2. Theses and Dissertations

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Open Access
A Comparative Study of AI-assisted Assessment and Teacher-Assessment in an EFL Writing Course
(SDU University, 2025) Fazilova A.
With the rapid development of artificial intelligence in education, automated writing evaluation (AWE) tools such as ChatGPT are becoming increasingly popular for providing feedback on students' written works. This study explores the perceptions of first-year EFL students about AI-assisted assessment compared to teacher assessment in an EFL writing course at a private Kazakhstani university. Over the period of one semester, 33 participants have written 4 essays and received both the teacher and ChatGPT feedback on each essay. Their perceptions have been compared and analyzed with a quantitative research design with elements of qualitative analysis. A questionnaire with Likert-scale closed-ended and open-ended questions was used. The results revealed that while students recognize the importance of AI-assisted assessment for surface-level corrections (grammar, vocabulary, structure), the majority of students prefer teacher feedback for its clarity, personalized support, and depth. Additionally, most students viewed the ideal approach as a combination of two types of feedback: AI for quick technical feedback and teachers for more complex aspects like structure, argumentation, and tone. The study concludes that although AWE tools have potential as supplementary support in EFL writing instruction, they cannot replace the human connection and pedagogical insight offered by teachers. Implications for integrating AI tools into classroom practice and teacher training are also discussed, along with recommendations for future research in this evolving field.
Open Access
A comparative study of air quality analysis in Almaty
(SDU University, 2025) Dauletkhan N.
Air pollution remains a pressing public health and environmental challenge in Almaty, Kazakhstan, where concentrations of fine particulate matter (PM2.5) frequently exceed World Health Organization limits. This study presents a comprehensive comparative analysis of statistical, machine learning (ML), deep learning (DL), and hybrid models for short-term PM2.5 forecasting using real-world meteorological and air quality data collected between 2020 and 2024. The methodology involved rigorous data preprocessing, including imputation techniques such as mean substitution, time-based mean, and Multiple Imputation by Chained Equations (MICE), followed by correlation analysis and normalization. Multiple models were implemented and evaluated: statistical models like Multiple Linear Regression (MLR), SARIMA, and Prophet; ML algorithms including Random Forest, Support Vector Regression (SVR), and XGBoost; DL architectures such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN); and hybrid combinations like CNN-ELM and CNN-LSTM. Model performance was assessed using MAE, RMSE, and R² across three imputation scenarios. Results indicated that LSTM consistently achieved the highest accuracy, particularly under the MICE imputation scenario, while Random Forest and XGBoost showed strong performance among ML models. Hybrid models like CNN-LSTM demonstrated promising results in capturing both spatial and temporal patterns. This research contributes to the development of robust, interpretable, and localized forecasting systems, offering valuable insights for environmental monitoring and public health planning in data-constrained urban regions
Open Access
Academic Integrity: Students’ Awareness and Instructors’ Promoting Strategies in English
(SDU University, 2025) Nesterova A.
With the emergence of artificial intelligence, maintaining academic integrity has become one of the most discussed topics. Thus, universities make attempts to decrease academic dishonesty. The aim of this research is doublefold: first, it seeks to identify students’ awareness of academic integrity. Second, it describes the instructors’ roles and strategies they use to maintain academic integrity. To achieve these aims, the adapted version of M-AIS was distributed among 187 students, majoring in the specialty “6B1702: Foreign Language: Two Foreign Languages” at one private university in Kazakhstan. As for instructors, 12 of them participated in semi-structured interviews. The research followed a mixed method design, in which the quantitative part was analyzed based on mean, standard deviation, and Spearman’s rank correlation. Overall, it was found that the students were fairly familiar with academic integrity and held positive attitudes towards it. However, they still perceived plagiarizing home assignments, getting unpermitted help, and mispresenting sources as trivial plagiarism and admitted to being engaged in them. In addition, there was a correlation between the student’s year of study, GPA, perceived severity of academic dishonesty, and self-reported engagement in it. The findings were interpreted using Hatch’s (2002) framework of typological analysis. As regards the instructors, their roles in promoting academic integrity were moderately active (ambassador). They taught different techniques to avoid plagiarism, assigned authentic assignments, or developed their own materials. The other part was determined to be passive (casual and detached) because they believed in students’ responsibility to learn about academic integrity. As for strategies, the instructors considered the Turnitin application and authentic assessment as effective ways of maintaining academic integrity. In addition, they made presentations to teach about academic integrity. On the other hand, they did not believe in the usefulness of honor codes and online proctoring applications.
