In this e-learning course, I developed a series of grammar lessons based on the required textbook for a course in English Composition, an introductory course in writing and critical analysis. Using common examples in Standard American English (conversational), students are introduced to complex grammatical concepts such as parallel structure, appositives, or relative clauses. To access the prototype model, click on the button above. This model was made using Rise 360.
To develop the virtual grammar lessons and quizzes each semester, I used the iterative SAM model.
Below, I annotate each step of the process.
Diagnostic on the first day of class each semester. The diagnostic uses Qualtrics XM, which has a survey path capability that allows for questions to become more difficult when a learner has answered correctly and less difficult when a learner has answered incorrectly.
Diagnostic questions are categorized based on topic (parallel structure, appositive, etc.) and difficulty (novice, intermediate, advanced).
The course is several weeks and each virtual lesson contains internal knowledge checks and then, of course, the quiz following each virtual lesson is full of knowledge checks.
In total, there are three virtual lessons and each is followed by a quiz. The SAM iterative model, then, is the more logical choice for ID project life cycle.
In the first prototype, students have knowledge checks in the virtual lesson. This evaluation uses multiple choice, short answer, and fill-in-the-blank questions and includes a post-lesson survey that asks reaction questions developed using Kirkpatrick model.
The “low-stakes” knowledge checks provide a soft form of evaluation. The post-lesson survey not only allows aggregate data for the next iterative design (which will be the quiz), it also requires students reflect on their feeling of preparedness. This reflection ensures that upon iteration, students study more or less based on their feeling prepared or unprepared.
Based on the post-lesson survey data as well as the data provided by the LMS (Canvas) on which questions in the virtual lesson were missed most often, I develop question format (multiple choice, short answer) and content for the quiz accordingly.
If the majority of students seem to struggle with parallel structure, for example, in the first virtual lesson, then I will reiterate parallel structure questions in the first quiz. This tests the hypothesis (above) that the post-quiz Kirkpatrick survey requires reflection and thereby encourages conscious change in study habits for future iterations.
Implementing the first quiz, I gauge ability in similar ways to the first virtual lesson: based on data provided by the LMS on which questions and question formats were missed by the majority of students, I incorporate these into the second virtual lesson.
The same post-lesson Kirkpatrick survey is used, and this process continues for each lesson (with low-stakes knowledge checks) and quiz (with high-stakes knowledge checks).
Preston Taylor Stone 