Learners, Teachers, and Technology: Personalization in 2015 and Beyond


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Many key questions about learning in the classroom can be boiled down to, “What should I do next?” From a teacher’s viewpoint, this question can mean, “Which topic should I teach next?” or “Do students need more review or are they ready to move on?” For a student, “what should I do next?” can mean, “Which math problem should I do next?” or “Which topic should I study next?”


It is possible to answer these student questions without much thought about any particular student. The student should do the next problem on the pre-printed math curriculum worksheet. He or she should move on to the next topic in the district scope and sequence and move to it at the same time everyone else does in order to stay on track for the year.


However, since 2012, the term “personalized learning” has changed the “what should I do next?” conversation. If you type “personalized learning” into Google Trends, you’ll see searches for it by month, displaying that the term has skyrocketed in the previous two years. For example, the most frequent search count for the term “personalized learning” in 2012 is lower than the least frequent search count in 2014.


The Department of Education’s 2014 definition of personalized learning is as follows:



  • Personalization refers to instruction that is paced to learning needs, tailored to learning preferences, and tailored to the specific interests of different learners. In an environment that is fully personalized, the learning objectives and content as well as the method and pace may all vary.

  • The promise of personalization is that learning and instruction can be varied based on the individual. Despite the hype, two big questions remain: who makes the decisions and how are they made? In discussions of personalization there are three candidates to make the decision: teachers, students, and technology.


The Student as the Decision Maker


Some advocates of personalized learning (for example, Barbara Bray here) emphasize the role of the student in driving learning decisions. In this view, students actively participate in the design of their learning, including how and what they learn. They bring their interests and background to bear on the decision of what to do next. On the other hand, others like Benjamin Riley, caution that students don’t have awareness of research-based learning progressions that detail the paths most likely to lead to in-depth understanding of complex concepts. In other words, how students make decisions about what to do next may not lead to the best learning outcomes.


Technology as the Decision Maker


On the other hand, many technologists use the term “personalized learning” to mean largely technology-based systems. These use data-driven approaches to serve content and activities to students based on statistical and instructional models. The technology allows the use of big data to investigate the learning outcomes of students who take path A versus path B through content. Even more, the technology-based systems can look at how these outcomes vary for students with a variety of characteristics and interests. Finally, the technology allows personalization to scale to a whole classroom whereas making separate paths for 30 students is a big request to make of a teacher.


However, the promise of these systems outstrips the current reality. Most current systems at scale are personalizing based solely on skill levels – not interests or preferences. Those with experience in statistics know that building models that involve dozens of variables and the interactions between those variables is a non-trivial exercise. The ability to do this and obtain results that educators would find reasonable has yet to be demonstrated. Finally, as Mike Caulfield cautions, structured discussion is key to learning, particularly learning how to talk and think like experts in a given field. If every student is working at his own pace on his own path, it is not clear how these interactions happen.


The Teacher as the Decision Maker


The third option for the decision maker is the teacher. Teachers are already making hundreds of decisions per day in the classroom. Teachers are the people best positioned to help students make connections between content and students’ lives, as well as lead instruction such as the structured discussions described above. However, personalizing to the level described by advocates of personalized learning, for all students requires far more time and tracking than is possible for most teachers. Realistically, teachers cannot create unique problem sets for all the students in their classroom.


In the end, there needs to be a sophisticated blending of learner, teacher, and technology. When does it make sense to give the learner choice and when does it not? When is it helpful to have technology systems provide content personalization and which decisions are best left to teachers? Do we create systems in which teachers provide the content guidelines within which the system chooses activity or systems that recommend guidelines within which teachers and students choose activity? I hope in the next years we can come to understand the roles and responsibilities that each has in improving learner outcomes.


Kristen DiCerbo is a principal research scientist at Pearson’s Center for Digital Data, Analytics, & Adaptive Learning.



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