Open Access
AI in chemistry education and ethical considerations
(SDU University, 2025) Balkyibek K.
This dissertation examines the integration of Artificial Intelligence (AI) tools, specifically ChatGPT, in chemistry education in Kazakhstan, aiming to identify challenges, opportunities, ethical considerations, and the practical effectiveness of these tools in enhancing learning outcomes. The study explored three key areas: the effectiveness of AI integration into chemistry education, the ethical and privacy concerns associated with student use of AI tools, and the primary advantages and disadvantages of employing AI in chemistry instruction. Using a mixed-method approach, the research combined quantitative surveys among 108 chemistry education students from Kazakh universities and qualitative expert evaluations of AI-generated chemistry solutions. The theoretical framework drew from existing literature on AI integration in education, ethical implications, and pedagogical impacts. Findings indicated that students strongly prefer ChatGPT due to its efficiency, clarity, and ability to facilitate independent learning, primarily utilizing it for problemsolving and exam preparation. However, significant limitations were observed, including accuracy issues, logical inconsistencies, and inadequate linguistic adaptation to the Kazakh language. Ethical concerns highlighted were academic integrity, dependency on technology, and unequal access to premium AI features. The dissertation contributes theoretically by providing empirical evidence of AI’s educational benefits and limitations, and practically by recommending structured AI integration strategies, specialized training, enhanced linguistic localization, and ethical guidelines. Ultimately, this research informs educators, policy-makers, and developers aiming to harness AI responsibly and effectively in chemistry education.
Open Access
An Inclusive Analysis of Mathematics Achievement and Attitudes in Diverse Educational Environments
(SDU University, 2025) Yuzeyeva Z.
This dissertation presents an inclusive analysis of mathematics education by examining the development of inclusive competence in future mathematics teachers within diverse educational environments in the Republic of Kazakhstan. Rooted in national and international frameworks on inclusive education, the research explores how cultural, linguistic, psychological, and regional factors affect teacher preparedness and attitudes toward working with learners with special educational needs (SEN). The study focuses on Almaty and rural areas as case settings, analyzing how educational equity and inclusivity are addressed in mathematics classrooms. A structural-logical model was developed to support the formation of inclusive competence through a task-based, personalized, and culturally responsive methodology. The model integrates motivational, cognitive, practical, and reflective components, preparing future teachers to adapt mathematics instruction for learners with a wide range of abilities and backgrounds. A three-stage pedagogical experiment involving 180 pre-service mathematics teachers and 28 in-service educators were conducted. Quantitative and qualitative data were collected to assess initial readiness, track development of inclusive attitudes, and evaluate the effectiveness of proposed instructional strategies. Results revealed significant improvements in teachers’ motivation, adaptability, and use of inclusive methods following implementation of the model. The findings highlight the importance of localized, inclusive teacher education that reflects Kazakhstan’s evolving educational landscape. This work contributes to the broader discourse on equitable mathematics education and supports ongoing efforts to create accessible and high-quality learning environments for all learners.
Open Access
An Investigation into the Development of Intercultural Competence in TEFL: A Study of Master’s Students’ Perspectives
(SDU University, 2025) Ibrayeva A.
The current study aims to investigate the perspective of master’s students on the development of intercultural competence (IC) in the Kazakhstani teaching English as a foreign language (TEFL) and to identify key challenges they face in practical application. The study involved ten working master’s students enrolled in TEFL programs at two Kazakhstani institutions through purposive sampling. Semi-structured interviews were used in a qualitative research design to acquire detailed information about the experiences and reflections of the participants. The research questions were addressed through thematic analysis of the data. The findings reveal the theoretical intercultural competence awareness of students, yet there is a lack of structured and practical inclusion of intercultural aspects in their academic curriculum. Participants reported that little experiential learning and intercultural engagement are incorporated into core teaching modules, and that intercultural competence development is frequently restricted to quick discussions in elective courses. The insufficient academic training, strict curriculum, and lack of support from the institution emerged as key barriers to intercultural competence integration in teaching. The students suggested the incorporation of practical tasks and intercultural competence integration mechanisms in the TEFL courses. This study contributes to the growing body of literature on IC development in the Kazakhstani TEFL context.
