Showing papers on "Concept map published in 2019"

Mapping knowledge risks: towards a better understanding of knowledge management

Abstract: This conceptual paper aims to identify, present, and analyze potential knowledge risks organizations might face. With the growing complexity of organizational environments and the plethora of new k...

Cooperative Learning Application with the Method of "Network Tree Concept Map" : Based on Japanese Learning System Approach

Abstract: Almost all countries pay full attention to the education system, especially countries with smaller populations have an advantage in this field. Relatively small countries and regions, including Hong Kong, Singapore, South Korea, Taiwan, Estonia and Finland, were at the top of the results of the 2015 Program for International Student Assessment (PISA) test. This test was carried out by evaluating academic achievement for 15 years around the world. The results of the last evaluation, Shanghai took the overall top position. Japan is one country that has a good learning concept, with 4th to 7th positions in various fields of competition. The application of cooperative learning with the development of learning methods is very important to improve the achievement of student learning outcomes. The purpose of this study is to observe the understanding of concepts, and how to think critically in analyzing exam questions. Cooperative learning application with a combination of the "Network Tree Concept Map" method as a depiction to increase students' interest and thinking power.

Effects of augmented reality-based multidimensional concept maps on students’ learning achievement, motivation and acceptance

Abstract: In this study, an augmented reality-based multidimensional concept map (ARMCM) learning system is proposed for conducting mobile learning activities. The subjects consisted of 65 students with an average age of 11 years in an elementary school in Taiwan. They were divided into an ARMCM group and a multidimensional concept map (MCM) group. An experiment was conducted to evaluate the effectiveness of the proposed approach. The experimental results suggested that students in the ARMCM group performed significantly better than those in the MCM group. Moreover, this study also found that the students using the ARMCM learning approach showed significantly higher motivation than those using the MCM learning approach, because ARMCM learning is able to simulate the complex knowledge that they needed to learn. ARMCM learning was easier to understand and easy to use, as it could simplify the content of the learning knowledge. Pedagogical implications, conclusion, and some suggestions based on this study are provided.

Computer-supported collaborative concept mapping: The effects of different instructional designs on conceptual understanding and knowledge co-construction

Abstract: Computer-supported collaborative concept mapping (CSCCM) leverages technology and concept mapping to support conceptual understanding, as well as collaborative learning to foster knowledge co-construction. This article investigated the effect of different instructional designs using CSCCM on students' conceptual understanding, and on the type of processes of knowledge co-construction that students engage. Participants (N = 120) were 10th graders enrolled in their physics course, randomly distributed in dyads. They were asked to draw concept maps related to the conservation of energy law, by using CSCCM with different instructional designs (i.e., control, Exp. 1 and Exp. 2). In the control condition, dyads worked collaboratively all the time. In both Exp. 1 and Exp. 2, dyads worked first individually (one week) and then collaboratively (two weeks). However, in Exp. 2, the individual concept map was shared with the peer before collaborating. Conceptual understanding improved significantly for learners in all three experimental conditions, especially in Exp. 2. Statistically significant differences were found in students' knowledge co-construction among the three conditions. Dyads in the control group showed a significantly higher use of quick consensus-building. Dyads in Exp. 1 showed a significantly higher reliance on externalization and elicitation. Dyads in Exp. 2 showed a significantly higher enacting of integration- and conflict-oriented consensus building. Accordingly, an instructional design like Exp. 2 optimizes CSCCM learning outcomes in terms of conceptual understanding and knowledge co-construction.

