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This paper aims to describe a method of teaching that is based on Novak's concept‐mapping technique.

The paper shows how concept mapping can be used to measure prior knowledge and how simple mapping exercises can promote the integration of teachers' and students' understandings in ways that are meaningful.

The concept‐mapping method facilitates quick and easy measures of student knowledge‐change so that teachers can identify the parts of the curriculum that are being understood and those that are not. This is possible even among very large student groups in the 50‐minute slots that are allocated to so much teaching in higher education.

Concept mapping is discussed in the wider context of student learning style. The styles literature has been criticised because it tends to encourage undue labelling of people or behaviours. The approach described here also uses “labels” to typify learning (using the terms non‐learning and rote or meaningful learning to identify different qualities of change).

The difference in this approach is that terms are attached to empirical measures of learning outcome, not to personal or psychological styles. Concept mapping makes learning visible so that the actual quality of the learning that has occurred can be seen and explored. Using concept mapping in the course of teaching means that learning is no longer a complex and intractable process, measurable only by proxy, but an observable phenomenon.

Finds a statistically significant relation between top‐down concept‐mapping and the right cerebral hemisphere, and between bottom‐up concept‐mapping and the right hemisphere. Correlates scores on concept‐mapping with scores on hemispheric tests, and compares the scores of the subjects on hemispheric tests with the preferable style of concept‐mapping. Concludes that top‐down concept‐mapping, the right hemisphere, and Frege’s logic are mutually related. Similarly, bottom‐up concept‐mapping, the left hemisphere, and Russell’s logic are mutually related.

The purpose of this paper is to present an automated ontology mapping and merging algorithm, namely OntoDNA, which employs data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities among distributed data sources in organizational memory and subsequently generate a merged ontology to facilitate resource retrieval from distributed resources for organizational decision making.

The OntoDNA employs unsupervised data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities to integrate distributed data sources in organizational memory. Unsupervised methods are needed as an alternative in the absence of prior knowledge for managing this knowledge. Given two ontologies that are to be merged as the input, the ontologies' conceptual pattern is discovered using FCA. Then, string normalizations are applied to transform their attributes in the formal context prior to lexical similarity mapping. Mapping rules are applied to reconcile the attributes. Subsequently, SOM and K‐means are applied for semantic similarity mapping based on the conceptual pattern discovered in the formal context to reduce the problem size of the SOM clusters as validated by the Davies‐Bouldin index. The mapping rules are then applied to discover semantic similarity between ontological concepts in the clusters and the ontological concepts of the target ontology are updated to the source ontology based on the merging rules. Merged ontology in a concept lattice is formed.

In experimental comparisons between PROMPT and OntoDNA ontology mapping and merging tool based on precision, recall and f‐measure, average mapping results for OntoDNA is 95.97 percent compared to PROMPT's 67.24 percent. In terms of recall, OntoDNA outperforms PROMPT on all the paired ontology except for one paired ontology. For the merging of one paired ontology, PROMPT fails to identify the mapping elements. OntoDNA significantly outperforms PROMPT due to the utilization of FCA in the OntoDNA to capture attributes and the inherent structural relationships among concepts. Better performance in OntoDNA is due to the following reasons. First, semantic problems such as synonymy and polysemy are resolved prior to contextual clustering. Second, unsupervised data mining techniques (SOM and K‐means) have reduced problem size. Third, string matching performs better than PROMPT's linguistic‐similarity matching in addressing semantic heterogeneity, in context it also contributes to the OntoDNA results. String matching resolves concept names based on similarity between concept names in each cluster for ontology mapping. Linguistic‐similarity matching resolves concept names based on concept‐representation structure and relations between concepts for ontology mapping.

The OntoDNA automates ontology mapping and merging without the need of any prior knowledge to generate a merged ontology. String matching is shown to perform better than linguistic‐similarity matching in resolving concept names. The OntoDNA will be valuable for organizations interested in merging ontologies from distributed or different organizational memories. For example, an organization might want to merge their organization‐specific ontologies with community standard ontologies.

Motivational interviewing (MI) has developed considerably since its inception, which may have led to diverse practice across contexts and differential understanding of core principles. Concept mapping is one potential method for offering insight into practitioner awareness, understanding and application of MI. The paper aims to discuss this issue.