Open Access
Application of CLIL teaching methods in chemistry lessons
(SDU University, 2025) Yntymakkyzy K.
This dissertation explores the use of the CLIL (Content and Language Integrated Learning) method in chemistry classes, focusing on its impact on students` academic performance and teachers` perceptions of its implementation in the classroom. The main research question of this study is: "how does the use of CLIL methods affect students` performance in chemistry and how do teachers perceive their use in the classroom?" approach was used to use mixed methods that combined quality interviews with seven teachers with CLIL experience and quantitative data from pre-and post-test tests conducted on experimental and control groups in three different schools. Data sources included academic databases such as Scopus, Research Gate, and Google Scholar. The results show that the effectiveness of CLIL varies depending on the educational context, student motivation, and pre-impact on CLIL. In school 1, where CLIL was newly introduced and motivation was high due to the preparation for the national test, students showed a significant improvement in post-test scores. School 2 (IB school) has shown moderate but statistically significant advances, indicating that CLIL can improve understanding of the subject even when the knowledge of the content is initially low. On the contrary, the 3rd school (Lyceum) showed the least improvement, which is probably due to the high base performance and previous experience of CLIL. However, achievements in all schools were statistically significant, confirming the overall positive impact of CLIL on students ` achievements in chemistry. The study concludes that while CLIL can be a powerful learning approach, its effectiveness depends on the specific learning environment and student background.
Open Access
Applications of the differentiation method in the study of the topic of trigonometric equations and inequalities
(SDU University, 2025) Kazhmukhanov A.
This dissertation comprehensively explores the effectiveness of applying the differentiation method in teaching trigonometric equations and inequalities. In today's educational system, the variation in students’ preparedness levels has become a pressing issue, especially when dealing with complex mathematical topics like trigonometry. The theoretical part of the study discusses the concept and importance of differentiated instruction, along with a review of both domestic and international practices. The methodological section presents approaches for designing level-specific tasks on trigonometric equations and inequalities. Levels A, B, and C were introduced, and customized tasks were developed using graphical methods, function properties, and identity transformations. A pedagogical experiment was conducted with students from grades 5, 6, and 11. The findings demonstrate a positive impact of differentiation on student performance, motivation, and independent learning. Even students with lower academic performance achieved success through assignments tailored to their capabilities. The study also considers the integration of artificial intelligence (AI) tools to support differentiated learning. Digital platforms allow teachers to accurately assess student levels and assign personalized tasks accordingly. The research concludes that the differentiation method is effective not only in teaching trigonometry but also applicable to other areas of mathematics. Therefore, broader implementation and enhanced teacher training in differentiation strategies are recommended.
Open Access
Designing chemistry project in an integrative STEM manner
(SDU University, 2025) Daribay A.
STEM (science, technology, engineering, and mathematics) was integrated into chemistry education; this study focused on project-based learning (PBL) as a pedagogical tool. The study examined how chemistry teachers conceptualize, design, and implement STEM-integrated projects and how these practices affect student engagement, teacher professional development, and student learning. A total of 79 chemistry teachers in Kazakhstan participated in the study using a mixed-method approach, including surveys, interviews, and classroom observations. The results indicate that the chemistry teachers are increasingly adopting STEM principles and focusing on real-world applications to improve student motivation and learning. The researchers noticed that teachers with advanced qualifications (i.e., pedagogical researchers) were more successful at implementing STEM practices, as they encouraged student-centered learning approaches, such as collaboration, experimental design, and integrating digital technology. With the aforementioned projects, the students exhibited increased interest, improved understanding of chemical concepts, and developed their critical thinking and problem-solving skills. Despite the successes of the STEM projects and activities, the teachers experienced significant challenges related to working in an un-coordinated education system, limited resources, not enough time to implement integrated PBL, and lack of institutional and administrative support. These issues inhibited the project-based teachers from fully embedding STEM practices into their curriculum, particularly in creating interdisciplinary opportunities and obtaining materials for project implementation. However, this research indicated that there were several benefits for both the students' learning and teacher professional development practices by employing STEM projects regardless of the limitations and challenges presented. The researchers concluded that programs for professional development for teacher support, better access to educational resources, and eventually a supportive framework by the institution were needed to integrate STEM for best practice in chemistry education. The recommendations offered by the research could provide insight into the challenges of STEM education and development for policymakers and educators in Kazakhstan and similar contexts to maximize the opportunities offered by STEM education.
