Investigating the Role of Cognitive Abilities in Computational Thinking for Young Learners

With the global movement to incorporate computer science instruction into elementary education, learners are being introduced to computer science and computational thinking (CS/CT) ideas at increasingly younger ages. At these early ages, young learners are developing cognitive abilities foundational to their education. While other discipline-based education fields, such as math, science, and reading, have long studied the role of cognitive abilities, such as short-term working memory and long-term retrieval, in their respective fields, similar research in computer science education is relatively sparse. In this exploratory study, we examined the relationship between cognitive abilities and CS/CT performance of fourth-grade students (ages 9-10) who underwent either an introductory CT curriculum based on Use–>Modify–>Create or the same curriculum with additional scaffolding from the TIPP&SEE metacognitive learning strategy. Our analysis revealed performance on CT assessments to be weakly correlated with working memory and long-term retrieval, with correlations increasing as the CT concepts grew more complex. This suggests that scaffolding beyond TIPP&SEE may be needed with more complex CT concepts. We also found that when using TIPP&SEE, students scoring below average on cognitive ability tests performed as well as students in the control condition with average cognitive ability scores. These results indicate TIPP&SEE’s potential in creating more equitable computing instruction. We hope that results from this initial exploration can help encourage further study into the role of cognitive abilities in CS/CT education for young learners.

[1]  Diana Franklin,et al.  An Analysis through an Equity Lens of the Implementation of Computer Science in K-8 Classrooms in a Large, Urban School District , 2019, SIGCSE.

[2]  Frederick A. Schrank.  Essentials of Wj III Cognitive Abilities Assessment , 2001 .

[3]  Mitchel Resnick,et al.  Designing ScratchJr: support for early childhood learning through computer programming , 2013, IDC.

[4]  Corrado Böhm,et al.  Flow diagrams, turing machines and languages with only two formation rules , 1966, CACM.

[5]  Michael J. Orosco,et al.  Growth in literacy, cognition, and working memory in English language learners. , 2015, Journal of experimental child psychology.

[6]  H. Kaiser.  The Application of Electronic Computers to Factor Analysis , 1960 .

[7]  D. Kostons,et al.  Effectiveness of learning strategy instruction on academic performance: A meta-analysis , 2014 .

[8]  R. Sternberg,et al.  Complex Cognition: The Psychology of Human Thought , 2001 .

[9]  Paul A. Kirschner,et al.  Cognitive load theory: implications of cognitive load theory on the design of learning , 2002 .

[10]  R. Estrella,et al.  Stuck in the Shallow End Education , Race , and Computing , 2008 .

[11]  Nelson Cowan,et al.  Theories of Working Memory: Differences in Definition, Degree of Modularity, Role of Attention, and Purpose. , 2018, Language, speech, and hearing services in schools.

[12]  Daniël Lakens,et al.  Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs , 2013, Front. Psychol..

[13]  J. J. Higgins,et al.  The aligned rank transform for nonparametric factorial analyses using only anova procedures , 2011, CHI.

[14]  R. Mayer,et al.  Nine Ways to Reduce Cognitive Load in Multimedia Learning , 2003 .

[15]  John Sweller,et al.  Cognitive Load Theory: New Conceptualizations, Specifications, and Integrated Research Perspectives , 2010 .

[16]  Roy D. Pea,et al.  Factors Influencing Computer Science Learning in Middle School , 2016, SIGCSE.

[17]  D. Bryant,et al.  A Synthesis of Mathematical and Cognitive Performances of Students With Mathematics Learning Disabilities , 2015, Journal of learning disabilities.

[18]  Alfred V. Aho,et al.  Computation and Computational Thinking , 2012, Comput. J..

[19]  Tina Seufert,et al.  The interplay between self-regulation in learning and cognitive load , 2018, Educational Research Review.

[20]  R. Cattell.  The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.

[21]  Daniel A. Hackman,et al.  Mapping the trajectory of socioeconomic disparity in working memory: parental and neighborhood factors. , 2014, Child development.

[22]  Maya Israel,et al.  Empowering K–12 Students With Disabilities to Learn Computational Thinking and Computer Programming , 2015 .

[23]  J. J. Higgins,et al.  THE ALIGNED RANK TRANSFORM PROCEDURE , 1990 .

