1932

Abstract

This article covers recent research activities in educational psychology that have an interdisciplinary emphasis and that accommodate twenty-first-century skills in addition to the traditional foundations of literacy, numeracy, science, reasoning (problem-solving), and academic subject matter. We emphasize digital technologies because they are capable of tracking learning data in rich detail and reliably delivering interventions that are tailored to individual learners in particular sociocultural contexts. This is a departure from inflexible pedagogical approaches that previously have been routinely adopted in most classrooms and other contexts of instruction with no precise record of learning and instructional activities. A good design of educational technology embraces the principles of learning science, identifies the basic types of learning that are needed, implements relevant technological affordances, and accommodates feedback from different stakeholders. This article covers research in literacy, collaborative problem-solving, motivation, emotion, and science, technology, engineering, and mathematics (STEM) areas.

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2022-01-04
2024-10-08
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Literature Cited

  1. Ackerman PL. 1988. Determinants of individual differences during skill acquisition: cognitive abilities and information processing. J. Exp. Psychol. Gen. 117:3288–318
    [Google Scholar]
  2. AERA (Am. Educ. Res. Assoc.), APA (Am. Psychol. Assoc.), NCME (Natl. Counc. Meas. Educ.) 1999. Standards for Educational and Psychological Testing Washington, DC: Am. Educ. Res. Assoc.
    [Google Scholar]
  3. Aleven V, McLaren B, Roll I, Koedinger K 2006. Toward meta-cognitive tutoring: a model of help seeking with a cognitive tutor. Int. J. Artif. Intell. Educ. 16:2101–28
    [Google Scholar]
  4. Am. Assoc. Phys. Teach 2006. Physics First: an informational guide for teachers, school administrators, parents, scientists, and the public Inf. Guide, Am. Assoc. Phys. Teach. College Park, MD:
    [Google Scholar]
  5. Anderson JR. 2007. How Can the Human Mind Occur in the Physical Universe? New York: Oxford Univ. Press
    [Google Scholar]
  6. Atkinson RC. 1968. Computerized instruction and the learning process. Am. Psychol. 23:225–39
    [Google Scholar]
  7. Atkinson RC 2015. Vannevar sets the stage. Past as Prologue: The National Academy of Education at 50 JM Feuer, AI Berman, RC Atkinson 151–57 Washington, DC: Natl. Acad. Educ.
    [Google Scholar]
  8. Autor D, Levy F, Murnane RJ 2003. The skill content of recent technological change: an empirical exploration. Q. J. Econ. 118:41279–334
    [Google Scholar]
  9. Azevedo R, Aleven V 2013. International Handbook of Metacognition and Learning Technologies 26 New York: Springer
    [Google Scholar]
  10. Azevedo R, Cromley JG. 2004. Does training on self-regulated learning facilitate students’ learning with hypermedia?. J. Educ. Psychol. 96:3523–35
    [Google Scholar]
  11. Baker RS. 2020. Big Data and Education Philadelphia: Univ. Pa., 6th ed..
    [Google Scholar]
  12. Bandura ANIMH (Natl. Inst. Ment. Health) 1986. Social Foundations of Thought and Action: A Social Cognitive Theory Englewood Cliffs, NJ: Prentice-Hall
    [Google Scholar]
  13. Biswas G, Segedy JR, Bunchongchit K. 2016. From design to implementation to practice a learning by teaching system: Betty's Brain. Int. J. Artif. Intell. Educ. 26:1350–64
    [Google Scholar]
  14. Bjork RA, Bjork EL. 2020. Desirable difficulties in theory and practice. J. Appl. Res. Memory Cogn. 9:4475–79
    [Google Scholar]
  15. Bjork RA, Dunlosky J, Kornell N. 2013. Self-regulated learning: beliefs, techniques, and illusions. Annu. Rev. Psychol. 64:417–44
    [Google Scholar]
  16. Bower GH. 2008. The evolution of a cognitive psychologist: a journey from simple behaviors to complex mental acts. Annu. Rev. Psychol. 59:127
