<p>     Farmers face significant uncertainty, like weather and prices. Micro-economic theory tells us that when facing uncertainty, an agent, or farmer, makes economic decisions based upon their expectations. This primitive is important for agricultural economics. The “classic” agricultural economic problems: acreage allocation, commodity storage, technology adoption, household labor engagement, etc., are all influenced by the expectations of farmers. Despite expectations pervasive inclusion in economic theory and the decades of attention from agricultural economists, we still know relatively little about how farmers form expectations. This Dissertation is aimed at this opportunity.</p> <p>     The first chapter estimates the degree to which information is incorporated in farmland value expectations. Theoretically, an agent’s expectation should represent all available information. However, there are reasons to believe that an agent may not possess all the pertinent information or they may not be able to interpret the information. Macroeconomists have developed two models to explain the degree to which information may not be incorporated into expectations, The Sticky and Noisy Information Models. I use expectations and actual values of Iowa farmland from 1964 to 2021 to estimate the degree to which new information is not reflected in expectations, or exhibit information rigidities. I find that Iowa farmland market participants do experience information rigidities. From a practical standpoint, farmland is farmers’ most important collateral, the presence of public, simple farmland information may help mitigate lending challenges as a result of farmland value expectations.</p> <p>     The second chapter addresses how commodity price information is incorporated into the financial expectations of farmers. I estimate how unknown or surprise information from a United States Department of Agriculture (USDA) report changes farmers’ attitudes and expectations of their financial conditions. This chapter, synthesizes literature from macroeconomics and commodity price analysis, and uses a unique source of data, the Purdue University/CME Group Ag Economy Barometer. The Ag Economy Barometer reflects the aggregate sentiment of farmers across the US. Like the consumer sentiment index from the University of Michigan, the Ag Economy Barometer can provide a snapshot of sentiment, a measure outside of fundamental economic indicators. Using the corn ending stocks values from the USDA World Agricultural Supply and Demand Estimates (WASDE), I find that</p> <p>farmers’ short– and long–term expectations and attitudes toward large farm investments are increased by information implying a higher corn price. However, this relationship does not exist in the reverse direction and when corn is not actively growing. As a result, if farmers are acting on these changes in expectations, they may be engaging in suboptimal decision making.</p> <p>     The third and final chapter explores the degree to which previous experience is reflected in expectations. The tales of the financial hardship during 1980’s Farm Financial Crisis have been shared across farmers’ dining room tables for decades. The most prominent anecdote relates to the rapid decline in farmland prices. As mentioned in the first chapter, the asset value of farmland is important to farmers. As a result, if experiences like the 1980’s Farm Financial Crisis have created a downward bias toward farmland values, the asset may be undervalued and frictions may exist in the farmland lending market. Macroeconomists show that consumers’ inflation expectations are directly related to their life experiences. I use a panel of farmland market participants in the Purdue Land Value and Cash Rent Survey to estimate the effect of previous experience on farmland value expectations. I find no</p> <p>significant effects. However, my estimates are using variation in cross sectional data. This modeling choice does not rule out the potential of the Farm Financial Crisis effecting all market participants in a similar way, a question outside of my analysis.</p> <p>     Each chapter of this Dissertation addresses how an agent forms their expectations, a necessary first step in my journey as a researcher. I am interested in the link between expectations and economic outcomes. I have built considerable knowledge on expectation formation and will deploy this knowledge exploring the role of expectations in farm outcomes, like acreage allocation, commodity storage, technology adoption, and household labor engagement. In my next step as a researcher, I plan to use the current theoretical advancements in behavioral economics, the explosion in empirical methods and computing, and the availability of data to re-visit the role of expectations in “classic” farm economics problems.</p>

