contextual vocabulary acquisition (CVA):|
Our ultimate goal is not merely to improve vocabulary acquisition, but also to increase students' reading comprehension of science, technology, engineering, and mathematics (STEM) texts, thereby leading to increased learning, by using a "miniature" (but real) example of the scientific method, viz., CVA.
The computational and educational strands of our research are fully integrated and jointly serve this ultimate goal. We are attempting to:
People know the meanings of more words than they are explicitly taught, so they must have learned most of them as a by-product of reading or listening. Some of this is the result of active processes of hypothesizing the meaning of unknown words from context.
How do readers do this? Most published strategies are quite vague; one simply suggests to "look" and "guess". This vagueness stems from a lack of relevant research about how context operates. There is no generally accepted cognitive theory of CVA, nor is there an educational curriculum or set of strategies for teaching it. If we knew more about how context operates, had a better theory of CVA, and knew how to teach it, we could more effectively help students identify context cues and know better how to use them.
AI studies of CVA (including our own) have necessarily gone into much more detail on what underlies the unhelpful advice to "guess", since natural-language-processing systems must operate on unconstrained input text independently of humans and can't assume a "fixed complete lexicon". But they have largely been designed to improve practical natural-language-processing (NLP) systems. Few, if any, have been applied in an educational setting, and virtually all have been ignored in the reading- and vocabulary-education literature.
AI algorithms for CVA can fill in the details that can
turn "guessing" into
these can then be taught to students.
Thus, the importance of our project stems from the twin needs
We are continuing the two-way flow of research results between the education and the AI teams, the education team providing data for improving the definition algorithms, the AI team providing the algorithms to be converted into a curriculum.
The AI team is:
The education team is building, implementing, and evaluating a curriculum designed to help secondary-school and college students become better able to use CVA processes to increase knowledge of word meanings, thereby leading to increased content learning and reading comprehension in STEM.
The curriculum is
based on our algorithms and uses teacher-modeled protocols that are
practiced by students with the teacher, in small groups, and alone.
We are developing student materials and a teacher's guide that
emphasize how our method of CVA is an example of the scientific
method "in the small", and we are
studying the curriculum's effectiveness.