VictorJuiz/thesis huggingface dataset:
Oxford admissions and knowledge as capital: understanding undergraduate admissions inequality through field

Securing the opportunity to study at the University of Oxford remains a culturally and professionally significant symbol, opening up opportunities and facilitating social advancement. Able students from a range of backgrounds and schools compete for undergraduate admission through a highly demanding decentralised process, crowned by the idiosyncratic tutorial interview. Unsurprisingly, the socioeconomic inequality of student success in this process remains a topic of regular, and at times heated, debate with little consensus on the reasons for this inequality. Yet a widely held assumption across the spectrum of opinion is the belief that the interview is a diagnostic test, with the tutors assessing an intelligence which is emergent and/or at the apex of what is studied in school. This study, premised on the belief that intelligence and hierarchies of knowledge are far more fluid and problematic concepts, seeks to identify the hitherto undervalued epistemological factor in admissions inequality. Using History as a case-study, it identifies the dissonance between historical knowledge and thinking in the school context and more disciplinary-linked university admissions context. Students, informed by the former, can best bridge this lack of epistemological through access to specialisms and networks most available for the socioeconomically privileged, contributing to the well-publicised inequality. This study mapped the gap in understandings of the models of intelligence of students from a range of school types and individuals involved in admissions at the University of Oxford. From this an intervention for the student group was designed, appropriating a form of Socratic dialogue, to close the identified gap. The study was forced to end prematurely due to COVID-19, meaning the results and conclusions are limited. However, it finds a clear gap in epistemological hierarchies between students and individuals involved in admissions and presents a model of a potential intervention which will inform further practitioner research.

Keywords: MLT 2020
MLT
How can we think geographically? an investigation into designing reasoning tasks to promote geographical thinking at KS3

Thinking geographically; “to have a trained capacity to construct a mental map and see patterns, to recognise relationships, to see movement, to take that map and clothe it in meaning” is a key term in the Geography Education Community (Morgan, 2013, p.275). However, my Part 2 research established that despite being familiar with term, what thinking geographically consists of, or looks like in the classroom is not clear for many teachers. Where this ambiguity exists, it is much harder for teachers to target its development. Therefore, this research aims to clarify what underpins thinking geographically, from a knowledge and learning perspective, but also – and perhaps most importantly – attempts to consider practical methods to support its development in a Key Stage Three classroom. The research began by examining what skills underpin thinking geographically. Through the course of the literature review, these skills appeared very similar to skills identified as ‘reasoning’: cause and effect reasoning, argumentation, gist reasoning, for example. Therefore, thinking geographically could be understood as discipline-specific utilization of reasoning skills: when students are thinking geographically, they are synchronously employing a variety of reasoning skills to make sense of geographical processes and systems. With this understanding, the research considered practical solutions to support the development of thinking geographically by targeting reasoning skills in a variety of tasks. Three different tasks are designed; two original and one inspired by the Thinking Through Geography ‘mystery’ task (Leat, 1997) and their nature and efficacy evaluated.

Keywords: MLT 2020
MLT
To what extent does the use of authentic prompts promote participation in Key Stage 3 Modern Foreign Languages students?

This study uses action research in a mixed, comprehensive secondary school in the South East of England to investigate the extent to which the use of authentic prompts promotes participation in Key Stage 3 Modern Foreign Languages (MFL) students. The investigation comprises of three Year 8 classes using thought-provoking and authentic current affairs resources in their Spanish lessons as a conversation prompt, with the support of a speaking mat containing useful vocabulary. The data were collected via a pre- and post-intervention questionnaire alongside individual and group interviews. In order to carry out the investigation, collaboration with the two other members of the MFL department was vital and allowed for discussion and reflection in both the planning and evaluation stages of the investigation. The quantitative and qualitative data collected provides evidence that highlights many of the issues impacting upon student participation explored in the literature review. With regards to the results of the study, students highlighted that anxiety, peer pressure, teacher interaction, grades and working in pairs were all factors affecting their willingness to participate. Whilst only one of the three planned waves of the intervention was able to be carried out due to the partial closing of UK schools in March 2020 following the outbreak of the Coronavirus pandemic, students reflected positively on the intervention and indicated that the use of authentic materials had a beneficial impact on the learning experience.

