Queries and Cues: Textual Stimuli for Reflective Thinking in Digital Mind-Mapping

2021 ◽  
pp. 1-19
Author(s):  
Ting-Ju Chen ◽  
Ronak Ranjitkumar Mohanty ◽  
Vinayak Krishnamurthy

Abstract Mind-mapping is useful for externalizing ideas and their relationships surrounding a central problem. However, balancing between the exploration of different aspects (breadth) of the problem with respect to the detailed exploration of each of its aspects (depth) can be challenging, especially for novices. The goal of this paper is to investigate the notion of “reflection-in-design” through a novel interactive digital mind-mapping workflow that we call “QCue”. The idea behind this workflow is to incorporate the notion of reflective thinking through two mechanisms: (1) offering suggestions to promote depth exploration through user's queries (Q), and (2) asking questions (Cue) to promote reflection for breadth exploration. This paper is an extension of our prior work where our focus was mainly on the algorithmic development and implementation of a cognitive support mechanism behind QCue enabled by ConceptNet (a graph-based rich ontology with “commonsense” knowledge). In this extended work, we first present a detailed summary of how QCue facilitated the breadth-depth balance in a mind-mapping task. Second, we present a comparison between QCue and conventional digital mind-mapping i.e. without our algorithm through a between-subjects user study. Third, we present new detailed analysis on the usage of different cognitive mechanisms provided by QCue. We further consolidate our prior quantitative analysis and build a connection with our observational analysis. Finally, we discuss in detail the different cognitive mechanisms provided by QCue to stimulate reflection in design.

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Ting-Ju Chen ◽  
Vinayak R. Krishnamurthy

Abstract In this paper, we report on our investigation of human-AI collaboration for mind-mapping. We specifically focus on problem exploration in pre-conceptualization stages of early design. Our approach leverages the notion of query expansion—the process of refining a given search query for improving information retrieval. Assuming a mind-map as a network of nodes, we reformulate its construction process as a sequential interaction workflow wherein a human user and an intelligent agent take turns to add one node to the network at a time. Our contribution is the design, implementation, and evaluation of algorithm that powers the intelligent agent (IA). This paper is an extension of our prior work (Chen et al., 2019, “Mini-Map: Mixed-Initiative Mind-Mapping Via Contextual Query Expansion,” AIAA Scitech 2020 Forum, p. 2347) wherein we developed this algorithm, dubbed Mini-Map, and implemented a web-based workflow enabled by ConceptNet (a large graph-based representation of “commonsense” knowledge). In this paper, we extend our prior work through a comprehensive comparison between human-AI collaboration and human-human collaboration for mind-mapping. We specifically extend our prior work by: (a) expanding on our previous quantitative analysis using established metrics and semantic studies, (b) presenting a new detailed video protocol analysis of the mind-mapping process, and (c) providing design implications for digital mind-mapping tools.


2021 ◽  
Author(s):  
Ting-Ju Chen ◽  
Shantanu Vyas ◽  
Vinayak R. Krishnamurthy

Abstract We present an experiment to study the role of mind-mapping as a tool for design opportunity identification and problem understanding. Our goal is to investigate how the quality of design opportunity statements change with two different techniques, namely, mind-mapping and free writing. Identifying design opportunities is an important step in new product development and little is currently understood in terms of what tools can provide cognitive support for problem clarification. In this work, we focus on mind-mapping as one example of a potential tool for providing such support. Mind-maps are well-known for their ability to enable the exploration of ideas in an unconstrained and structured way. To study their role in helping problem exploration, we conducted a between-subject user study with 28 participants to investigate how information structure and organization affect the exploration of ideas in a given design context. Further, we propose new evaluation metrics to quantitatively assess key elements presented in the design opportunity statements generated after exploring the problem domain. We report on the quantitative results, the exploration behaviors, and the general user feedback about the experience. Finally, we discuss the implications of these findings on design problem identification and future digital mind-mapping tools for exploratory tasks.


2018 ◽  
Vol 4 (1) ◽  
pp. 6-10
Author(s):  
Irma Al Hamid ◽  
Agus Yudiawan

Morals in presenting subject matter are sometimes monotonous. Teachers are more likely to use the lecture method in learning, thereby making students bored. Therefore, researchers chose one cooperative learning model, namely the Mind Mapping learning model to improve student learning outcomes. This model uses a grouping system consisting of 2-5 people, which in the implementation stage includes the discussion stage, thinking together to express ideas from the problem concept given by the teacher, asking questions, and answering questions. This study aims to determine the increase in learning outcomes in the subjects of Islamic Morals through the Mind Mapping learning model. This type of research is Classroom Action Research conducted in 2 cycles, namely Cycle I conducted 2 meetings and Cycle II 2 meetings. The subjects of this study were students of Class VII MTs Az-Zikra, Sorong City in the odd semester of the 2016-2017 academic year of 21 students. The object of research is student learning outcomes which are divided into three domains, namely cognitive, affective and psychomotor. Data collection techniques used were observation and tests. The collected data were analyzed both qualitative and quantitative data. Qualitative data is data in the form of a sentence that gives a description of student expressions, the level of understanding of a subject, the learning process takes place and the like. While quantitative data uses descriptive statistical analysis with the application of SPSS 16.0, for example, finding an average value. The results of research based on the cognitive domain showed that there was an increase in the learning outcomes of the moral creed ie from the average value of student learning outcomes in Cycle I was 67.43 (low category) and in Cycle II amounted to 74.52 (low category) but there was an increase in the Cycle I. In Cycle I the number of students who finished as many as 9 people (42.85%), while in Cycle II it increased to 15 people (71.42%), and students who completed had reached 71.42% exceeding the expected target ie 65%. The affective domain looks changes after the teaching and learning process, both from the attitudes, behavior, interests, emotions, motivation, cooperation and coordination of each student which is done through direct observation and interaction. The psychomotor domain is seen from the results of observations or activities of students obtained during the learning process, such as asking questions, answering, responding, dare to submit opinions and draw conclusions from the material that has been submitted. Based on these results it can be concluded that there is an increase in learning outcomes of moral subjects in Sorong City Az-Zikra Class VII students after learning by using Mind Mapping Learning Model.


2019 ◽  
Vol 42 ◽  
Author(s):  
Mark Alfano

Abstract Reasoning is the iterative, path-dependent process of asking questions and answering them. Moral reasoning is a species of such reasoning, so it is a matter of asking and answering moral questions, which requires both creativity and curiosity. As such, interventions and practices that help people ask more and better moral questions promise to improve moral reasoning.


2016 ◽  
Vol 39 ◽  
Author(s):  
Arnon Lotem ◽  
Oren Kolodny ◽  
Joseph Y. Halpern ◽  
Luca Onnis ◽  
Shimon Edelman

AbstractAs a highly consequential biological trait, a memory “bottleneck” cannot escape selection pressures. It must therefore co-evolve with other cognitive mechanisms rather than act as an independent constraint. Recent theory and an implemented model of language acquisition suggest that a limit on working memory may evolve to help learning. Furthermore, it need not hamper the use of language for communication.


PsycCRITIQUES ◽  
1983 ◽  
Vol 28 (5) ◽  
Author(s):  
No authorship indicated
Keyword(s):  

2007 ◽  
Author(s):  
F. Lucidi ◽  
A. Zelli ◽  
L. Mallia ◽  
C. Grano ◽  
C. Violani

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