Uncovering Types of Knowledge in Concept Maps

Abstract: Concept maps have been shown to have a positive impact on the quality of student learning in a variety of disciplinary contexts and educational levels from primary school to university by helping students to connect ideas and develop a productive knowledge structure to support future learning. However, the evaluation of concept maps has always been a contentious issue. Some authors focus on the quantitative assessment of maps, while others prefer a more descriptive determination of map quality. To our knowledge, no previous consideration of concept maps has evaluated the different types of knowledge (e.g., procedural and conceptual) embedded within a concept map, or the ways in which they may interact. In this paper we consider maps using the lens provided by the Legitimation Code Theory (LCT) to analyze concept maps in terms of semantic gravity and semantic density. Weaving between these qualitatively, different knowledges are considered necessary to achieve professional knowledge or expert understanding. Exemplar maps are used as illustrations of the way in which students may navigate their learning towards expertise and how this is manifested in their concept maps. Implications for curriculum design and teaching evaluation are included.

Systematic Literature Review of Knowledge Sharing Barriers and Facilitators in Global Software Development Organizations Using Concept Maps

Abstract: Knowledge is the most important resource in software development. The success of software development relies on knowledge sharing between software developers working across the globe. Global software development has brought many benefits to the software industry; however, at the same, time knowledge sharing across diverse team members is one of the main concerns of global software development organizations. This paper provides a systematic literature review of 42 studies on knowledge sharing barriers and facilitators from 2010 to 2017 and classifies them into five main categories: Individual, Organizational, Technological, Cultural, and Geographical. In order to synthesize and represent the complexity of the knowledge sharing factors in a more manageable and visual manner, this paper proposes concept maps for each category. The identified factors can be strategically used as the guidelines in the global software development organizations to boost the culture of knowledge sharing.

Finding the Intersection of Neuroplasticity, Stroke Recovery, and Learning: Scope and Contributions to Stroke Rehabilitation.

Abstract: Aim. Neural plastic changes are experience and learning dependent, yet exploiting this knowledge to enhance clinical outcomes after stroke is in its infancy. Our aim was to search the available evidence for the core concepts of neuroplasticity, stroke recovery, and learning; identify links between these concepts; and identify and review the themes that best characterise the intersection of these three concepts. Methods. We developed a novel approach to identify the common research topics among the three areas: neuroplasticity, stroke recovery, and learning. A concept map was created a priori, and separate searches were conducted for each concept. The methodology involved three main phases: data collection and filtering, development of a clinical vocabulary, and the development of an automatic clinical text processing engine to aid the process and identify the unique and common topics. The common themes from the intersection of the three concepts were identified. These were then reviewed, with particular reference to the top 30 articles identified as intersecting these concepts. Results. The search of the three concepts separately yielded 405,636 publications. Publications were filtered to include only human studies, generating 263,751 publications related to the concepts of neuroplasticity ( ), stroke recovery ( ), and learning ( ). A cluster concept map (network graph) was generated from the results; indicating the concept nodes, strength of link between nodes, and the intersection between all three concepts. We identified 23 common themes (topics) and the top 30 articles that best represent the intersecting themes. A time-linked pattern emerged. Discussion and Conclusions. Our novel approach developed for this review allowed the identification of the common themes/topics that intersect the concepts of neuroplasticity, stroke recovery, and learning. These may be synthesised to advance a neuroscience-informed approach to stroke rehabilitation. We also identified gaps in available literature using this approach. These may help guide future targeted research.

Mind map our way into effective student questioning: A principle-based scenario

Abstract: Student questioning is an important self-regulative strategy and has multiple benefits for teaching and learning science. Teachers, however, need support to align student questioning to curricular goals. This study tests a prototype of a principle-based scenario that supports teachers in guiding effective student questioning. In the scenario, mind mapping is used to provide both curricular structure as well as support for student questioning. The fidelity of structure and the process of implementation were verified by interviews, video data and a product collection. Results show that the scenario was relevant for teachers, practical in use and effective for guiding student questioning. Results also suggest that shared responsibility for classroom mind maps contributed to more intensive collective knowledge construction.