    [Google Scholar]
  17. Braasch JLG, Bråten I, McCrudden MT 2018. Handbook of Multiple Source Use New York: Routledge
    [Google Scholar]
  18. Bransford JD. 2013. The Jasper Project: Lessons in Curriculum, Instruction, Assessment, and Professional Development New York: Routledge
    [Google Scholar]
  19. Britt MA, Rouet JF, Durik AM. 2017. Literacy Beyond Text Comprehension: A Theory of Purposeful Reading New York: Taylor & Francis
    [Google Scholar]
  20. Brown PC, Roediger HL III, McDaniel MA. 2014. Make It Stick: The Science of Successful Learning Cambridge, MA: Harvard Univ. Press
    [Google Scholar]
  21. Bybee J, McClelland JI. 2005. Alternatives to the combinatorial paradigm of linguistic theory based on domain general principles of human cognition. Linguistic Rev 22:381–410
    [Google Scholar]
  22. Carnevale AP, Smith N. 2013. Workplace basics: the skills employees need and employers want. Hum. Resour. Dev. Int. 16:5491–501
    [Google Scholar]
  23. Carroll JM, Holliman AJ, Weir F, Baroody AE. 2019. Literacy interest, home literacy environment and emergent literacy skills in preschoolers. J. Res. Read. 42:1150–61
    [Google Scholar]
  24. Carvalho PF, Goldstone RL. 2017. The sequence of study changes what information is attended to, encoded and remembered during category learning. J. Exp. Psychol. Learn. Memory Cogn. 43:111699–719
    [Google Scholar]
  25. Cattell JM. 1886. The time taken up by cerebral operations: Parts 1 & 2. Mind 11:42220–42
    [Google Scholar]
  26. Chi MTH, Wylie R 2014. ICAP: a hypothesis of differentiated learning effectiveness for four modes of engagement activities. Educ. Psychol. 49:4219–43
    [Google Scholar]
  27. Cohn N, Magliano JP. 2020. Visual narrative research: an emerging field in cognitive science. Topics Cogn. Sci. 12:1197–223
    [Google Scholar]
  28. Conway CM, Pisoni DB. 2008. Neurocognitive basis of implicit learning of sequential structure and its relation to language processing. Ann. N.Y. Acad. Sci. 1145:113–31
    [Google Scholar]
  29. Csikszentmihalyi M. 1990. Flow: The Psychology of Optimal Experience New York: Harper & Row
    [Google Scholar]
  30. Dede C, Ho A, Mitros P. 2016. Big data analysis in higher education: promises and pitfalls. EDUCAUSE Rev 51:522–34
    [Google Scholar]
  31. D'Mello SK. 2013. A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. J. Educ. Psychol. 105:41082–99
    [Google Scholar]
  32. D'Mello SK, Graesser AC. 2012. AutoTutor and affective AutoTutor: learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Trans. Interact. Intell. Syst. 2:423
    [Google Scholar]
  33. D'Mello SK, Kappas A, Gratch J 2018. The affective computing approach to affect measurement. Emot. Rev. 10:2174–83
    [Google Scholar]
  34. D'Mello SK, Lehman B, Pekrun R, Graesser AC 2014. Confusion can be beneficial for learning. Learn. Instr. 29:153–70
    [Google Scholar]
  35. Dunlosky J, Lipko A. 2007. Metacomprehension: a brief history and how to improve its accuracy. Curr. Dir. Psychol. Sci. 16:4228–32
    [Google Scholar]
  36. Durgunoglu AY, Verhoeven L 2013. Literacy Development in a Multilingual Context: Cross-Cultural Perspectives New York: Routledge
    [Google Scholar]
  37. Ehri LC. 2005. Learning to read words: theory, findings, and issues. Sci. Stud. Read. 9:2167–88
    [Google Scholar]
  38. Ekman P. 1992. An argument for basic emotions. Cogn. Emot. 6:3–4169–200
    [Google Scholar]
  39. Elliot S. 2017. Computers and the Future of Skill Demand Paris: OECD
    [Google Scholar]
  40. Ericsson KA 2018. The differential influence of experience, practice, and deliberate practice on the development of superior individual performance of experts. The Cambridge Handbook of Expertise and Expert Performance KA Ericsson, RR Hoffman, A Kozbelt, AM Williams 745–69 Cambridge, UK: Cambridge Univ. Press, 2nd ed..
