Knewton, a technology firm founded in 2008, has developed an “adaptive learning platform” that received significant media attention (also here, here, here and here), as well as funding and recognition early last fall and, again, in February this year (here and here). Although the firm is not alone in the adaptive learning game – e.g., Dreambox, Carnegie Learning – Knewton’s partnership with Pearson puts the company in a whole different league.
Adaptive learning takes advantage of student-generated information; thus, important questions about data use and ownership need to be brought to the forefront of the technology debate.
Adaptive learning software adjusts the presentation of educational content to students’ needs, based on students’ prior responses to such content. In the world of research, such ‘prior responses’ would count and be treated as data. To the extent that adaptive learning is a mechanism for collecting information about learners, questions about privacy, confidentiality and ownership should be addressed.
Adaptive learning platforms are built on the premise that learning is optimized “when students receive exactly the content that they need at exactly the right time.” Although products like Knewton are not on the market yet, available information – e.g., see this short video – suggests that the platform is designed in a manner that might appeal to learners. For example, the software includes game- and social network site- features (e.g., rewards, points, badges, “study buddies” etc.) and combines them with attributes of more traditional course management systems, as well as data summarization/visualization capabilities.
As valuable these and other features may be, there’s nothing truly revolutionary so far. In fact, without actual access to the software, it is difficult to assess whether the product does indeed live up to the sexy promise of adaptive learning. But what do we know about adaptive learning more generally?
Adaptive learning can be viewed as part of learning analytics, an emerging field for which time-to-adoption is estimated at four years, according to the 2011 Horizon Report. Learning analytics (LA) applies the model of analytics to the specific goal of improving learning outcomes as measured by grades, retention, and completion. A recent Educause brief explained it this way:
LA collects and analyzes the “digital breadcrumbs” that students leave as they interact with various computer systems to look for correlations between those activities and learning outcomes. LA software compares a student’s activity with others in the class, with students who previously took the course, and/or against other rubrics to create a model for how each student is likely to fare. In this way, LA capitalizes on the vast quantities of data that most colleges and universities collect to find patterns that can be used to improve learning. […] Learning analytics tools can track far more data than an instructor can alone, and […] identify factors that are unexpectedly associated with student learning and course completion.
While there is no doubt in my mind that these technologies have much potential, the extent to which existing platforms can dynamically and intelligently adapt to different learners is less clear at the moment. As Horizon 2011 notes, referring to learning analytics, “It is still very early and most of the work in this area is conceptual” (p. 32).
Platforms like Knewton work like other recommendation systems, such as Netflix or Pandora. For those unfamiliar with it, Pandora generates music recommendations based on the recurrent elements present in the songs a user enjoys. For this approach to work, two steps are necessary. First, we need to understand and be able to represent the phenomena we want to personalize – i.e., in music, we need to identify features that are important in a song. Second, we need input from users. In other words, we need people’s reactions to song suggestions. Data on users’ preferences (likes/dislikes) are essential for the system to return good (i.e., user- adapted) suggestions.
Predictive/adaptive learning works similarly in that it incorporates and exploits digitally-captured learner activity. In other words, the platform takes advantage of how students interact with content, learns from it, and offers adapted material in return.
Knewton, for example, is working with several higher education institutions in the country, including the University of Nevada Las Vegas, Penn State University, and Washington State University, which are currently test beds for the platform. In other words, data from students at these universities are informing the product’s development.
There is enormous interest in information about how learners interact with educational materials. Being able to collect data about student progress in real time is a very powerful idea. Essentially, it enables us to observe and understand the process of learning. Questions such as ‘how do people learn?’ or ‘what exactly influences learning outcomes?’ have always been critical, and adaptive learning may help to address them. But who will have access to student data?
At the moment most of these and similar data belong to the companies that collect them. In fact, to some extent, public money is indirectly financing data collection for the development of a for-profit product. Ironically, when such software products are finished, they may be sold back to students – much like those students whose data helped fine-tune the starting algorithms.
Social scientists have, for some time now, taken issue with the fact that valuable data are largely controlled by companies such as Facebook, Google and government agencies, for profit and national security purposes, respectively (see here and here). But what about other stakeholders, including the research community, and other purposes such as generating knowledge that informs policy and serves the public interest? Wouldn’t we be better served if scientists and educators also had access to these data and could use them to help and guide their students?
- Esther Quintero