Every entry below is real and worth citing. The does not support line is what keeps you from being quoted badly eighteen months from now.
Quasi-experimental
Ill communication: Technology, distraction and student performance
Beland, L.-P., & Murphy, R. (2016). Labour Economics, 41, 61–76.
Examination performance in English schools improved after phone bans, with gains concentrated among lower-achieving students.
that a restriction may particularly benefit students who are currently struggling.
a general claim that banning phones raises test scores. Different country, earlier technology, and a policy environment predating generative tools.
doi.org/10.1016/j.labeco.2016.04.004
Cross-sectional
School phone policies and their association with mental wellbeing, phone use, and social media use (SMART Schools)
Goodyear, V. A., Randhawa, A., Adab, P., Al-Janabi, H., Fenton, S., Jones, K., Michail, M., Morrison, B., Patterson, P., Quinlan, J., Sitch, A., Twardochleb, R., Wade, M., & Pallan, M. (2025). The Lancet Regional Health – Europe, 51, Article 101211.
Across 30 English secondary schools and 1,227 adolescents, restrictive phone policies showed no association with better mental wellbeing and no association with lower overall phone use.
caution about promising wellbeing outcomes from a restriction.
the claim that phone policies do not work. The design compared schools that already differed; absence of an association is not evidence that a policy change produces no effect.
doi.org/10.1016/j.lanepe.2025.101211
National evaluation
Dutch national phone ban evaluation
Netherlands Ministry of Education (2025).
Roughly three quarters of schools reported improved concentration and about two thirds reported improved climate following restrictions.
that staff and students experience a restricted environment as calmer, which is a real and reportable effect.
an academic outcome claim. These are perceptions, not measured attainment, and only about a third reported academic gain. Read alongside SMART Schools, this is a measurement gap rather than a contradiction.
Survey
72% of U.S. high school teachers say cellphone distraction is a major problem
Hatfield, J. (2024, June 12). Pew Research Center.
Nationally representative teacher survey on classroom cellphone distraction.
that the problem is real and widely experienced, which is sufficient grounds to act.
any prediction about what a given remedy will produce.
Meta-analysis
What forty years of research says about the impact of technology on learning
Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011). Review of Educational Research, 81(1), 4–28.
A second-order meta-analysis pooling 25 meta-analyses across 1,055 primary studies. Overall random-effects mean of 0.35 (validation subset 0.33). The moderator analysis reports 0.42 (k = 10) where technology supported instruction and 0.31 (k = 15) where it was used for direct instruction.
the claim that how a task uses a device matters more than whether the device is present, the one thing a device policy cannot reach.
a claim that direct instruction or drill is ineffective. At 0.31 it is a moderate positive effect. The corpus also predates smartphones and generative AI, so it says nothing about phone policy.
doi.org/10.3102/0034654310393361
Methodological
The great rewiring: Is social media really behind an epidemic of teenage mental illness?
Odgers, C. L. (2024). Nature, 628, 29–30. See also Odgers & Jensen (2020), JCPP, 61(3); Taylor et al. (2024), Affective Science, 5.
A sustained methodological challenge to causal claims about adolescent technology effects, emphasizing within-person designs, the weakness of aggregate screen-time measures, and how offline vulnerability shapes online risk.
precision about causal language, and attention to variation across students rather than population averages.
a claim that devices are harmless. Small average effects can coexist with concentrated harms. Odgers is a counterweight to Haidt, not a rebuttal of concern.
Theory
Examining productive failure, productive success, unproductive failure, and unproductive success in learning
Kapur, M. (2016). Educational Psychologist, 51(2), 289–299.
Distinguishes immediate performance from longer-term learning, and names the case where apparent success conceals a less productive learning process.
the core instructional concept of the session: a polished product is not sufficient evidence of learning.
any claim about device policy. Kapur is about task design, and the extension to institutional decisions is an argument, not a finding.
doi.org/10.1080/00461520.2016.1155457
Framework
When the output looks like learning: Pedagogical friction and human agency in the age of generative AI
Miner, M. J. (2026). i.e.: Inquiry in Education, 18(1), Article 4.
The four-dimension framework used throughout the session: noetic, rhetorical, existential, and infrastructural friction, and the distinction between productive and exclusionary difficulty.
a shared vocabulary for separating difficulty that builds capacity from difficulty that blocks participation.
an empirical claim about outcomes. It is a theoretical framework; the associated dissertation research has collected no data.
digitalcommons.nl.edu/ie/vol18/iss1/4/