Fostering deeper learning in a flipped classroom: Effects of knowledge graphs versus concept maps

Abstract: Flipped classroom is an approach that has been increasingly used in K‐12 and higher education. Many studies on the flipped classroom have focused on student behaviors, with inadequate attention to student thinking, which is crucial to learning. Meanwhile, prior studies have examined the effects of visualization tools, such as concept map, on improving student learning through in‐depth thinking. Another related approach is knowledge graph, which presents a set of entities and their relationships in a graph as well as in a machine language for further processing and reasoning. It has a potential to support collaborative knowledge construction by automatic combination of individual knowledge graphs. To compare the effects of knowledge graph and concept map on student learning in a flipped classroom, we conducted an experimental study in fifth grade class at an elementary school. Students in the experimental group used a knowledge graph tool in the Learning Cell System, while those in the control group used a concept map tool, XMind, to support their learning of ancient Chinese poetry. The results showed that learning with knowledge graph resulted in better performance in the breadth and depth of subject knowledge as reflected in the students' learning products (ie, concept maps or knowledge graphs). Practitioner NotesWhat is already known about this topic The flipped classroom can improve student learning by enabling active participation and interactions.The concept map is an effective learning tool that can foster meaningful understanding and higher order thinking in both traditional and flipped classrooms.The knowledge graph is a related tool that presents a set of entities and their semantic relationships in a graph as well as in a machine language to support further processing.The knowledge graph can support collaborative knowledge construction by automatic combination of individual knowledge graphs.What this paper adds We conducted an experimental study to compare the effects of knowledge graph and concept map on student learning in a flipped classroom.Compared with concept map, learning with knowledge graph resulted in better performance in the breadth and depth of subject knowledge as reflected in the students' learning products (ie, concept maps or knowledge graphs).Implications for practice and/or policy Learning with knowledge graph fosters more cognitive engagement in exploring the relationships between concepts represented in both individual and converged knowledge graphs.The converged knowledge graph offers the teacher a big picture of the entire class in student understanding.Relevant training should be provided to students and teachers for effective use and learning with the knowledge graph tool. [ABSTRACT FROM AUTHOR] uracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Incorporation and evaluation of serial concept maps for vertical integration and clinical reasoning in case-based learning tutorials: Perspectives of students beginning clinical medicine.

Abstract: Background: Concept maps and case-based learning (CBL) are recognized and useful strategies to enhance undergraduate medical learning. However, research on the use of a mixed approach is limited.Ai...

Developing Clinical Reasoning Skills through Argumentation with the Concept Map Method in Medical Problem-Based Learning.

Abstract: This study aims to explore the effects of argumentation with the concept map method during medical problem-based learning (PBL) on individual clinical reasoning. Individual clinical reasoning ability was assessed through problem-solving performance and arguments that students constructed during individual clinical reasoning processes. Toulmin’s model of argument was utilized as a structure for arguments. The study also explored whether there would be any differences between the firstand second-year medical students. Ninety-five medical students participated in this study, and they took two PBL modules. During PBL, they were asked as a group to construct concept maps based on their argumentation about a case under discussion. Before and after each PBL, they were asked to write individual clinical problem-solving tests. One-way, within-subjects ANOVAs were conducted to examine the quality of arguments and clinical problem-solving performance in three individual tests. The results provided evidence that utilizing argumentation with the concept map method during PBL positively affects the development of clinical reasoning skills by individual students.

A Concept Map-Based Learning Paths Automatic Generation Algorithm for Adaptive Learning Systems

Abstract: A concept map is an important knowledge visualization tool in adaptive learning systems. The weak concepts of students can be identified by analyzing the concept maps to provide learning guidance and teaching suggestions to students and teachers. However, in the current researches, it is difficult to analyze effective information from the concept maps when the number of concepts is large and the associations between concepts are complicated. It rarely reflects the learning performance of different student groups, which do not reflect the characteristics of the adaptive learning systems. A learning path automatic generation algorithm, Learning paths generation (LPG) algorithm, for adaptive learning systems is proposed. The LPG algorithm fully considers the learning performance of different student groups. The concept maps with different learning features are generated by the clustering algorithm and association rules mining, and several simplified learning paths are generated by using the topological sorting algorithm. Experiments show that the LPG algorithm has a good discrimination for the student groups and can generate different learning paths according to the students’ learning performance.