Keywords: MLT 2020
MLT
An investigation into the impact of retrieval practice strategies on science teachers and students

This project describes an investigation into the effect of retrieval practice on student performance in AO1 questions. Analysis of student performance in AO1questions in chemistry showed their mean average to be lower than their performance in AO2 and AO3 questions, when compared to similar centres.
A review of the literature on retrieval practice shows how testing can improve student learning outcomes. Interviews with teachers at the start of the project revealed that knowledge of key facts was assumed to be taking place across the science department without explicit strategies to support learning. Interviews also revealed that teacher understanding and use of retrieval practice strategies was limited.
An intervention was designed to improve student performance in AO1 questions through the use of retrieval practice strategies. Weekly retrieval practice quizzes were developed using interleaved questions from different topics, focussing on AO1 questions as identified by the exam board. Analysis of student data at the end of the intervention showed an increase in performance in not only AO1 questions, but also in AO2 and AO3 questions, compared to a previous cohort.
Feedback from students post intervention suggested that the introduction of regular testing also reduced their test anxiety.

Keywords: MLT 2020
MLT
Investigating talk moves in key stage 3 mathematics classrooms

This study aims to explore whole-class discussion in Key Stage 3 mathematics classrooms. It is widely recognised that the quality of language heard and spoken by students during such discussions influences mathematical progression. Despite this recognition, literature demonstrates that achieving productive mathematical speech during whole-class discussion is difficult. American researchers Chapin, O’Connor, and Anderson (2009) advocate a five-step talk move framework which helps support teachers in such endeavours. This study uses their talk move framework as a lens to examine the speech of teachers and students during whole-class mathematical discussions in my school setting. Through use of video recordings and subsequent classroom transcripts it aims to understand which talk moves are currently used by teachers in their classrooms and then seeks to understand any changes following the teachers’ participation in focused professional development. This research also explores students’ verbal reactions to observed talk moves. Analysis of our teachers’ talk moves reveal some noteworthy themes. These themes include: how teachers use talk moves in reaction to student error; students’ verbal engagement with peers following talk moves; and how participation structures vary according to teachers’ individual use of talk moves. Classroom discussion remains on the agenda for next year’s department development meetings and has also been added as a feature in my school’s upcoming professional learning calendar. It is hoped that findings from this study will help guide the professional conversations in these settings.

Keywords: MLT 2020
MLT
To what extent can flipped learning strategies enhance the success of SRL in A Level Economics

This research looks to support the deeper learning of A-level economists through assessing the merits of flipped learning to improve the use, and success, of self-regulated learning (SRL). With a focus on autonomy, self-efficacy and self-assessment cycles, successes were expected not only through improved academic results but also by enhancing students’ wider transferable skills for use beyond 6th form education.
The research was conducted with a lower 6th form cohort of A-level economists over an initial two week intervention period. A second phase was scheduled, but the COVID-19 pandemic halted these plans due to the nationwide closure of schools in March 2020. The research not only sought to uncover whether students’ learning outcomes would improve, but also to study the behaviours and motivations which influenced their success.
The research revealed that most students used SRL successfully and experienced improved learning outcomes, however, there was a noticeable decline in usage of one element of the SRL framework as the intervention progressed. The data showed that motivational influences played a significant role in reducing the effective use of self-assessment tools, resulting in diminishing learning outcomes. This impacted all students in the sample, including those displaying mastery behaviours.
It is suggested that a full adoption of flipped learning is not advisable given the cognitive load and implicit need for independent decision making, resulting in declining use of SRL. However, a short term adoption of flipped learning within a blended approach to education should enhance both learning outcomes and wider problem-solving skills.

Keywords: MLT 2020
MLT
Exploring the effect of a chapter map intervention, with a thinking aloud model, on reading comprehension attainment and confidence, for pupils with severe learning disabilities diagnosed with autism spectrum disorder

This research and development project investigated how to support students with Autism Spectrum Disorder, categorised as having Severe Learning Disabilities, with their reading comprehension. My previous research had highlighted how students, diagnosed with Autism Spectrum Disorder, at my current school who do learn to read words show a reading pattern of high decoding skills but low reading comprehension. An intervention was designed, which combined thinking-aloud techniques with a chapter map design, to build confidence and skills in reading comprehension.
Participants were selected based on their decoding skills and diagnoses. Three students were found who fitted into these criteria. Unfortunately, due to the worldwide pandemic, two students were unable to complete the intervention. For the remaining participant, the intervention had to be shortened and original timings had to be changed.
The findings showed that the intervention had little impact on reading comprehension for the student who undertook it. However, this could be partially explained by the situation caused by the pandemic, such as timings being changed and anxiety caused to the student by changes to routine.

