In this quantitative study, the relationships between high school students' preference for solution methods, geometry performance, task difficulty, and gender were investigated. The data was collected from 161 high school students from six different schools at a county located in central Florida in the United States. The study was conducted during the 2013-2014 school year. The participants represented a wide range in socioeconomic status, were from a range of grades (10-12), and were enrolled in different mathematics courses (Algebra 2, Geometry, Financial Algebra, and Pre-calculus). Data were collected primarily with the aid of a geometry test and a geometry questionnaire. Using a think-aloud protocol, a short interview was also conducted with some students. For the purpose of statistical analysis, students' preferences for solution methods were quantified into numeric values, and then a visuality score was obtained for each student. Students' visuality scores ranged from -12 to +12. The visuality scores were used to assess students' preference for solution methods. A standardized test score was used to measure students' geometry performance. The data analysis indicated that the majority of students were visualizers. The statistical analysis revealed that there was not an association between preference for solution methods and students' geometry performance. The preference for solving geometry problems using either visual or nonvisual methods was not influenced by task difficulty. Students were equally likely to employ visual as well as nonvisual solution methods regardless of the task difficulty. Gender was significant in geometry performance but not in preference for solution methods. Female students' geometry performance was significantly higher than male students' geometry performance. The findings of this study suggested that instruction should be focused on incorporating both visual and nonvisual teaching strategies in mathematics lesson activities in order to develop preference for both visual and nonvisual solution methods.

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This study investigates the effects of including a recipient in writing instructions on the quality of writing produced by L2 English students. To evaluate the effect of the recipient, writing performances are first assessed in texts written in response to instructions that do not explicitly specify a recipient. They are subsequently compared to writing performances by the same students in texts produced for similar instructions but this time with an explicitly stated recipient. The results of the main experiment are complemented with test-taker surveys that capture the students’ own perceptions about the differences between the two tasks. The participating pupils are from two classes at an inner-city municipal Upper Secondary school in Sweden, one at level ENG05 and the other at level ENG07. The study is inspired by communicative language teaching where the writer’s adaptation to the recipient is central. The study tests for the recipient effect along a broad spectrum of linguistic and communicative measures. The results show that students in both classes performed at a higher level with an explicit recipient across all four communicative measures and two out of four linguistic measures. The study also identified a generally stronger recipient effect in ENG05 than in ENG07 and a mixed pattern when comparing students with differing abilities in English. The main finding of the test-taker survey is that students prefer doing tasks with a specified recipient but are slightly more familiar with tasks without a recipient. They also report that they rarely imagine an English-speaking recipient when writing a task and that they often write with the teacher in mind.