Reducing Cognitive Load During Closed Concept Map Construction and Consequences on Reading Comprehension and Retention

Abstract: Computer-assisted concept map building from provided pieces by using Kit-build is an activity which can promote comprehension and retention. However, users are burdened with searching for pieces and organizing the layout, which are believed to increase the overall cognitive load of the activity. In this paper, we describe the Airmap interface, which uses automatic layout management and spatial separation to improve cognitive load during concept map building. Two experiments were done in which participants ($N= 60, N=50$) built a map after reading a text. Results show that Airmap is successful in reducing cognitive load without significant differences in immediate reading comprehension. However, there is a significant difference in performance after a two week retention period. Results give new insight into the retention enhancing aspects of building closed concept maps, indicating that the cognitive load reduced by the new interface is of the germane type, affecting how deep in memory users commit the information.

An innovative consensus map-embedded collaborative learning system for ER diagram learning: sequential analysis of students’ learning achievements

Abstract: Although computer-supported collaborative learning has been successfully applied in educational settings to improve group learning performance, most such systems still lack effective strategies for...

Elicitation of Tacit Knowledge Using Soft Systems Methodology

Abstract: Elicitation of tacit knowledge is of paramount importance in organizations for novice people’s use of experts’ knowledge. One of the shortcomings of knowledge elicitation techniques is lack of consensus among experts in eliciting tacit knowledge. In other words, experienced individuals have different viewpoints regarding the statement of complicated knowledge activities. Hence, using soft thinking approach, it is tried to establish agreement among experts and solve the problems of previous techniques. In the present study, a combination of concept mapping, scenario making, teach back, and card sorting techniques within the framework of soft systems methodology have been used. In this way, a methodology is presented for eliciting tacit knowledge.

Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering

Abstract: This article addresses some fundamental issues of concept mapping relevant to discipline-based education. The focus is on manufacturing knowledge representation from the viewpoints of both human and machine learning. The concept of new-generation manufacturing (Industry 4.0, smart manufacturing, and connected factory) necessitates learning factory (human learning) and human-cyber-physical systems (machine learning). Both learning factory and human-cyber-physical systems require semantic web-embedded dynamic knowledge bases, which are subjected to syntax (machine-to-machine communication), semantics (the meaning of the contents), and pragmatics (the preferences of individuals involved). This article argues that knowledge-aware concept mapping is a solution to create and analyze the semantic web-embedded dynamic knowledge bases for both human and machine learning. Accordingly, this article defines five types of knowledge, namely, analytic a priori knowledge, synthetic a priori knowledge, synthetic a posteriori knowledge, meaningful knowledge, and skeptic knowledge. These types of knowledge help find some rules and guidelines to create and analyze concept maps for the purposes human and machine learning. The presence of these types of knowledge is elucidated using a real-life manufacturing knowledge representation case. Their implications in learning manufacturing knowledge are also described. The outcomes of this article help install knowledge-aware concept maps for discipline-based education.

Representation of knowledge in digital educational environments: A systematic review of literature

Abstract: The representation of knowledge is a process widely used in education for its potential to generate deep learning, metacognition, and also in mapping the student's cognitive structure while developing a broad spectrum of thinking skills. Notwithstanding the abovementioned benefits, the development and evolution of new digital ecologies of learning is still an unexplored field for knowledge representation systems. As part of a larger study, this article shows the process and results of a systematic review of literature on knowledge representation systems, with the purpose of identifying the foundations and main applicable instruments of digital educational environments. Among the most representative findings of this review is that despite the existence of a large number of educational experiences that have incorporated both physical and digital knowledge representation tools, their use has been restricted almost entirely to the understanding of concepts and the assessment of learning in non-collaborative environments. These findings suggest the relevance of studying the representation of knowledge in digital collaborative contexts that facilitate the development of thinking skills for the digital age, and the need for co-creation and transformation of knowledge. Together these suggest a new perspective on knowledge representation for digital ecologies of learning.

