<p>I collected two types of data throughout Weeks 1-14, with the original purpose of enhancing teaching and learning in ENGL620. The data included weekly assignment recordings and weekly surveys.</p><p>The primary data were students' speech data, which were collected through 14 weekly timed speaking assessments conducted from Week 1 to Week 14. These assignments were made available on Monday at midnight and were required to be completed and submitted by Sunday at midnight). The assignments were delivered, and responses were collected using Extempore (<a href="http://www.extemporeapp.com/" target="_blank">www.extemporeapp.com</a>), a website specifically designed to support oral English assessment and practice.</p><p>To conduct more comprehensive assessments of students’ performances, I incorporated two OEPT item types into the weekly assignments, including PROS and CONS (referred to as “PC”) and LINE GRAPH (referred to as “LG”). See Appendix B for the assignment items. The PC item presented challenging scenarios ITAs may encounter and required the test-takers to make a decision and discuss the pros and cons associated with the decision. An example item is “<i>Imagine you have a student who likes to come to your office hours but often talks about something irrelevant to the course. What would you do in this situation? What are the pros and cons associated with the decision?</i>”. The LG item asked students to describe a line graph illustrating two or three lines and provide possible reasons behind those trends. It can be argued that the two tasks targeted slightly different language abilities and background knowledge. The two item types were selected because they represented two key skills that the OEPT tests. The PC task focused on stating one’s decision and presenting an argument within a personal context, while the LG item assessed students’ ability to describe visual information and engage in discussions about broader topics such as gender equality, employment, economic growth, college policy. The PC and LG items are the most difficult items in the test (Yan et al., 2019). Therefore, progress in the two tasks can be a good indicator of improvement in the speaking skills required in this context. All the items were either taken from retired OEPT items or developed by the researcher following the specifications for OEPT item development. In particular, the design of the items aimed to avoid assuming prior specific knowledge and to ensure that students could discuss them without excessive cognitive load.</p><p>For each task, the students were allocated 2 minutes for preparation and a maximum of 2 minutes to deliver their response to the assigned topic. The responses were monologic, resembling short classroom presentations. During the preparation time, the participants were permitted to take notes. Each item only allowed for one attempt, which aimed to capture students’ online production of speech and their utilization of language resources. Table 2 presents the descriptive statistics of the responses.</p><p>The PC prompt was deliberately kept consistent for Week 2 and Week 12 randomly selected as time points at the beginning and end of the semester. This deliberate choice of using the same prompt at these two distinct stages serves multiple purposes. Firstly, it provides a valuable perspective for analyzing growth over time. This approach adds depth to the study results and conclusions by providing additional evidence and triangulation. Second, this approach addresses one of the specific challenges identified by Ortega and Iberr-Shea (2005) in studies involving multiple data collection points, as maintaining consistency in the prompt can minimize potential variations in task difficulty or topic-related factors.</p><p>After completing each speaking assignment, the students were requested to rate the level of difficulty for each item on a scale of 1 (Very Easy) to 5 (Very difficult). Additionally, they were asked to fill out a weekly survey using Qualtrics. The Qualtrics survey contained six questions related to the frequency of their English language use outside of the classroom and their focus on language skills in the previous and upcoming week. These questions were considered interesting as potential contributing factors to changes in their performances throughout the semester. Refer to Appendix C for the survey questions.</p>

This project details the designing and implementation of an English for Specific Purposes (ESP) Curriculum to prepare English learners to become Certified Nursing Assistants (CNA) at Utah Valley Regional Medical Center (UVRMC) in Provo, Utah. UVRMC, which is owned by Intermountain Health Care (IHC), employs a group of about 40 non-native speakers of English. They work as housekeepers and have interest in learning English and consequently acquiring new skills they could use in better jobs to improve the quality of their lives. UVRMC would like these employees to obtain additional education in order to provide them with better employment opportunities. UVRMC allowed two graduate students at the Department of Linguistics and English Language at Brigham University to design and implement an ESP course to help UVRMC housekeepers improve their language skills in preparation to apply and participate in a Certified Nursing Assistant (CNA) course offered through IHC University. This report covers the linguistic needs analysis of the participants, situational analysis of UVRMC in terms of the support given to the curriculum, the designing of goals and objectives, the syllabus, the teaching of the syllabus, some material development, and the assessment of language learning. It also describes the instruments used to obtain information during each step of the designing of the curriculum and its implementation, analyzes that information, presents results, assesses the curriculum's efficacy, and explains the implications for other ESP curricula in the field of nursing and other scientific fields.

This diploma work focuses on Adaptation of Czech Language Leaving Examination for the Deaf, which deaf student in Czech republic pass as a part of the their Leaving Examination. As a background of this main theme examines also present situation in the research of reading literacy. In the main part of this work is this examination presented in detail and there are also presented the results of deaf students and some other statisticts information. Examples of test units and student's written works are attached too. The other part of this work describes examinations of Czech language as a foreign language and give some detail information about the most important exams. In conclusion, the author of this work is trying to explore the way how the deaf students language skills in other countries are examined in their Leaving Examination.

In an increasingly interconnected world where English has become the lingua franca of business, culture, entertainment, and academia, learners of English as a second language (L2) have been steadily growing. This has contributed to an increasing demand for automatic spoken language assessment systems for formal settings and practice situations in Computer-Assisted Language Learning. One common misunderstanding about automated assessment is the assumption that machines should replicate the human process of assessment. Instead, computers are programmed to identify, extract, and quantify features in learners' productions, which are subsequently combined and weighted in a multidimensional space to predict a proficiency level or grade. In this regard, transferring human assessment knowledge and skills into an automatic system is a challenging task since this operation should take into account the complexity and the specificities of the proficiency construct. This PhD thesis presents research conducted on methods and techniques for the automatic assessment and feedback of L2 spoken English, mainly focusing on the application of deep learning approaches. In addition to overall proficiency grades, the main forms of feedback explored in this thesis are feedback on grammatical accuracy and assessment related to particular aspects of proficiency (e.g., grammar, pronunciation, rhythm, fluency, etc.). The first study explores the use of written data and the impact of features extracted through grammatical error detection on proficiency assessment, while the second illustrates a pipeline which starts from disfluency detection and removal, passes through grammatical error correction, and ends with proficiency assessment. Grammar, as well as rhythm, pronunciation, and lexical and semantic aspects, is also considered in the third study, which investigates whether it is possible to use systems targeting specific facets of proficiency analytically when only holistic scores are available. Finally, in the last two studies, we investigate the use of self-supervised learning speech representations for both holistic and analytic proficiency assessment. While aiming at enhancing the performance of state-of-the-art automatic systems, the present work pays particular attention to the validity and interpretability of assessment both holistically and analytically and intends to pave the way to a more profound and insightful knowledge and understanding of automatic systems for speaking assessment and feedback.