[EN] The stupefying success of Artificial Intelligence (AI) for specific problems, from recommender systems to self-driving cars, has not yet been matched with a similar progress in general AI systems, coping with a variety of problems. This dissertation deals with the long-standing problem of creating more general AI systems, through the analysis of their development and the evaluation of their cognitive abilities. Firstly, this thesis contributes with a general-purpose learning system that meets several desirable characteristics in terms of expressiveness, comprehensibility and versatility. The system works with approaches that are inherently general: inductive programming and reinforcement learning. The system does not rely on a fixed library of learning operators, but can be endowed with new ones, so being able to operate in a wide variety of contexts. This flexibility, jointly with its declarative character, makes it possible to use the system as an instrument for better understanding the role (and difficulty) of the constructs that each task requires. The learning process is also overhauled with a new developmental and lifelong approach for knowledge acquisition, consolidation and forgetting, which is necessary when bounded resources (memory and time) are considered. Secondly, this thesis analyses whether the use of intelligence tests for AI evaluation is a much better alternative to most task-oriented evaluation approaches in AI. Accordingly, we make a review of what has been done when AI systems have been confronted against tasks taken from intelligence tests. In this regard, we scrutinise what intelligence tests measure in machines, whether they are useful to evaluate AI systems, whether they are really challenging problems, and whether they are useful to understand (human) intelligence. Finally, the analysis of the concepts of development and incremental learning in AI systems is done at the conceptual level but also through several of these intelligence tests, providing further insight for the understanding and construction of general-purpose developing AI systems. / [ES] El éxito abrumador de la Inteligencia Artificial (IA) en la resolución de tareas específicas (desde sistemas de recomendación hasta vehículos de conducción autónoma) no ha sido aún igualado con un avance similar en sistemas de IA de carácter más general enfocados en la resolución de una mayor variedad de tareas. Esta tesis aborda la creación de sistemas de IA de propósito general así como el análisis y evaluación tanto de su desarrollo como de sus capacidades cognitivas. En primer lugar, esta tesis contribuye con un sistema de aprendizaje de propósito general que reúne distintas ventajas como expresividad, comprensibilidad y versatilidad. El sistema está basado en aproximaciones de carácter inherentemente general: programación inductiva y aprendizaje por refuerzo. Además, dicho sistema se basa en una biblioteca dinámica de operadores de aprendizaje por lo que es capaz de operar en una amplia variedad de contextos. Esta flexibilidad, junto con su carácter declarativo, hace que sea posible utilizar el sistema de forma instrumental con el objetivo de facilitar la comprensión de las distintas construcciones que cada tarea requiere para ser resuelta. Por último, el proceso de aprendizaje también se revisa por medio de un enfoque evolutivo e incremental de adquisición, consolidación y olvido de conocimiento, necesario cuando se trabaja con recursos limitados (memoria y tiempo). En segundo lugar, esta tesis analiza el uso de tests de inteligencia humana para la evaluación de sistemas de IA y plantea si su uso puede constituir una alternativa válida a los enfoques actuales de evaluación de IA (más orientados a tareas). Para ello se realiza una exhaustiva revisión bibliográfica de aquellos sistemas de IA que han sido utilizados para la resolución de este tipo de problemas. Esto ha permitido analizar qué miden realmente los tests de inteligencia en los sistemas de IA, si son significativos para su evaluación, si realmente constituyen problemas complejos y, por último, si son útiles para entender la inteligencia (humana). Finalmente se analizan los conceptos de desarrollo cognitivo y aprendizaje incremental en sistemas de IA no solo a nivel conceptual, sino también por medio de estos problemas mejorando por tanto la comprensión y construcción de sistemas de propósito general evolutivos. / [CA] L'èxit aclaparant de la Intel·ligència Artificial (IA) en la resolució de tasques específiques (des de sistemes de recomanació fins a vehicles de conducció autònoma) no ha sigut encara igualat amb un avanç similar en sistemes de IA de caràcter més general enfocats en la resolució d'una major varietat de tasques. Aquesta tesi aborda la creació de sistemes de IA de propòsit general així com l'anàlisi i avaluació tant del seu desenvolupament com de les seues capacitats cognitives. En primer lloc, aquesta tesi contribueix amb un sistema d'aprenentatge de propòsit general que reuneix diferents avantatges com ara expressivitat, comprensibilitat i versatilitat. El sistema està basat en aproximacions de caràcter inherentment general: programació inductiva i aprenentatge per reforç. A més, el sistema utilitza una biblioteca dinàmica d'operadors d'aprenentatge pel que és capaç d'operar en una àmplia varietat de contextos. Aquesta flexibilitat, juntament amb el seu caràcter declaratiu, fa que siga possible utilitzar el sistema de forma instrumental amb l'objectiu de facilitar la comprensió de les diferents construccions que cada tasca requereix per a ser resolta. Finalment, el procés d'aprenentatge també és revisat mitjançant un enfocament evolutiu i incremental d'adquisició, consolidació i oblit de coneixement, necessari quan es treballa amb recursos limitats (memòria i temps). En segon lloc, aquesta tesi analitza l'ús de tests d'intel·ligència humana per a l'avaluació de sistemes de IA i planteja si el seu ús pot constituir una alternativa vàlida als enfocaments actuals d'avaluació de IA (més orientats a tasques). Amb aquesta finalitat, es realitza una exhaustiva revisió bibliogràfica d'aquells sistemes de IA que han sigut utilitzats per a la resolució d'aquest tipus de problemes. Açò ha permès analitzar què mesuren realment els tests d'intel·ligència en els sistemes de IA, si són significatius per a la seua avaluació, si realment constitueixen problemes complexos i, finalment, si són útils per a entendre la intel·ligència (humana). Finalment s'analitzen els conceptes de desenvolupament cognitiu i aprenentatge incremental en sistemes de IA no solament a nivell conceptual, sinó també per mitjà d'aquests problemes millorant per tant la comprensió i construcció de sistemes de propòsit general evolutius. / Martínez Plumed, F. (2016). Incremental and developmental perspectives for general-purpose learning systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/67269

The purpose of this thesis was to explore how preschool-aged girls self-regulate when they are challenged by a naturally occurring task or activity within the preschool environment. My work as a graduate research assistant to Dr. Wanda Boyer enabled me access to a large grounded theory study entitled, A Foundational Measure of Early Childhood Self-Regulation, from which participants were chosen. Since I endeavoured to capture a holistic picture of preschool-aged girls’ self-regulation, as they encounter a naturally occurring difficult activity, a multiple case research design, within the qualitative paradigm, was used. In keeping with the qualitative case study tradition, this study utilized multiple data sources such as demographic information, transcriptions of interviews and videotaped observations from the larger study. The results of pattern matching, within-case and cross-case analyses are presented within four central themes: (a) initiating, modulating, and ceasing a difficult task, (b) strategies used across six dimensions of regulation, (c) interactions occurring during the regulation of a difficult task and, (d) the common use of physical strategies. This descriptive thesis concludes with key findings, proposed implications, and suggestions for future research.