Title of a CMC2018 Innovative Experience

Juan Valdés1, María Sánchez1 & John M. Smith2

1Universidad del Mapeo Conceptual, España

2Mario James, University of Cmap, Germany

Email: {jvaldes, msanchez}@universidadmapeo.edu, jsmith@univcmap.edu

www.mapping.org

Introduction

Concept mapping is a process of meaning-making. It implies taking a list of concepts – a concept being a perceived regularity in events or objects, or records of events or objects, designated by a label (Yates & Ribeiro-Neto, 1999; Cañas et al., 2000) – and organizing it in a graphical representation where pairs of concepts and linking phrases form propositions. Hence, key to the construction of a concept map is the set of concepts on which it is based. Coming up with an initial list of concepts to include in a map is really just an issue of retrieving from long-term memory. In fact, rote learners are particularly good at listing concepts. A more difficult task during concept map construction is finding the “linking phrase” that appropriately expresses the relationship between two concepts to form a meaningful proposition.

Aiding Model Extension

An initiative is now under way to develop methods to aid the user during concept map construction. These aids are designed in response to observations of snags which may arise during concept mapping. During concept mapping, users often stop and wonder what other concepts they should add to the concept map they are working on, frequently spending time looking for the right word to use in a concept or linking phrase; they search for other concept maps that may be relevant to the one they are constructing, and they search through the Web looking for additional material that could help them enhance their maps. The following sections describe three methods developed to address these issues.

Suggester for Concepts

The goal of the concept suggester is to facilitate concept map construction by proactively searching and suggesting new concepts to the user (Novak & Gowin, 1984). In contrast to the proposition and resource suggester, discussed in the next section, the concept suggester proposes collections of terms, each of them representing a concept that is novel (i.e., not contained in the current map) but potentially relevant. This can (1) help the user to remember familiar concepts that might otherwise be forgotten, and (2) give the user the opportunity to further explore and understand new and potentially relevant concepts.

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Target Concept Maps and most relevant terms of six
Artificial Topics	Topic 1:

technology

penetration

revolutionary

systems

protection	Topic 2:

air

dry

composition

nitrogen

oxygen	Topic 3:

entry

atmosphere

system

esa

level	Topic 4:

landing

sites

specific

nasa

mars	Topic 5:

mars

lowell

history

percival

planet	Topic 6:

fluvial

history

erosion

activity

glaciers
Originating Map	0.1	0	0	0	0.25	0	0.4	0	0.15	0	0.3	0
Climate History	0	0	0.15	0.15	0.15	0.2	0.05	0.08	0.1	0.05	0.7	0.72
Deep Access	0.75	0.72	0.05	0.05	0.2	0.2	0.15	0.08	0.1	0.05	0.1	0
Earth’s Atmosphere	0	0	0.5	0.5	0	0	0	0	0	0	0.1	0.07
Table 1: Caption for Table

Summary

This is the summary of this innovative experience.

Acknowledgements

This Research Project was supported by Grant #CMC-200-19-4 from the Concept Mapping Foundation. We thank Joe Doe and Jane Smith for their critical review of previous version of the manuscript and Mary Lamb for her assistance during data collection.

References

Cañas, A. J., Ford, K. M., Coffey, J., Reichherzer, T., Carff, R., Shamma, D., & Breedy, M. (2000). Herramientas para Construir y Compartir Modelos de Conocimiento basados en Mapas Conceptuales. Revista de Informática Educativa, 13(2), 145-158.

Novak, J. D. (1998). Learning, creating, and using knowledge: Concept Maps as Facilitative Tools in Schools and Corporations. Mahweh, NJ: Lawrence Erlbaum Associates.

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Alberto J. Cañas

2018-06-29

Alberto Cañas