[pt] No contexto de uma sociedade digital emergem abordagens educativas mediadas pelas tecnologias digitais que visam respeitar o tempo, o ritmo e a forma de aprendizagem de cada aluno individualmente. As principais ideias embutidas na lógica das aprendizagens híbrida e adaptativa – autonomia, protagonismo do aluno, personalização do ensino, acompanhamento e avaliação contínua da aprendizagem – não são novas e também estão presentes na educação tradicional exclusivamente presencial. A principal diferença reside no fato de reconhecer na tecnologia o potencial de expansão das formas e lugares de aprendizagem dos alunos, oportunizando uma via paralela e integradora, não substitutiva e nem tampouco de caráter simplesmente complementar ao ensino presencial, para alcance dos objetivos de aprendizagem. O objetivo geral desta investigação é apresentar a concepção, operacionalização e implicações de duas abordagens educativas mediadas pelas tecnologias digitais, a aprendizagem híbrida e a aprendizagem adaptativa. A tese foi estruturada no formato multipaper contendo três estudos independentes que resguardam unidade temática e alinhamento teórico, mantendo assim uma inter-relação. A adoção de um modelo alternativo para a escrita da tese de doutorado responde a demanda do objeto de investigação, que é plural, de caráter difuso e multifacetado. O primeiro estudo, uma revisão sistemática da literatura a partir da produção acadêmica na área de Informática na Educação, buscou identificar as concepções e características de modelos híbridos e adaptativos que objetivam a personalização da aprendizagem. O segundo estudo é o relato de uma experiência de formação online destinada a professores que procurou explorar os conceitos de aprendizagem híbrida e adaptativa, assim como mobilizar mecanismos para sua operacionalização. O terceiro estudo pretendeu verificar, a partir da percepção de professores das licenciaturas e da análise dos currículos de seus cursos, se a formação inicial de professores está alinhada às abordagens educativas emergentes supracitadas, assim como seu entrelaçamento com as políticas públicas educacionais, específicas e complementares, que visam a promoção da incorporação das tecnologias digitais à educação. Compõe também este trabalho uma introdução, onde é apresentado o enquadramento teórico-conceitual transversal aos três estudos, assim como uma síntese conclusiva, onde se apresenta uma discussão integrada dos resultados. Os resultados da pesquisa mostram a necessidade de maior interlocução entre os profissionais das áreas de Informática e Educação no desenvolvimento de ambientes e sistemas que suportem a aprendizagem híbrida e adaptativa, o que implica na realização de mais pesquisas de caráter experimental e, principalmente, na reconfiguração da formação docente, inicial e continuada, no sentido de preparar efetivamente os professores para exercerem seu papel, que já não é mais de transmissores do conhecimento, mas de mediadores no processo de aprendizagem, ressignificando seu modus operandi com base em uma tríade de princípios norteadores: design, curadoria, e orientação. / [en] In the digital society emerge educational approaches mediated by digital technologies that aim to respect the time, pace and form of learning of each student individually. The main ideas embedded in the logic of blended and adaptive learning - autonomy, student protagonism, customization, monitoring and continuous evaluation of learning - are not new and are also present in traditional classroom-based education. The main difference lies in the fact of recognizing in technology the potential of expanding the forms and places of learning, providing a parallel and integrative way, not substitutive and not simply complementary to face-to-face teaching, in order to achieve learning objectives. The general objective of this research is to present the conception, operationalization and implications of two educational approaches mediated by digital technologies, blended learning and adaptive learning. The thesis was structured in the multipaper format containing three independent studies that safeguard thematic unit and theoretical alignment, thus maintaining an interrelation. The adoption of an alternative model for writing the thesis responds to the demand for the research object, which is plural, diffuse and multifaceted. The first study, a systematic survey of the literature from the academic production in the area of Informatics in Education, sought to identify the conceptions and characteristics of hybrid and adaptive models that aim at personalization of learning. The second study is the report of an online training experience for teachers that sought to explore the concepts of blended and adaptive learning, as well as to mobilize mechanisms for its operationalization. The third study aimed to verify, from the teachers perception of the degree programs and the curriculum analysis of their courses, if the initial teacher training is in line with the emerging educational approaches mentioned above, as well as their intertwining with specific and complementary public educational policies, which aim to promote the incorporation of digital technologies into education. This work has also an introduction, where the theoretical and conceptual framework of the three studies is presented, as well as a conclusive synthesis, presenting an integrated discussion of the results. The research results show the need for greater interaction between professionals in the areas of Informatics and Education in the development of environments and systems that support personalized learning, which implies in the accomplishment of more experimental research and, mainly, in the reconfiguration of the teacher training in order to effectively prepare teachers to play their role, which is no longer a transmitter of knowledge but a mediator in the learning process, re-signifying its modus operandi based on the principles of curatorship, design and orientation.

Cette thèse analyse le rôle de l’apprentissage dans deux cadres de modélisation distincts. Dans le cas dunouveau modèle canonique avec apprentissage adaptatif, les caractéristiques les plus marquantes des dynamiquesd’apprentissage concernent la capacité des règles de politique monétaire à assurer la convergencevers l’équilibre en anticipations rationnelles. Le mécanisme de transmission de la politique monétaire estcelui de l’effet de substitution associé au canal de la consommation. Dans le cas d’un modèle multi-agentsqui relâche des hypothèses restrictives du nouveau modèle canonique, tout en restant structurellementproche de celui-ci, les variables agrégées évoluent à bonne distance de cet équilibre, et on observe desdynamiques nettement différentes. La politique monétaire influence les variables agrégées de manièremarginale via l’effet de revenu du canal de la consommation. En présence d’un processus d’apprentissagesocial évolutionnaire, l’économie converge vers un faible niveau d’activité économique. L’introductiond’un processus caractérisé par le fait que les agents apprennent individuellement à l’aide de leurs modèlesmentaux atténue le caractère dépressif des dynamiques d’apprentissage. Ces différences entre les deuxcadres de modélisation démontrent la difficulté de généraliser les résultats du nouveau modèle canonique. / This thesis analyses the role of learning in two different modelling frameworks. In the new canonicalmodel with adaptive learning, the most remarkable characteristics of the learning dynamics deal withthe capacity of monetary policy rules to guaranty convergence to the rational expectations equilibrium.The transmission mechanism of the monetary policy is based on the substitution effect associated to theconsumption channel. In the case of an agent-based model which relaxes some restrictive assumptionsof the new canonical model - but is endowed with a similar structure - aggregate variables evolve atsome distance from the rational expectations equilibrium. Monetary policy has a marginal impact onthe agregated variables via the wealth effect of the consumption channel. When agents learn accordingto an evolutionnary social learning process, the economy converges to regions of low economic activity.The introduction of a process where agents learn individually by using their mental models induces lessdepressive learning dynamics. These differences between the two modelling frameworks show that thegeneralisation of the results of the new canonical model is not easy to achieve.

