Paraphrasing refutation text and knowledge form: examples from repairing relational database design misconceptions
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Paraphrasing refutation text and knowledge form: examples from repairing relational database design misconceptions General M. Ntshalintshali1 · Roy B. Clariana2
© Association for Educational Communications and Technology 2020
Abstract This experimental study examined the effects of conceptual change-oriented refutation text (RT) on declarative knowledge and conceptual knowledge. Information Science undergraduates (N = 66) enrolled in two sections of a course with different instructors but the same syllabus were randomly assigned to one of four RT treatments that included read only vs. reading plus paraphrasing, with either set 1 or set 2 RTs, each RT set addressed five separate misconceptions. Pretest and posttest assessed the declarative and conceptual aspects of all ten misconceptions. For conceptual knowledge, pretest-to-posttest results show that reading and paraphrasing RTs is superior to only reading the RTs (ES = .40). Unexpectedly, conceptual knowledge improved for all misconceptions, both for the assigned RTs as well as those not assigned, thus RTs had a broad structural rather than a narrow attentional influence. However, declarative knowledge scores significantly and substantially decreased from pretest-to-posttest, indicating that the conceptual gains observed here came at the cost of declarative knowledge. Misconceptions are represented here as multiword chunks using a Pathfinder network approach, and conceptual improvement is explained as the effects of refutation text as a form of structural feedback acting on these chunks. Future research is needed to further consider the effects of addressing multiple misconceptions at once, and also on how RTs impact different kinds of learning outcomes. Keywords Refutation text · Misconception · Conceptual knowledge · Declarative knowledge · Structural knowledge · Pretests
This article is a report of the Ntshalintshali’s dissertation, Ph.D. graduation May 2014, Clariana was the student’s dissertation chair. * Roy B. Clariana [email protected] General M. Ntshalintshali [email protected] 1
DataState, LLC, Yardley, PA, USA
2
The Pennsylvania State University, University Park, PA, USA
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G. M. Ntshalintshali, R. B. Clariana
Misconceptions and Learning Jonassen et al. (2005) describe misconceptions as concept miss-assignment within a bounded domain-knowledge ontological space that hinders deeper understanding (Chi and Roscoe 2002). Van den Broek and Kendeou (2008) refer to misconceptions simply as knowledge that is not accurate, while Hancock (1940) describes misconceptions as false beliefs that cannot be rationalized or justifiably held up to normative standards of a knowledge domain. A misconception is defined here as a unique kind of robust context-specific error that is often difficult to identify and correct with formal instruction (Chi et al. 2012). Unlike typical errors, misconceptions persist and so traditional instruction has difficulty correcting them (Keleş et al. 2011), thus misconceptions are a source of concern bo
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