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With the immense increase in the computational power available for the material science community in recent years, a range of new discoveries were made possible. Accurate investigations of large scale atomic systems, however, still come with an extremely high computational demand. While the recent development of Graphics Processing Unit (GPU) accelerated supercomputing might offer a solution to some extent, most well known electronic structure codes have yet to be fully ported to utilize this new power. With a soaring demand for new and better materials from both science and industry, a more efficient approach for the investigation of material properties needs to be implemented. The use of Machine Learning (ML) to obtain Interatomic Potentials (IP) which far outperform the classical potentials has increased greatly in recent years. With successful implementation of ML methods utilizing neural networks or Gaussian basis functions, the accuracy of ab-initio methods can be achieved at the demand of simulations with empirical potentials. Most ML approaches, however, require high accuracy data sets to be trained sufficiently. If no such data is available for the system of interest, the immense cost of creating a viable data set from scratch can quickly negate the benefit of using ML. In this diploma project, the elastic and thermodynamic properties of the Ti0.5Al0.5N random alloy at elevated temperature are therefore investigated using an Active Learning (AL) approach with the Machine Learning Interatomic Potentials (MLIP) package. The obtained material properties are found to be in good agreement with results from computationally demanding ab-initio studies of Ti0.5Al0.5N, at a mere fraction of the demand. The AL approach requires no high accuracy data sets or previous knowledge about the system, as the model is initially trained on low accuracy data which is removed from the training set (TS) at a later stage. This allows for an iterative process of improving and expanding the data set used to train the IP, without the need for large amounts of data.

This thesis aims at exploring the potential of playful annotations in interactive textbooks, to engage the teacher and the learner in an active learning process. This research focus was taken after a Field study consisting of a range of semi-structured interviews, surveys, and discussions with teachers and students from a pilot school provided with an interactive textbook platform called Gleerups. This latter is a Swedish publisher which spreads a large offer of educational textbooks across Sweden. The thesis topic was chosen in order to find and suggest ways to approach the learning and reading phase in an active way while also focusing on the teacher-learner relationship.The design contributions include proposals for improvements taking the shape of scenarios and sketches using field research and qualitative studies. It is based on an analysis of related examples and cross disciplinary literature, grounding the research in education and learning theories. Finally, a prototype encompassing the main features raised from the research is presented.The thesis ends with outcomes and reflections from findings, as well as discussions with stakeholders and teachers that initiated the research.

There is a significant body of literature showing improved student outcomes in higher education STEM courses when evidence-based instructional practices (EBIPs) are used. Despite this, traditional, lecture-style instruction remains the primary means of instruction in these courses. However, given the situation of the sudden shift to online teaching as a result of the COVID-19 pandemic, faculty were participating in training programs with greater frequency, and thus learning more about the use of EBIPs than ever before. Through the lens of Kurt Lewin’s theory of organizational change in the three stages of unfreezing, change, and refreezing, this explanatory mixed methods study sought to explore through a survey and interviews whether this shift to online teaching and the resulting increase in training participation did, in fact, result in changes in instructional practices, implementation, and perceptions of EBIPs, and whether any changes were sustained upon the return to in-person instruction.The survey tool used in this study was a subset of the Teaching Practices Inventory, developed by the Carl Wieman Science Education Initiative from the University of British Columbia. This generated a modified “extent of use of research-based teaching practices” (METP) score, as well as METP sub-scores in five subcategories of the survey. These results, as well as data obtained from demographic questions and questions about teaching responsibilities and training participation, informed the selection of twelve participants for semi-structured interviews. Through one-way ANOVA testing, the quantitative analysis showed a statistically significant increase in METP (p < .001) from Pre-Covid to Post-Covid scores. Statistical significance was also found in the subcategories of In Class Features (p = .003) and Collaboration (p = .005). Two-way ANOVA testing was also done to explore statistical significance for demographic subcategories, which was found to exist for gender, tenure status, and various categories relating to participation in training and professional development. Interview data supported the quantitative data analysis, and offered further insight and context for the changes that have been made and sustained, including changes regarding the use of educational technology tools, introduction of authentic learning experiences, streamlining of content, and intentional alignment of activities and assessments with course goals. Additional analysis showed faculty relied on virtual collaboration to develop community with other instructors, and realized the importance of student feedback to inform their instruction and of fostering a classroom community. Most significantly, the ability to see first-hand the effect of the pandemic on students and to have a window into their personal lives caused faculty to make sweeping changes with respect to their beliefs in the affective domains of learning, emphasizing the need for empathy, flexibility, and equity-mindedness in their classrooms. This study showed that faculty became convinced of the need for change, consistent with Lewin’s unfreezing stage, not solely through training and professional development, but largely through the realizations about the individuality of students that faculty experienced during the pandemic. This occurred simultaneously with an increase in virtual collaboration as well as the influence of changes peers had made and suggested upon the return to in-person instruction. The recognition of the need to center students in learning combined with these outside influences resulted in the increased use of EBIPs upon the return to in-person instruction, therefore creating the desired change. Lastly, these practices have been maintained as of two years after the return to in-person, thus indicating refreezing, and further data showed that faculty continue to adapt their practices to create more inclusive and student-centered learning environments. / Policy, Organizational and Leadership Studies

