The Planned and the Emergent: An Alternative Model of Learning and Literacy

Within academic institutions, writing centers are uniquely situated, socially rich sites for exploring learning and literacy. I examine the work of the Michigan Tech Writing Center's UN 1002 World Cultures study teams primarily because student participants and Writing Center coaches are actively engaged in structuring their own learning and meaning-making processes. My research reveals that learning is closely linked to identity formation and leading the teams is an important component of the coaches' educational experiences. I argue that supporting this type of learning requires an expanded understanding of literacy and significant changes to how learning environments are conceptualized and developed. This ethnographic study draws on data collected from recordings and observations of one semester of team sessions, my own experiences as a team coach and UN 1002 teaching assistant, and interviews with Center coaches prior to their graduation. I argue that traditional forms of assessment and analysis emerging from individualized instruction models of learning cannot fully account for the dense configurations of social interactions identified in the Center's program. Instead, I view the Center as an open system and employ social theories of learning and literacy to uncover how the negotiation of meaning in one context influences and is influenced by structures and interactions within as well as beyond its boundaries. I focus on the program design, its enaction in practice, and how engagement in this type of writing center work influences coaches' learning trajectories. I conclude that, viewed as participation in a community of practice, the learning theory informing the program design supports identity formation —a key aspect of learning as argued by Etienne Wenger (1998). The findings of this study challenge misconceptions of peer learning both in writing centers and higher education that relegate peer tutoring to the role of support for individualized models of learning. Instead, this dissertation calls for consideration of new designs that incorporate peer learning as an integral component. Designing learning contexts that cultivate and support the formation of new identities is complex, involves a flexible and opportunistic design structure, and requires the availability of multiple forms of participation and connections across contexts.

Actas da II International Conference «Learning and Teaching in Higher Education/ Learning Orchestration in Higher Education»

É ponto assente que o desenvolvimento social e económico de um país depende do conhecimento e do nível educativo dos seus cidadãos. Num momento de crise económica profunda em que a sustentabilidade do ensino superior se encontra ameaçada e a sua organização com rumo incerto, importa, mais do que nunca, criar um espaço de reflexão em que a ciência se pronuncie, aproximando e colocando em evidência os contributos que em vários domínios se vão produzindo. Nesta conferência, que se pretende em continuidade com a realizada em 2010, elegemos como tema central a qualidade do ensino e da aprendizagem e, tendo em conta a realidade em que nos inscrevemos, alargamos a discussão aos contextos e aos modelos de organização que os podem consubstanciar. Pode parecer um paradoxo centrar esta conferência na componente mais pedagógica do ensino superior quando toda a pressão de avaliação interna e externa das instituições e dos docentes incide particularmente sobre a produção científica. Mas talvez não. Assumimos que a reflexão de cariz pedagógico continua a não ser uma prática corrente no meio universitário português que, durante gerações, se habituou a um ensino universitário tradicional destinado apenas a uma pequena elite e se alheou dos desafios da massificação de que foi alvo. A ideia de que pedagogia apenas diz respeito à relação do professor com crianças ou jovens até ao ensino secundário permaneceu latente, tornando estéril qualquer discussão que, na perspectiva de alguns, seria desnecessária e até contraproducente no ensino superior. Um novo paradigma terá de ser assumido por um ensino universitário que busca o ideal da excelência, colocando no centro do debate o equilíbrio entre as dimensões pedagógica e investigativa e uma redobrada atenção à sociedade e às mudanças se, verdadeiramente, o que pretendemos é ganhar o desafio da qualidade.

Some common fallacies about learning and teaching English as a Lingua Franca

Over the past decades, English language teachers have become familiar with several terms which attempt to describe the role of English as a language of international communication. Presently, the term English as a lingua franca (ELF) seems to be one of the most favoured and adopted to depict the global use of English in the 21st century. Basically, the concept of ELF im-plies cross-cultural, cross-linguistic interactions involving native and non-native speakers. Conse-quently, the ELF paradigm suggests some changes in the language classroom concerning teachers’ and students’ goals as far as native speaker norms and cultures are concerned. Based on Kachru’s (1992) fallacies, this article identifies thirteen misconceptions in ELT regarding learning and teach-ing English varieties and cultures, suggesting that an ethnocentred and linguacentred approach to English should be replaced by an ELF perspective which recognizes the diversity of communicative situations involving different native and non-native cultures and varieties of English

The Palace of Quinta do Marquês do Alegrete: restitution strategies for space, time and memory

