Continuously updated as needed.
| ⏰ Stopwatch Online| 📍 Padlet | 🐳 MK316 APP Hub | 📗 Coding4ET |
| Digital Classrooms | Level | Days | Credits | Description |
|---|---|---|---|---|
| 1. English Pronunciation Practice | 📒 UG (1st Yr.) | Tu&Thur | 3 | Designed specifically for first-year English Education majors, this course enhances English pronunciation skills by exploring English sounds and their rules. Students will engage with various vowel and consonant sounds, intonation patterns, and stress rules. This knowledge is crucial for improving pronunciation and comprehension in spoken English, forming a vital foundation for effective English communication and teaching. |
| 2. Digital Literacy and English Education | 📙 UG (2nd Yr.) | Tu | 2 | This course for undergraduate English teacher candidates emphasizes digital literacy, with an understanding of basic coding as a helpful tool. It explores how these skills can enrich language teaching by enabling the creation of customized educational tools and interactive experiences, catering to diverse student needs in a digital world. |
| 3. Introduction to Coding and Language App Design | 📗 Grad (TESOL) | Tu | 2 | In this course, we focus on learner-centered, customized digital tools to enhance TESOL teaching. Discover how digital literacy for educators enables the creation of adaptive learning environments, where technology is tailored to meet diverse student needs and foster more effective and engaging language instruction. |
| 4. Corpus Linguistics and English Education | 📘 Grad (MA, PhD) | Wed | 3 | This is an introductory course exploring corpus analysis methods to innovate English teaching and curriculum design. Graduate students will engage with real-world language data, applying findings to enhance teaching methodologies, material development and assessment strategies in language education. |
| 5. Seminar | 📘 Grad (MA, PhD) | Thur (Mon) | 1 | This seminar focuses on frequency data handling and relevant basic statistics, offering lectures and practical sessions to develop foundational skills in statistical analysis and data interpretation. Using Python coding, students will learn how to summarize the frequency data visually (e.g., pie, bar, associate plots), frequency data tables, Chi-Squared tests, and related quantitative analysis methods. |
| Date | Groups |
|---|---|
| June 11 | G1, G5, G6 |
| June 18 | G2, G3, G4 |