In total, 29 professionals from a range of disciplines, including counselling, education and health, completed concept maps about MI, following brief training at the UK regional MI interest network. In total, 17 completed maps were submitted for analysis using quantitative and qualitative methods.

A total of 186 concepts and 175 propositional links were found within the 17 maps. The most commonly identified concepts were: change, empathy, collaboration, open-ended questions, affirmations, reflections, summaries (OARS), client centred and compassion. The concept maps also suggested differing levels of expertise across network members using concept mapping morphology classification.

The sample was small scale and located in one region of the UK. Maps were submitted anonymously meaning that participant data could not be matched to the maps.

Concept mapping is a potentially useful method for auditing practice and developing skills in MI, as well as exploring participants’ understanding of related concepts and therapeutic mechanisms.

MI has a strong evidence-based across a variety of disciplines and contexts. Refining practitioner skills in MI has implications for the integrity of delivery, and improved client outcomes in areas such as substance use, health promotion and educational disaffection.

This is the first study to investigate concept mapping as a means of understanding MI practice. It has potential implications for training, monitoring, supervision and development in MI practice.

This paper aims to apply vector space model (VSM)-PCR model to compute the similarity of Fault zone ontology semantics, which verified the feasibility and effectiveness of the application of VSM-PCR method in uncertainty mapping of ontologies.

The authors first define the concept of uncertainty ontology and then propose the method of ontology mapping. The proposed method fully considers the properties of ontology in measuring the similarity of concept. It expands the single VSM of concept meaning or instance set to the “meaning, properties, instance” three-dimensional VSM and uses membership degree or correlation to express the level of uncertainty.

It provides a relatively better accuracy which verified the feasibility and effectiveness of VSM-PCR method in treating the uncertainty mapping of ontology.

The future work will focus on exploring the similarity measure and combinational methods in every dimension.

This paper presents an uncertain mapping method of ontology concept based on three-dimensional combination weighted VSM, namely, VSM-PCR. It expands the single VSM of concept meaning or instance set to the “meaning, properties, instance” three-dimensional VSM. The model uses membership degree or correlation which is used to express the degree of uncertainty; as a result, a three-dimensional VSM is obtained. The authors finally provide an example to verify the feasibility and effectiveness of VSM-PCR method in treating the uncertainty mapping of ontology.

Mind mapping can be a pedagogical tool that helps students brainstorm how to organize information in a way that incorporates creative and active learning, as well as critical thinking. In this article, we describe the application of using mind maps in an undergraduate course focused on teaching leadership theory as an effort to help students think more holistically about how theories intersect with their lives. The assignment description, rubric, and details of the application are provided. Examples of ways students have organized their maps to integrate theories into their lives (e.g., based on themselves, organizations, or metaphors) are also included. Through this practice, we found that mind mapping leadership concepts to areas of students’ lives develop their ability to describe how leadership theories operate holistically in their life, rather than limiting their understanding to one or two popular theories. We recommend leadership educators consider mind mapping as a pedagogical tool to teach theories and other content that require understanding in a broader context.

The purpose of this paper is to explain and develop a classification of cognitive structures (or typologies of thought), previously designated as spoke, chain and network thinking by Kinchin et al.

The paper shows how concept mapping can be used to reveal these conceptual typologies and endeavours to place the concept‐mapping method in the broader context of learning styles and learning theory.

The findings suggest that spoke structures are indicative of a naïve epistemology, or of “learning‐readiness”; chain structures are indicators of “goal‐orientation” and networks are indicators of expertise. Furthermore, change that comprises simple elaboration of existing spokes or chains is likely to be the result of surface learning styles and the emergence of networks indicative of deep learning. The utility of these different cognitive approaches is discussed.

The work is limited by the general lack of empirical testing, but the approach is presented as an important source of hypotheses for future research.

The practical implications of the research are considerable. First, concept mapping provides a framework for documenting and assessing understanding at “novice” and “expert” levels. Second, where definitive criteria can be developed from the learning styles literature, cognitive change in the course of learning can be evaluated to distinguish between deep versus surface or holist versus serialist approaches, for example.

The papers original and comprises a synthetic approach to the study of learning style and learning theory through the use of the concept‐mapping method. It has both practical and theoretical value because it suggests a new approach and is an important source of testable hypotheses.