Topological Scoring of Concept Maps for Cybersecurity Education

Abstract: Concept maps are a well-known pedagogical tool for organizing and representing knowledge and developing a deep understanding of concepts. Unfortunately, the grading of concept maps tends to be manual and tedious thereby, posing serious limitation for an instructor to use them in class efficiently. To automate the assessment and grading, the topology and structural features of concept maps are utilized. However, they have never been explored for cybersecurity education. This paper evaluates the effectiveness of topological scoring of the concept maps for two cybersecurity courses: digital forensics, and SCADA system security. We create a dataset of 41 high-quality concept maps developed with expert knowledge. We utilize waterloo rubric to manually validate the quality of the concept maps based-on their contents and further compare the rubric outcome (obtained via manual analysis) with the automated topological scoring of the maps. The evaluation results show that the topological scoring is promising. However, it is not equally effective and warrants for advanced techniques to better utilize the topology of the maps. The dataset is made publicly available for further research on this topic.

Case-Based Group Learning Using Concept Maps to Achieve Multiple Educational Objectives and Behavioral Outcomes:

Abstract: Knowledge integration is an important aspect of education. In clinical education, there is an emphasis on the integration of basic medical science with clinical practice to provide a higher order o...

Constructing Educational Concept Maps with Multiple Relationships from Multi-Source Data

Abstract: Concept map is an useful tool to help people organize and improve knowledge. Particularly in educational domain, it is beneficial for students and teachers to improve the learning and teaching quality. Traditionally, manual educational concept maps, provided by teachers, are quite time-consuming and limited to teachers' experience. Thus, it is meaningful to automatically construct high-quality concept maps. However, existing data-driven solutions only focus on either separate data source or single pedagogic relationship, which are not sufficient to satisfy actual demands. To this end, we propose a novel framework, named Extracting Multiple Relationships Concept Map (EMRCM), to construct multiple relations concept maps from Multi-source Data. Specifically, we design various targeted evidences to explore diverse information of multi-source data from different perspectives. Then, we employ three classic classifiers to bulid the predictive model for extracting key concepts and multiple concept relationships using the proposed evidences. We create a real dataset for empirically studying this problem. Extensive experiments on a real-world dataset show the effectiveness of our method.

Science Mapping the Knowledge Base on Educational Leadership and Management in Africa, 1960-2018.

Abstract: This review of research employed science mapping to analyse the intellectual structure of the knowledge base on educational leadership and management in Africa. The review analysed 645 Scop...

A Concept Map of Evolutionary Biology to Promote Meaningful Learning in Biology

Abstract: Is it possible to teach biology without mentioning evolution? The answer is yes, but it is not possible for students to understand biology without the evolutionary context on which the meaning and intellectual value of biological concepts depend. Meaningful learning of evolution requires (1) that the students incorporate new knowledge into a cognitive structure linked with higher-order concepts; (2) a well-organized knowledge structure; and (3) a positive emotional attachment and identification (affective commitment) to the subject by the learner. Concept maps are useful tools in meaningful learning. We present a concept map that organizes concepts of history of life and the processes that generate it, and the hierarchical relationships among them. Biological evolution is a compelling account of life on Earth and of human origins. It constitutes a unifying explanatory framework that can generate a powerful affective commitment to the subject. The concept map provided here is tied to the Next Generation Science Standards (NGSS).