Palaces, as an architectural typology, can be found in recreation Quintas that surrounded the main cities in Portugal, which preserved its rural character since the 16th century until the middle of the 19th century. Consisting of cultivated land and farm buildings, the palace of the Quinta was the owner´s temporary residence, for summer holidays and festive events, with gardens, pavilions, fountains and lakes for recreational purposes and leisure. The focus on palaces, as a historic building and as in need of new uses, clearly shows how current the debate on contemporary interventions in this heritage typology is. Interventions in architectural heritage require multidisciplinary teams to identify conservation strategies which enable a qualified use of its spaces, such as for example the experience of security and well-being, which can contribute to a better quality of life and simultaneously to the quality of the urban environment. This paper presents the Palace of Quinta Alegre and its rehabilitation project for contemporary use and public esteem, both of which are considered fundamental prerequisites for its sustainable maintenance in space, in time and in memory. [versão Portuguesa] Sob a denominação de tipologia arquitectónica, o edifício Palácio pode ser encontrado nas Quintas de Recreio que rodeavam as principais cidades Portuguesas, tendo preservado o seu carácter rural, desde o século XVI até metade do século XIX. Consistindo as Quintas em terra cultivada e edifícios rurais, o Palácio da Quinta consistia na residência temporária do proprietário, para férias de verão e eventos comemorativos, dispondo de jardins, pavilhões, fontes e lagos para recreação e lazer. O tema dos Palácios, entendido como edifício histórico que procura novos usos, demonstra como é actual o debate sobre intervenções contemporâneas nesta tipologia de valor patrimonial. A intervenção em património arquitectónico requer a definição de estratégias de conservação por equipas multidisciplinares que permitam estabelecer um uso qualificado dos seus espaços, proporcionando experiências sensoriais de bem-estar e segurança, contribuindo para uma melhor qualidade de vida e, simultaneamente, para a qualidade do ambiente urbano em que se insere. Este artigo tem por objectivo apresentar o Palácio da Quinta Alegre e o projecto de reabilitação, devolvendo-o a um uso contemporâneo e à estima pública, factores fundamentais para a sua manutenção sustentável no espaço, no tempo e na memória.

Modeling Alzheimer disease with iPSCs and mouse model for the identification of risk factors, new targets, and potential therapeutical strategies

Alzheimer's disease (AD) is the most common neurodegenerative disease in elderly. Donepezil is the first-line drug used for AD. In section one, the experimental activity was oriented to evaluate and characterize molecular and cellular mechanisms that contribute to neurodegeneration induced by the Aβ1-42 oligomers (Aβ1-42O) and potential neuroprotective effects of the hybrids feruloyl-donepezil compound called PQM130. The effects of PQM130 were compared to donepezil in a murine AD model, obtained by intracerebroventricular (i.c.v.) injection of Aβ1-42O. The intraperitoneal administration of PQM130 (0.5-1 mg/kg) after i.c.v. Aβ1-42O injection improved learning and memory, protecting mice against spatial cognition decline. Moreover, it reduced oxidative stress, neuroinflammation and neuronal apoptosis, induced cell survival and protein synthesis in mice hippocampus. PQM130 modulated different pathways than donepezil, and it is more effective in counteracting Aβ1-42O damage. The section two of the experimental activity was focused on studying a loss of function variants of ABCA7. GWA studies identified mutations in the ABCA7 gene as a risk factor for AD. The mechanism through which ABCA7 contributes to AD is not clear. ABCA7 regulates lipid metabolism and critically controls phagocytic function. To investigate ABCA7 functions, CRISPR/Cas9 technology was used to engineer human iPSCs and to carry the genetic variant Y622*, which results in a premature stop codon, causing ABCA7 loss-of-function. From iPSCs, astrocytes were generated. This study revealed the effects of ABCA7 loss in astrocytes. ABCA7 Y622* mutation induced dysfunctional endocytic trafficking, impairing Aβ clearance, lipid dysregulation and cell homeostasis disruption, alterations that could contribute to AD. Though further studies are needed to confirm the PQM130 neuroprotective role and ABCA7 function in AD, the provided results showed a better understanding of AD pathophysiology, a new therapeutic approach to treat AD, and illustrated an innovative methodology for studying the disease.

Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models

Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.

