Human memory is imperfect; thus, periodic review is required for the long-term preservation of knowledge and skills. However, students at every educational level are challenged by an ever-growing amount of material to review and an ongoing imperative to master new material. We developed a method for efficient, systematic, personalized review that combines statistical techniques for inferring individual differences with a psychological theory of memory. The method was integrated into a semester-long middle-school foreign-language course via retrieval-practice software. Using a cumulative exam administered after the semester’s end, we compared time-matched review strategies and found that personalized review yielded a 16.5% boost in course retention over current educational practice (massed study) and a 10.0% improvement over a one-size-fits-all strategy for spaced study.
AtkinsonR. C. (1972). Optimizing the learning of a second-language vocabulary. Journal of Experimental Psychology, 96, 124–129.
2.
BjorkR. (1994). Memory and metamemory considerations in the training of human beings. In MetcalfeJ.ShimamuraA. (Eds.), Metacognition: Knowing about knowing (pp. 185–205). Cambridge, MA: MIT Press.
3.
CarpenterS.PashlerH.CepedaN. (2009). Using tests to enhance 8th grade students’ retention of U.S. history facts. Applied Cognitive Psychology, 23, 760–771.
4.
CenH.KoedingerK.JunkerB. (2006). Learning factors analysis—a general method for cognitive model evaluation and improvement. In IkedaM.AshleyK. D.ChanT.-W. (Eds.), Intelligent Tutoring Systems: 8th International Conference (pp. 164–175). Berlin, Germany: Springer.
5.
CenH.KoedingerK.JunkerB. (2008). Comparing two IRT models for conjunctive skills. In WoolfB. P.AïmeurE.NkambouR.LajoieS. (Eds.), Intelligent Tutoring Systems: 9th International Conference (pp. 796–798). Berlin, Germany: Springer.
6.
CepedaN.PashlerH.VulE.WixtedJ.RohrerD. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132, 354–380.
7.
CepedaN.VulE.RohrerD.WixtedJ.PashlerH. (2008). Spacing effects in learning: A temporal ridgeline of optimal retention. Psychological Science, 19, 1095–1102.
8.
ChiM.KoedingerK.GordonG.JordanP.VanLehnK. (2011). Instructional factors analysis: A cognitive model for multiple instructional interventions. In PechenizkiyM.CaldersT.ConatiC.VenturaS.RomeroC.StamperJ. (Eds.), Proceedings of the 4th International Conference on Educational Data Mining (pp. 61–70). Retrieved from http://educationaldatamining.org/EDM2011/proceedings-2
9.
CohenM. S.YanV. X.HalamishV.BjorkR. A. (2013). Do students think that difficult or valuable materials should be restudied sooner rather than later?Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 1682–1696.
10.
CustersE. (2010). Long-term retention of basic science knowledge: A review study. Advances in Health Science Education: Theory and Practice, 15, 109–128.
11.
CustersE.ten CateO. (2011). Very long-term retention of basic science knowledge in doctors after graduation. Medical Education, 45, 422–430.
12.
De BoeckP.WilsonM. (Eds.).(2004). Explanatory item response models: A generalized linear and nonlinear approach. New York, NY: Springer.
13.
DunloskyJ.RawsonK. A.MarshE. J.NathanM. J.WillinghamD. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14, 4–58.
14.
KerfootB.FuY.BakerH.ConnellyD.RitcheyM.GenegaE. (2010). Online spaced education generates transfer and improves long-term retention of diagnostic skills: A randomized controlled trial. Journal of the American College of Surgeons, 211, 331–337.
15.
KhajahM.LindseyR.MozerM. C. (2013). Maximizing students’ retention via spaced review: Practical guidance from computational models of memory. In KnauffM.PauenM.SebanzN.WachsmuthI. (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 758–763). Austin, TX: Cognitive Science Society.
16.
LeitnerS. (1972). So lernt man lernen [How to learn]. Freiburg im Breisgau, Germany: Herder.
17.
MassonM.LoftusG. (2003). Using confidence intervals for graphically based data interpretation. Canadian Journal of Experimental Psychology, 57, 203–220.
18.
Metzler-BaddeleyC.BaddeleyR. (2009). Does adaptive training work?Applied Cognitive Psychology, 23, 254–266.
19.
MozerM.PashlerH.CepedaN.LindseyR.VulE. (2009). Predicting the optimal spacing of study: A multiscale context model of memory. In BengioY.SchuurmansD.LaffertyJ.WilliamsC.CulottaA. (Eds.), Advances in neural information processing systems22. Retrieved from http://books.nips.cc/nips22.html
20.
NelsonT.DunloskyJ. (1991). When people’s judgments of learning (JOL) are extremely accurate at predicting subsequent recall: The delayed-JOL effect. Psychological Science, 2, 267–270.
21.
PavlikP.AndersonJ. (2005). Practice and forgetting effects on vocabulary memory: An activation-based model of the spacing effect. Cognitive Science, 29, 559–586.
22.
PavlikP.AndersonJ. (2008). Using a model to compute the optimal schedule of practice. Journal of Experimental Psychology: Applied, 14, 101–117.
23.
PavlikP.CenH.KoedingerK. (2009). Performance factors analysis—a new alternative to knowledge tracing. In DimitrovaV.MizoguchiR. (Eds.), Proceeding of the Fourteenth International Conference on Artificial Intelligence in Education (pp. 531–538). Brighton, England: IOS Press.
24.
RaschG. (1961). On general laws and the meaning of measurement in psychology. In NeymanJ. (Ed.), Proceedings of the Fourth Berkeley Symposium on Math, Statistics, and Probability (pp. 321–333). Berkeley: University of California Press.
25.
SeabrookR.BrownG.SolityJ. (2005). Distributed and massed practice: From laboratory to classroom. Applied Cognitive Psychology, 19, 107–122.
VanLehnK.JordanP.LitmanD. (2007). Developing pedagogically effective tutorial dialogue tactics: Experiments and a testbed. In Proceedings of the SLaTE Workshop on Speech and Language Technology in Education (pp. 17–20). Retrieved from http://www.isca-speech.org/archive/slate_2007
28.
van RijnD. H.van MaanenL.van WoudenbergM. (2009). Passing the test: Improving learning gains by balancing spacing and testing effects. In HowesA.PeeblesD.CooperR. P. (Eds.), Proceedings of the Ninth International Conference on Cognitive Modeling. Retrieved from http://sideshow.psyc.bbk.ac.uk/rcooper/iccm2009/proceedings/
29.
WagenmakersE.-J.GrünwaldP.SteyversM. (2006). Accumulative prediction error and the selection of time series models. Journal of Mathematical Psychology, 50, 149–166.
30.
WozniakP.GorzelanczykE. (1994). Optimization of repetition spacing in the practice of learning. Acta Neurobiologiae Experimentalis, 54, 59–62.
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