Design thinking support: information systems versus reasoning

Numerous attempts have been made to conceive and implement appropriate information systems to support architectural designers in their creative design thinking processes. These information systems aim at providing support in very diverse ways: enabling designers to make diverse kinds of visual representations of a design, enabling them to make complex calculations and simulations which take into account numerous relevant parameters in the design context, providing them with loads of information and knowledge from all over the world, and so forth. Notwithstanding the continued efforts to develop these information systems, they still fail to provide essential support in the core creative activities of architectural designers. In order to understand why an appropriately effective support from information systems is so hard to realize, we started to look into the nature of design thinking and on how reasoning processes are at play in this design thinking. This investigation suggests that creative designing rests on a cyclic combination of abductive, deductive and inductive reasoning processes. Because traditional information systems typically target only one of these reasoning processes at a time, this could explain the limited applicability and usefulness of these systems. As research in information technology is increasingly targeting the combination of these reasoning modes, improvements may be within reach for design thinking support by information systems

Combining Exploratory Learning With Structured Practice to Foster Conceptual and Procedural Fractions Knowledge

Robust domain knowledge consists of conceptual and procedural knowledge. The two types of knowledge develop together, but are fostered by different learning tasks. Exploratory tasks enable students to manipulate representations and discover the underlying concepts. Structured tasks let students practice problem-solving procedures step-by-step. Educational technology has mostly relied on providing only either task type, with a majority of learning environments focusing on structured tasks. We investigated in two quasi-experimental studies with 8-10 years old students from UK (N = 121) and 10-12 years old students from Germany (N = 151) whether a combination of both task types fosters robust knowledge more than structured tasks alone. Results confirmed this hypothesis and indicate that students learning with a combination of tasks gained more conceptual knowledge and equal procedural knowledge compared to students learning with structured tasks only. The results illustrate the efficacy of combining both task types for fostering robust fractions knowledge

Scaffolding online peer collaboration to enhance ill-structured problem solving with computer-based cognitive support.

The results reveal significant effects of procedure and metacognitive question prompts in ill-structured problem solving at both overall and univariate levels. However, there was no significant effect of online peer collaboration and no significant interaction. This study supported some previous research on using question prompts as a scaffolding strategy to support problem solving. Further, these findings support a redefined IDEAL problem solving model for solving ill-structured problems. The findings suggest many implications for instructional designers, educators in web-based learning environments, and educational researchers. These implications and the limitations of this study are discussed.The present study investigated the effects of question prompts and online peer collaborations on solving ill-structured problems. Sixty undergraduate students were randomly assigned to one of the four treatment groups: collaboration with question prompts, individual with question prompts, collaboration without question prompts, and individual without question prompts. Question prompts were designed to both facilitate problem solving procedure and promote students' metacognition. Students worked either individually or collaboratively with partners via MSN Messenger during the problem solving processes

Design activities: how to analyze cognitive effort associated to cognitive treatments?

Working memory issues are important in many real activities. Thus, measuring cognitive effort (or mental load) has been a main research topic for years in cognitive ergonomics, though no consensual method to study such aspect has been proposed. In addition, we argue that cognitive effort has to be related to an analysis of the evolution of cognitive processes, which has been called "time processing". Towards this end, we present and discuss paradigms that have been used for years to study writing activities and, in experiments reported in this paper, for studying design activities, such as computer-graphic tasks or web site desig

Designing a training tool for imaging mental models

The training process can be conceptualized as the student acquiring an evolutionary sequence of classification-problem solving mental models. For example a physician learns (1) classification systems for patient symptoms, diagnostic procedures, diseases, and therapeutic interventions and (2) interrelationships among these classifications (e.g., how to use diagnostic procedures to collect data about a patient's symptoms in order to identify the disease so that therapeutic measures can be taken. This project developed functional specifications for a computer-based tool, Mental Link, that allows the evaluative imaging of such mental models. The fundamental design approach underlying this representational medium is traversal of virtual cognition space. Typically intangible cognitive entities and links among them are visible as a three-dimensional web that represents a knowledge structure. The tool has a high degree of flexibility and customizability to allow extension to other types of uses, such a front-end to an intelligent tutoring system, knowledge base, hypermedia system, or semantic network

Hypermedia support for argumentation-based rationale: 15 years on from gIBIS and QOC

