Computational Morphology and the Teaching of Indigenous Languages

Computer-assisted language learning is by now so common around the world as to be something of a default, and the teaching of the indigenous languages of the Americas is already benefiting from the new technology. Intelligent computer-assisted language learning relies on software that has relatively sophisticated models of the target language and/or the learner. An example is the use of a program that has an explicit model of some aspect of the grammar of the target language and can analyze or generate words or sentences. Many indigenous languages of the Americas are characterized by complex morphology, and morphology must play a significant role in the instruction of these languages. This paper describes how morphological analyzers and generators can handle the complex morphology of languages such as K’iche’ and Quechua and discusses a potential application of this technology to the teaching of such languages. Computer-Assisted Language Learning In recent years, computers have become so important in language teaching that it hard to imagine a class without them. Students use computers to do exercises practicing what they have learned in the class, they access documents from the Internet, they interact with other learners or with native speakers of the target language on the Internet, and they write papers with word processing software that may be especially adapted to second language learning. The field of computer-assisted language learning (CALL) has its own conferences and its own journals, CALICO Journal, Computer Assisted Language Learning, and Language Learning and Technology. Computers are even a part of the language curriculum in relatively impoverished parts of the world, including regions where indigenous languages are taught as part of a bilingual curriculum or as second languages to legacy speakers or non-indigenous people. More recently, advances in computational linguistics and artificial intelligence have led to what is referred to as intelligent computer-assisted language learning (ICALL; see Heift & Schulze, 2007 for an overview), which can also be considered a subfield within the area of intelligent tutoring systems and other applications of artificial intelligence (AI) and computational linguistics in education more generally (a field with its own journals, the International Journal of Artificial Intelligence in Education and the Journal of Interactive Learning Research). A common feature of applications of AI in education has been a model of the learner, based on the learner’s errors; examples within ICALL are Heift (2001) and Amaral & Meurers (2007). Another possibility for ICALL is the incorporation of explicit models of the language being learned (e.g., Nagata, 2002). Such programs, or more specifically, a proposal for their incorporation into the teaching of indigenous languages, are the subject of the rest of this article. Grammar in Second Language Instruction Before discussing the potential use of intelligent software in indigenous language instruction, we need to consider briefly the role of grammar in language teaching more generally. No issue has been more controversial in the field of second language teaching than that of how (or whether) grammar is to be taught. While the explicit teaching of grammar played a significant role in traditional language teaching, the influence of behaviorist psychological theory and later the ideas of Stephen Krashen (1985) called into question the value of making learners aware of the sorts of generalizations that linguists themselves make when analyzing a language. More recently, the question has been framed in terms of (1) whether second language learners benefit from focus on linguistic form, with or without the associated meaning, as opposed to focus exclusively on meaning, and (2) whether explicit instruction is superior to implicit. There is now an emerging consensus that focus on form is