Language instruction is no longer delivered exclusively by humans, and the trends are pointing to a future where a substantial part of language instruction could (at least theoretically) be delivered by machines and algorithms. Language education providers that employ this new technology now need to make design choices on the pace and degree of personalisation they deliver in their online courses.
Origins of Machine-Powered Instruction
The origins of modern Machine-Powered Instruction take root in the 1980’s when language distance courses started transitioning from paper to digital interface with the rise of the personal computer and CD-Roms that were sophisticated enough to direct a learner down a particular learning path. When the internet exploded worldwide, “digital courses” (often completely standardised) evolved to become “online self-study courses” with more options, more flexibility and could be updated even after the course was purchased or sold.
The Future of Machine-Powered Instruction
These days the interface may differ substantially - web application, mobile application, virtual classroom, virtual reality headset, augmented reality screen - but the key idea for MPI remains the same: a self-study course where the instruction is pre-programmed (increasingly using artificial intelligence (AI) to improve the experience).
The most helpful way to understand the different self-study courses is through their product design choices with regard to delivery and approach of instruction:
Delivery: Paced vs Self-Paced Instruction
Approach: Standardized vs Personal Instruction.
Paced vs Self-Paced Instruction
Paced vs. Self-Paced Instruction refers to when the instruction is delivered and/or the learning takes place.
A paced course is one where the learner completes the work by following a predetermined schedule, often with a cohort of other learners from the same online school.
In contrast, a self-paced course is where the learner completes the work whenever they choose and often work through the subject areas they care most deeply about themselves.
Standardised Instruction vs Personal Instruction
Standardised Instruction is where learning is delivered in the same way for everyone in that online course. In this case, “learning” may refer to the way the course goals, course content, class materials, approach, methodology, and/or assessment are delivered. Learners have little to no input with regard to what will be learned and how. This is typically decided by the online school.
Personal Instruction is where the learner makes (or is involved in) most of the decisions about what will be learned and how. At its finest, a personal approach implies the learner not only contributes to deciding what will be learned and how, but also to the creation of the overall curriculum.
Personalized Instruction is close to Personal Instruction but not quite. Personalized Instruction in language learning broadly refers to instruction that is customized to meet the specific educational needs of the learner. However, this instruction (including learning goals, class materials, assessment, etc.) is generally pre-created and pre-packaged without the learner’s input, i.e. by someone who hasn’t met and will never meet the learner. It has been suggested that “personalized learning” is something we do to learners, whereas “personal learning” is something learners do for themselves.
For example: If a student is brand new to learning to speak English and was considering learning with an online teacher and/or a self-study app, then it’s easier if we compare the 3 options: standardised, personalised and personal learning instruction.
Standardised instruction example: The student learns how to speak English by following the same process as any/all students. An old-school technology example of this is a CD-ROM where every learner follows the exact same pre-programmed learning path. Everything is predetermined: the curriculum, hours spent per unit, test, etc.
Personalised instruction example: The curriculum and content are predetermined but there are more choices available to the student (e.g. what topics are taught/skipped, timing, duration, etc) or the group the student is part of. An example using a web application might be where the app is optimised for Vietnamese (A1) learners of English. A more advanced example is the app offering multiple learning “tracks,” and customising the track as the learner moves along, based on his/her input or responses. Essentially, there are options and it is a huge step forward from standardised instruction and generally yields more effective results. However, the options are still pre-made and predetermined, created in advance by someone who has not met the individual learner, and in essence, limited in number.
Personal instruction example: This is the most advanced form of personalisation. The curriculum and content are not predetermined or prefabricated by the online school or teacher. Instead, the self-study application somehow collaborates with the student to understand the student's motivations, goals and preferences. Based on these, they create the curriculum together (it is important to clarify that by “curriculum” we do not refer exclusively to class materials, but also to learning goals, methodology, and assessment). The curriculum will therefore be unique to that individual at that point in time and therefore, the options for curricula are, by definition, unlimited.
These product design choices are expressed in famous self-study courses that you may be familiar with, although we are already seeing the authors of these machine-powered online courses start to experiment with enabling technology and mix with human-powered instruction such as Babbel, Altissia, Visiagora and ABA English.
Each of these choices has advantages and disadvantages. Advances in technology make a huge difference to the effectiveness of Machine-Powered Instruction. The more that AI makes decisions for the learner, and the better the AI needs to be at identifying and adapting to new data, the more personal the instruction can be.
Brian & I are still seeking feedback on this post as part of the pre-release of our upcoming book, LangTech...
What are your thoughts?