Competency-based education (CBE) has become well-accepted as a powerful way to personalize learning. Today's advanced technologies have enhanced CBE even further. Practitioners in the field are seeking means to take advantage of technology to increase CBE's effectiveness and efficiency, especially for adult learners. Microlearning and digital open badges are two examples. This dissertation, which consists of three articles, aimed to provide more in-depth insights into the two innovative approaches. The first article is a literature review of the current understanding of microlearning. While microlearning is commonly defined as breaking down learning into manageable bite-size chunks, the review of the existing literature identified key principles for effective microlearning while also suggesting gaps in the research. Because of the limited number of peer-reviewed and research-based articles about microlearning, this literature review justified microlearning as a practical approach for workplace learning through CBE and digital open badges, which were relatively more well-studied. The article concluded with suggestions on how to design and facilitate effective microlearning experiences. The second and third articles from this dissertation resulted from an ongoing design-based research (DBR) project began in 2018. The study aims to contribute theories and practices about developing microcredentials and microlearning experiences to support self-directed learning (SDL) in educational settings. The project started with implementing competency-based microcredentials to train student instructors to teach software workshops at the Brigham Young University multimedia lab (Clement et al. 2020). It is in the second iteration to offer microcredentials to all students on campus through project-based assessments. Following the timeline of this project, the second article presents a case study that discusses microcredential use for student instructor training at the multimedia lab. We collected surveys and interviews from the current and former employees to determine if the badge-assisted training design has been meeting its intended goals for tracking skills. The result shows that while the badge-assisted training is effective for tracking skills and progression. It also provides insights to inform the next iteration's design. The third article is a product from the second iteration of this DBR project. The objective was to understand if and how microcredentials could promote continuous SDL. We collected 104 survey responses and 7 interviews from students who attended the software training workshops. Our findings suggest that marketing digital open badges as individual skills identification may be insignificant for supporting continuous SDL, but their stackable feature is. We aim to provide insights for practitioners to avoid similar pitfalls when implementing digital open badges through our reflections and suggestions.

High-power fiber lasers have emerged as a cornerstone in the realm of laser technology. Characterized by their exceptional efficiency, ruggedness, and versatility, fiber lasers are experiencing widespread use in manufacturing, medical, defense, science, and in long range sensing. Unfortunately, high-power applications require strict spatial and spectral performance characteristics to be maintained, which has yet to be perfected. This dissertation discusses the power scaling of ytterbium-doped fiber laser amplifiers, presenting three significant advancements. First, a novel photonic lantern-based method is introduced for real-time monitoring of laser beam modal content and beam quality. Initial tests highlight the photonic lantern's efficiency in predicting the onset of modal instability while simultaneously measuring the laser's output beam quality, M2. Second, this work achieved 2.2 kW single-mode narrow-linewidth laser delivery through a 5-tube nested antiresonant hollow core fiber, maintaining over 95% transmission efficiency and near diffraction-limited beam quality. Lastly, this research explores active-gain fiber designs to mitigate nonlinear effects for further power scaling. One design employing confined-doping strategies, achieving a 2.4x increase in the maximum output power before the onset of stimulated Brillouin scattering. Additionally, a second experiment employing a bend-insensitive fiber design demonstrated a transverse modal instability threshold nearly 3x that of its step-index counterpart. Collectively, this work presents a novel approach to power scale, deliver, and monitor multi-kW Yb-doped fiber laser amplifiers enabling the next-generation of applications requiring the strictest spatial and spectral performance.

In the distal lung, alveolar epithelial type I cells (AT1s) comprise the vast majority of alveolar surface area and are uniquely flattened to allow the diffusion of oxygen into the capillaries. This structure along with a quiescent, terminally differentiated phenotype has made AT1s particularly challenging to isolate or maintain in cell culture. As a result, there is a lack of established models for the study of human AT1 biology, and in contrast to alveolar epithelial type II cells (AT2s), little is known about the mechanisms regulating their differentiation. Here we engineer a human in vitro AT1 model system through the directed differentiation of induced pluripotent stem cells (iPSC). We first define the global transcriptomes of primary adult human AT1s, suggesting gene-set benchmarks and pathways, such as Hippo-LATS-YAP/TAZ signaling, that are enriched in these cells. Next, we generate iPSC-derived AT2s (iAT2s) and find that activating nuclear YAP signaling is sufficient to promote a broad transcriptomic shift from AT2 to AT1 gene programs. The resulting cells express a molecular, morphologic, and functional phenotype reminiscent of human AT1 cells, including the capacity to form a flat epithelial barrier which produces characteristic extracellular matrix molecules and secreted ligands. Our results indicate a role for Hippo-LATS-YAP signaling in the differentiation of human AT1s and demonstrate the generation of viable AT1-like cells from iAT2s, providing an in vitro model of human alveolar epithelial differentiation and a potential source of human AT1s that until now have been challenging to viably obtain from patients.

