Distinguishing complex ideas about climate change: knowledge integration vs. specific guidance

ABSTRACT We compared two forms of automated guidance to support students’ understanding of climate change in an online inquiry science unit. For specific guidance, we directly communicated ideas that were missing or misrepresented in student responses. For knowledge integration guidance, we provided hints or suggestions to motivate learners to analyze features of their response and seek more information. We guided both student-constructed energy flow diagrams and short essays at total of five times across an approximately week-long curriculum unit. Our results indicate that while specific guidance typically produced larger accuracy gains on responses within the curriculum unit, knowledge integration guidance produced stronger outcomes on a novel essay at posttest. Closer analysis revealed an association between the time spent revisiting a visualization and posttest scores on this summary essay, only for those students in the knowledge integration condition. We discuss how these gains in knowledge integration extend laboratory results related to ‘desirable difficulties’ and show how autonomous inquiry can be fostered through automated guidance.

[1]  M. Linn,et al.  Sixth-Grade Students’ Progress in Understanding the Mechanisms of Global Climate Change , 2015 .

[2]  Fred Paas,et al.  Effects of Process-Oriented Worked Examples on Troubleshooting Transfer Performance , 2006 .

[3]  Janice D. Gobert,et al.  From Log Files to Assessment Metrics: Measuring Students' Science Inquiry Skills Using Educational Data Mining , 2013, Journal of the Learning Sciences.

[4]  Jennifer L. Chiu,et al.  Evidence for effective uses of dynamic visualisations in science curriculum materials , 2015 .

[5]  Raymond W. Kulhavy,et al.  Feedback complexity and corrective efficiency , 1985 .

[6]  Anna N. Rafferty,et al.  Computer-Guided Inquiry to Improve Science Learning , 2014, Science.

[7]  Marcia C. Linn,et al.  A Design-based Approach to Fostering Understanding of Global Climate Change , 2012 .

[8]  Stephanie B. Corliss,et al.  Professional Development for Technology-Enhanced Inquiry Science , 2011 .

[9]  Joseph Krajcik,et al.  A Scaffolding Design Framework for Software to Support Science Inquiry , 2004, The Journal of the Learning Sciences.

[10]  Robert K. Atkinson,et al.  Using animations and visual cueing to support learning of scientific concepts and processes , 2011, Comput. Educ..

[11]  John R. Anderson,et al.  Implications of the ACT-R Learning Theory: No Magic Bullets , 2000 .

[12]  K. Koedinger,et al.  Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system , 2011, Learning and Instruction.

[13]  K. Koedinger,et al.  Exploring the Assistance Dilemma in Experiments with Cognitive Tutors , 2007 .

[14]  J. Levin.  Computers, teachers, peers: Science learning partners , 2003 .

[15]  J. Sweller.  COGNITIVE LOAD THEORY, LEARNING DIFFICULTY, AND INSTRUCTIONAL DESIGN , 1994 .

[16]  Douglas B. Clark,et al.  WISE design for knowledge integration , 2003 .

[17]  Marcia C. Linn,et al.  The Science of Learning and the Learning of Science Introducing Desirable Difficulties , 2006 .

[18]  J. Bruner.  The act of discovery. , 1961 .

[19]  Ngss Lead States.  Next generation science standards : for states, by states , 2013 .

[20]  Chen-Lin C. Kulik,et al.  The Instructional Effect of Feedback in Test-Like Events , 1991 .

[21]  R. Bjork.  Memory and metamemory considerations in the training of human beings. , 1994 .

[22]  Libby Gerard,et al.  Automated Scoring of Constructed-Response Science Items: Prospects and Obstacles , 2014, Educational Measurement: Issues and Practice.

[23]  Milena K. Nigam,et al.  The Equivalence of Learning Paths in Early Science Instruction: Effects of Direct Instruction and Discovery Learning , 2022 .

[24]  James D. Klein,et al.  Control of Practice and Level of Feedback in Computer-Based Instruction , 1995 .

[25]  Pål J. Kirkeby Hansen,et al.  Knowledge about the Greenhouse Effect and the Effects of the Ozone Layer among Norwegian Pupils Finishing Compulsory Education in 1989, 1993, and 2005—What Now? , 2010 .

[26]  E. Bjork,et al.  Processing strategies and the generation effect: Implications for making a better reader , 2004, Memory & cognition.

[27]  V. Shute.  Focus on Formative Feedback , 2007 .

[28]  R. Mayer.  Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. , 2004, The American psychologist.

[29]  J. Sweller,et al.  The Use of Worked Examples as a Substitute for Problem Solving in Learning Algebra , 1985 .

[30]  Esther Ziegler,et al.  Delayed benefits of learning elementary algebraic transformations through contrasted comparisons , 2014 .

[31]  Manu Kapur.  Productive Failure , 2006, ICLS.

[32]  Fred Paas,et al.  Reflection prompts and tutor feedback in a web-based learning environment: effects on students' self-regulated learning competence , 2004, Comput. Hum. Behav..

[33]  R. Butler.  Task-involving and ego-involving properties of evaluation: Effects of different feedback conditions on motivational perceptions, interest, and performance. , 1987 .

[34]  T. Wickens,et al.  Examining the Spacing Effect in Advertising: Encoding Variability, Retrieval Processes, and Their Interaction , 2005 .

[35]  Cheryl E. Sanders,et al.  Advice and Feedback: Elements of Practice for Problem Solving , 1994 .

[36]  Michael I. Swart,et al.  Applying Grounded Coordination Challenges to Concrete Learning Materials: A Study of Number Line Estimation. , 2014 .

[37]  Ryan Shaun Joazeiro de Baker,et al.  Off-task behavior in the cognitive tutor classroom: when students "game the system" , 2004, CHI.

[38]  U. Wilensky,et al.  Thinking Like a Wolf, a Sheep, or a Firefly: Learning Biology Through Constructing and Testing Computational Theories—An Embodied Modeling Approach , 2006 .

[39]  3 Optimizing Long-Term Retention and Transfer , 2022 .

[40]  John R. Anderson,et al.  Cognitive Tutors: Lessons Learned , 1995 .

[41]  T. Gog,et al.  In the eyes of the beholder: How experts and novices interpret dynamic stimuli , 2010 .

[42]  M. Thackrey.  In the Mind's Eye: Enhancing Human Performance , 1993 .

[43]  Marcia C. Linn,et al.  Designing guidance for interpreting dynamic visualizations: Generating versus reading explanations , 2014 .

[44]  Joseph Krajcik,et al.  Constructing Extended Inquiry Projects: Curriculum Materials for Science Education Reform , 2000 .

[45]  R. Mayer,et al.  Narrative games for learning: Testing the discovery and narrative hypotheses. , 2012 .

[46]  Jonathan M. Vitale,et al.  Taking advantage of automated assessment of student‐constructed graphs in science , 2015 .

[47]  Vincent Aleven,et al.  Intelligent Tutoring Goes To School in the Big City , 1997 .

[48]  Fred Paas,et al.  Process-Oriented Worked Examples: Improving Transfer Performance Through Enhanced Understanding , 2004 .

[49]  Daniel L. Schwartz,et al.  Physically Distributed Learning: Adapting and Reinterpreting Physical Environments in the Development of Fraction Concepts , 2005, Cogn. Sci..

[50]  Jean McKendree,et al.  Effective Feedback Content for Tutoring Complex Skills , 1990, Hum. Comput. Interact..

[51]  Cindy E. Hmelo-Silver,et al.  Understanding Complex Systems: Some Core Challenges , 2006 .

[52]  John R. Anderson,et al.  Cognitive principles in the design of computer tutors , 1984 .