Mary Cullinane, Houghton Mifflin Harcourt’s first Chief Content Officer, writes on Why Free Is Not the Future of Digital Content in Education. She makes the case that technology advances will not make digital content eventually free, just like in the music industry. This is because technology is adding value, not just reducing the cost of delivery or production. She writes:
As students engage with the content, the content learns more about the students and it also becomes “smarter”. A digital engine compares students’ responses to those of all other users. Equipped with that data, this adaptive learning system doesn’t just show that a student answered incorrectly. It knows why she did, and uses those insights to create a customized learning path.
In doing so, technology helps solves a big problem that has always confronted teachers: students learn at different paces. Advanced students can get bored and struggling students can give up. Now, as a teacher, I can put content in front of each learner that is personalized to his or her needs. It’s something teachers have been doing through the ages, but technology brings it to the next level of adaptivity.
However, this is not an argument that precludes “free”. Technology has added tremendous value for many services (take, for example, instant messaging) and kept the price at zero. Adaptive learning is just simply not the domain of publishers who have large repositories, author networks and organized funding. It can be done in a free, open manner too (just take a look at Khan’s work, for example). What is true, though, is that content development for adaptive learning can become expensive very quickly. We have come a long way since Ms. Lindquist : The Tutor. Now paths through content leading to mastery can be uncovered through collective intelligence, rather than having to be enumerated as before. However, adaptive content still has design requirements that are in addition to regular content development and learning design.
The other thing I would watch against is taking this as a magic wand that “solves a big problem” for teachers and personalizes learning for students. Personalized learning is a very difficult thing to crack, and it is not the same as recommendation systems such as the ones we see today that crowdsource learning patterns. Ultimately, these systems seek to harmonize existing goals (like solving an algebraic equation or learning a grammar construct) where the ontology is precise and the domain exhibits structured rules. Much of learning, however, is not that. Nor is it always goal-seeking.
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