Presented by Mariana Bertho, Aleksey Novikov, Adriana Picoral, Bruna Sommer-Farias, and Shelley Staples (all University of Arizona).
Participants in this free, live webinar were introduced to the Multilingual Academic Corpus of Assignments – Writing and Speech (MACAWS), an ongoing project building a corpus of assignments (written texts, spoken discourse, and multimedia products such as blogs) produced by learners in Russian and Portuguese language programs at University of Arizona. This online platform allows teachers and learners to perform searches, and filter results by course level, assignment macro-genre (i.e. description, narration, etc.) and topic (i.e. family, culture), learner L1 background, and more. The MACAWS platform also offers tools for incorporating corpus search results (concordance lines) into language learning materials.
In the webinar, participants were introduced to the interactive Data-driven Learning (iDDL) tool based on the Data-driven Learning (DDL) approach to foreign language teaching. At the core of Data-driven Learning (DDL) is the premise that language is highly patterned, and that it can be taught inductively by guiding learners through the pattern discovery process. In turn, iDDL is a dynamic way of integrating concordance lines into online pedagogical materials using learning management systems (e.g. Moodle, D2L, etc) or any other online platforms that support embedding (e.g. Google sites). Along with a window containing concordance lines, the instructor can add questions to scaffold students’ noticing patterns.
During this webinar, participants learned to:
- navigate MACAWS for linguistic and/or rhetorical features relevant to their teaching context;
- implement Data-driven Learning (DDL) principles for inductive learning;
- develop iDDL pedagogical materials for their instructional contexts.
K-12 participants could request a certificate for one hour of Continuing Education during the registration process.
The MACAWS project is co-sponsored by the College of Social and Behavioral Sciences.