Mobile learning (m-learning) is still in its infancy, and great efforts should be made so as to investigate the potentials of an educational paradigm shift from the traditional one-size-fits-all teaching approaches to an adaptive learning that can be delivered via mobile devices. Thus, the next challenge has been identified from this implication: How learning design can be implemented so as to benefit from the m-learning characteristics and achieve adaptation and personalization of the individual learning process in different contexts? An important factor for achieving personalized and adaptive m-learning has been the pedagogically meaningful and technically feasible processing of learners’ contextual information. Therefore in this work, design and delivery of personalized educational scenarios are suggested to be re-thought so as to benefit from the affordances of mobile technologies and the learners’ context / El aprendizaje móvil (m-learning) se encuentra todavía en su infancia y grandes esfuerzos se deben hacer para investigar el cambio de paradigma educativo, desde la forma de enseñanza tradicional de 'un modelo único para todos' a un aprendizaje adaptativo que se pueda entregar a través de dispositivos móviles. De esta manera, el siguiente desafío ha sido identificado por esta implicación: ¿Cómo se puede implementar el diseño instruccional con el fin de beneficiarse de las características del m-learning y lograr la adaptación y personalización del proceso de aprendizaje personal en diferentes contextos? Un importante factor para lograr un m-learning personalizado y adaptable ha sido el procesamiento de la información contextual de los estudiantes. Por lo tanto, en este trabajo se sugiere que sean re-pensados el diseño y la entrega de escenarios educativos orientados a la personalización del aprendizaje y que se beneficien de las potencialidades de las tecnologías móviles y el contexto de los estudiantes

Knowledge tracing is concerned with tracking a student’s knowledge as she/he engages with exercises in an (online) learning platform. A commonly used state-of-theart knowledge tracing model is Deep Knowledge Tracing (DKT) which models the time dimension as a sequence of completed exercises per student by using a Long Short-Term Memory Neural Network (LSTM). However, a common problem in this sequence-based model is too much instability in the time dimension of the modelled knowledge of a student. In other words, the student’s knowledge on a skill changes too quickly and unreliably. We propose dynamic student embeddings as a stable method for encoding the time dimension of knowledge tracing systems. In this method the time dimension is encoded in time slices of a fixed size, while the model’s loss function is designed to smoothly align subsequent time slices. We compare the dynamic student embeddings to DKT on a large-scale real-world dataset, and we show that dynamic student embeddings provide a more stable knowledge tracing while retaining good performance. / Kunskapsspårning handlar om att modellera en students kunskaper då den arbetar med uppgifter i en (online) lärplattform. En vanlig state-of-the-art kunskapsspårningsmodell är Deep Knowledge Tracing (DKT) vilken modellerar tidsdimensionen som en sekvens av avslutade uppgifter per student med hjälp av ett neuronnät kallat Long Short-Term Memory Neural Network (LSTM). Ett vanligt problem i dessa sekvensbaserade modeller är emellertid en för stor instabilitet i tidsdimensionen för studentens modellerade kunskap. Med andra ord, studentens kunskaper förändras för snabbt och otillförlitligt. Vi föreslår därför Dynamiska Studentvektorer som en stabil metod för kodning av tidsdimensionen för kunskapsspårningssystem. I denna metod kodas tidsdimensionen i tidsskivor av fix storlek, medan modellens förlustfunktion är utformad för att smidigt justera efterföljande tidsskivor. I denna uppsats jämför vi de Dynamiska Studentvektorer med DKT i en storskalig verklighetsbaserad dataset, och visar att Dynamiska Studentvektorer tillhandahåller en stabilare kunskapsspårning samtidigt som prestandan bibehålls.

Focusing on personalized grammar tasks, this study dives into the integration of Generative Artificial Intelligence into English as a Second Language education. By utilizing a mixed methods approach, incorporating both qualitative and quantitative analyses the study explores how personalized learning can be improved by employing ChatGPT. Results from the study indicate that GAI-driven personalization significantly enhances student engagement and motivation. This offers a promising path for tailoring education to individual learner needs toward a more inclusive classroom. A central outcome of this study is the proposal of a new theoretical framework the Personalization-Motivation Integration Framework (PMIF). This framework clarifies the synergistic effects of integrating content and topic personalization to significantly boost student motivation and reach a more inclusive learning environment. This adds to the growing research about AI's potential in education as it indicates that these technologies can significantly enhance teaching and offer a more tailored and inclusive learning environment.