By Jonathan A. Bower

Personalizing Learning with Student Control vs. Algorithms
In general, the large amount of research on the subject of student agency shows that “the degree to which students learn how to control their own learning … is highly related to outcomes.1”
Furthermore, for computer-aided instruction, “when the student is in ‘control’ over his or her
learning … then the effects were greater than when the teacher was in ‘control’ over these dimensions of learning.2” Students who feel that they are in control of their learning are more highly motivated to do the often difficult work of acquiring a new skill like reading. Studies by Kanevsky & Keighley on student engagement show that, “Five interdependent features … distinguished boring from learning experiences: control, choice, challenge, complexity and caring
teachers. The extent to which these five C's were present determined the extent of students’
engagement and productivity.3”
Student-controlled software gives students control through choice. Students choose their own
challenges across a range of complexity; for example in reading, evolving from simple alphabet
activities to the application of complex analysis to text. The key item that software cannot provide is a caring teacher. That’s where peer learning comes in4: students can share a screen withpeers, who usually care about their co-learners, and with caring parents who certainly do. In thisway, student-controlled software provides truly engaging and effective learning experiences.
And what about algorithms? Computer-assisted instruction is one of the many interventions
studied by researcher John Hattie who found that:• normal mental development and exposure to a teacher for a year generates an averagelearning gain of 0.375, while• the use of computer-assisted instruction also shows a gain of exactly 0.37 per year.
Students who use algorithmically-controlled software see no benefit above students who don’t!
We shouldn’t be surprised. The algorithms used are rarely sophisticated enough to account forthe different learning pathways of real students. As Richard Culatta says, “a model where astudent is simply clicking through digital content at their own pace does not meet the criteria for
personalized learning.”6 Even “Individualized Instruction” programs based on student-response
algorithms do not provide a truly personalized learning experience, and as shown by Hattie’s
analysis if the research, do not contribute in a meaningful way to learning.
Flink Learning implements all of this best practice research by putting students in charge of theirown learning in order to maximize learning outcomes. Flink Learning products are studentcontrolled. This means that students choose not only when and where they use them, but also,
what learning activities they perform, or build ̶ without limitations. They are free to choose anylevel, any activity, and to repeat activities as they wish/need. They use information about theirperformance on each activity, and about what they need to accomplish, to decide whether torepeat it, move on to a more difficult one, or to do something else entirely. The result is both
long-term engagement and accelerated learning.
1

Hattie, John. Visible Learning: a Synthesis of Meta-Analyses Relating to Achievement, 2009, P. 48.
Ibid. P. 225.
3
Lannie Kanevsky & Tacey Keighley. To produce or not to produce? Understanding boredom and thehonor in underachievement, Roeper Review, Volume 26, 2003 - Issue 1, P. 20-28
4
See Peer Learning is Better than 1-1 Computing by Jonathan Bower
5
That is, an effect size of 0.37 from the school experience over one year. Effect sizes ranged from -0.3 to1.44.
6
Tweet: Richard Culatta,CEO, International Society for Technology in Education
2For more information, contact Jon Bower at 508-641-4035 or at jon@flinklearning.com.