Temporary teams learning and performance: the case of 3D printing technology in orthopaedic surgery

This dissertation contributes to the scholarly debate on temporary teams by exploring team interactions and boundaries.The fundamental challenge in temporary teams originates from temporary participation in the teams. First, as participants join the team for a short period of time, there is not enough time to build trust, share understanding, and have effective interactions. Consequently, team outputs and practices built on team interactions become vulnerable. Secondly, as team participants move on and off the teams, teams’ boundaries become blurred over time. It leads to uncertainty among team participants and leaders about who is/is not identified as a team member causing collective disagreement within the team. Focusing on the above mentioned challenges, we conducted this research in healthcare organisations since the use of temporary teams in healthcare and hospital setting is prevalent. In particular, we focused on orthopaedic teams that provide personalised treatments for patients using 3D printing technology. Qualitative and quantitative data were collected using interviews, observations, questionnaires and archival data at Rizzoli Orthopaedic Institute, Bologna, Italy. This study provides the following research outputs. The first is a conceptual study that explores temporary teams’ literature using bibliometric analysis and systematic literature review to highlight research gaps. The second paper qualitatively studies temporary relationships within the teams by collecting data using group interviews and observations. The results highlighted the role of short-term dyadic relationships as a ground to share and transfer knowledge at the team level. Moreover, hierarchical structure of the teams facilitates knowledge sharing by supporting dyadic relationships within and beyond the team meetings. The third paper investigates impact of blurred boundaries on temporary teams’ performance. Using quantitative data collected through questionnaires and archival data, we concluded that boundary blurring in terms of fluidity, overlap and dispersion differently impacts team performance at high and low levels of task complexity.

Applied deep learning and data science with a human-centric and data-centric approach

The term Artificial intelligence acquired a lot of baggage since its introduction and in its current incarnation is synonymous with Deep Learning. The sudden availability of data and computing resources has opened the gates to myriads of applications. Not all are created equal though, and problems might arise especially for fields not closely related to the tasks that pertain tech companies that spearheaded DL. The perspective of practitioners seems to be changing, however. Human-Centric AI emerged in the last few years as a new way of thinking DL and AI applications from the ground up, with a special attention at their relationship with humans. The goal is designing a system that can gracefully integrate in already established workflows, as in many real-world scenarios AI may not be good enough to completely replace its humans. Often this replacement may even be unneeded or undesirable. Another important perspective comes from, Andrew Ng, a DL pioneer, who recently started shifting the focus of development from “better models” towards better, and smaller, data. He defined his approach Data-Centric AI. Without downplaying the importance of pushing the state of the art in DL, we must recognize that if the goal is creating a tool for humans to use, more raw performance may not align with more utility for the final user. A Human-Centric approach is compatible with a Data-Centric one, and we find that the two overlap nicely when human expertise is used as the driving force behind data quality. This thesis documents a series of case-studies where these approaches were employed, to different extents, to guide the design and implementation of intelligent systems. We found human expertise proved crucial in improving datasets and models. The last chapter includes a slight deviation, with studies on the pandemic, still preserving the human and data centric perspective.

Computational creativity: an interdisciplinary approach to sequential learning and creative generations

Creativity seems mysterious; when we experience a creative spark, it is difficult to explain how we got that idea, and we often recall notions like ``inspiration" and ``intuition" when we try to explain the phenomenon. The fact that we are clueless about how a creative idea manifests itself does not necessarily imply that a scientific explanation cannot exist. We are unaware of how we perform certain tasks, such as biking or language understanding, but we have more and more computational techniques that can replicate and hopefully explain such activities. We should understand that every creative act is a fruit of experience, society, and culture. Nothing comes from nothing. Novel ideas are never utterly new; they stem from representations that are already in mind. Creativity involves establishing new relations between pieces of information we had already: then, the greater the knowledge, the greater the possibility of finding uncommon connections, and the more the potential to be creative. In this vein, a beneficial approach to a better understanding of creativity must include computational or mechanistic accounts of such inner procedures and the formation of the knowledge that enables such connections. That is the aim of Computational Creativity: to develop computational systems for emulating and studying creativity. Hence, this dissertation focuses on these two related research areas: discussing computational mechanisms to generate creative artifacts and describing some implicit cognitive processes that can form the basis for creative thoughts.