    [Google Scholar]
  41. Eur. Comm 2019. The Digital Competence Framework 2.0 Brussels, Belg: Eur. Comm.
    [Google Scholar]
  42. Falmagne JC, Albert D, Doble C, Eppstein D, Hu X 2013. Knowledge Spaces: Applications in Education Berlin: Springer
    [Google Scholar]
  43. Fang Y, Ren Z, Hu X, Graesser AC. 2019. A meta-analysis of the effectiveness of ALEKS on learning. Educ. Psychol. 39:101278–92
    [Google Scholar]
  44. Feldman RD 2020. Learning Science: Theory, Research, and Practice New York: McGraw-Hill
    [Google Scholar]
  45. Fiore SM, Graesser A, Greiff S. 2018. Collaborative problem-solving education for the twenty-first-century workforce. Nat. Hum. Behav. 2:6367–69
    [Google Scholar]
  46. Fischer C, Pardos ZA, Baker RS, Williams JJ, Smyth P et al. 2020. Mining big data in education: affordances and challenges. Rev. Res. Educ. 44:1130–60
    [Google Scholar]
  47. Fischer F, Hmelo-Silver CE, Goldman SR, Reimann R 2018. International Handbook of the Learning Sciences New York: Routledge
    [Google Scholar]
  48. Fletcher JD, Tobias S, Wisher RL 2007. Learning anytime, anywhere: advanced distributed learning and the changing face of education. Educ. Res. 36:296–102
    [Google Scholar]
  49. Flum H, Kaplan A. 2006. Exploratory orientation as an educational goal. Educ. Psychol. 41:299–110
    [Google Scholar]
  50. Follmer DJ. 2018. Executive function and reading comprehension: a meta-analytic review. Educ. Psychol. 53:142–60
    [Google Scholar]
  51. Forsyth CM, Graesser AC, Millis K. 2020. Predicting learning in a multi-component serious game. Technol. Knowl. Learn. 25:2251–77
    [Google Scholar]
  52. Gašević D, Kovanović V, Joksimović S, Siemens G 2014. Where is research on massive open online courses headed? A data analysis of the MOOC research initiative. Int. Rev. Res. Open Distrib. Learn. 15:5134–76
    [Google Scholar]
  53. Gee JP. 2007. What Video Games Have to Teach Us About Learning and Literacy New York: Palgrave Macmillan
    [Google Scholar]
  54. Gilbert S, Slavina A, Sinatra AM, Bonner D, Johnston J et al. 2018. Creating a team tutor using GIFT. Int. J. Artif. Intell. Educ. 28:2286–313
    [Google Scholar]
  55. Gobert J, Moussavi R, Li H, Pedro MS, Dickler R 2018. Real-time scaffolding of students’ online data interpretation during inquiry with Inq-ITS using educational data mining. Cyber-Physical Laboratories in Engineering and Science Education ME Auer, AKM Azad, A Edwards, T de Jong 191–218 Cham, Switz: Springer
    [Google Scholar]
  56. Goldman SR, Britt MA, Brown W, Cribb G, George M et al. 2016. Disciplinary literacies and learning to read for understanding: a conceptual framework for disciplinary literacy. Educ. Psychol. 51:2219–46
    [Google Scholar]
  57. Goodwin AP, Jiménez RT 2020. Read. Res. Q. 55:S1)
    [Google Scholar]
  58. Gotwals AW, Songer NB. 2010. Reasoning up and down a food chain: using an assessment framework to investigate students’ middle knowledge. Sci. Educ. 94:2259–81
    [Google Scholar]
  59. Graesser AC. 2015. Deeper learning with advances in discourse science and technology. Policy Insights Behav. Brain Sci. 2:142–50
    [Google Scholar]
  60. Graesser AC. 2016. Conversations with AutoTutor help students learn. Int. J. Artif. Intell. Educ. 26:1124–32
    [Google Scholar]
  61. Graesser AC 2020. Learning science principles and technologies with agents that promote deep learning. Learning Science: Theory, Research, and Practice RS Feldman 2–33 New York: McGraw-Hill
    [Google Scholar]
  62. Graesser AC, D'Mello S 2012. Emotions during the learning of difficult material. The Psychology of Learning and Motivation 57 B Ross 183–225 Oxford, UK: Elsevier
    [Google Scholar]
  63. Graesser AC, D'Mello S, Person NK 2009. Metaknowledge in tutoring. Handbook of Metacognition in Education D Hacker, J Donlosky, AC Graesser 361–82 New York: Taylor & Francis
    [Google Scholar]
  64. Graesser AC, Fiore SM, Greiff S, Andrews-Todd J, Foltz PW, Hesse FW. 2018a. Advancing the science of collaborative problem solving. Psychol. Sci. Public Interest. 19:259–92
    [Google Scholar]
  65. Graesser AC, Hu X, Sottilare R 2018b. Intelligent tutoring systems. International Handbook of the Learning Sciences F Fischer, CE Hmelo-Silver, SR Goldman, P Reimann 246–55 New York: Routledge
    [Google Scholar]
  66. Graesser AC, Li H 2022. Intelligent tutoring systems and conversational agents. International Encyclopedia of Education R Tierney, F Rizvi, K Ercikan, G Smith Elsevier, 4th ed.. In press
    [Google Scholar]
