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We used a 50Hz sampling frequency to collect tri-axle acceleration from the cows. For the traditional Machine learning approach, we segmented the data to calculate features, selected the important features, and applied machine learning algorithms for classification. We compared the performance of various models and found a robust model with relatively low computation and high accuracy. For the deep learning approach, we designed an end-to-end trainable Convolutional Neural Networks (CNN) to predict activities for given segments, applied distillation, and quantization to reduce model size. In addition to the fixed window size approach, we used CNN to predict dense labels that each data point has an individual label, inspired by semantic segmentation. In this way, we could have a more precise measurement for the composition of activities. Summarily, physically monitoring the well-being of crowded animals is labor-intensive, so we proposed a solution for timely and efficient measuring of cattle’s daily activities using wearable sensors and machine learning models. / M.S. / Animal agriculture has intensified over the past several decades, and animals are managed increasingly as large groups. This group-based management has significantly increased productivity. However, animals are often located remotely on large expanses of pasture, which makes continuous monitoring of daily activities to assess animal health and well-being labor-intensive and challenging [37]. Remote monitoring of animal activities with wireless sensor nodes integrated with machine learning algorithms is a promising solution. The machine learning models will predict the activities of given accelerometer segments, and the pre-dicted result will be uploaded to the cloud. The challenges would be the limitation in power consumption and computation. To propose a precise measurement of individual cattle in the herd, we experimented with several different types of machine learning methods with different advantages and drawbacks in performance and efficiency.

E-learning continues to gain popularity as a way of delivering instruction in the workplace. However, adoption of e-learning is often considered without determining organizational readiness for e-learning. Comacchio and Scapolan (2004) found that bandwagon pressures such as fear of losing competitive advantage often drive e-learning adoption decisions. Many organizations use various types of analysis to determine instructional need but often at a course level. An e-learning readiness analysis tool will add to existing tools but focus on the workplace organization as the unit of study. The purpose of this design and development study is to create an analysis tool for determining e-learning readiness in organizations. Four existing e-learning readiness models, Aydin and Tasci (2005); Chapnick (2005); Borotis and Poulymenakou (2005) and Psycharis (2005), were used as a basis for identifying factors affecting e-learning readiness which informed the tool design. Using developmental research-based practices the tool was developed for use by practitioners. This study describes the design and development of the tool and the expert review used in the validation of the tool. / Ph. D.

The proliferation of teams and team-based activities emphasizes the need to understand knowledge sharing behaviors in order to facilitate team performance. Knowledge sharing in teams is valuable and indispensable for both academic and corporate organizations in order to meet and manage team effectiveness. Knowledge is driven by people who behave in different ways based on their environment and its accompanying factors. Considering what factors facilitate knowledge sharing behaviors in teams within an academic environment is an important benchmark for knowledge management researchers and instructional designers. Instructors and professors plan various thorough and organized collaborative opportunities for teams in their classrooms to encourage knowledge sharing. Similarly, understanding the specific factors of a collaborative context before setting team procedures better facilitates knowledge sharing behaviors. Therefore, the research problem addressed in this study was to predict what contextual factors promote perceptions toward knowledge sharing behaviors in students enrolled in graduate courses from a business school, as measure by a self-reported questionnaire. Prior studies on student teams state that team climate and leadership contributes to student knowledge sharing behavioral patterns. These studies emphasize the importance of recognizing specific factors that function with climate and leadership to contribute towards knowledge sharing behaviors and attitudes toward knowledge sharing; this would allow instructional designers to more fully understand the process. Furthermore, other studies related to team knowledge sharing behaviors reported certain specific factors, like organizational context, interpersonal and team characteristics, and cultural characteristics as crucial in influencing knowledge sharing behaviors. Specifically, in regard to team context, existing studies mentioned five factors - climate, leadership, rewards and incentives, structure, and support - that encourage knowledge sharing behaviors and attitude towards knowledge sharing in teams. Thus, in this study, the researcher investigated team climate, leadership, rewards and incentives, task structure, and task support to determine in what manner these factors influence student knowledge sharing behaviors as well as attitudes toward knowledge sharing in graduate business courses. This study used the quantitative methodologies. Multiple regression and correlation analysis were used to measure students' self-reported perceptions of what contextual factors impacted their knowledge sharing behaviors and attitudes toward knowledge sharing during team project work. The findings of this study show that in the studied context, students reported that task structure affected their knowledge sharing behaviors more than the rest of the identified factors. Correspondingly, rewards and incentives impacted their attitudes toward knowledge sharing behaviors. The findings also indicate negative correlations of team climate and leadership with attitudes toward knowledge sharing. Correspondingly, this study delineates certain implications for instructional designers for assisting knowledge sharing behaviors in teams. The study results contribute to the body of literature that suggest the importance of motivating and supporting detailed task structure and procedures for promoting knowledge sharing behaviors in student teams. / Ph. D.

