Is it the end for books and pencils?

As part of the #EdbookNZ challenge for connected educators month 2016, I have been kindly asked by @vanschaijik to blog about a topic in education. This year the topic is buzz words in teaching. I encourage comments that disrupt what I have written, which you can do in the comments section.

So after much thought, I have chosen the buzzword of 'adaptive technologies' to see how it really stacks up against my most favourite of words - evidence.

Our world is experiencing an evolution in adaptive technologies. Adaptive technologies are systems that adapt to us as individuals. Such systems are ubiquitous like using the smartphone app Didi Chuxing to hail a cab here in China. So with the spread of adaptive technologies, it is not surprising that they are increasingly found in education.

At present, the standard classroom is still like the one below, the teacher standing at the front transmitting the same content to all students at the same time.

However, there are now new perceived demands for personalised learning. It is argued that such a massive increase in the demand for such education may only be met with technology (National Science Foundation Task Force on Cyberlearning, 2008). However, it can also be suggested that personalised learning is just merely an upgrade of the automation processes prevalent in education since Victorian times. 

One of the major hallmarks of adaptive technology is big data. This allows in the view of supporters, a number of opportunities including enabling educators to capture the benefits of diverse learning environments and the use of techniques such as data mining and machine learning to expand our understanding of learning as a basis for guiding more effective pedagogy.

However, detractors also point out problems. For example, although the data gathered can investigate relationships among variables, they do not usually include context. Additionally, the ownership of and access to data is complicated (Office of Science and Technology Policy, 2012). Finally, the gathering of information on individual students raises privacy concerns (Nissenbaum, 2010; Pitman & McLaughlin, 2000; Glenn, 2008).

So with these issues surrounding privacy now in mind, let us now consider the track record of technology implementation in education. The introduction of computers to the classroom has been well-supported and sometimes even successful. Resistance runs deep in our profession and the push to equip students with digital devices has met with resistance. In the context of the traditional classroom, internet-connected devices risk distracting from traditional learning. A recent OECD study found that the students who spent most of their time on computers performed worse than their peers in standardised testing. The OECD report concluded that:

“adding 21^st century technologies to 20^th century teaching practices (emphasis added) will just dilute the effectiveness of teaching.”

So it is tempting, as educators, to dismiss the whole trend as the latest fad, destined to upset curricula and enrapture education ministers for a few years until the next fad comes along. But there is one reason why adaptive learning might prove an exception. In at least some contexts, it may work. Take, for example, a once-struggling middle school in South Carolina, that credits adaptive technology for boosting test scores, student engagement, and even teacher attendance.

However, a few success stories are not yet convincing, especially when considering that the technology may enable a traditional education system that may no longer be fit for purpose. Furthermore, a look back at the history of educational technology shows a path that is littered with failed efforts to automate the teaching process. Adaptive technology isn’t quite there yet with controlled studies of its efficacy yielding at best, conflicting results.

As an example, the effectiveness of adaptive algebra software, developed at Carnegie Mellon University has been closely studied, and in some settings, it has appeared to substantially boost students’ performance. Yet a 2010 review of the research found “no discernible effects” on high school students’ test scores.

Proponents of adaptive technology look at these results and conclude that properly implementing the technology simply requires an adjustment period on the part of the students, the teachers, or both. Then you’ll be rewarded with significant improvements in learning.

As Carnegie Learning co-founder Ken Koedinger puts it:

“So far in educational technology, we’re in the Model T stage.” 

Opponents look at educational technology’s track record as a whole and see something different: a long history of big promises and underwhelming results. Audrey Watters, a vocal critic of educational technology states:

“The research is quite mixed: Some shows there is really no effect when compared to traditional instruction; some shows a small effect. I’m not sure we can really argue it’s an effective way to improve education.”

Stephen Laster, McGraw-Hill Education's digital officer is adamant that the teacher is still irreplaceable.

“We think education is inherently social, and that students need to learn from well-trained and well-versed teachers. But we also know that that time together, shoulder-to-shoulder, is more and more costly, and more and more precious.”

In this view, the role of adaptive technology is to automate more of education allowing the teacher to focus on what humans do best. How much you can automate, depends on what you’re teaching. In maths, the objectives are easily measurable. Students need to be able to correctly solve problems with just one correct answer. If they can do that, they’ve mastered the material.

So an adaptive technology focused on skills, practice, and mastery is a good fit and you can see if it is effective by seeing how many learners have achieved a measurable number of questions right. In this case, adaptive technology does allow students can see right away what they’ve got right and wrong. So far, so good, but what happens when you have a class where the questions are something like, “What would Oscar Wilde think of Instagram?”

There are other downsides, too. I had two students who were in the same class, but they couldn’t use the same book because everyone has to have their own login code This last point is symbolic of a deeper problem with personalised learning: It treats learning as a solo endeavour.

Think of two different classrooms. One in New Zealand, where you have students, struggling quietly at their devices solving a physics problem. In the second which I see in China, a group of students are huddled around a textbook, working their way through the same problem. They may well have come into the class with different skill levels. However, because they were all assigned the same page numbers and exercises at the same time, they can learn together, helping and correcting one another as they go. The collaborative process by which they solve a problem involves a different kind of adaptive learning. One in which humans adapt to one another, rather than waiting for software to adapt to them.

The speed and apparent efficacy of today’s adaptive technology can mask deeper limitations. MIT digital learning scholar Justin Reich argues in a blog post for Education Week

“computers are good at assessing the kinds of things—quantitative things, computational things—that computers are good at doing. Which is to say that they are good at assessing things that we no longer need humans to do anymore.”

The inference is that adaptive technology won’t teach learners the underlying skills that they’ll need to tackle complex, real-world problems. More importantly, it won’t prepare them for a future in which rote jobs are increasingly done by computers.

So, let’s for the sake of argument, say that adaptive technology already works, we can take a classroom of students with different experiences and teach them all a well-defined set of objectives. Let’s state that adaptive technology will excel where the mastery of skills can be assessed through multiple choice or short-answer quizzes. Let’s further assume that the predictive algorithms will continue to evolve, the data will grow more robust, the content will continue to improve, and teachers will get better at integrating it into their classrooms.

Where I grow uneasy is at the point. At a time when schools face cost cutting and intense pressure to boost students’ achievement rates, it’s easy to imagine choosing adaptive technology at the expense of us, the human teachers. This also might drive out those teachers who resist the push to rebuild their classes and teaching styles to suit the technology.

To see the possible implications, consider the following research. Students were asked the following question: “There are 125 sheep and five dogs in a flock. How old is the shepherd?”

My concern is that adaptive technology might train generations of students to become ever more efficient at applying rote memorization without ever learning to critique and challenge what is being taught.

So perhaps adaptive technology will be a false step on the path to a better learning. But there’s also a risk in marking a step wrong too early. It would be a mistake, in criticising today’s educational technology, to romanticise the status quo.

Ideally, we'll find ways to make use of a promising new technology without turning our students into sheep, which even as a teacher from New Zealand is not a good thing.

References:

Glenn, D. (2008). Huge databases offer a research goldmine and privacy worries. Chronicle of Higher Education, 54(35), A10. 

National Science Foundation Task Force on Cyber-learning. (2008). Fostering learning in a networked world: The cyberlearning opportunity and challenge. Washington, DC: National Science Foundation.

Nissenbaum, H. (2010). Privacy in context: Technology, policy, and the integrity of social life. Palo Alto, CA: Stanford University Press.

Pitman, J. and McLaughlin, B. (2000). Making cyberspace safe for children. Educational Leadership, 67(6), 67-71.