  67. Gray W. 1916. Standardized Oral Reading Paragraphs Bloomington, Ill: Public Sch.
    [Google Scholar]
  68. Hall KL, Vogel AL, Huang GC, Serrano KJ, Rice EL et al. 2018. The science of team science: a review of the empirical evidence and research gaps on collaboration in science. Am. Psychol. 73:4532–48
    [Google Scholar]
  69. Hattie JAC, Donoghue GM 2016. Learning strategies: a synthesis and conceptual model. NPJ Sci. Learn. 1:116013
    [Google Scholar]
  70. Heffernan NT, Heffernan CL. 2014. The ASSISTments ecosystem: building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. Int. J. Artif. Intell. Educ. 24:4470–97
    [Google Scholar]
  71. Helsper E, Smirnova S 2019. Youth inequalities in digital interactions and well-being. Educating 21st Century Children: Emotional Well-Being in the Digital Age T Burns, F Gottschalk 163–84 Paris: OECD
    [Google Scholar]
  72. Hesse F, Care E, Buder J, Sassenberg K, Griffin P 2015. A framework for teachable collaborative problem solving skills. Assessment and Teaching of 21st Century Skills: Methods and Approach P Griffin, E Care 37–56 Dordrecht, Neth: Springer
    [Google Scholar]
  73. Hmelo-Silver CE. 2004. Problem-based learning: What and how do students learn?. Educ. Psychol. Rev. 16:3235–66
    [Google Scholar]
  74. Hossain Z, Bumbacher E, Brauneis A, Diaz M, Saltarelli A et al. 2017. Design guidelines and empirical case study for scaling authentic inquiry-based science learning via open online courses and interactive biology cloud labs. Int. J. Artif. Intell. Educ. 28:4478–507
    [Google Scholar]
  75. Huey EB. 1908. The Psychology and Pedagogy of Reading New York: MacMillan
    [Google Scholar]
  76. Jackson GT, McNamara DS. 2013. Motivation and performance in a game-based intelligent tutoring system. J. Educ. Psychol. 105:41036–49
    [Google Scholar]
  77. Johnson WL, Lester JC 2016. Face-to-face interaction with pedagogical agents: twenty years later. Int. J. Artif. Intell. Educ. 26:125–36
    [Google Scholar]
  78. Johnson WL, Valente A 2009. Tactical language and culture training systems: using AI to teach foreign languages and cultures. AI Mag 30:272–83
    [Google Scholar]
  79. Johnson-Laird PN. 1983. Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness Cambridge, MA: Harvard Univ. Press
    [Google Scholar]
  80. Jordan MI, Mitchell TM. 2015. Machine learning: trends, perspectives, and prospects. Science 349:255–60
    [Google Scholar]
  81. Kendeou P, Butterfuss R, Kim J, Van Boekel M 2019. Knowledge revision through the lenses of the three-pronged approach. Memory Cogn 47:133–46
    [Google Scholar]
  82. Kim HY, LaRusso MD, Hsin LB, Harbaugh AG, Selman RL, Snow CE. 2018. Social perspective-taking performance: construct, measurement, and relations with academic performance and engagement. J. Appl. Dev. Psychol. 57:24–41
    [Google Scholar]
  83. Kintsch W. 1998. Comprehension: A Paradigm for Cognition Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  84. Koedinger KR, Corbett AC, Perfetti C. 2012. The Knowledge-Learning-Instruction (KLI) framework: bridging the science-practice chasm to enhance robust student learning. Cogn. Sci. 36:5757–98
    [Google Scholar]
  85. Kulik JA, Fletcher JD. 2015. Effectiveness of intelligent tutoring systems: a meta-analytic review. Rev. Educ. Res. 85:171–204
    [Google Scholar]
  86. Lamb R, Akmal T, Petrie K 2015. Development of a cognition-priming model describing learning in a STEM classroom. J. Res. Sci. Teach. 52:3410–37
    [Google Scholar]
  87. Lemons CJ, Vaughn S, Wexler J, Kearns DM, Sinclair AC. 2018. Envisioning an improved continuum of special education services for students with learning disabilities: considering intervention intensity. Learn. Disabil. Res. Pract. 33:3131–43
    [Google Scholar]
  88. Lesgold AM. 2019. Learning for the Age of Artificial Intelligence: Eight Educational Competencies New York: Routledge
    [Google Scholar]
  89. Li H, Gobert J, Graesser A, Dickler R 2018. Advanced educational technology for science inquiry assessment. Policy Insights Behav. Brain Sci. 5:2171–78
    [Google Scholar]
  90. Li Y, Schoenfeld AH, diSessa AA, Graesser AC, Benson LC et al. 2020. On computational thinking and STEM education. J. STEM Educ. Res. 3:147–66
    [Google Scholar]
  91. Ma W, Adesope OO, Nesbit JC, Liu Q. 2014. Intelligent tutoring systems and learning outcomes: a meta-analysis. J. Educ. Psychol. 106:4901–18
    [Google Scholar]
  92. Magliano JP, McCrudden MT, Rouet J-F, Sabatini J. 2018. The modern reader: Should changes to how we read affect research and theory?. The Routledge Handbook of Discourse Processes343–61 New York: Routledge/Taylor & Francis, 2nd ed..
