Online education is popular from a consumer perspective, but there are elements of face-to-face instruction and assessment that are difficult to reproduce online (Bassoppo-Moyo 2006). The difficulty of reproducing valued elements of a face-to-face setting leads to concerns regarding the overall quality of the online learning experience. Videoconferencing is one technology that has been used to incorporate elements of a face-to-face environment. However, videoconferencing over the Internet is fraught with technical difficulties and live discussions remove one of the main benefits of distance education: time flexibility. A more recent development has been to use asynchronous video as a communications method in online courses. Griffiths and Graham (2009) described several pilots using asynchronous video in online courses at Brigham Young University. Asynchronous video conveys the verbal and nonverbal signals necessary for immediacy and social presence and retains the time flexibility benefit of distance education. Following the pilot studies, a prototype design theory titled the Asynchronous Video Learning Model (AVLM) was created for the use of asynchronous video in online courses. A study was designed to study a practical implementation of AVLM. The major purpose of the study was to observe and analyze the practical experiences of participants and improve the AVLM model. A class named IPT286 (Using Instructional Technology in Teaching) taught by the department of IP&T at BYU was redesigned to be an online class using AVLM. Data were gathered during the semester and then analyzed according to the methods described in this study. Results showed that many of the principles of the AVLM model were successfully implemented and led to positive experiences. Some elements of the model were not adequately implemented which led to some negative experiences. In addition, experiences led to new elements being added to the model. The study also revealed some interesting principles related to general learning theory. The data consistently revealed the importance of relationships in the learning process. Relationships between students and the instructor were shown to influence the student learning experience, and therefore the personality and style of the instructor impacted overall student learning to some degree.

The development of a multi-objective decision making and learning model for the use in unmanned systems is the focus of this project. Starting with traditional game theory and psychological learning theories developed in the past, a new model for machine learning is developed. This model incorporates a no-regret decision making model with a Bayesian learning process which has the ability to adapt to errors found in preconceived costs associated with each objective. This learning ability is what sets this model apart from many others. By creating a model based on previously developed human learning models, hundreds of years of experience in these fields can be applied to the recently developing field of machine learning. This also allows for operators to more comfortably adapt to the machine's learning process in order to better understand how to take advantage of its features. One of the main purposes of this system is to incorporate multiple objectives into a decision making process. This feature can better allow its users to clearly define objectives and prioritize these objectives allowing the system to calculate the best approach for completing the mission. For instance, if an operator is given objectives such as obstacle avoidance, safety, and limiting resource usage, the operator would traditionally be required to decide how to meet all of these objectives. The use of a multi-objective decision making process such as the one designed in this project, allows the operator to input the objectives and their priorities and receive an output of the calculated optimal compromise.

This thesis looks at the development of a physical 3D learning model designed to introduce key stage 3 students to the basics of SolidWorks with the ultimate aim of developing the model to a level where schools can use it in the education of students. The purpose of this thesis is to identify any problems with the author’s final year undergraduate project (a three-dimensional card model of Tower Bridge, which features instructions to help teach the fundamentals of SolidWorks), and to create a new learning material based on those findings. The creation of the new learning material was in part based on feedback during visits to local secondary schools. Small scale user trials were also conducted throughout the product development in order to gain first-hand insight into how the solution was meeting its objectives, i.e. being a viable learning pack for Secondary Schools. The overall project aim was to create 3 – Dimensional teaching material designed to assist in classrooms for secondary education. This aim was partially realised in that a clear and concise learning path was created. However, due to lack of engagement from local secondary schools it was not feasible to conduct user trials. These trials and subsequent review have been suggested as possible future work. It should be noted that apart from the Tower Bridge product, reviewed in chapter 3, all work presented within this thesis was conducted as part of this master study.

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<p dir="ltr">Data pipelines are core machine learning components essential for moving data through various stages and applying transformations to enhance data quality for model training, thereby improving performance and efficiency. However, as data volumes grow, optimizing these pipelines becomes increasingly complex, which can impact performance and increase the costs of finding the optimal pipeline. Data-centric systems are found across various sectors, including finance, education, marketing, and healthcare, which are trained on historical data. After that, systems need to be monitored, and continuous testing is required to ensure the performance of new incoming data. However, when the system encounters failures with new incoming data, debugging is needed to find the data point that is causing the system to fail. Finding the optimal pipeline for new data can also be daunting. In this research, we aim to address these challenges by proposing an approach that uses the GRASP method to find the new pipeline and a data profile to find the cause of the disconnect between the pipeline and data.</p>

The spatial variation of wave climate plays a crucial role in erosion, sediment transport, and the design of management actions in coastal areas. Low energy wave systems occur frequently and over a wide range of geographical areas. There is a lack of studies assessing wave model performance in low-energy environments at a regional scale. Therefore, this research aims to model a low energy wave system using a high-resolution dataset. The specific objectives of this study involves 1) using cluster analysis and extensive field measurements to understand the spatial behavior of ocean waves, 2) develop a physics based model of wind-driven waves using high-resolution measurements, and 3) compare machine learning and physics-based models in simulating wave climates. The findings of this study indicate that clustering can effectively assess the spatial variation of the wave climate in a low energy system, with depth identified as the most important influencing factor. Additionally, the physics-based model showed varying performance across different locations within the study area, accurately simulating wave climates in some locations but not in others. Finally, the machine learning model demonstrated overall acceptable performance and accuracy in simulating wave climates and revealed better agreement with observed data in estimating central tendency compared to the physics-based model. The physics-based model performed more favorably for dispersion metrics. These findings contribute to our understanding of coastal dynamics. By providing insights into the spatial behavior of wave climates in low energy systems and comparing the performance of physics-based model and machine learning model, this research contributes to the development of effective coastal management strategies and enhances our understanding of coastal processes.