Large scale Gaussian process (GP) regression is infeasible for larger data sets due to cubic scaling of flops and quadratic storage involved in working with covariance matrices. Remedies in recent literature focus on divide-and-conquer, e.g., partitioning into sub-problems and inducing functional (and thus computational) independence. Such approximations can speedy, accurate, and sometimes even more flexible than an ordinary GPs. However, a big downside is loss of continuity at partition boundaries. Modern methods like local approximate GPs (LAGPs) imply effectively infinite partitioning and are thus pathologically good and bad in this regard. Model averaging, an alternative to divide-and-conquer, can maintain absolute continuity but often over-smooth, diminishing accuracy. Here I propose putting LAGP-like methods into a local experts-like framework, blending partition-based speed with model-averaging continuity, as a flagship example of what I call precision aggregated local models (PALM). Using N_C LAGPs, each selecting n from N data pairs, I illustrate a scheme that is at most cubic in n, quadratic in N_C, and linear in N, drastically reducing computational and storage demands. Extensive empirical illustration shows how PALM is at least as accurate as LAGP, can be much faster in terms of speed, and furnishes continuous predictive surfaces. Finally, I propose sequential updating scheme which greedily refines a PALM predictor up to a computational budget, and several variations on the basic PALM that may provide predictive improvements. / Doctor of Philosophy / Occasionally, when describing the relationship between two variables, it may be helpful to use a so-called ``non-parametric" regression that is agnostic to the function that connects them. Gaussian Processes (GPs) are a popular method of non-parametric regression used for their relative flexibility and interpretability, but they have the unfortunate drawback of being computationally infeasible for large data sets. Past work into solving the scaling issues for GPs has focused on ``divide and conquer" style schemes that spread the data out across multiple smaller GP models. While these model make GP methods much more accessible to large data sets they do so either at the expense of local predictive accuracy of global surface continuity. Precision Aggregated Local Models (PALM) is a novel divide and conquer method for GP models that is scalable for large data while maintaining local accuracy and a smooth global model. I demonstrate that PALM can be built quickly, and performs well predictively compared to other state of the art methods. This document also provides a sequential algorithm for selecting the location of each local model, and variations on the basic PALM methodology.

