Effect of Problem Solving Support and Cognitive Styles on Idea Generation (original) (raw)
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2007
This study investigated the effect of two problem-solving techniques:(a) free-association with a direct reference to the problem, called shortly direct, and (b) free-association with a remote and postponed reference to the problem, called remote, on fluency and originality of ideas in solving ill-structured problems. The research design controlled for possible effects of cognitive style for problem-solving–adaptor vs. innovator. The results showed that both groups significantly outscored a control group on fluency and originality.
Impact of Guidance on the Problem-Solving Efforts of Instructional Design Novices
Performance …, 2009
This exploratory study examined differences in the problem representations of a case-based situation by expert and novice instructional designers. The experts and half of the novices (control group) received identical directions for case analysis, while the other novices (treatment group) received additional guidelines recommending analysis strategies that experts tend to use. After participants' case analyses were scored on four dimensions of problem representation, a Wilcoxon nonparametric test was performed. Significant differences were noted between experts and control novices on the total score and on two dimensions of problem representation. Treatment novices did not differ significantly from experts, while control and treatment novices differed significantly on one dimension. Implications for future research and practice are discussed.
Problematizing Helps! A Classroom Study of Computer-Based Guidance for Invention Activities
International Journal of Artificial Intelligence in Education, 2019
A successful instructional method is to engage learners with exploratory problemsolving before providing explanations of the canonical solutions and foundational concepts. A key question is whether and what type of guidance will lead learners to explore more productively and how this guidance will affect subsequent learning and transfer. We investigate this question through the design and study of the Invention Coach, an adaptive, computer-based learning environment that problematizes students' understanding as they invent fundamental physics equations. Problematizing guidance (Reiser Journal of the Learning Sciences, 13(3), 273-304, 2004), which encourages learners to grapple with domain complexity, is well-suited to the goals of Invention. However, there are few examples of technology-based learning environments that were explicitly designed to problematize and scant research on their efficacy. In an experimental study, 199 middle schoolers worked with either motivational, task + motivational, or problematizing + task + motivational guidance versions of the Coach while inventing. Students who engaged with the problematizing Coach were better able to transfer their knowledge to novel domains in the short term, and their transfer gains were comparable to those provoked by human tutors. While students in the problematizing condition were less likely to invent the correct solutions, they engaged in more targeted and efficient exploration of the solution space and were less likely to report experiences of difficulty. Findings suggest that problematizing guidance has the potential to effectively support exploratory problem-solving, when the goal is to facilitate productive exploration and transfer from subsequent instruction. The work also has implications for the design of problematizing guidance.
Multiple studies have shown benefits of problem-solving prior to instruction (cf. Productive Failure, Invention) in comparison to direct instruction. However, students' solutions prior to instruction are usually erroneous or incomplete. In analogy to guided discovery learning, it might therefore be fruitful to lead students towards the discovery of the canonical solution. In two quasi-experimental studies with 104 students and 175 students, respectively, we compared three conditions: problem-solving prior to instruction, guided problem-solving prior to instruction in which students were led towards the discovery of relevant solution components, and direct instruction. We replicated the beneficial effects of problem-solving prior to instruction in comparison to direct instruction on posttest items testing for conceptual knowledge. Our process analysis further revealed that guidance helped students to invent better solutions. However, the solution quality did not correlate with the posttest results in the guided condition, indicating that leading students towards the solution does not additionally promote learning. This interpretation is supported by the finding that the two conditions with problem-solving prior to instruction did not differ significantly at posttest. The second study replicated these findings with a greater sample size. The results indicate that different mechanisms underlie guided discovery learning and problem-solving prior to instruction: In guided discovery learning, the discovery of an underlying model is inherent to the method. In contrast, the effectiveness of problem-solving prior to instruction does not depend on students' discovery of the canonical solution, but on the cognitive processes related to problem-solving, which prepare students for a deeper understanding during subsequent instruction.
Learning Styles Difference in Difficulties of Generating Idea
The generation of an idea that goes through several phases is affected by individual factors, interests, preferences and motivation. The purpose of this research was to analyze the difference in difficulties of generating ideas according to individual learning styles. A total of 375 technical students from four technical universities in Malaysia were randomly selected as samples. The Kolb Learning Styles Inventory and a set of developed questionnaires were used in this research. The results showed that the most dominant learning style among technical students is Doer. A total of 319 (85.1%) technical students faced difficulties in solving individual assignments. Most of the problem faced by technical students is the difficulty of generating ideas for solving individual assignments. There was no significant difference in difficulties of generating ideas according to students' learning styles. Therefore, students need to learn higher order thinking skills enabling students to generate ideas and consequently complete assignments.
Instructional design models for well-structured and III-structured problem-solving learning outcomes
Although problem solving is regarded by most educators as among the most important learning outcomes, few instructional design prescriptions are available for designing problem-solving instruction and engaging learners. This paper distinguishes between well-structured problems and ill-structured problems. Well-structured problems are constrained problems with convergent solutions that engage the application of a limited number of rules and principles within welldefined parameters. Ill-structured problems possess multiple solutions, solution paths, fewer parameters which are less manipulable, and contain uncertainty about which concepts, rules, and principles are necessary for the solution or how they are organized and which solution is best. For both types of problems, this paper presents models for how learners solve them and models for designing instruction to support problem-solving skill development. The model for solving wellstructured problems is based on information processing theories of learning, while the model for solving ill-structured problems relies on an emerging theory of ill-structured problem solving and on constructivist and situated cognition approaches to learning.