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Horiguchi, Tomoya; Hirashima, Tsukasa; Hayashi, Yusuke – Journal of Computer Assisted Learning, 2023
Background: In learning mechanics, students often believe that "force is exerted on moving objects." As this misconception called "motion implies a force" (MIF) is difficult to correct, various teaching methods have been proposed, such as showing refutational/explanatory text (Palmer & Flanagan, 1997; Takagaki, 2004),…
Descriptors: Scientific Concepts, Misconceptions, Concept Formation, Instructional Effectiveness
Žanko, Žana; Mladenovic, Monika; Krpan, Divna – Journal of Computer Assisted Learning, 2022
Background and Context: Most studies about programming misconceptions are conducted at the undergraduate and graduate levels. Since the age level for starting learning programming is getting lower, there is a need for determining programming misconceptions for younger learners. Objective: Our goal is to determine programming misconceptions and…
Descriptors: Programming, Misconceptions, Grade 5, Elementary School Students
Christina Areizaga Barbieri; Brianna L. Devlin – Journal of Computer Assisted Learning, 2024
Background: Providing students with worked out problem solutions is a beneficial instructional technique in STEM disciplines, and studying examples that have been worked out incorrectly may be especially helpful for reducing misconceptions in students with low prior content knowledge. However, past results are inconclusive and the effects of…
Descriptors: STEM Education, Misconceptions, Fractions, Error Patterns
Fischer, Julian; Machts, Nils; Bruckermann, Till; Möller, Jens; Harms, Ute – Journal of Computer Assisted Learning, 2022
Background: The professional knowledge of pre-service teachers is highly important for effective and successful teaching. In recent years, many research groups have been engaged in developing simulated classroom environments to capture especially the pedagogical knowledge (PK) of pre-service teachers, neglecting the content-related facets of…
Descriptors: Preservice Teachers, Preservice Teacher Education, Biology, Pedagogical Content Knowledge
Dersch, Anna-Sophia; Renkl, Alexander; Eitel, Alexander – Journal of Computer Assisted Learning, 2022
Background: Previous research has shown that teachers hold misconceptions about multimedia learning (e.g., multimedia instruction needs to be adapted to students' learning styles), which may be at odds with evidence-based teaching. Objectives: Refutation texts are a classical method to reduce misconceptions and thus to stimulate conceptual change.…
Descriptors: Teacher Attitudes, Misconceptions, Attitude Change, Persuasive Discourse
Yang, Der-Ching; Sianturi, Iwan Andi J. – Journal of Computer Assisted Learning, 2019
When solving a mathematical problem, students who do not have sufficient conceptual understanding may perform poorly and exhibit misconceptions. This study was aimed to examine students' conceptual understanding and significant misconceptions when solving number sense-related problems. An online three-tier diagnostic test was administered to 125…
Descriptors: Concept Formation, Diagnostic Tests, Misconceptions, Problem Solving
Papadopoulos, Pantelis M.; Natsis, Antonis; Obwegeser, Nikolaus; Weinberger, Armin – Journal of Computer Assisted Learning, 2019
The aim of the present study (n = 113) was to examine how (objective and subjective) information on peers' preparation, confidence, and past performance can support students in answering correctly in audience response systems (aka clickers). The result analysis shows that in the "challenging" questions, in which answers diverged,…
Descriptors: Feedback (Response), Audience Response Systems, Self Esteem, Student Attitudes
Liu, T.-C.; Lin, Y.-C.; Kinshuk – Journal of Computer Assisted Learning, 2010
Simulation-based computer assisted learning (CAL) is recommended to help students understand important statistical concepts, although the current systems are still far from ideal. Simulation-Assisted Learning Statistics (SALS) is a simulation-based CAL that is developed with a learning model that is based on cognitive conflict theory to correct…
Descriptors: Experimental Groups, Computer Assisted Instruction, Lecture Method, Misconceptions

Lim, C. P. – Journal of Computer Assisted Learning, 2001
Discussion of visualization and animation in computer-assisted learning packages focuses on a case study of secondary students in England in an economics course. Highlights include visualization and animation as conceptual anchors; as sources of misconceptions; and the role of instructional activities. (LRW)
Descriptors: Case Studies, Computer Assisted Instruction, Economics Education, Foreign Countries

Niedderer, H.; And Others – Journal of Computer Assisted Learning, 1991
Described is how an iconic model building software can be used to help students gain a deeper qualitative conceptual understanding of physics concepts. The program, STELLA, links research about misconceptions and new teaching strategies with the use of modern information technology tools. (31 references) (KR)
Descriptors: Computer Assisted Instruction, Concept Formation, Learning Strategies, Misconceptions
Li, S. C.; Law, N.; Lui, K. F. A. – Journal of Computer Assisted Learning, 2006
While simulations have widely been used to facilitate conceptual change in learning science, results indicate that significant disparity or gap between students' prior conceptions and scientific conceptions still exists. To bridge the gap, we argue that the applications of computer simulation in science education should be broadened to enable…
Descriptors: Computer Simulation, Science Education, Concept Formation, Models

Brna, P. – Journal of Computer Assisted Learning, 1991
A methodology for confronting students with the inconsistencies entailed by their own beliefs is outlined. This methodology is illustrated using the dynamics domain of physics and a computer modeling program, DYNALAB. (KR)
Descriptors: Case Studies, Cognitive Development, Computer Assisted Instruction, Concept Formation