Using Kolb’s Learning Cycle to Improve Student Sustainability Knowledge

Abstract: Engineers are increasingly called upon to develop and implement innovative solutions that serve a growing population, while simultaneously exploiting fewer resources and minimizing environmental impacts. As such, improvements in undergraduate curricula are needed to train students to operate under a sustainable development paradigm. A learning-cycle-based sustainability module was adapted and implemented in a cornerstone design course within a civil engineering program at a large, research-intensive institution in the United States. One cornerstone cohort participated in a peer-lecture version of the module, while the second cohort participated in a peer-discussion version. Concept maps, scored using three different methods, were used to measure changes in students’ sustainability knowledge. A self-report survey was used to measure changes in students’ perceptions of their sustainability knowledge and skills. Students in both the peer-lecture and peer-discussion cohorts demonstrated improved sustainability knowledge networks and confidences after participation in the module. However, peer-lecture students showed greater improvements in knowledge connectedness (a feature of expert-like knowledge) than peer-discussion students. Regardless of cohort, cornerstone students demonstrated greater gains in knowledge and confidence than did a cohort of capstone students who participated in an earlier implementation of the module. Future implementations may be most impactful if the peer-discussion format is integrated into early design courses.

Curricular Concept Maps as Structured Learning Diaries: Collecting Data on Self-Regulated Learning and Conceptual Thinking for Learning Analytics Applications

Abstract: The collection and selection of the data used in learning analytics applications deserve more attention. Optimally, selection of data should be guided by pedagogical purposes instead of data availability. Using design science research methodology, we designed an artifact to collect time-series data on students’ self-regulated learning and conceptual thinking. Our artifact combines curriculum data, concept mapping, and structured learning diaries. We evaluated the artifact in a case study, verifying that it provides relevant data, requires a limited amount of effort from students, and works in different educational contexts. Combined with learning analytics applications and interventions, our artifact provides possibilities to add value for students, teachers, and academic leaders.

Computer-Based Concept Mapping as a Method for Enhancing the Effectiveness of Concept Learning in Technology-Enhanced Learning

Abstract: Technology enhanced learning is a wide area that covers all uses of digital technology to support learning and teaching activities. The computer-based concept mapping has shown potential in enhancing meaningful learning in education. Concept mapping is an important tool that is used in the field of education to help students in understanding the basic concepts and the relationships between them. This research proposes a computer-based concept mapping (CBCM) environment combined with Google classroom to help students reduce their misconceptions and to improve their problem solving skills. Furthermore, it examines the effect of CBCM on the sustainability of concept learning according to student views. The participants were first-year engineering students. The study was conducted in a physics class, and a true-experimental design was used. The experimental group students learned with the Google classroom combined with computer-based concept mapping (CBCM), while the concept group students learned with Google classroom and the traditional method. Data were collected from a physics concept test, problem solving inventory, and semi-structured interviews. The research results indicated that teaching in the CBCM environment combined with Google Classroom provides meaningful learning by correcting the misconceptions of the students. Moreover, there was a significant increase in the problem solving skills of the experimental group as compared to the control group. According to the students’ views, it was determined that CBCM enhances the sustainability of concept learning. The results of this study can help educators and researchers to integrate computer-based concept mapping (CBCM) techniques into Google Classroom.

Advance in Detecting Key Concepts as an Expert Model: Using Student Mental Model Analyzer for Research and Teaching (SMART)

Abstract: While key concepts embedded within an expert’s textual explanation have been considered an aspect of expert model, the complexity of textual data makes determining key concepts demanding and time consuming. To address this issue, we developed Student Mental Model Analyzer for Teaching and Learning (SMART) technology that can analyze an expert’ textual explanation to elicit an expert concept map from which key concepts are automatically derived. SMART draws on four graph-based metrics (i.e., clustering coefficient, betweenness, PageRank, and closeness) to automatically filter key concepts from experts’ concept maps. This study investigated which filtering method extract key concepts most accurately. Using 18 expert textual data, we compared the accuracy levels of those four competing filtering methods by referring to four accuracy measures (i.e., precision, recall, F-measure, and N-similarity). The results showed the PageRank filtering method outperformed the other methods in all accuracy measures. For example, on average, PageRank derived 79% of key concepts as accurately as human experts. SMART’s automatic filtering methods can help human experts save time when building an expert model, and it can validate their decision making on a list of key concepts.