Open Access
Detecting social conflicts in kindergartens using deep learning and computer vision
(SDU University, 2025) Kengesbay D.
Early conflict detection in kindergartens plays a significant role in ensuring a harmonious learning atmosphere and in promoting the social growth of young children. While most previous works have only addressed conflict detection through adults, in this paper, we specifically address conflict detection in kindergartens using deep learning, utilizing both spatial and temporal information to improve performance. The application of deep learning and computer vision in automatically detecting and analyzing early conflicts among young children is discussed in this paper. Using video footage, we leverage state-of-theart RNNs and 3D CNNs for high-accuracy detection of conflict instances. Crucial visual cues—facial expressions, gestures, poses, vocal tone, and movement—are examined for the extraction of tension or aggression signs. The model is evaluated on real kindergarten video data, with promising conflict detection and classification results. The findings indicate the potential of AI-supported tools in assisting teachers in class management, child behavior monitoring, early intervention mechanisms, and the fostering of a good social environment
Open Access
Determining the challenges of chemistry teachers in Integrative STEM Teaching
(SDU University, 2025) Zhibekaiym I.
This study examines the attitudes of school chemistry teachers toward integrated STEM education and the challenges they face when implementing STEM approaches in chemistry lessons. As STEM education continues to gain global importance, understanding teachers’ experiences and barriers is essential for improving pedagogical strategies and professional development in science education. A mixed-methods approach was used, combining quantitative data from a teacher survey and qualitative data from semi-structured interviews. Survey results were analyzed using descriptive statistics, factor analysis, independent samples t-tests, and one-way ANOVA to explore differences based on teachers’ demographic characteristics such as experience, academic degree, professional category, and school level. Interview data were analyzed using inductive content analysis to capture teachers’ authentic views and the specific difficulties they face in practice. The findings show that although chemistry teachers generally express positive attitudes toward STEM education, they encounter significant challenges due to a lack of methodological tools, resources, and difficulties with interdisciplinary integration. Differences in perceived challenges were also noted based on teaching experience and education level. Additionally, teachers reported struggling with implementing complex STEM activities such as project-based tasks, laboratory work, and interdisciplinary collaboration. The results emphasize the need for targeted professional development and systematic support to ensure effective integration of STEM practices in chemistry education. These findings are valuable for policymakers, curriculum developers, and teacher education institutions seeking to enhance STEM implementation in secondary education.
Open Access
Developing beginner-level young learners’ reading skills in Kazakhstani public schools
(Suleyman Demirel University, 2020-12-25) Karakat Dauletkyzy
This dissertation focuses on examining how English teachers develop reading skills of primary EFL learners in public schools in Kazakhstan. This qualitative study was carried out using semi-structured interviews with 15 English teachers working in primary public schools across Almaty, Kazakhstan. The findings suggest that a lot of English teachers use the phonics method to teach reading to primary school learners. Teachers find the phonics method to be more effective than other methods. The results of the study indicate that teacher training and gaining experience in the primary EFL classroom play the biggest role in teaching reading better. Teacher training programs should pay more attention to teaching methods aimed at primary EFL learners. The phonics method should be part of the English teacher-training curriculum.
Open Access
Developing students’ grammar skills using “Learning English grammar tenses” application at the intermediate level
(Suleyman Demirel University, 2021-06-18) Karman A.
This research looks specifically at using the mobile application as a material resource and how to prepare and manage the grammar lesson at the intermediate level, at the secondary school. This dissertation presents practical tasks aimed at developing grammar skills. The main goal of thіs study is to identify effective tasks that can be performed with the help of mobile devices as well as with the “Learning English grammar tenses” application.
Open Access
Development and optimization of physics-informed neural networks for solving partial differential equations
(SDU University, 2025) Sharimbayev B.