Keywords: MLT 2020
MLT
Predicting fluoroquinolone resistance in Mycobacterium tuberculosis

Tuberculosis killed an estimated 1.5 million people in 2020 and the spread of drug-resistant strains is an increasing threat. Fluoroquinolones are among the safest and most effective drugs used to treat drug-resistant tuberculosis, but fluoroquinolone resistance has emerged. Rapid resistance diagnosis is key to provide patients with effective treatment and monitor the spread of resistant strains. Sequence-based tools, including whole genome sequencing (WGS), provide a promising solution whereby known resistance associated mutations can be quickly detected and used to infer resistance. Several catalogues of mutations with known associations have now been produced, however, these catalogues will never be exhaustive owing to the inevitable emergence of novel and rare resistant mutations.
In this thesis I explore a geographically diverse dataset of thousands of Mycobacterium tuberculosis isolates with WGS and phenotypic resistance measurements, to improve our understanding of patterns associated with fluoroquinolone resistance. I investigate the reliability of the assumptions of current sequence-based diagnostics and show how resistance patterns can affect the performance of predictive tools. I apply and evaluate two methods, that utilise the chemical and structural interactions resulting from genetic mutations, to predict the effects of mutations on fluoroquinolone resistance; machine learning algorithms and free energy calculation.
I find that fluoroquinolone resistance is widespread and the associations with resistance are complex; genetic and geographic background, resistance to other antitubercular drugs and minor populations with resistance associated mutations are important. I show that structure-based predictive methods, and the machine learning approach particularly, can successfully predict fluoroquinolone resistance and susceptibility. Such tools could increase the success of resistance prediction from WGS data by complimenting the catalogue-based predictive approach and predicting the effects of novel mutations. Overall, this work shows that genetics-based predictive diagnostics have the potential to provide personalised, effective treatment regimens for tuberculosis.

Keywords: tuberculosis
antibiotic resistance
free energy calculations
fluoroquinolones
antimicrobial resistance
machine learning
Advisor: Walker, A
Fowler, P
The role of LARP1 in communication between ovarian tumours and their immune microenvironment

Ovarian cancer is notoriously lethal, killing almost 60% of women within 5 years of their diagnosis. Despite advances in survival for many other cancers through the introduction of immunotherapy, the mainstay of ovarian cancer treatment remains chemotherapy. Clinical trials of immunotherapy agents have proven resolutely unsuccessful in ovarian cancer, a finding that has challenged scientists and clinicians over the last decade. Specifically, how does ovarian cancer exist in a context in which it continues to grow and metastasise without recognition by the innate or adaptive immune system? Improving our understanding of this axis will require a closer exploration of the relationship between ovarian cancer cells and the immune cells within their microenvironment.
In this thesis, we explore this relationship through the perspective of the oncogenic RNA-binding protein LARP1, based on our finding that LARP1 is present both in cancer cells and in immunosuppressive macrophages from the earliest stages of ovarian cancer formation. Macrophages are crucial regulators of the tumour microenvironment, and their polarisation state determines whether they accept or reject cancer cells. We show, for the first time, that tumour LARP1 is responsible for reprogramming macrophages to support and perpetuate tumour growth. We find that LARP1 is crucial for anti-inflammatory macrophage polarisation and show that this is enabled by LARP1-dependent metabolic activity. Tumour LARP1 expression promotes anti-inflammatory polarisation in macrophages in the vicinity, and LARP1 expression itself is upregulated in macrophages by proximity to tumour cells. LARP1 is therefore implicated in two ways in anti-inflammatory tumour-associated macrophage phenotype: intrinsically within the macrophages, and by influencing secreted factors from the tumour cells.
In this work, we introduce new roles for LARP1 in the tumour microenvironment and implicate it in ovarian tumour-immune system crosstalk. Our findings pose important questions about how this axis can be interrupted and what additional factors must work alongside LARP1 to maintain this immunosuppressive environment.