Sustainable futuring. Learning and unlearning the past A critical discourse study of ‘fashion sustainability’-related discursive formations in Vogue Italia (1965-2021)

Recent scholarly works on the relationship between ‘fashion’ and ‘sustainability’ have identified a need for a systemic transition towards fashion media ‘for sustaianbility’. Nevertheless, the academic research on the topic is still limited and rather circumscribed to the analysis of marketing practices, while only recently some more systemic and critical analyses of the symbolic production of sustainability through fashion media have been undertaken. Responding to this need for an in-depth investigation of ‘sustainability’-related media production, my research focuses on the ‘fashion sustainability’-related discursive formations in the context of one of the most influential fashion magazines today – Vogue Italia. In order to investigate the ways in which the ‘sustainability’ discourse was formed and has evolved, the study considered the entire Vogue Italia archive from 1965 to 2021. The data collection was carried out in two phases, and the individualised relevant discursive units were then in-depth and critically analysed to allow for a grounded assessment of the media giant’s position. The Discourse-Historical Approach provided a methodological base for the analysis, which took into consideration the various levels of context: the immediate textual and intertextual, but also the broader socio-cultural context of the predominant, over-production oriented and capital-led fashion system. The findings led to a delineation of the evolution of the ‘fashion sustainability’ discourse, unveiling how despite Vogue Italia’s auto-determination as attentive to ‘sustainability’-related topics, the magazine is systemically employing discursive strategies which significantly mitigate the meaning of the ‘sustainable commitment’ and thus the meaning of ‘fashion sustainability’.

Cardiac functional and structural abnormalities in a mouse model of CDKL5 deficiency disorder

CDKL5 (cyclin-dependent kinase-like 5) deficiency disorder (CDD) is a rare and severe neurodevelopmental disease that mostly affects girls who are heterozygous for mutations in the X-linked CDKL5 gene. The lack of CDKL5 protein expression or function leads to the appearance of numerous clinical features, including early-onset seizures, marked hypotonia, autistic features, and severe neurodevelopmental impairment. Mouse models of CDD, Cdkl5 KO mice, exhibit several behavioral phenotypes that mimic CDD features, such as impaired learning and memory, social interaction, and motor coordination. CDD symptomatology, along with the high CDKL5 expression levels in the brain, underscores the critical role that CDKL5 plays in proper brain development and function. Nevertheless, the improvement of the clinical overview of CDD in the past few years has defined a more detailed phenotypic spectrum; this includes very common alterations in peripheral organ and tissue function, such as gastrointestinal problems, irregular breathing, hypotonia, and scoliosis, suggesting that CDKL5 deficiency compromises not only CNS function but also that of other organs/tissues. Here we report, for the first time, that a mouse model of CDD, the heterozygous Cdkl5 KO (Cdkl5 +/-) female mouse, exhibits cardiac functional and structural abnormalities. The mice also showed QTc prolongation and increased heart rate. These changes correlate with a marked decrease in parasympathetic activity to the heart and in the expression of the Scn5a and Hcn4 voltage-gated channels. Moreover, the Cdkl5 +/- heart shows typical signs of heart aging, including increased fibrosis, mitochondrial dysfunctions, and increased ROS production. Overall, our study not only contributes to the understanding of the role of CDKL5 in heart structure/function but also documents a novel preclinical phenotype for future therapeutic investigation.

Deep learning and spatial transcriptomics to investigate thyroid tumors

There are many diseases that affect the thyroid gland, and among them are carcinoma. Thyroid cancer is the most common endocrine neoplasm and the second most frequent cancer in the 0-49 age group. This thesis deals with two studies I conducted during my PhD. The first concerns the development of a Deep Learning model to be able to assist the pathologist in screening of thyroid cytology smears. This tool created in collaboration with Prof. Diciotti, affiliated with the DEI-UNIBO "Guglielmo Marconi" Department of Electrical Energy and Information Engineering, has an important clinical implication in that it allows patients to be stratified between those who should undergo surgery and those who should not. The second concerns the application of spatial transcriptomics on well-differentiated thyroid carcinomas to better understand their invasion mechanisms and thus to better comprehend which genes may be involved in the proliferation of these tumors. This project specifically was made possible through a fruitful collaboration with the Gustave Roussy Institute in Paris. Studying thyroid carcinoma deeply is essential to improve patient care, increase survival rates, and enhance the overall understanding of this prevalent cancer. It can lead to more effective prevention, early detection, and treatment strategies that benefit both patients and the healthcare system.

Dynamic Human Robot Interaction Framework Using Deep Learning and Robot Operating System (ROS): a practical approach

Trying to explain to a robot what to do is a difficult undertaking, and only specific types of people have been able to do so far, such as programmers or operators who have learned how to use controllers to communicate with a robot. My internship's goal was to create and develop a framework that would make that easier. The system uses deep learning techniques to recognize a set of hand gestures, both static and dynamic. Then, based on the gesture, it sends a command to a robot. To be as generic as feasible, the communication is implemented using Robot Operating System (ROS). Furthermore, users can add new recognizable gestures and link them to new robot actions; a finite state automaton enforces the users' input verification and correct action sequence. Finally, the users can create and utilize a macro to describe a sequence of actions performable by a robot.