Memory systems select from environmental stimuli those to encode permanently. Repeated stimuli separated by timed spaces without stimuli can initiate Long-Term Potentiation (LTP) and long-term memory (LTM) encoding. These processes occur in time scales of minutes, and have been demonstrated in many species. This study reports on using a specific timed pattern of three repeated stimuli separated by 10 min spaces drawn from both behavioral and laboratory studies of LTP and LTM encoding. A technique was developed based on this pattern to test whether encoding complex information into LTM in students was possible using the pattern within a very short time scale. In an educational context, stimuli were periods of highly compressed instruction, and spaces were created through 10 min distractor activities. Spaced Learning in this form was used as the only means of instruction for a national curriculum Biology course, and led to very rapid LTM encoding as measured by the high-stakes test for the course. Remarkably, learning at a greatly increased speed and in a pattern that included deliberate distraction produced significantly higher scores than random answers (p < 0.00001) and scores were not significantly different for experimental groups (one hour spaced learning) and control groups (four months teaching). Thus learning per hour of instruction, as measured by the test, was significantly higher for the spaced learning groups (p < 0.00001). In a third condition, spaced learning was used to replace the end of course review for one of two examinations. Results showed significantly higher outcomes for the course using spaced learning (p < 0.0005). The implications of these findings and further areas for research are briefly considered.
Arne Duncan, US Secretary of State for Education, tweeted in 2013: 'let teens sleep, start school later'. This paper examines early starts and their negative consequences in the light of key research in the last 30 years in sleep medicine and circadian neuroscience. An overview of the circadian timing system in adolescence leading to changes in sleep patterns is given and underpins the conclusion that altering education times can both improve learning and reduce health risks. Further research is considered from education, sleep medicine and neuroscience studies illustrating these improvements. The implementation of later starts is briefly considered in light of other education interventions to improve learning. Finally, the impact of introducing research-based later starts synchronized to adolescent biology is considered in practical and policy terms.Keywords: education; adolescence; school start times; circadian; sleep deprivation; learning; health Introduction Time is a fundamental variable in human biology and in education, yet currently the two disciplines measure time in different ways. For the educator, student development is defined by age and the daily timetable by social conventions that vary between countries, regions and even individual schools. In contrast, biological time is measured in developmental changes in the body, and over the day by our internal biological clock. It is no surprise given the relative novelty of mechanical clocks in evolutionary timescales that our ability to function optimally, including in learning, varies with biological time rather than conventional social times.
While many studies have shown the benefits of later school starts, including better student attendance, higher test scores, and improved sleep duration, few have used starting times later than 9:00 a.m. Here we report on the implementation and impact of a 10 a.m. school start time for 13 to 16-year-old students. A 4-year observational study using a before-after-before (A-B-A) design was carried out in an English state-funded high school. School start times were changed from 8:50 a.m. in study year 0, to 10 a.m. in years 1–2, and then back to 8:50 a.m. in year 3. Measures of student health (absence due to illness) and academic performance (national examination results) were used for all students. Implementing a 10 a.m. start saw a decrease in student illness after 2 years of over 50% (p < 0.0005 and effect size: Cohen's d = 1.07), and reverting to an 8:50 a.m. start reversed this improvement, leading to an increase of 30% in student illness (p < 0.0005 and Cohen's d = 0.47). The 10:00 a.m. start was associated with a 12% increase in the value-added number of students making good academic progress (in standard national examinations) that was significant (<0.0005) and equivalent to 20% of the national benchmark. These results show that changing to a 10:00 a.m. high school start time can greatly reduce illness and improve academic performance. Implementing school start times later than 8:30 a.m., which may address the circadian delay in adolescents' sleep rhythms more effectively for evening chronotypes, appears to have few costs and substantial benefits.
University days generally start at fixed times in the morning, often early morning, without regard to optimal functioning times for students with different chronotypes. Research has shown that later starting times are crucial to high school students' sleep, health, and performance. Shifting the focus to university, this study used two new approaches to determine ranges of start times that optimize cognitive functioning for undergraduates. The first is a survey-based, empirical model (SM), and the second a neuroscience-based, theoretical model (NM). The SM focused on students' self-reported chronotype and times they feel at their best. Using this approach, data from 190 mostly first and second year university students were collected and analyzed to determine optimal times when cognitive performance can be expected to be at its peak. The NM synthesized research in sleep, circadian neuroscience, sleep deprivation's impact on cognition, and practical considerations to create a generalized solution to determine the best learning hours. Strikingly the SM and NM results align with each other and confirm other recent research in indicating later start times. They add several important points: (1) They extend our understanding by showing that much later starting times (after 11 a.m. or 12 noon) are optimal; (2) Every single start time disadvantages one or more chronotypes; and (3) The best practical model may involve three alternative starting times with one afternoon shared session. The implications are briefly considered.
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