1999; Fischer & Bidell, 1998). In other words, proficient and advanced students who learn how to play the school game, sit quietly, and get through the traditional tasks of schools as quickly and efficiently as possible are likely going to struggle when it is time to apply their learning outside of school. Teaching and learning with the end goal of a good grade on the material from the book is less effective and lacks the emotional aspect of learning. This embodies the old saying that someone is “book smart, but not street smart.” To challenge and push learners, especially those question 4 students, it is our job to make learning emotional and to connect their learning to what they will need to know and be able to do outside of the classroom.
Emotion and cognition go hand in hand. In education, we ask students to learn, pay attention, remember, make decisions, motivate, and collaborate with others. Emotion affects all of these important learning factors. The question isn’t whether we should pay attention to emotions. For educators the question becomes, How do we leverage the emotional aspects of learning in education?
Immordino-Yang (2016) shares another key finding: the toggling that takes place when the brain is looking out (actively learning) or looking in (resting). While we all know the brain is never truly at rest (it is always working to keep us alive and manage biological functions necessary for life), we do have times when we turn off external stimuli and rest our brains to a certain degree. Daydreaming, reflecting, and just thinking are key components of what takes place when we turn off the external stimuli. In listening to and reading study after study (Buckner & Vincent, 2007; Esposito et al., 2006; Fox et al., 2005; Raichle et al., 2001; Seeley et al., 2007) cited in Immordino-Yang’s (2016) work, it seems logical to suggest that it is important for educators to consider providing students the opportunity to spend time looking out and looking in.
While we certainly don’t want to make a claim or post the headline that says, “Neuroscience says personalized learning works,” we do feel validated because what we know about the connection between emotions and learning supports the personalized learning strategies we describe in this book. In personalized learning, teachers give students opportunities to emotionally connect with what they are learning and time for self-reflection. Based on all that she has done in the field, we asked Immordino-Yang what her ideal classroom would look like. She shared that her ideal classroom, which would of course look different in each environment, would be one where all students are engaged and generally willing to share what they are doing. Students may say they are doing great, not doing great, or just doing OK, but they would know why this is so and what it would take to do better (M. Immordino-Yang, personal communication, April 2017). Immordino-Yang also said in her ideal environment, the teacher would be able to tell you one thing about which each student is an expert. To us, this sounds a lot like personalized learning.
Research on Personalized Learning
While there is not a great deal of research about personalized learning, the limited extant research is promising. Some specific studies include a 2014 Bill and Melinda Gates Foundation report featuring RAND Corporation research and a 2015 follow-up report (Pane, Steiner, Baird, & Hamilton, 2015). The two-year study (Bill and Melinda Gates Foundation, 2014) includes five thousand students attending twenty-three charter schools that began implementing personalized learning in 2012. There are some promising results, as gains in mathematics and reading scores are significantly higher than a comparison group’s. Effect sizes are .41 in reading and .29 in mathematics (Bill and Melinda Gates Foundation, 2014). Note that effect sizes allow researchers looking at others’ work to compare their results, even if they used different statistical measures. Effect size predicts whether or not the strategy would work and it helps predict how much range in the scenarios.
In a 2015 follow-up report, the RAND Corporation uses a larger study of sixty-two schools involving more than eleven thousand students, which again reveals gains in mathematics (.27) and reading (.19) when compared to control groups (Pane et al., 2015). Perhaps even more promising, the 2015 report states the schools in the original study continue to see gains, and those who had the most growth are students who began with lower achievement levels. A 2017 report (Pane, Steiner, Baird, Hamilton, & Pane, 2017) notes that schools that were awarded funding through the NGLC (Next Generation Learning Challenges) experienced positive achievement effects in mathematics and reading, with statistical significance in reading, and that levels of achievement relative to grade-level norms appeared to benefit.
Additionally, Jim Rickabaugh shares impressive data about work from districts in Wisconsin (J. Rickabaugh, personal communication, March 4, 2017). He notes that in an unpublished report from the Institute for Personalized Learning, where he serves as senior advisor, there are specific examples from three different districts showing increases in projected growth in areas such as mathematics and reading on Northwest Evaluation Association Measures of Academic Progress tests after incorporating personalized learning strategies. In the study cited in this report, all seventh-grade students were evaluated by how they performed on the Northwest Evaluation Association (NWEA) Measures of Academic Progress (MAP) assessments. This is significant, as even the top-performing students were measured for academic growth. In this example, 73.6 percent of the students saw growth in their own learning. In another middle school implementing personalized learning strategies, a significant number of students completed top-level mathematics courses and were ready for precalculus when they entered high school. In yet another middle school, a district with scores typically above the eighth-grade norm-referenced test, data indicate that at each grade level at the middle school, students, even the top performers, showed an average 25 percent growth in college readiness in English, mathematics, reading, and science. The report also shares qualitative findings that reference the power of personalized learning.
While it would be wonderful to have a broader range of research that specifically ties to personalized learning, the best case for the topic comes from Professor John A. C. Hattie’s (2009, 2015) work, which includes a great deal of deep research that reflects the underpinnings of personalized learning. Hattie, who many consider to be the most influential education researcher, regularly updates a ranked list of the influences that impact student learning (Visible Learning, n.d.b). Of the top items, we find the ones in the following list to be in direct alignment with personalized learning. Note the numbers in parentheses are the effect sizes. Hattie determines that the average effect size of all the strategies or interventions is 0.40. The list ranges from 1.62 (teacher estimates of achievement) at the top to -0.9 (physical influences of ADHD) at the bottom.
■ Teacher estimates of achievement (1.62): Teachers knowing their learners, developing a plan to ensure student success, and then following the plan
■ Self-reported grades (1.33): Teachers getting to know learners by learning what the students’ expectations are, and then working with the students to exceed them
■ Cognitive task analysis (1.29): Instructional strategies that require a lot of cognitive activity from the learner and include items such as decision making, problem solving, memory, attention, and judgment
■ Strategy to integrate with prior knowledge (.93): In order to acquire deeper learning, deliberately activating prior knowledge and then making relations and extensions beyond what students have learned at the surface phase
■ Teacher credibility (.90): Students’ perception about whether or not the teacher is high quality
■ Teacher clarity (.75): Teachers providing a clear explanation about what is expected of students (goals and success criteria) before providing instruction
■ Feedback (.70): Teachers providing immediate feedback, which aligns very closely with formative assessment, to learners to maximize student learning; this also includes feedback from the student to the teacher
While these items do not specifically mention personalized
