Appraising research on personalized learning: Definitions, theoretical alignment, advancements, and future directions

Abstract This article introduces a special issue comprising research on efforts to personalize learning in different academic subjects. We first consider the emergence of personalized learning (PL) and the myriad of definitions that describe its essential features. Thereafter, we introduce the articles in the special issue by examining their alignment to extant theories of learning, the instructional design features that personalize the learning experience based on a learner characteristic, and the relationships between PL design and outcomes achieved in an educational context. Based on observations of contemporary PL research, we identify key issues to be addressed by the field and recommendations for future researchers to undertake to advance a PL theory. Chief among issues with PL are the role of technology, the agency of the learner, and the absence of a consistent theoretical grounding to motivate PL design choices. Future directions that would advance PL include the adoption of a theory of change in PL design, a design-based research approach to refine PL initiatives, more intensive and iterative research in authentic classroom contexts, and a greater focus on student input into and ownership of the PL experience.

[1]  E. Deci,et al.  The "What" and "Why" of Goal Pursuits: Human Needs and the Self-Determination of Behavior , 2000 .

[2]  Helen Crompton,et al.  Mobile technology, learning, and achievement: Advances in understanding and measuring the role of mobile technology in education , 2020, Contemporary Educational Psychology.

[3]  Matthew L. Bernacki,et al.  Students authoring personalized “algebra stories”: Problem-posing in the context of out-of-school interests , 2015 .

[4]  Seymour Papert,et al.  Mindstorms: Children, Computers, and Powerful Ideas , 1981 .

[5]  Vincent Aleven,et al.  Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra. , 2012, EDM 2012.

[6]  E. Beese A process perspective on research and design issues in educational personalization , 2019, Theory and Research in Education.

[7]  Chris S. Hulleman,et al.  Enhancing interest and performance with a utility value intervention. , 2010 .

[8]  M. Peters,et al.  Transforming American Education: Learning Powered by Technology , 2011 .

[9]  Candace Walkington,et al.  Personalizing Algebra to Students’ Individual Interests in an Intelligent Tutoring System: Moderators of Impact , 2018, International Journal of Artificial Intelligence in Education.

[10]  Mingyu Feng,et al.  An Efficacy Study of a Digital Core Curriculum for Grade 5 Mathematics , 2019, AERA Open.

[11]  L. Schauble,et al.  Design Experiments in Educational Research , 2003 .

[12]  Steven Netcoh Balancing freedom and limitations: A case study of choice provision in a personalized learning class , 2017 .

[13]  Seymour Papert,et al.  The Children's Machine , 1993 .

[14]  M. McDaniel,et al.  Learning Styles , 2008, Psychological science in the public interest : a journal of the American Psychological Society.

[15]  S. Järvelä,et al.  Self-Regulated, Co-Regulated, and Socially Shared Regulation of Learning , 2011 .

[16]  Erika A. Patall Constructing motivation through choice, interest, and interestingness , 2013 .

[17]  Daniel F. McCaffrey,et al.  Effectiveness of Cognitive Tutor Algebra I at Scale , 2014 .

[18]  B. B.,et al.  BOOK REVIEW Make Learning Personal: The What, Who, Wow, Where, And Why , 2020 .

[19]  Gwo-Jen Hwang,et al.  Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017 , 2019, Comput. Educ..

[20]  Mingyu Feng,et al.  Online Mathematics Homework Increases Student Achievement , 2016 .

[21]  Allan Collins,et al.  Design Research: Theoretical and Methodological Issues , 2004 .

[22]  Luis C. Moll,et al.  Funds of knowledge for teaching: Using a qualitative approach to connect homes and classrooms , 1992 .

[23]  Candace Walkington,et al.  Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. , 2013 .

[24]  Beth A. Rogowsky,et al.  Matching Learning Style to Instructional Method: Effects on Comprehension. , 2015 .

[25]  Edward L. Deci,et al.  The initiation and regulation of intrinsically motivated learning and achievement. , 1992 .

[26]  Carol Ann Tomlinson,et al.  Differentiation of Instruction in the Elementary Grades. ERIC Digest. , 2000 .

[27]  R. Reber,et al.  Effects of Example Choice on Interest, Control, and Learning , 2009 .

[28]  Erika A. Patall,et al.  The effects of choice on intrinsic motivation and related outcomes: a meta-analysis of research findings. , 2008, Psychological bulletin.

[29]  B. Means,et al.  The Effectiveness of Online and Blended Learning: A Meta-Analysis of the Empirical Literature , 2013, Teachers College Record: The Voice of Scholarship in Education.

[30]  Noel Enyedy,et al.  New Interest, Old Rhetoric, Limited Results, and the Need for a New Direction for Computer-Mediated Learning , 2014 .

[31]  Matthew L. Bernacki,et al.  The Role of Situational Interest in Personalized Learning , 2018, Journal of Educational Psychology.

[32]  Larry Cuban Rethinking education in the age of technology: The digital revolution and schooling in America , 2010 .

[33]  John F. Pane,et al.  Continued Progress: Promising Evidence on Personalized Learning: Survey Results Addendum , 2015 .

[34]  Etienne Wenger,et al.  Situated Learning: Legitimate Peripheral Participation , 1991 .

[35]  M. Lepper,et al.  Intrinsic motivation and the process of learning: Beneficial effects of contextualization, personalization, and choice. , 1996 .

[36]  Gordon Ellis,et al.  Grand challenges for engineering , 2009, IEEE Engineering Management Review.

[37]  Vincent Aleven,et al.  Active Learners: Redesigning an Intelligent Tutoring System to Support Self-regulated Learning , 2013, EC-TEL.

[38]  S. Hidi,et al.  The Four-Phase Model of Interest Development , 2006 .

[39]  L. Hamilton,et al.  Continued Progress: Promising Evidence on Personalized Learning , 2015 .

[40]  K. Squire,et al.  Design-Based Research: Putting a Stake in the Ground , 2004 .

[41]  R. Reber,et al.  Supporting interest of middle school students in mathematics through context personalization and example choice , 2015 .

[42]  B. Bloom Learning for Mastery. Instruction and Curriculum. Regional Education Laboratory for the Carolinas and Virginia, Topical Papers and Reprints, Number 1. , 1968 .

[43]  E. Deci,et al.  Overview of self-determination theory: An organismic-dialectical perspective. , 2002 .

[44]  Candace Walkington,et al.  Motivating Students by “Personalizing” Learning Around Individual Interests: A Consideration of Theory, Design and Implementation Issues , 2014 .

[45]  K. Ann Renninger,et al.  The Power of Interest for Motivation and Engagement , 2015 .

[46]  Susan E. Thomas Future Ready Learning: Reimagining the Role of Technology in Education. 2016 National Education Technology Plan. , 2016 .