We introduce recurrent collective classification (RCC), a variant of ICA analogous to recurrent neural network prediction. RCC accommodates any differentiable local classifier and relational feature functions. We provide gradient-based strategies for optimizing over model parameters to more directly minimize the loss function. In our experiments, this direct loss minimization translates to improved accuracy and robustness on real network data. We demonstrate the robustness of RCC in settings where local classification is very noisy, settings that are particularly challenging for ICA. As a new way to train generative models, generative adversarial networks (GANs) have achieved considerable success in image generation, and this framework has also recently been applied to data with graph structures. We identify the drawbacks of existing deep frameworks for generating graphs, and we propose labeled-graph generative adversarial networks (LGGAN) to train deep generative models for graph-structured data with node labels. We test the approach on various types of graph datasets, such as collections of citation networks and protein graphs. Experiment results show that our model can generate diverse labeled graphs that match the structural characteristics of the training data and outperforms all baselines in terms of quality, generality, and scalability. To further evaluate the quality of the generated graphs, we apply it to a downstream task for graph classification, and the results show that LGGAN can better capture the important aspects of the graph structure. / Doctor of Philosophy / Graphs are one of the most important and powerful data structures for conveying the complex and correlated information among data points. In this research, we aim to provide more robust and accurate models for some graph specific tasks, such as collective classification and graph generation, by designing deep learning models to learn better task-specific representations for graphs. First, we studied the collective classification problem in graphs and proposed recurrent collective classification, a variant of the iterative classification algorithm that is more robust to situations where predictions are noisy or inaccurate. Then we studied the problem of graph generation using deep generative models. We first proposed a deep generative model using the GAN framework that generates labeled graphs. Then in order to support more applications and also get more control over the generated graphs, we extended the problem of graph generation to conditional graph generation which can then be applied to various applications for modeling graph evolution and transformation.

The study focuses on AI and source criticism and teachers' views and teaching in this. The teachers interviewed are teachers at junior high school but also high school in the subjects SO and civic studies. The purpose of the survey has been to account for and problematize civic studies teachers' approach to AI and its connection to source criticism.The teachers who were interviewed all have different experiences in the teaching profession, but they all are educated in civic studies at university. The teachers are working in the central parts of Sweden. The question that the study answers are:1. What is the teachers view of AI and how does it affect their teaching of source criticism? The teachers in the study agree on the importance of source criticism in teaching. They all teach about source criticism in their teaching, some a little more while others a little less. Time has been the biggest reason why source criticism has been given a little less space in some teachers' teaching.All the teachers in the study have changed their teaching because of AI. The teachers with more experience and more material have had to make a bigger change in their teaching, while the younger ones were already thrown into a world where AI was already active. One of the teachers testifies about how his teaching philosophy has taken a turn for the worse with AI as he cannot teach in the way he really wants.

Den mest förekommande användningen av HBTQIA+litteratur i gymnasieskolans svenskundervisning är främst för att diskutera sexualitet, och avstår från att arbeta och analysera texterna utifrån andra centrala teman. Därför syftar denna uppsats till att exemplifiera hur HBTQIA+litteratur kan och bör användas på ett liknande sätt som litteratur där sexualiteten inte är det som är drivande för textens handling. Fortsättningsvis syftar denna uppsats till att hjälpa svensklärare att implementera HBTQIA+litteratur i sin undervisning, för att i sin tur ska leda till att HBTQIA+elever känner sig representerade i undervisningsinnehållet. Uppsatsen grundar sig i en kvalitativ innehållsanalys, där romanen Profeterna av Robert Jones Jr har analyserats utifrån olika kriterier som tagits fram av Logan m.fl. som är till för att underlätta valet av HBTQIA+litteratur som kan användas i svensklärares undervisning.

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