As everything in our media environment becomes more tailored to an individual’s tastes and skills, education too is searching for ways to put the technology’s capacity for individualization to use. Whether to boost student engagement, diagnose a student’s learning gaps more precisely, or provide automated differentiation, personalized learning is the object of much hope for those who see technology’s potential for improving education.
Still, that potential is far from reality, and policy impediments are part of the reason. In recent months, several reports have attempted to explain the lag. They offer useful ways of thinking about the problem, but they also beg a larger question: Are we doing enough to better understand personalized learning from the teacher perspective?
One of these reports came from Bellwether Education Partners. In A Policy Playbook for Personalized Learning, Carolyn Chuong and Sara Mead released a set of policy recommendations for promoting personalized learning in public schools. The “playbook,” as they call it, focuses on building both supply of and demand for high-quality personalized learning, especially those tools and models driven by new technologies, and removing policy barriers to effective implementation. Though the authors seem to use supply and demand as stylized descriptors to distinguish between policies that apply to consumers and those that apply to producers, their recommendations are mainly focused on the market mechanisms at work in the development of personalized learning models.
Currently, many of those forces are market failures, naturally arising to block effective use of technology to promote student learning. The framework set out by Chuong and Mead offers smart recommendations to empower schools, districts and states. For example, to overcome information asymmetries that prevent teachers, schools and districts from knowing the quality of a product before they buy it, Chuong and Mead propose that states establish an “approved model” designation and provide annual accountability report cards on those approved models. To surmount the high fixed costs associated with many major overhauls of a school’s IT resources, the authors suggest that districts set aside funds that schools can access for one-time costs or set up a revolving loan fund that would provide districts or schools with interest free loans to cover such costs. To get around the structural incompatibility between the way schools currently function and the flexibility required to execute personalized learning effectively, Chuong and Mead propose changes to the class size, teacher certification and “seat time” requirements.
Very few of these changes are directed at teachers specifically. And yet a second report out this summer — a new survey from the Organization for Economic Co-operation and Development (OECD) — suggests that teachers should be central to any strategy to expand technology use. In conducting the Teaching and Learning International Survey (TALIS), the OECD researchers examined which teachers in different countries used certain teaching practices, one of which was the use of information and computer technology (ICT). The survey reveals a great deal of variety across the OECD countries in their use of ICT (pictured below in dark blue):
Why so much variability in the use of technology? As is shown in the third graph below, the variability was explained best not by school or country level factors, but overwhelmingly by teacher level characteristics. (Note: the first two graphs also show the variability of two other teaching strategies that the OECD survey examined: the use of small groups and assignments of projects lasting longer than one week.)
In defense of Chuong and Mead’s approach, which privileges policy changes at school, district and state level over those focused at teachers, school- and country-level factors account for more (about 7 percentage points) of the variation in use of ICT than in the use of long projects or small groups. The OECD report recognizes that this difference likely flows from the cost concerns addressed by Chuong and Mead. As the OECD authors explain, “…when it comes to practices that require more resources, such as the use of ICT, school- and country-level factors tend to play slightly more of a role.”
Nevertheless, teacher factors remain easily the biggest factor determining technology uptake and any plan that seeks to make serious inroads to adoption will face fundamental barriers to success without components that seek to invest and train teachers in the best technologies and instructional models.
Focusing on teachers is part of New America’s recent analyses on learning technologies. Last month, Lindsey Tepe emphasized the importance of training teachers to use new technologies in her assessment of the progress on the president’s ConnectED initiative. Similarly, Lisa Guernsey’s report, Envisioning a Digital Age Architecture, devoted much of its attention to “boosting the workforce” and asked whether teacher preparation programs are up to the task of modeling how technology can be integrated effectively. None of this is to detract from the value of Chuong and Mead’s policy prescriptions. But given the role teachers play in the use of technology, its important to see that theirs is not an exhaustive list of policy changes that must take place for personalized learning to be a success.
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