This week I will keep with my current trend of analyzing the Mango language learning approach from different second language learning (SLA) perspectives, theories, and hypotheses. In this blog I will adopt the skill acquisition theory of SLA, specifically McLaughlin’s (1987, 1990) information-processing model and Anderson’s (1983, 1985) Active Control of Thought (ACT) model and see how and whether Mango Languages takes in to account this theory of SLA.
The skill acquisition theory of SLA views language learning as similar to learning other skills (i.e., math, driving a car, etc.). This means that it requires practice. It is through practice that the information being learned moves from short-term memory (STM), where it begins, to long-term memory (LTM), where it is said to be stored once learned. Second language (L2) input is initially stored in STM. According to McLaughlin’s information-processing model, the initial input is retrieved from STM for production by what he refers to as “controlled processing.” This simply means that learner must make a controlled effort to retrieve the information, i.e., vocabulary, grammar, sounds, etc., from their STM in order to produce the desired response. According to this model, repeated activation of this ‘knowledge’ in the STM moves it to LTM where it becomes available for rapid retrieval with minimally controlled effort by the language learner. McLaughlin calls this shift from controlled to automatic processing, automatization.
The process of automatization is also important in Anderson’s ACT model. According to Anderson, it is through automitization that declarative knowledge, i.e., knowledge that something is the case, shifts to become procedural knowledge, i.e., knowledge of how to do something. To understand the difference between declarative and procedural knowledge, imagine you are learning to drive a car. For example, you will be told that if the engine is revving too much that you need to change to a higher gear. You will also be told how to change gears. This knowledge of the indicators that it is time to shift to a higher gear and knowing theoretically how you should do this is an example of declarative knowledge. However, when it is time to actually perform this task the student driver will most certainly not perform well, at least not their first time. That is, simply knowing what to do does not necessarily mean that you will know how to do it successfully. In order for a skill to be automatic, or proceduralized, you must go through the declarative stage before acquiring the procedural knowledge needed to perform the task successfully. However, for any of you that remember driver’s ed or have teenagers currently taking driver’s ed. (God help us! I’m one of this bunch!), you know that while learning you have to practice, practice, practice. That’s the idea behind McLaughlin and Anderson’s models. Practice is the key! Therefore, declarative knowledge of the L2 is necessary but not sufficient for successful language acquisition.
Mango Languages recognizes this and applies the skill acquisition theory to our language learning software. The Mango system is programmed to request output from the student on newly presented vocabulary and phrases, as well as perform automatically generated quizzes throughout the course at certain spaced intervals in order to implement the concept of automatization through repeated activation of material. The student is also able to repeat any slide, lesson, chapter and, even entire course, any number of times.
So, what do you think? Are you willing to hand your car over to a 16 year old who has only read the operator’s manual? Or, do you agree with McLaughlin and Anderson that practice makes perfect? Or at least almost perfect?!