepts but neither for abstract nor social concepts. Conclusions Within the framework of strong and weak embodiment theories, the data support weak embodiment theory as the most explicative one.Publicación Un Estudio sobre la Competencia Lectora en Adultos con Discapacidad Intelectual y del Desarrollo ante Textos con Contenidos de Clínica y Salud(Colegio Oficial de la Psicología de Madrid, 2018) León, José A.; Jastrzebska, Olga; Martínez Huertas, José ÁngelEl objetivo de este estudio fue analizar el nivel de la competencia lectora en personas adultas con Discapacidad Intelectual y del Desarrollo (PcDID) en la Comunidad de Madrid ante textos clínicos de diferente complejidad léxica y semántica. Participaron un total de 61 personas adultas con discapacidad intelectual y 31 estudiantes universitarios del último curso de grado como grupo control. Los resultados mostraron que las PcDID mejoran su rendimiento en tareas de comprensión lectora cuando se dispone del tiempo y apoyos necesarios. Así, si se dispone de una mayor cantidad de tiempo, el rendimiento de una parte importante de las PcDID mejora considerablemente, consiguiendo en algunos casos aproximarse al rendimiento de los estudiantes universitarios. El nivel de escolarización de las PcDID fue determinante en su rendimiento en las distintas pruebas.Publicación Automated summary evaluation with inbuilt rubric method: An alternative to constructed responses and multiple-choice tests assessments(Taylor and Francis Group, 2019-02-09) Jastrzebska, Olga; Olmos, Ricardo; León, José A.; Martínez Huertas, José ÁngelAutomated Summary Evaluation is proposed as an alternative to rubrics and multiple-choice tests in knowledge assessment. Inbuilt rubric is a recent Latent Semantic Analysis (LSA) method that implements rubrics in an artificially-generated semantic space. It was compared with classical LSA’s cosine-based methods assessing knowledge in a within-subjects design regarding two validation sources: a comparison with the results of rubric scores and multiple-choice tests, and the sensitivity of predicting the academic level of the test-taker. Results showed a higher reliability for inbuilt rubric (from Pearson correlation coefficient .81 to .49) over the classical LSA method (from .61 to .34) and a higher sensitivity using binary logistic regressions and effect sizes to predict academic level. It is concluded that inbuilt rubric has a qualitatively higher reliability and validity than classical LSA methods in a way that is complementary to models based on semantic networks. Thus, it is concluded that new Automated Summary Evaluation approaches such as the inbuilt rubric method can be practical in terms of reliability and efficiency, and, thus, they can offer an affordable and valuable form of knowledge assessment in different educational levels.Publicación Distilling vector space model scores for the assessment of constructed responses with bifactor Inbuilt Rubric method and latent variables(['Springer', 'Psychonomic Society'], 2022-01-11) Olmos, Ricardo; Jorge Botana, Guillermo de; León, José A.; Martínez Huertas, José ÁngelIn 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 riteria. 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.Publicación Enhancing topic-detection in computerized assessments of constructed responses with distributional models of language(Elsevier, 2021-12-15) Olmos, Ricardo; León, José A.; Martínez Huertas, José ÁngelUsually, computerized assessments of constructed responses use a predictive-centered approach instead of a validity-centered one. Here, we compared the convergent and discriminant validity of two computerized assessment methods designed to detect semantic topics in constructed responses: Inbuilt Rubric (IR) and Partial Contents Similarity (PCS). While both methods are distributional models of language and use the same Latent Semantic Analysis (LSA) prior knowledge, they produce different semantic representations. PCS evaluates constructed responses using non-meaningful semantic dimensions (this method is the standard LSA assessment of constructed responses), but IR endows original LSA semantic space coordinates with meaning. In the present study, 255 undergraduate and high school students were allocated one of three texts and were tasked to make a summary. A topic- detection task was conducted comparing IR and PCS methods. Evidence from convergent and discriminant validity was found in favor of the IR method for topic-detection in computerized constructed response assessments. In this line, the multicollinearity of PCS method was larger than the one of IR method, which means that the former is less capable of discriminating between related concepts or meanings. Moreover, the semantic representations of both methods were qualitatively different, that is, they evaluated different concepts or meanings. The implications of these automated assessment methods are also discussed. First, the meaningful coordinates of the Inbuilt Rubric method can accommodate expert rubrics for computerized assessments of constructed responses improving computer-assisted language learning. Second, they can provide high-quality computerized feedback accurately detecting topics in other educational constructed response assessments. Thus, it is concluded that: (1) IR method can represent different concepts and contents of a text, simultaneously mapping a considerable variability of contents in constructed responses; (2) IR method semantic representations have a qualitatively different meaning than the LSA ones and present a desirable multicollinearity that promotes the discriminant validity of the scores of distributional models of language; and (3) IR method can extend the performance and the applications of current LSA semantic representations by endowing the dimensions of the semantic space with semantic meanings.