| Course Description: | "Practical Computational Text Analysis" is a comprehensive 3-hour course designed to familiarize students with the basics of computational text analysis. The course is structured into three key sections. The first part provides a theoretical introduction to foundational computational methods used in text analysis, including concordance analysis, word frequency analysis, and topic modeling. This is followed by a demonstration of "Voyant Tools," an online platform that enables students to apply these methods to their own texts. The final segment is a hands-on practical session where students will engage in the computational analysis of specific text corpora. Throughout the course, students will gain a solid understanding of textual computational methods, learn to utilize Voyant Tools for their analyses, and acquire practical experience in handling real-world text data. This course aims to equip students with essential skills in computational text analysis, preparing them for further academic and professional applications in this field. |
| Course Aim: | The aim of the "Practical Text Analysis" course, especially relevant to language teaching, is to equip language educators and students with the essential skills and tools for computational text analysis. This course focuses on understanding and utilizing computational methods such as concordancing, word frequency analysis, and topic modelling to enhance language learning and teaching. By learning how to use "Voyant Tools," an online platform for text analysis, educators can better analyze language usage, identify linguistic patterns, and develop more effective teaching materials. The hands-on session with specific text corpora will allow participants to apply these methods in real-world language teaching contexts, facilitating the creation of data-driven language learning activities and resources. Ultimately, this course aims to empower language teachers with the knowledge and skills to integrate computational text analysis into their teaching practices, thereby enriching the language learning experience for their students. |
| Anticipated Outcomes | The objective of the "Practical Text Analysis" course, particularly relevant to language teaching, is to equip language educators and students with essential skills and tools for computational text analysis. This course specifically focuses on comprehending and applying computational methods, such as concordancing, word frequency analysis, and topic modeling, to enhance language learning and teaching. By mastering the use of "Voyant Tools," an online platform for text analysis, educators can analyze language usage, identify linguistic patterns, and develop more effective teaching materials. The hands-on session with specific text corpora allows participants to apply these methods in real-world language teaching contexts, facilitating the creation of data-driven language learning activities and resources Ultimately, the course aims to empower language teachers with the knowledge and skills to seamlessly integrate computational text analysis into their teaching practices, enriching the language learning experience for their students. The anticipated outcomes of the "Practical Text Analysis" course, particularly in the context of language teaching, include: Enhanced Analytical Skills: Participants will develop a strong foundation in computational methods such as concordancing, word frequency analysis, and topic modeling—essential for deep linguistic analysis and understanding language patterns. Proficiency in Digital Tools: Educators and students will gain proficiency in using "Voyant Tools," enabling them to integrate technology into language teaching and learning effectively. Improved Material Development: Armed with acquired knowledge, language teachers can create more engaging and effective teaching materials, tailoring content to address specific linguistic features or learner needs. Data-Driven Teaching Approaches: The course enables educators to adopt data-driven approaches in language teaching by analyzing real-world text corpora, allowing them to base teaching strategies on actual language usage patterns. Innovative Teaching Strategies: Exposure to computational text analysis encourages teachers to explore innovative teaching strategies, incorporating findings from analyses into classroom activities for more interactive and relevant learning experiences. Research Opportunities: For those involved in academic research, the course opens up new avenues for investigation in the field of language education, facilitating more empirical and data-backed research studies. Enhanced Student Engagement: By integrating technology and data-driven insights into language teaching, educators can create a dynamic and engaging learning environment, fostering increased student interest and participation. |
| Trainer Bio | George Mikros is currently a Professor and the Coordinator of the MA Program in Digital Humanities and Societies at the Department of Middle Eastern Studies at Hamad Bin Khalifa University in Qatar. Prior to this role, from 1999 to 2019, he served as a Professor of Computational and Quantitative Linguistics at the University of Athens, Greece, where he founded and became the Director of the Computational Stylistics lab. Since 2013, he has also held the position of Adjunct Professor at the Department of Applied Linguistics at the University of Massachusetts, Boston, USA. Professor Mikros has authored 5 monographs and over 100 papers published in peer-reviewed journals, conference proceedings, and edited volumes. Since 2007, he has been elected as a Member of the Council of the International Association of Quantitative Linguistics (IQLA). During the period from 2018 to 2021, he served as its president. He has been a keynote speaker at numerous international conferences, workshops, and summer schools focusing on Digital Humanities, AI, Forensic Linguistics, and Quantitative Linguistics. His primary research interests include computational stylistics, quantitative linguistics, computational linguistics, and forensic linguistics. |