Multi-choice questions (MCQs) have long been a staple in language resources and exams, and with AI making them easier to produce than ever, I've been making growing use of them in my frenchteacher resources. In the past, I avoided them since they took so long to write. But while technology has simplified their creation, the art of designing effective MCQs — whether for assessment or practice —still requires thought and precision. Here are some issues to consider when writing and using MCQs. Assessment 1. Objectivity One of the greatest strengths of MCQs is their objectivity . Unlike level-based markscehems (or "rubrics" in the USA), where subjectivity can creep in, MCQs provide reliable scores, an important aspect of assessment. When designed well, they should offer a reliable snapshot of student listening and reading comprehension, or sometimes lexical and grammatical knowledge. 2. The three-option rule Research and practice show that three options are statistically a...
I'm not sure who first coined the terms fine-tuning and rough-tuning of input, but they have certainly become associated with the work of Stephen Krashen. In this post, I'll explain what the terms refer to and what they might mean for language teacher practice. We all know that a prequisite for first and additional language acquisition is input students can understand ( comprehensible input , to use Krashen's familar term). Krashen used the formula i + 1 to describe input which is at or just above the learner's current level. This would imply giving students aural and written texts, dialogues, etc, which contain a large majority of vocabulary students already know (Paul Nation and others write about 95-98% knowledge), using grammatical constructions which students are already familiar with. This is where the distinction between fine-tuning and rough-tuning of input comes in. There is no precise definition of this, but essentially if you finely tune the input you go out...