Briana B. Morrison
Lauren E. Margulieux
Mark Guzdial
ABSTRACT
Recent empirical results suggest that the instructional material used to teach computing may actually overload students' cognitive abilities. Better designed materials may enhance learning by reducing unnecessary load. Subgoal labels have been shown to be effective at reducing the cognitive load during problem solving in both mathematics and science. Until now, subgoal labels have been given to students to learn passively. We report on a study to determine if giving learners subgoal labels is more or less effective than asking learners to generate subgoal labels within an introductory CS programming task. The answers are mixed and depend on other features of the instructional materials. We found that student performance gains did not replicate as expected in the introductory CS task for those who were given subgoal labels. Computer science may require different kinds of problem-solving or may generate different cognitive demands than mathematics or science.
- Atkinson, R. K., Catrambone, R., and Merrill, M. M., 2003. Aiding Transfer in Statistics: Examining the Use of Conceptually Oriented Equations and Elaborations During Subgoal Learning. Journal of Educational Psychology. 95, 4 (2003), 762.Google Scholar
Cross Ref
- Atkinson, R. K., Derry, S., Renkl, A., and Wortham, D., 2000. Learning from examples: Instructional principles from the worked examples research. Review of educational research. 70, 2 (2000), 181--214.Google Scholar
- Atkinson, R. K. 2002. Optimizing learning from examples using animated pedagogical agents. Journal of Educational Psychology. 94, 2 (2002), 416.Google ScholarCross Ref
- Atkinson, R. K. and Derry, S. J. 2000. Computer-based examples designed to encourage optimal example processing: A study examining the impact of sequentially presented, subgoal-oriented worked examples. (2000).Google Scholar
- Bjork, R. A. 1994. Memory and metamemory considerations in the training of human beings. Metacognition: Knowing about Knowing. MIT Press.Google Scholar
- Bransford, J. D., Brown, A., and Cocking, R. R., 2000. How People Learn: Brain, Mind, Experience, and School. National Academy Press.Google Scholar
- Catrambone, R. 1995. Aiding subgoal learning: Effects on transfer. Journal of educational psychology. 87, 1 (1995), 5.Google ScholarCross Ref
- Catrambone, R. 1996. Generalizing solution procedures learned from examples. Journal of Experimental Psychology: Learning, Memory, and Cognition; Journal of Experimental Psychology: Learning, Memory, and Cognition. 22, 4 (1996), 1020.Google ScholarCross Ref
- Catrambone, R. 1994. Improving examples to improve transfer to novel problems. Memory & Cognition. 22, 5 (1994), 606--615.Google ScholarCross Ref
- Catrambone, R. 1998. The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General. 127, 4 (1998), 355.Google ScholarCross Ref
- Chi, M.T., Bassok, M., Lewis, M.W., Reimann, P., and Glaser, R., 1989. Self-explanations: How students study and use examples in learning to solve problems. Cognitive science. 13, 2 (1989), 145--182.Google Scholar
- Eiriksdottir, E. and Catrambone, R. 2011. Procedural instructions, principles, and examples how to structure instructions for procedural tasks to enhance performance, learning, and transfer. Human Factors: The Journal of the Human Factors and Ergonomics Society. 53, 6 (2011), 749--770.Google ScholarCross Ref
- Leppink, J, Paas, F., van der Vleuten, C., van Gog, T., and van Merriënboer, J., 2013. Development of an instrument for measuring different types of cognitive load. Behavior research methods. 45, 4 (2013), 1058--1072.Google Scholar
- Leppink, J., Paas, F., van Gog, T., van der Vleuten, C., and van Merriënboer, J., 2014. Effects of pairs of problems and examples on task performance and different types of cognitive load. Learning and Instruction. 30, (2014), 32--42.Google ScholarCross Ref
- Margulieux, L. E., Guzdial, M., and Catrambone, R., 2012. Subgoal-labeled instructional material improves performance and transfer in learning to develop mobile applications. Proceedings of the ninth annual international conference on International computing education research (2012), 71--78. Google ScholarDigital Library
- Margulieux, L. E. and Catrambone, R. 2014. Improving problem solving performance in computer-based learning environments through subgoal labels. Proceedings of the first ACM conference on Learning@ scale conference (2014), 149--150. Google ScholarDigital Library
- Van Merriënboer, J. J. and Sweller, J. 2005. Cognitive load theory and complex learning: Recent developments and future directions. Educational psychology review. 17, 2 (2005), 147--177.Google Scholar
- Morrison, B. B., Dorn, B., and Guzdial, M., 2014. Measuring cognitive load in introductory CS: adaptation of an instrument. Proceedings of the tenth annual conference on International computing education research (2014), 131--138. Google ScholarDigital Library
- Palmiter, S. and Elkerton, J. 1993. Animated demonstrations for learning procedural computer-based tasks. Human-Computer Interaction. 8, 3 (1993), 193--216. Google ScholarDigital Library
- Plass, J. L., Moreno, R., and Brünken, R., 2010. Cognitive load theory. Cambridge University Press.Google Scholar
- Renkl, A. and Atkinson, R. K. 2002. Learning from examples: Fostering self-explanations in computer-based learning environments. Interactive learning environments. 10, 2 (2002), 105--119.Google Scholar
- Renkl, A. and Atkinson, R. K. 2003. Structuring the transition from example study to problem solving in cognitive skill acquisition: A cognitive load perspective. Educational psychologist. 38, 1 (2003), 15--22.Google Scholar
- Spanjers, I. A., van Gog, T., van Merriënboer, J., 2012. Segmentation of worked examples: Effects on cognitive load and learning. Applied Cognitive Psychology. 26, 3 (2012), 352--358.Google ScholarCross Ref
- Sweller, J., van Marriënboer, J., Paas, F., 1998. Cognitive architecture and instructional design. Educational psychology review. 10, 3 (1998), 251--296.Google Scholar
- Sweller, J., Ayres, P., and Kalyuga, S., 2011. Cognitive load theory. Springer.Google Scholar
- Sweller, J. 2010. Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational psychology review. 22, 2 (2010), 123--138.Google Scholar
- Tew, A. E. and Guzdial, M., 2011. The FCS1: a language independent assessment of CS1 knowledge. Proceedings of the 42nd ACM technical symposium on Computer science education (2011), 111--116. Google ScholarDigital Library
- van Gog, T. and Paas, F., 2012. Cognitive Load Measurement. Encyclopedia of the Sciences of Learning. Springer.Google Scholar
Index Terms
- Subgoals, Context, and Worked Examples in Learning Computing Problem Solving
Recommendations
Subgoals Help Students Solve Parsons Problems
SIGCSE '16: Proceedings of the 47th ACM Technical Symposium on Computing Science EducationWe report on a study that used subgoal labels to teach students how to write while loops with a Parsons problem learning assessment. Subgoal labels were used to aid learning of programming while not overloading students' cognitive abilities. We wanted ...
Using Subgoals to Improve Student Performance in CS1: (Abstract Only)
SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science EducationSubgoal labels are function-based instructional explanations that describe the problem-solving steps to the learner, highlighting the solution process. There is strong evidence that the use of subgoal labels within worked examples improves student ...
Subgoal Labeled Worked Examples in K-3 Education
SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science EducationWorked examples are step-by-step instructions that are used to demonstrate and teach problem-solving processes. Subgoal labels are used to group the steps of worked examples into cohesive units that may help the learner to identify key information about ...
Comments