Concept maps have been described as a valuable tool for exploring curriculum knowledge. However, less attention has been given to the use of them to visualise contested and tacit knowledge, i.e. the values and perceptions of teachers that underpin their practice. This paper aims to explore the use of concept mapping to uncover academics’ views and help them articulate their perspectives within the framework provided by the concepts of pedagogic frailty and resilience in a collaborative environment.

Participants were a group of five colleagues within a Biochemical Science Department, working on the development of a new undergraduate curriculum. A qualitative single-case study was conducted to get some insights on how concept mapping might scaffold each step of the collaborative process. They answered the online questionnaire; their answers were “translated” into an initial expert-constructed concept map, which was offered as a starting point to articulate their views during a group session, resulting in a consensus map.

Engaging with the questionnaire was useful for providing the participants with an example of an “excellent” map, sensitising them to the core concepts and the possible links between them, without imposing a high level of cognitive load. This fostered dialogue of complex ideas, introducing the potential benefits of consensus maps in team-based projects.

An online questionnaire may facilitate the application of the pedagogic frailty model for academic development by scaling up the mapping process. The map-mediated facilitation of dialogue within teams of academics may facilitate faculty development by making explicit the underpinning values held by team members.

The definition of modeling languages is a key‐prerequisite for model‐driven engineering. In this respect, Domain‐Specific Modeling Languages (DSMLs) defined from scratch in terms of metamodels and the extension of Unified Modeling Language (UML) by profiles are the proposed options. For interoperability reasons, however, the need arises to bridge modeling languages originally defined as DSMLs to UML. Therefore, the paper aims to propose a semi‐automatic approach for bridging DSMLs and UML by employing model‐driven techniques.

The paper discusses problems of the ad hoc integration of DSMLs and UML and from this discussion a systematic and semi‐automatic integration approach consisting of two phases is derived. In the first phase, the correspondences between the modeling concepts of the DSML and UML are defined manually. In the second phase, these correspondences are used for automatically producing UML profiles to represent the domain‐specific modeling concepts in UML and model transformations for transforming DSML models to UML models and vice versa. The paper presents the ideas within a case study for bridging ComputerAssociate's DSML of the AllFusion Gen CASE tool with IBM's Rational Software Modeler for UML.

The ad hoc definition of UML profiles and model transformations for achieving interoperability is typically a tedious and error‐prone task. By employing a semi‐automatic approach one gains several advantages. First, the integrator only has to deal with the correspondences between the DSML and UML on a conceptual level. Second, all repetitive integration tasks are automated by using model transformations. Third, well‐defined guidelines support the systematic and comprehensible integration.

The paper focuses on the integrating direction DSMLs to UML, but not on how to derive a DSML defined in terms of a metamodel from a UML profile.

Although, DSMLs defined as metamodels and UML profiles are frequently applied in practice, only few attempts have been made to provide interoperability between these two worlds. The contribution of this paper is to integrate the so far competing worlds of DSMLs and UML by proposing a semi‐automatic approach, which allows exchanging models between these two worlds without loss of information.

The paper's first objective is to develop a new conceptual framework for categorizing and designing cognitive, specifically comparative, causal mapping (CCM) research by building upon the theory‐centred and participant‐centred perspectives. The second purpose is to enable the discerned study prototypes by introducing a new CCM software application, CMAP3.

Building upon the distinction between theory‐centred (etic) and participant‐centred (emic) perspectives in social research, we first construct and apply a conceptual framework for analysing and categorising extant CCM studies in terms of their objectives and basic design. Next, after noting the important role and basic tasks in computerising causal mapping studies, we present a new CCM software application.

The theory‐centred/participant‐centred perspectives define four causal mapping study prototypes, each with different goals, basic designs and methodological requirements. Noting the present lack of widely accessible software for qualitatively oriented CCM studies, we introduce CMAP3, a new non‐commercial Windows application, and summarise how it is used in related research.

The framework and the studies representing the prototypes demonstrate the versatility of CCM methods and that the proposed framework offers a new, systematic approach to categorising and designing CCM studies. Research technically, CMAP3 can support the defined CCM‐prototypes, based on a low‐structured (inductive/qualitative) or a structured (nomothetic/quantitative) methodological approach/stance, and having therefore different needs of data acquisition, processing, coding, aggregation/comparison, and analysis of the emerging aggregated cause maps’ contents or structure.