[24]  Diana Franklin,et al.  A K-8 Debugging Learning Trajectory Derived from Research Literature , 2019, SIGCSE.

[25]  Diana Franklin,et al.  K-8 Learning Trajectories Derived from Research Literature: Sequence, Repetition, Conditionals , 2017, ICER.

[26]  M. Sheridan,et al.  Minds Under Siege: Cognitive Signatures of Poverty and Trauma in Refugee and Non‐Refugee Adolescents , 2019, Child development.

[27]  Jeannette M. Wing.  An introduction to computer science for non-majors using principles of computation , 2007, SIGCSE.

[28]  L. Verhoeven,et al.  How working memory relates to children’s reading comprehension: the importance of domain-specificity in storage and processing , 2016, Reading and writing.

[29]  Richard E. Clark,et al.  Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching , 2006 .

[30]  Febrian Febrian.  The Instruction to Overcome the Inert Knowledge Issue in Solving Mathematical Problem , 2016 .

[31]  Annegret Goold,et al.  Factors affecting performance in first-year computing , 2000, SGCS.

[32]  Briana B. Morrison,et al.  Cognitive Sciences for Computing Education , 2019, The Cambridge Handbook of Computing Education Research.

[33]  A Learning Trajectory for Variables Based in Computational Thinking Literature: Using Levels of Thinking to Develop Instruction , 2020, Computer Science Education.

[34]  Marjorie Montague,et al.  Metacognitive Strategy Use of Eighth-Grade Students With and Without Learning Disabilities During Mathematical Problem Solving , 2011, Journal of learning disabilities.

[35]  James Daniel Lehman,et al.  Correlates of Success in Introductory Programming: A Study with Middle School Students. , 2016 .

[36]  M. Hillemeier,et al.  Executive function deficits in kindergarten predict repeated academic difficulties across elementary school , 2019, Early Childhood Research Quarterly.

[37]  Jean Salac,et al.  Supporting Diverse Learners in K-8 Computational Thinking with TIPP&SEE , 2021, SIGCSE.

[38]  Juhani Tuovinen,et al.  Optimising student cognitive load in computer education , 2000, ACSE '00.

[39]  Diane Ebert-May,et al.  The Other Half of the Story: Effect Size Analysis in Quantitative Research , 2013, CBE life sciences education.

[40]  Jeffrey T. Steedle,et al.  Working memory, fluid intelligence, and science learning , 2006 .

[41]  Jean Salac,et al.  TIPP&SEE: A Learning Strategy to Guide Students through Use - Modify Scratch Activities , 2020, SIGCSE.

[42]  D. Hinkle,et al.  Applied statistics for the behavioral sciences , 1979 .

[43]  Mark Guzdial,et al.  Subgoals, Context, and Worked Examples in Learning Computing Problem Solving , 2015, ICER.

[44]  M. Reber.  Development during Middle Childhood: The Years from Six to Twelve , 1986 .

[45]  L. Fuchs,et al.  Fraction Intervention for Students With Mathematics Difficulties: Lessons Learned From Five Randomized Controlled Trials , 2017, Journal of learning disabilities.

[46]  Nell K. Duke,et al.  Diagrams, Timelines, and Tables—Oh, My! Fostering Graphical Literacy , 2013 .

[47]  Ann L. Brown,et al.  How people learn: Brain, mind, experience, and school. , 1999 .

[48]  Tugba Altan,et al.  Cognitive load in multimedia learning environments: A systematic review , 2019, Comput. Educ..

[49]  Jean Salac,et al.  Exploring Student Behavior Using the TIPP&SEE Learning Strategy , 2020, ICER.

[50]  K. McGrew,et al.  Exploring the Relations between Cattell–Horn–Carroll (CHC) Cognitive Abilities and Mathematics Achievement , 2017 .

[51]  M. Pressley,et al.  Cognitive Strategies Instruction: From Basic Research to Classroom Instruction , 2009 .

[52]  Alison G. Boardman,et al.  Efficacy of Collaborative Strategic Reading With Middle School Students* , 2011 .

[53]  S. Pollak,et al.  How developmental neuroscience can help address the problem of child poverty , 2020, Development and Psychopathology.

[54]  Robert J. Mislevy,et al.  Implications of Evidence‐Centered Design for Educational Testing , 2007 .