    [Google Scholar]
  93. Maki RH 1998. Test predictions over text material. Metacognition in Educational Theory and Practice DJ Hacker, J Dunlosky, AC Graesser 117–44 Mahwah, NJ: Erlbaum
    [Google Scholar]
  94. Mayer RE. 2009. Multimedia Learning New York: Cambridge Univ. Press, 2nd ed..
    [Google Scholar]
  95. Mayer RE. 2019. Computer games in education. Annu. Rev. Psychol. 70:531–49
    [Google Scholar]
  96. McCrudden MT, Magliano JP, Schraw G 2011. Text Relevance and Learning from Text Charlotte, NC: Inf. Age
    [Google Scholar]
  97. McNamara DS, Graesser AC, McCarthy PM, Cai Z. 2014. Automated Evaluation of Text and Discourse with Coh-Metrix Cambridge, MA: Cambridge Univ. Press
    [Google Scholar]
  98. McNeill KL, Lizotte DJ, Krajcik J, Marx RW 2006. Supporting students’ construction of scientific explanations by fading scaffolds in instructional materials. J. Learn. Sci. 15:2153–91
    [Google Scholar]
  99. Millis K, Long DL, Magliano JP, Wiemer K 2019. Deep Comprehension: Multidisciplinary Approaches to Understanding, Enhancing, and Measuring Comprehension New York: Routledge
    [Google Scholar]
  100. NAEP (Natl. Assess. Educ. Prog.) 2019. NAEP report card: 2019 NAEP reading assessment Rep., Inst. Educ. Sci., Natl. Cent. Educ. Stat. Washington, DC:
    [Google Scholar]
  101. NASEM (Natl. Acad. Sci. Eng. Med.) 2018. How People Learn II: Learners, Contexts, and Cultures Washington, DC: Natl. Acad. Press
    [Google Scholar]
  102. Nathan M, Koedinger K 2000. Teachers’ and researchers’ beliefs about the development of algebraic reasoning. J. Res. Math. Educ. 31:2168–90
    [Google Scholar]
  103. New Lond. Group 1996. A pedagogy of multiliteracies: designing social futures. Harvard Educ. Rev. 66:160–92
    [Google Scholar]
  104. NGSS (Next Gener. Sci. Stand.) Lead States 2013. Next Generation Science Standards: For States, by States Washington, DC: Natl. Acad. Press
    [Google Scholar]
  105. Norman DA 2013. The Design of Everyday Things New York: Basic Books
    [Google Scholar]
  106. NRC (Natl. Res. Counc.) 2011. Assessing 21st Century Skills: Summary of a Workshop Washington, DC: Natl. Acad. Press
    [Google Scholar]
  107. Nye BD, Graesser AC, Hu X. 2014. AutoTutor and family: a review of 17 years of natural language tutoring. Int. J. Artif. Intell. Educ. 24:4427–69
    [Google Scholar]
  108. OECD (Organ. Econ. Coop. Dev.) 2013. OECD Skills Outlook 2013: First Results from the Survey of Adult Skills Paris: OECD
    [Google Scholar]
  109. OECD (Organ. Econ. Coop. Dev.) 2015. Adults, Computers and Problem Solving: What's the Problem? Paris: OECD
    [Google Scholar]
  110. OECD (Organ. Econ. Coop. Dev.) 2017. PISA 2015 Results, Vol. V: Collaborative Problem Solving Paris: OECD
    [Google Scholar]
  111. OECD (Organ. Econ. Coop. Dev.) 2019a. PISA 2018 Results, Vol. I–III: Combined Executive Summaries Paris: OECD
    [Google Scholar]
  112. OECD (Organ. Econ. Coop. Dev.) 2019b. PISA 2021 ICT Framework Paris: OECD
    [Google Scholar]
  113. OECD (Organ. Econ. Coop. Dev.) 2021. The Assessment Frameworks for Cycle 2 of the Programme for the International Assessment of Adult Competencies Paris: OECD
    [Google Scholar]
  114. Olney AM, D'Mello S, Person N, Cade W, Hays P et al. 2012. Guru: a computer tutor that models expert human tutors. Intelligent Tutoring Systems SA Cerri, WJ Clancey, G Papdourakis, K Panourgia 256–61 Lect. Notes Comput. Sci. 7315 Berlin: Springer