Having developed, used and evaluated some of the early IBIS-based approaches to design rationale (DR) such as gIBIS and QOC in the late 1980s/mid-1990s, we describe the subsequent evolution of the argumentation-based paradigm through software support, and perspectives drawn from modeling and meeting facilitation. Particular attention is given to the challenge of negotiating the overheads of capturing this form of rationale. Our approach has maintained a strong emphasis on keeping the representational scheme as simple as possible to enable real time meeting mediation and capture, attending explicitly to the skills required to use the approach well, particularly for the sort of participatory, multi-stakeholder requirements analysis demanded by many design problems. However, we can then specialize the notation and the way in which the tool is used in the service of specific methodologies, supported by a customizable hypermedia environment, and interoperable with other software tools. After presenting this approach, called Compendium, we present examples to illustrate the capabilities for support security argumentation in requirements engineering, template driven modeling for document generation, and IBIS-based indexing of and navigation around video records of meetings

Effect Of Expert Modeling On Ill-Structured Problem Solving In An Undergraduate General Education Honors Course

Abstract Effect of Expert Modeling on Ill-Structured Problem Solving in an Undergraduate General Education Honors Course by Minakshi Lahiri May 2016 Advisor: Dr. Ke Zhang Major: Instructional Technology Degree: Doctor of Philosophy This dissertation research was based on David H. Jonassen’s recommendation that not all problems are the same and different types of problems require different approaches of instruction and scaffolding (Jonassen & Hung, 2008). Jonassen (2011) provided a set of recommended components (problem types, case components, cognitive supports) for designing effective Problem Based Learning Environments (PBLEs). The purpose of this research was to investigate the effect of using expert modeling of ill-structured problem solving as a scaffolding strategy on undergraduate students’ problem solving outcome. Expert’s analytical guideline to approach and solve an ill structured problem and an example of the expert’s problem solving report was used as scaffold for the problem solving task. The problem solving performance of the undergraduate students were measured on the three major problem solving learning outcomes as listed below: i. Ability to define problem ii. Ability to analyze issues critically and comprehensively iii. Ability to evaluate proposed solutions/hypotheses to problems The above mentioned problem solving outcomes and performance scales and categories were defined by a rubric that was developed following the guidelines from the Association for American Colleges and Universities (AACU) problem solving VALUE rubric (Valid Assessment of Learning in Undergraduate Education). Participants of this study were from 2015 Fall freshmen cohort of Honors College, in a public urban research university in the mid-west of USA. Six Honors College First Year sections participated in this study. Three sections formed the Control group and another three sections formed the Treatment group. The sections were assigned to Control or Treatment group depending on the instructor and was determined with a coin toss. For practical feasibility, three Control Group sections were taught by the same instructor and three Treatment Group sections were taught by same instructor. Students who were less than 18 years of age at the beginning of the fall semester of 2015 were not considered in the study. Total number of participants who qualified for the study, Treatment and Control group combined was 144. Two groups received an identical problem Task I. 122 participant scores from treatment and control sections combined were analyzed for problem solving Task I to give a baseline problem solving score for the two groups. After Task I, 122 participants were considered for the data analysis of the problem solving task - Task II in this study. There were 54 Participants in the Control Group and 68 participants in the Treatment Group for Task II. The treatment group received the treatment (expert modeling scaffolding) along with Task II and the control group received only the problem solving task - Task II, no scaffold. The problem solving reports from the two groups were graded using the rubric by two reviewers using blind review mechanism for reliability. Reflection responses (optional) were also collected from the treatment group participants on their problem solving experience with the scaffold. Percentage agreement and Cohen’s Kappa were calculated as measures of reliability. Results of the quantitative data analysis indicated that the treatment group performed significantly better than the control group in the overall problem solving outcome as well as for the components “Ability to define problem” and “Ability to evaluate proposed solutions”. The result was slightly insignificant for the category “Analyze issues critically and comprehensively”. Qualitative data analysis of the treatment group reflection responses were highly positive and indicated that the learners perceived that the scaffold strategy was beneficial for them and that they learned from the experts analytical guidelines. The participants thought that the expert modeling benefited them by providing a useful tool and framework that they could use in future for other similar problem solving situations; the scaffolding strategy helped them organize and structure the information and helped them follow expert’s strategies on critical thinking and problem solving while approaching and working on the problem solving task