This thesis talks about using physics-informed neural networks (PINNs) to solve Poisson equations in both one-dimensional and two-dimensional areas. These equations are common in many physical problems, like heat transfer and electrostatics. The results from PINNs are compared to the finite difference method (FDM), which is a classical numerical method often used to solve these kinds of equations. The study shows that PINNs can give results that are close to those from FDM, with the added benefit of being more flexible for different types of problems. Another part of this work focuses on using multi-task learning with PINNs. In this part, the neural network does more than one job. It not only finds the solution of the differential equation, but it also learns unknown values or parameters that are part of the equation. For example, in one test problem, the equation had a source term and a coefficient that changes depending on the position. The PINN was able to learn both of them correctly while still solving the equation with a low training error. The results show that PINNs can work well even when the equation is more complex or has unknown parts. The model showed good performance on new or unseen data and was able to find the correct hidden values in the system. Because of this, PINNs may be very useful in future applications for solving advanced problems in science and engineering, especially where traditional methods might be harder to use.
Open Access
Development of a Medical Decision Support System for Lung Disease Diagnosis Using Deep Learning
(SDU University, 2025) Medeuova Zh.
Lung diseases such as Chronic Obstructive Pulmonary Disease (COPD), asthma, and pneumonia remain a leading cause of morbidity and mortality worldwide, with developing countries like Kazakhstan experiencing a rising burden due to environmental and lifestyle factors. Traditional diagnostic methods, while effective, are often limited by cost, accessibility, and dependence on expert interpretation, especially in low-resource settings. To address these challenges, this study proposes a novel artificial intelligence DL based decision support system that classifies lung conditions into three categories like normal, COPD. Using multimodal data: chest X-rays and lung sound recordings. The system utilizes a dual-stream deep learning architecture with late fusion to extract and integrate features from each modality independently, removing the need for synthetically paired datasets. This approach not only improves diagnostic scalability and accuracy but also provides a practical, low-cost solution for early lung disease detection, particularly in underserved regions. The model’s performance demonstrates the viability of using real-world, unpaired data for multimodal diagnostics, offering a significant step toward accessible, AI-driven respiratory healthcare
Open Access
Development of a Mobile Application for Preparation for English Exam at Unified National Testing
(SDU University, 2025) Anuarbekova A.
This research project focuses on the development and evaluation of BikiUNT, a Telegrambased educational chatbot designed to support high school students in Kazakhstan preparing for the Unified National Testing (UNT) in English. The study aims to address the lack of accessible, localized, and exam-oriented digital resources for learners, particularly in underserved and rural areas. Through a pilot implementation involving 20 students, the study assessed the chatbot’s effectiveness in terms of usability, perceived usefulness, motivation, and learning engagement. Key features such as grammar modules, multimedia lessons, diagnostic testing, and gamification were explored. The results indicate high user satisfaction and suggest that BikiUNT can serve as a scalable and cost-effective EdTech solution for exam preparation in the national context.
Open Access
DEVELOPMENT OF AN ATTITUDE SCALE FOR PHYSICS COURSES AND A REVIEW OF STUDENT ATTITUDES
(SDU University, 2025) Zhumabek B.
This study examines the attitudes of Kazakhstani students in grades 9 to 11 toward physics, using a sample of 195 participants from public, private, and specialized schools, including Nazarbayev Intellectual Schools (NIS) and Bilim-Innovation Lyceums (BIL). The sample included 106 girls and 89 boys, reflecting a diverse educational context. Data were collected through a 21-item attitude scale adapted from Aydın Gürler and Baykara (2020), covering six factors: Motivation, Interest, Self-efficacy, Academic Engagement, Physics Anxiety, and Math-related Anxiety. Internal consistency was excellent, with Cronbach’s alpha values ranging from 0.96 to 1.00. Items were rated on a five-point Likert scale. Descriptive statistics (means, SDs, variances) and independent samples t-tests were used to analyze differences by gender and school type. Students showed generally positive attitudes toward physics: Motivation and Interest (M = 3.48), Self-efficacy (M = 3.46), and Engagement (M = 3.41) were relatively high. Physics Anxiety (M = 2.63) and Math Anxiety (M = 3.10) revealed the emotional complexity of physics learning. While gender differences were minimal, female students reported slightly higher anxiety levels. School type was more influential: students from NIS and BIL scored higher in motivation, engagement, and confidence than those in general schools, highlighting the role of institutional context and teaching approach. The findings emphasize the need for physics educators to address both cognitive and emotional aspects of learning. Especially in general schools, student-centered and inquiry-based strategies could improve engagement and reduce anxiety. Curriculum developers should also consider pacing and content design in early physics instruction. This study offers reliable data on student attitudes in Kazakhstan and validates a six-factor scale as a tool for measuring affective dimensions of physics learning. Future research could include qualitative methods, longitudinal tracking, or intervention studies.