Learning and reasoning with common sense is a challenging problem in Artificial Intelligence (AI). Humans have the remarkable ability to interpret images and text from different perspectives in multiple modalities, and to use large amounts of commonsense knowledge while performing visual or textual tasks. Inspired by that ability, we approach commonsense learning as leveraging perspectives from multiple modalities for images and text in the context of vision and language tasks. Given a target task (e.g., textual reasoning, matching images with captions), our system first represents input images and text in multiple modalities (e.g., vision, text, abstract scenes and facts). Those modalities provide different perspectives to interpret the input images and text. And then based on those perspectives, the system performs reasoning to make a joint prediction for the target task. Surprisingly, we show that interpreting textual assertions and scene descriptions in the modality of abstract scenes improves performance on various textual reasoning tasks, and interpreting images in the modality of Visual Question Answering improves performance on caption retrieval, which is a visual reasoning task. With grounding, imagination and question-answering approaches to interpret images and text in different modalities, we show that learning commonsense knowledge from multiple modalities effectively improves the performance of downstream vision and language tasks, improves interpretability of the model and is able to make more efficient use of training data. Complementary to the model aspect, we also study the data aspect of commonsense learning in vision and language. We study active learning for Visual Question Answering (VQA) where a model iteratively grows its knowledge through querying informative questions about images for answers. Drawing analogies from human learning, we explore cramming (entropy), curiosity-driven (expected model change), and goal-driven (expected error reduction) active learning approaches, and propose a new goal-driven scoring function for deep VQA models under the Bayesian Neural Network framework. Once trained with a large initial training set, a deep VQA model is able to efficiently query informative question-image pairs for answers to improve itself through active learning, saving human effort on commonsense annotations. / Ph. D.

Procrastination is a pervasive problem in education. In computer science, procrastination and lack of necessary time management skills to complete programming projects are viewed as primary causes of student attrition. The most effective techniques known to reduce procrastination are resource-intensive and do not scale well to large classrooms. In this thesis, we examine three course interventions designed to both reduce procrastination and be scalable for large classrooms. Reflective writing assignments require students to reflect on their time management choices and how these choices impact their classroom performance. Schedule sheets force students to plan out their work on an assignment. E-mail alerts inform students of their current progress as they work on their projects, and provide ideas on improving their work behavior if their progress is found to be unsatisfactory. We implemented these interventions in a junior-level course on data structures. The study was conducted over two semesters and 330 students agreed to participate in the study. Data collected from these students formed the basis of our analysis of the interventions. We found a statistically significant relationship between the time a project was completed and the quality of that work, with late work being of lower quality. We also found that the e-mail alert intervention had a statistically significant effect on reducing the number of late submissions. This result occurred despite students responded negatively to the treatment. / Master of Science

Yes / A large majority of primary school pupils fail to achieve 30-min of daily, in-school moderate-to-vigorous physical activity (MVPA). The aim of this study was to investigate MVPA accumulation and subject frequency during academic lesson segments and the broader segmented school day. Methods: 122 children (42.6% boys; 9.9 ± 0.3 years) from six primary schools in North East England, wore uniaxial accelerometers for eight consecutive days. Subject frequency was assessed by teacher diaries. Multilevel models (children nested within schools) examined significant predictors of MVPA across each school-day segment (lesson one, break, lesson two, lunch, lesson three). Results: Pupils averaged 18.33 ± 8.34 min of in-school MVPA, and 90.2% failed to achieve the in-school 30-min MVPA threshold. Across all school-day segments, MVPA accumulation was typically influenced at the individual level. Lessons one and two—dominated by maths and English—were less active than lesson three. Break and lunch were the most active segments. Conclusion: This study breaks new ground, revealing that MVPA accumulation and subject frequency varies greatly during different academic lessons. Morning lessons were dominated by the inactive delivery of maths and English, whereas afternoon lessons involved a greater array of subject delivery that resulted in marginally higher levels of MVPA. / This research was funded by Redcar and Cleveland Borough Council.

Yes / This paper explores teachers’ educational values and how they shape their judgements about physically active learning (PAL). Twenty one teachers from four primary schools in Norway participated in focus groups. By conceptualising PAL as a didaktikk approach, the findings indicated that teachers engaged with PAL in a way that reflected their professional identity and previous experiences with the curriculum. Teachers valued PAL as a way of getting to know pupils in educational situations that were different from those when sedentary. These insights illustrate how PAL, as a didaktikk approach to teaching, can shift teachers’ perceptions of pupils’ knowledge, learning, and identity formation in ways that reflect the wider purposes of education. The paper gives support to a classroom discourse that moves beyond the traditional, sedentary one-way transfer of knowledge towards a more collaborative effort for pupils’ development. / This work was supported by Norwegian Directorate for Higher Education and Skills: [Grant Number 2019-1-NO01-KA203-060324]. The authors of this manuscript were supported and funded by the European Union ERASMUS+Strategic Partnership Fund as part of the Activating Classroom Teachers (ACTivate) project.