Creating a Corpus-Based Teaching Material
How did the process work?
We chose to teach the use of 'many' and 'much' quantifiers from the "Party" unit in the coursebook by MEB for preparatory students. By examining language corpora, we made it possible for students to identify linguistic patterns. For this reason, we introduced the COCA (Corpus of Contemporary American English) website by giving usage instructions in our material. Thus, we had the students observe for themselves in which words 'many' and 'much' are used. In the search section, we tried not to confuse too much by limiting the corpus to only word usage. In order to prepare the lesson plan, we used the ASSURE model to design the exercise. By assessing students' requirements, this strategy allowed us to successfully incorporate materials and technology into the teaching process. Through corpus analysis, students learned the language structure on their own and then completed tasks in the material to put what they had learned into practice. By applying Canva to design the content, we also added visual interest to it.
Possible problems that may occur in the application of the material in the real classroom:
Technology access, students' corpus confusion, and time management issues may arise when teaching this material. Additionally, scanning the QR code, logging into COCA and learning how to use it (despite the instructions provided) may be difficult. Also, if the teacher lacks corpus use and analysis knowledge, it may be difficult to apply the material.
Here is our corpus based material and our assure model based lesson plan. You can review them by clicking on the links, feel free to leave a comment.
click here for Corpus based teaching material
click here for Assure model based lesson plan
We completed this task with my group friends Başak and Belinay. If you would like to check out their blogs, here are the links:
(In case the qr or kahoot link in the material does not work, screenshots of our kahoot questions are below.)
picture by: https://deepai.org/machine-learning-model/text2img (created by ai)


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