    [Google Scholar]
  115. Olson DR, Torrance N 2001. The Making of Literate Societies Malden, MA: Blackwell
    [Google Scholar]
  116. O'Neil HF, Baker EL, Perez RS 2016. Using Games and Simulation for Teaching and Assessment New York: Routledge
    [Google Scholar]
  117. O'Reilly T, Wang Z, Sabatini J 2019. How much knowledge is too little? When a lack of knowledge becomes a barrier to comprehension. Psychol. Sci. 30:91344–51
    [Google Scholar]
  118. Owen VE, Roy M, Thai KP, Burnett V, Jacobs D et al. 2019. Detecting wheel-spinning and productive persistence in educational games. Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019) CF Lynch, A Merceron, M Desmarais, R Nkambou 378–83 Montreal: IEDMS
    [Google Scholar]
  119. Oyserman D, Lewis NA Jr., Yan VX, Fisher O, O'Donnell SC, Horowitz E. 2017. An identity-based motivation framework for self-regulation. Psychol. Inq. 28:2–3139–47
    [Google Scholar]
  120. Pane JF, Griffin BA, McCaffrey DF, Karam R. 2014. Effectiveness of cognitive tutor algebra I at scale. Educ. Eval. Policy Anal. 36:2127–44
    [Google Scholar]
  121. Paris SG, Newman RS. 1990. Development aspects of self-regulated learning. Educ. Psychol. 25:187–102
    [Google Scholar]
  122. Pashler H, Bain P, Bottge B, Graesser AC, Koedinger K et al. 2007. Organizing instruction and study to improve student learning: a practice guide NCER Rep. 2007–2004 Inst. Educ. Sci. Washington, DC:
    [Google Scholar]
  123. Pavlik PI, Anderson JR. 2008. Using a model to compute the optimal schedule of practice. J. Exp. Psychol. Appl. 14:2101–17
    [Google Scholar]
  124. Pekrun R. 2006. The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educ. Psychol. Rev. 18:4315–41
    [Google Scholar]
  125. Pellegrino JW 2017. Teaching, learning and assessing 21st century skills. Pedagogical Knowledge and the Changing Nature of the Teaching Profession S Guerriero 223–51 Paris: OECD
    [Google Scholar]
  126. Perfetti CA, Liu Y, Fiez JA, Tan L-H 2010. The neural bases of reading: the accommodation of the brain's reading network to writing systems. The Neural Basis of Reading PL Cornelissen, M Kringelbach, PC Hansen, K Pugh 147–72 Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  127. Pomerance L, Greenberg J, Walsh K. 2016. Learning About Learning: What Every Teacher Needs to Know Washington, DC: Natl. Counc. Teach. Qual.
    [Google Scholar]
  128. Preston JL, Molfese PJ, Frost SJ, Mencl WE, Fulbright RK et al. 2016. Print-speech convergence predicts future reading outcomes in early readers. Psychol. Sci. 27:175–84
    [Google Scholar]
  129. Pugh KR, Landi N, Preston JL, Mencl WE, Austin AC et al. 2013. The relationship between phonological and auditory processing and brain organization in beginning readers. Brain Lang 125:2173–83
    [Google Scholar]
  130. Rayner K, Foorman BR, Perfetti CA, Pesetsky D, Seidenberg M 2001. How psychological science informs the teaching of reading. Psychol. Sci. Public Interest. 2:231–74
    [Google Scholar]
  131. Ritter S, Anderson JR, Koedinger KR, Corbett A. 2007. Cognitive Tutor: applied research in mathematics education. Psychon. Bull. Rev. 14:2249–55
    [Google Scholar]
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