[55]  Peter J. Denning,et al.  The long quest for computational thinking , 2016, Koli Calling.

[56]  Colleen M. Lewis,et al.  Building upon and enriching grade four mathematics standards with programming curriculum , 2012, SIGCSE '12.

[57]  K. McGrew,et al.  Revisiting the Relations Between the WJ-IV Measures of Cattell-Horn-Carroll (CHC) Cognitive Abilities and Reading Achievement During the School-Age Years , 2017 .

[58]  T. Gog,et al.  Development of an instrument for measuring different types of cognitive load , 2013, Behavior Research Methods.

[59]  Michail N. Giannakos,et al.  A Global Snapshot of Computer Science Education in K-12 Schools , 2015, ITiCSE-WGR.

[60]  Daniel L. Schwartz,et al.  Learning Theories and Education: Toward a Decade of Synergy , 2006 .

[61]  D. Fuchs,et al.  A Meta-Analysis of Working Memory Deficits in Children With Learning Difficulties , 2016, Journal of learning disabilities.

[62]  Philip Sands,et al.  Addressing cognitive load in the computer science classroom , 2019, Inroads.

[63]  K. Taber.  The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education , 2017, Research in Science Education.

[64]  Cynthia Selby,et al.  Computational Thinking: The Developing Definition , 2013 .

[65]  G. Evans,et al.  Childhood poverty, chronic stress, and young adult working memory: the protective role of self-regulatory capacity. , 2013, Developmental science.

[66]  F. Paas,et al.  Cognitive-Load Theory: Methods to Manage Working Memory Load in the Learning of Complex Tasks , 2020, Current Directions in Psychological Science.

[67]  Jean Salac,et al.  Comprehending Code: Understanding the Relationship between Reading and Math Proficiency, and 4th-Grade CS Learning Outcomes , 2020, SIGCSE.

[68]  Mark Guzdial,et al.  Measuring cognitive load in introductory CS: adaptation of an instrument , 2014, ICER '14.

[69]  M. Tine.  Working Memory Differences Between Children Living in Rural and Urban Poverty , 2014, Journal of cognition and development : official journal of the Cognitive Development Society.

[70]  H. Swanson,et al.  Intelligence, Working Memory, and Learning Disabilities , 2015 .

[71]  Siegfried Dewitte,et al.  Commentary: “Poverty impedes cognitive function” and “The poor's poor mental power” , 2015, Front. Psychol..

[72]  Dawn P. Flanagan,et al.  The Cattell‐Horn‐Carroll Theory of Cognitive Abilities , 2014 .

[73]  Nelson Cowan,et al.  Working Memory Underpins Cognitive Development, Learning, and Education , 2014, Educational psychology review.

[74]  Mark Guzdial,et al.  Expanding access to K-12 computer science education: research on the landscape of computer science professional development , 2013, SIGCSE '13.

[75]  Diana Franklin,et al.  Decomposition: A K-8 Computational Thinking Learning Trajectory , 2018, ICER.

[76]  P. Curzon,et al.  Computational thinking - a guide for teachers , 2015 .

[77]  Peter J. Denning,et al.  Computational Thinking , 2019 .

[78]  R. Cohen Kadosh,et al.  What Expertise Can Tell About Mathematical Learning and Cognition , 2018, Mind, Brain, and Education.

[79]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[80]  Kimberly J. Vannest,et al.  Encyclopedia of special education : a reference for the education of children, adolescents, and adults with disabilities and other exceptional individuals , 2014 .

[81]  S. Dymock.  Teaching Expository Text Structure Awareness , 2005 .

[82]  Cathy Newman Thomas,et al.  Applying a Universal Design for Learning Framework to Mediate the Language Demands of Mathematics , 2015 .

[83]  Peter J. Denning,et al.  Remaining trouble spots with computational thinking , 2017, Commun. ACM.

[84]  John T. E. Richardson,et al.  Eta Squared and Partial Eta Squared as Measures of Effect Size in Educational Research. , 2011 .

[85]  Rachel Cole,et al.  A Landscape Study of Computer Science Education in NYC: Early Findings and Implications for Policy and Practice , 2018, SIGCSE.

[86]  Joyce Malyn-Smith,et al.  Computational thinking for youth in practice , 2011, INROADS.