Keywords: Tumour-associated macrophages
Exosomes
Extracellular vesicles
Immuno-oncology
RNAi in primary immune cells
Macrophages
Ovarian cancer
Macrophage polarisation
La-related binding protein 1 (LARP1)
Tumour microenvironment
RNA-binding protein
Macrophage metabolism
THP-1
Advisor: Blagden, S
Coosemans, A
Improving representation learning through variational autoencoding

Representation learning aims to distill useful knowledge from raw data and apply this knowledge to a wide range of applications. This ability to extract information that is useful not only for selected tasks but also generalizes to new settings is a key step towards artificial intelligence.
In this thesis, we focus on representations derived through a specific type of generative model, i.e. variational autoencoders (VAEs). VAEs have several desirable properties. Thanks to the use of variational inference and the convenient model assumptions of Gaussian posteriors and a simple prior, VAEs are often easy to train and exhibit fast convergence. The probabilistic modelling formulation allows VAEs to derive a smooth latent representation of the raw data (i.e. semantically similar data samples are likely to be projected to nearby regions in the latent space). VAEs compress the raw data to a much lower dimension latent space. Working with the low-dimensional representations rather than the raw data can significantly reduces costs in memory and computation. With these advantages, VAEs have been widely applied to many applications, including robotics, drug discovery and digital content creation.
Despite the widespread application of VAEs, improving the generative modelling of VAEs further remains an active research topic. In this thesis, we focus on two challenges in the VAE training: 1) over-regularized posterior distributions are often encountered in VAEs with Gaussian decoders and simple prior models; 2) the auto-encoding function may cause severe information drift and alter the information in the raw data in successive encodings. We propose solutions to both phenomena. Specifically, we optimize a variance parameter in the Gaussian decoder to balance competing loss terms in the ELBO objective. We adopt a flexible prior model that is implemented as a VAE in the latent space to mitigate the over-regularization effects. To reduce the information drift, we propose to modify the ELBO objective with a consistency loss that penalizes such drift. We show that these proposals can effectively address the challenges identified previously and improve the likelihood score of the VAEs.
In addition to the contributions related to improving VAEs, we also demonstrate the power of VAEs' representation learning in two important machine learning applications. Firstly, we show that a VAE's ability to compress complicated, high-dimensional data is key to achieving good performance in anomaly detection. We design a VAE-LSTM anomaly detection system that can accurately identify anomalous effects in a time serious. Secondly, we show that a classifier which incorporates a VAE module can give better calibrated predictions. This is the result of a VAEs' capability of expressing the uncertainty between similar data samples in the spread of the posterior distribution and of identifying out-of-distribution samples.

Keywords: Unsupervised learning
Representation learning
Variational autoencoders
Uncertainty estimation
Anomaly detection
Advisor: Trigoni, N
Roberts, S
Structural studies of low X-ray-scattering-contrast sites in light-element-doped inorganic energy materials

The agile ammonia synthesis is a collection of energy generation and storage infrastructure, which allows sustainable, local, and independent energy provision by converting renewable energy to hydrogen and ammonia, as well as in battery energy storage systems such as lithium-ion batteries. Many of these systems utilise archetypal inorganic materials, in particular due to enhanced performance from the incorporation of light-element dopants. Here, three components of the agile ammonia synthesis pathway are investigated in order to better understand the complex structure-activity relationship, in particular by using complex in-situ, in-operando, and inelastic synchrotron X-ray powder diffraction.
The nitrogen doping of titania is shown to impart increased structural and electronic defects that can be exploited by photo-absorption of visible light to split water using nothing else but solar irradiance. The generation of oxygen vacancies was observed by anisotropic thermal expansion, and supported by DFT calculations and photocatalytic performance. The use of palladium nanoparticles as an electrochemical energy store by lithium intercalation has been evaluated, and in-depth phase transitions characterised. Though the intercalation process reduces the order of the host framework, the cycling capacity remains constant, indicating amorphisation does not equate to performance degradation. The location of preferential substitutional aluminium sites in zeolite ZSM-5 has been yielded by observing probe molecule adsorption, but also directly by exploiting high-contrast anomalous scattering. The results of various techniques are in broad agreement of the siting, which will have impact the understanding of zeolite synthesis and performance mechanisms.

Keywords: xray diffraction
energy materials
resonance diffraction
electrochemistry
ammonia
zeolites
lithium ion batteries
catalysis
hydrogen
phototcatalysis
synchrotron science
Advisor: Tsang, E
Tang, C
Day, S