This study goal was to first determine the baseline Global Forecast System (GFS) skill in forecasting borderline (non-bomb:0.75-0.95, bomb: 1.-1.25) bomb events, and second to determine if machine learning (ML) techniques as a post-processor can improve the forecasts. This was accomplished by using the Tempest Extreme cyclone tracking software and ERA5 analysis to develop a case list during the period of October to March for the years 2008-2021. Based on the case list, GFS 24-hour forecasts of atmospheric base state variables in 10-degree by 10-degree cyclone center subdomains was compressed using S-mode Principal Component Analysis. A genetic algorithm was then used to determine the best predictors. These predictors were then used to train a logistic regression as a baseline ML skill and a Support Vector Machine (SVM) model. Both the logistic regression and SVM provided an improved bias over the GFS baseline skill, but only the logistic regression improved skill.

Cette étude acoustico-perceptive concerne les limites de la perception et de la production des voyelles orales du français [i, e, ɛ, a, u, o, ɔ, y, ø, œ], en isolation et en contextes consonantiques divers, chez dix tchécophones, futures enseignantes de Français Langue Étrangère (FLE). Les résultats montrent que (1) La maîtrise phonétique des voyelles dépend de leurs graphies et de l’entourage consonantique. (2) Les voyelles fermées [i, y, u] et le [a] sont globalement maîtrisées avec authenticité. (3) Les capacités de perception des contrastes entre les voyelles moyennes e/ɛ, ø/œ et o/ɔ ainsi que leur production sont limitées. Ces résultats ne sont que partiellement en accord avec les prédictions établies à partir du Speech Learning Model (SLM) de Flege (1995), basé sur la notion de similarité phonétique qui existe entre la langue maternelle (LM) et la langue étrangère (LE). / This acoustic-perceptual study concerns the limits of perception and production of French oral vowels [i, e, ɛ, a, u, o, ɔ, y, ø, œ], in isolation and in different consonantal contexts, in ten pre-service Czech teachers of French as a Foreign Language (FFL). The results show that (1) Phonetic proficiency in vowels depends on their spellings and consonantal context. (2) Vowels [i, y, u] and [a] are generally mastered with authenticity. (3) The ability to hear contrasts between the vowels e/ɛ, ø/œ and o/ɔ and pronounce them is limited. These results are only partially consistent with the predictions established in the Speech Learning Model (SLM) by Flege (1995), based on the notion of phonetic similarity between the mother tongue (MT) and the foreign language (FL).

Cette étude acoustico-perceptive concerne les limites de la perception et de la production des voyelles orales du français [i, e, ɛ, a, u, o, ɔ, y, ø, œ], en isolation et en contextes consonantiques divers, chez dix tchécophones, futures enseignantes de Français Langue Étrangère (FLE). Les résultats montrent que (1) La maîtrise phonétique des voyelles dépend de leurs graphies et de l’entourage consonantique. (2) Les voyelles fermées [i, y, u] et le [a] sont globalement maîtrisées avec authenticité. (3) Les capacités de perception des contrastes entre les voyelles moyennes e/ɛ, ø/œ et o/ɔ ainsi que leur production sont limitées. Ces résultats ne sont que partiellement en accord avec les prédictions établies à partir du Speech Learning Model (SLM) de Flege (1995), basé sur la notion de similarité phonétique qui existe entre la langue maternelle (LM) et la langue étrangère (LE). / This acoustic-perceptual study concerns the limits of perception and production of French oral vowels [i, e, ɛ, a, u, o, ɔ, y, ø, œ], in isolation and in different consonantal contexts, in ten pre-service Czech teachers of French as a Foreign Language (FFL). The results show that (1) Phonetic proficiency in vowels depends on their spellings and consonantal context. (2) Vowels [i, y, u] and [a] are generally mastered with authenticity. (3) The ability to hear contrasts between the vowels e/ɛ, ø/œ and o/ɔ and pronounce them is limited. These results are only partially consistent with the predictions established in the Speech Learning Model (SLM) by Flege (1995), based on the notion of phonetic similarity between the mother tongue (MT) and the foreign language (FL).

Adaptive video streaming is perpetually influenced by unpredictable network conditions, whichcauses playback interruptions like stalling, rebuffering and video bit rate fluctuations. Thisleads to potential degradation of end-user Quality of Experience (QoE) and may make userchurn from the service. Video QoE modelling that precisely predicts the end users QoE underthese unstable conditions is taken into consideration quickly. The root cause analysis for thesedegradations is required for the service provider. These sudden changes in trend are not visiblefrom monitoring the data from the underlying network service. Thus, this is challenging toknow this change and model the instantaneous QoE. For this modelling continuous time, QoEratings are taken into consideration rather than the overall end QoE rating per video. To reducethe user risk of churning the network providers should give the best quality to the users. In this thesis, we proposed the QoE modelling to analyze the user reactions change over timeusing machine learning models. The machine learning models are used to predict the QoEratings and change patterns in ratings. We test the model on video Quality dataset availablepublicly which contains the user subjective QoE ratings for the network distortions. M5P modeltree algorithm is used for the prediction of user ratings over time. M5P model gives themathematical equations and leads to more insights by given equations. Results of the algorithmshow that model tree is a good approach for the prediction of the continuous QoE and to detectchange points of ratings. It is shown that to which extent these algorithms are used to estimatechanges. The analysis of model provides valuable insights by analyzing exponential transitionsbetween different level of predicted ratings. The outcome provided by the analysis explains theuser behavior when the quality decreases the user ratings decrease faster than the increase inquality with time. The earlier work on the exponential transitions of instantaneous QoE overtime is supported by the model tree to the user reaction to sudden changes such as video freezes.