The present study set out to evaluate the relationship between list-method directed forgetting and one’s individual differences. Previous research has found personality and emotion as having an influence in how well participants were able to intentionally forget stimuli. Participants were split into a remember group and a forget group of 22 each and tasked to memorize a list of 10 words. They were then given a free recall test and the results for individual differences such as Need for Cognition, Mini-IPIP personality test, and Beck’s Depression Inventory were analyzed. Our first hypothesis presumes that participants in the forget group will have impaired recall of words. The second hypothesis predicts that individual differences have an effect with how many words participants recall. Results in this study indicated that while individual measures proved not significant between both groups, overall recall for the first list was lower than recall for the second list. There were also indications of an interaction between amount recalled from lists and whether they were in the remember group or in the forget group. Analyses showed that remember group had a recall mean similar in lists 1 and list 2, while the forget group had a higher recall mean in list 2 and a lower recall mean in the list 1, indicating that directed forgetting had taken place in the forget group.

Advanced architecture processors provide features such as caches and branch prediction that result in improved, but variable, execution time of software. Hard real-time systems require tasks to complete within timing constraints. Consequently, hard real-time systems are typically designed conservatively through the use of tasks? worst-case execution times (WCET) in order to compute deterministic schedules that guarantee task?s execution within giving time constraints. This use of pessimistic execution time assumptions provides real-time guarantees at the cost of decreased performance and resource utilization. In soft real-time systems, however, meeting deadlines is not an absolute requirement (i.e., missing a few deadlines does not severely degrade system performance or cause catastrophic failure). In such systems, a guaranteed minimum probability of completing by the deadline is sufficient. Therefore, there is considerable latitude in such systems for improving resource utilization and performance as compared with hard real-time systems, through the use of more realistic execution time assumptions. Given probability distribution functions (PDFs) representing tasks? execution time requirements, and tasks? communication and precedence requirements, represented as a directed acyclic graph (DAG), this dissertation proposes and investigates algorithms for constructing non-preemptive stochastic schedules. New PDF manipulation operators developed in this dissertation are used to compute tasks? start and completion time PDFs during schedule construction. PDFs of the schedules? completion times are also computed and used to systematically trade the probability of meeting end-to-end deadlines for schedule length and jitter in task completion times. Because of the NP-hard nature of the non-preemptive DAG scheduling problem, the new stochastic scheduling algorithms extend traditional heuristic list scheduling and genetic list scheduling algorithms for DAGs by using PDFs instead of fixed time values for task execution requirements. The stochastic scheduling algorithms also account for delays caused by communication contention, typically ignored in prior DAG scheduling research. Extensive experimental results are used to demonstrate the efficacy of the new algorithms in constructing stochastic schedules. Results also show that through the use of the techniques developed in this dissertation, the probability of meeting deadlines can be usefully traded for performance and jitter in soft real-time systems.

A study was conducted to determine the effects of directed discovery-based teaching methods (hands-on learning) and weekly quizzes on short-term learning and long-term retention of course material in an introductory geosciences laboratory course. Assessment of learning was accomplished using percentages of correct responses to questions on two tests, using percentages from the first semester of the study as a baseline to which data from each subsequent semester were compared to determine the effects of the study variable that was introduced. Student evaluations of value, meaning, and enjoyment of the course were also investigated through the use of an essay question at the end of the second test. The study revealed that directed discovery-based methods were successful for the teaching of some subject material, but not for all, and that the method did not necessarily enhance learning of scientific vocabulary. Weekly quizzes resulted in improved learning in all subject areas. Simultaneous use of traditional and directed-discovery teaching methods as well as weekly quizzes is recommended.

This research presents a hybrid algorithm that combines List Scheduling (LS) with a Genetic Algorithm (GA) for constructing non-preemptive schedules for soft real-time parallel applications represented as directed acyclic graphs (DAGs). The execution time requirements of the applications' tasks are assumed to be stochastic and are represented as probability distribution functions. The performance in terms of schedule lengths for three different genetic representation schemes are evaluated and compared for a number of different DAGs. The approaches presented in this research produce shorter schedules than HLFET, a popular LS approach for all of the sample problems. Of the three genetic representation schemes investigated, PosCT, the technique that allows the GA to learn which tasks to delay in order to allow other tasks to complete produced the shortest schedules for a majority of the sample DAGs.

Bayesian network is a popular machine learning tool for modeling uncertain dependence relationships among the random factors of a domain. It represents the relations qualitatively by using a directed acyclic graph (DAG) and quantitatively by using a set of conditional probability distributions. Several exact algorithms for learning optimal Bayesian networks from data have been developed recently. However, these algorithms are still inefficient to some extent. This is not surprising because learning Bayesian network has been proven to be an NP-Hard problem. Based on a critique of these algorithms, this thesis introduces a new algorithm based on heuristic search for learning optimal Bayesian.

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