The ACTFL Proficiency Guidelines identify memorized words and phrases as a hallmark of novice-level speech. For this reason, research by Cox (2017) found rehearsed content to be a major hindrance to interviewees being rated at higher sublevels on the Oral Proficiency Interview-computer (OPIc). To further investigate, an analysis of these memorized segments to determine patterns of lexico-grammatical and discursive features was conducted. In this study, researchers utilized a Praat analysis to compare prosodic features (specifically, mean length of utterance, number of silent pauses, and articulation rate) of speech segments marked as memorized and those which were not. A qualitative analysis was also conducted by identifying via a grounded theory approach any notable patterns both within single interviews as well as between speakers. Articulation rates differed significantly between the spontaneous and rehearsed segments; however, the strongest evidence of memorization lay in the transcriptions and the patterns that emerged within and across interviews.

The joint European Space Agency and Japan Aerospace Exploration Agency mission Comet Interceptor seeks to perform a flyby of a Small Solar System Body (SSSB), through use of a multi-element spacecraft. It comprises a primary spacecraft and two subspacecraft, the latter of which will encounter the intercepted object at a small enough distance that its end-of-life might occur at an impact of either the object itself or its potential coma. The Optical Periscopic Imager for Comets (OPIC) is an instrument implemented on one of these small probes which will generate monochromatic images during the encounter. Given a limited data budget before the possible impact, there is a need for data prioritization to ensure that only the most scientifically relevant data is collected. To enable this, algorithms for autonomously cropping an object nucleus from an image were developed during this thesis work. As the computational capabilities of OPIC are limited, the algorithms were required to be of low computational complexity. Additionally, given that the close environment of SSSB in general and comets in particular often exhibit considerable quantities of gas and dust which can generate cluttering in images, the algorithms developed were required to be resistant to noise. Three image cropping algorithms were developed with varying computational complexities. These were tested for cropping accuracy and relative execution times on data from both previous space missions as well as simulated photorealistic images. All three algorithms were able to properly find a bounding box of an object nucleus and any of its significant plumes. The accuracy in cropping correctness of the region borders generated increased with the computational complexity of the algorithms.

This study explores the effect of an explicit training module on improving language learner ability to accurately self-assess second-language proficiency. There was a total of 409 intermediate and advanced level participants across six languages: French, Italian, Japanese, Portuguese, Russian and Spanish. Most of the participants had extensive immersion backgrounds. The Language Ability Self Evaluation Resource (LASER) was used to measure the perceived proficiency of the participants (self-assessed language ability). The Oral Proficiency Interview – Computer (OPIc) was used to measure the actual proficiency of the participants (rater-assessed language ability). The participants were randomly assigned to either the control or experimental group. Both groups started and ended the self-assessment the same way, with the experimental group receiving the training directly before the self-assessment questions. The training module was designed to include clarification, instruction, modeling, practice and immediate feedback while only adding 10 minutes to the overall assessment time. The limited time was dictated by the overall desire for maximum usability within already busy curricula and by already busy students. All participants completed the OPIc within 30 days of taking the LASER. The results were unexpected, revealing that those learners who received the training were more likely to over-assess their own proficiency level than learners who received no training at all. Possible reasons for this outcome are explored and possible ways to improve the assessment tool are discussed.

This study explored the effectiveness of a Willingness to Communicate (WTC) scale with advanced foreign language (L2) learners. The WTC instrument included sections on communicating with native speakers of the L2 and peer language learners in various settings (at home, abroad, in-class and online). As most prior research had focused on beginning language learners, for this study, we recruited participants who began to learn their respective languages in informal, long-term immersion settings. Participants took the WTC survey as part of a larger self-assessment instrument with a subset of 600 intermediate and advanced level Spanish (n = 339), Portuguese (n = 155), and French (n = 106) L2 students taking an Oral Proficiency Interview (computerized). The instrument was found to be reliable (Cronbach α = .88), and there was a significant difference [t (5) = 2.97, p = .031] in WTC between sections for online and in-class settings. However, the WTC had no significant relationship (Pearson's r2 = .0005) with OPIc score. Thus, while WTC might help beginning learners reach advanced level language, it might not discriminate among learners who are already advanced.

This study investigates the interplay between temporal fluency measures, self-assessment, and language proficiency scores in novice- to intermediate- level language learners of Spanish and French. Analyzing data from 163 participants, the research employs both traditional linear regression and advanced XGBoost machine learning models. Findings demonstrate a moderate positive correlation between self-assessment and Oral Proficiency Interview by Computer (OPIc) scores, underscoring the dependable self-awareness of learners. Notably, XGBoost performs as well as linear regression in predicting OPIc scores and has more potential, underlining the efficacy of advanced methodologies. The study identifies Mean Length of Utterance (MLU) as a crucial predictor, highlighting specific temporal fluency measures' significance in determining proficiency. These findings contribute to language assessment practices, advocating for the integration of machine learning for enhanced precision in predicting language proficiency and informing tailored instructional approaches.

In recent years there has been an increase in the number of apartments in Sweden. The officials are estimating that approximately 1000 primary schools need to be built to meet the 20% increase in students. Facing the rising demand; my thesis makes a contribution to sustainable school design via optimisation of the building orientation.This study utilises Rhinoceros with Grasshopper visual programming language alongside Ladybug, Honeybee and Owl scripts, as well as Climate Studio plugin. The environmental performance analysis software’s results include horizontal and vertical illuminance, Daylight Factor, as well as melanopic Equivalent Daylight (D65) Illuminance. Those findings are further supported with on-site measurements and observations in Iggesund school.

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