Open Access
Digital storytelling in EFL teaching in Kazakhstan
(Suleyman Demirel University, 2021-06-18) Nazerke Bakyt
The purpose of this paper is to study the use of digital storytelling in teaching English as a foreign language in Kazakhstani educational institutions. To examine English teachers’ experiences, practices, and attitudes towards using digital storytelling in the teaching process, we utilized semi-structured interviews. The study was conducted among in-service early career English teachers working in public and private secondary schools. The findings suggest that English teachers use digital storytelling in their teaching but not very frequently. Study participants also indicated that they would benefit from additional training and experience with using digital storytelling as a teaching technique in their work. According to the participants, the main barriers to using digital storytelling in the classroom are the lack of technology, teachers’ methodological preparation, and time limitations. Despite these barriers, the teachers who participated in the study appreciate the substantial benefits of using digital storytelling in teaching English such as enhanced opportunities for developing students’ speaking skills, creativity, and critical thinking.
Open Access
Effectiveness of Hands-On Chemistry Experiments in High School Education
(SDU University, 2025) Aldenov R.
This master’s dissertation explores the impact of different instructional strategies on students’ academic performance and engagement in chemistry education, with a focus on the effectiveness of practical, laboratory-based teaching methods. The study compares two groups of secondary school students: a primary group that received instruction through traditional lecture-based methods, and a focused (experimental) group taught using systematic, hands-on laboratory activities. A total of 42 students were involved in the research, equally divided into the two groups. Both groups followed the same curriculum content and were taught by similarly qualified instructors, ensuring the internal validity of the comparative design. However, the focused group engaged in practical work, including laboratory experiments aligned with curriculum objectives, while the primary group experienced conventional instruction with minimal laboratory exposure. To evaluate the outcomes, a mixed-methods approach was utilized. Data were collected through a Chemistry Achievement Test (pre- and post-intervention), a laboratory skills assessment test, and a structured questionnaire measuring student perceptions. Descriptive and inferential statistical analyses-including independent samples t-tests, and ANCOVA-were performed to assess academic growth, control for baseline differences, and examine the robustness of instructional impact. This study concludes that practical, laboratory-based teaching methods lead to more effective learning outcomes, particularly by enhancing scientific thinking, motivation, and personalized academic growth. The findings advocate for the broader integration of experimental learning strategies in chemistry curricula and underscore the need for policy support in expanding laboratory infrastructure, teacher training, and time allocation for hands-on instruction.
Open Access
Efficacy of Cognitive-Behavioral Interventions in Reducing Foreign Language Speaking Anxiety: A Case Study of master's degree students
(SDU University, 2025) Jaxybekova I.
This study investigates the effectiveness of cognitive-behavioral interventions (CBIs) in reducing foreign language speaking anxiety (FLSA) among master’s degree students in Kazakhstan. Despite high English language proficiency, many graduate students face moderate to high levels of speaking anxiety, during such activities as classroom participation, oral exams and presentations. Such phenomenon may not only lower genetal performance of a student, but also limit students’ confidence and professional development. To address this issue, a three-week intervention program was designed using core CBT strategies: cognitive restructuring, graduate exposure and relaxation technique. A mixed-method case study approach was used, involving 30 master’s students from both public and private universities of Kazakhstan. Quantitative data was collected using Foreign Language Classroom Anxiety Scale (FLCAS) before and after interventions. Data was analyzed on a group-level, showing reduction in speaking anxiety, the most significant drop was evident in unprepared speaking, fear of negative evaluation and public correction. Qualitative data was gathered through semi-structured interviews after group interventions, as a complementary tool. Data was analyzed thematically and four key themes were revealed: (1) increased awareness through gradual practice , (2) improved emotional regulation, (3) enhanced speaking confidence, and (4) application of practices beyond the classroom. Study contributes to limited research on postgraduate students’ speaking anxiety, providing valuable insights from Kazakhstan’s educational context. Research findings suggest that brief, low-cost and adaptable interventions can have a meaningful impact on learners’ psychological readiness to speak.