Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website

• MEHMET FILIZ ^[1] ; DAVID TRUMPOWER ^[1] ; ARUN VANAPALLI ^[1]
  1. [1] University of Ottawa

     University of Ottawa

     Canadá

     
• Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 37, Nº. 2, 2014, págs. 297-317
• Idioma: inglés
• DOI: 10.15446/rce.v37n2spe.47939
• Títulos paralelos:
  • Exploración de la herramienta de aseguramiento estructural móvil: mapas conceptuales para websites de aprendizaje
• Enlaces
  • Texto completo
• Resumen
  • español

    En este artículo se describe cómo el algoritmo de búsqueda de ruta convierte puntajes de conceptos pareados en mapas conceptuales. También se presenta cómo este algoritmo ha sido utilizado para desarrollar estos mapas conceptuales para aprendizaje (\url{http://www.conceptmapsforlearning.com}) basados en los principios del aseguramiento formativo efectivo. Las redes de búsqueda de ruta, una de las herramientas de representación de redes, ayudan a memorizar a los estudiantes y enunciar las relaciones entre mapas más que las herramientas de expresión espacial (tales como el escalonamiento multidimensional). Por tanto, las redes de búsqueda de rutas han sido usadas en varios estudios de estructura del conocimiento incluyendo la identificación de malos conceptos usados por los estudiantes. Para lograr esto, cada mapa de conocimiento tanto del estudiante como del experto son comparados vía el software de búsqueda de ruta y se remarcan las diferencias entre éstos. Después que los malos conceptos son identificados, el software de búsqueda falla en entregar una retroalimentación en estos nodos conceptuales. Para superar esta debilidad, se desarrolla una herramienta de mapa conceptual móvil que manda retroalimentaciones visuales, textuales y remediales (e.g. vídeos, enlaces a páginas web y applets) en las relaciones de los conceptos. Adicionalmente, los estudiantes son preguntados acerca de qué entienden de la retroalimentación brindada y se les da la oportunidad de revisar sus mapas de conocimiento después de recibir varios tipos de retroalimentación.

  • English

    In this paper, we describe how the pathfinder algorithm converts relatedness ratings of concept pairs to concept maps; we also present how this algorithm has been used to develop the Concept Maps for Learning website (\url{http://www.conceptmapsforlearning.com}) based on the principles of effective formative assessment. The pathfinder networks, one of the network representation tools, claim to help more students memorize and recall the relations between concepts than spatial representation tools (such as Multi-Dimensional Scaling). Therefore, the pathfinder networks have been used in various studies on knowledge structures, including identifying students misconceptions. To accomplish this, each students knowledge map and the expert knowledge map are compared via the pathfinder software, and the differences between these maps are highlighted. After misconceptions are identified, the pathfinder software fails to provide any feedback on these misconceptions. To overcome this weakness, we have been developing a mobile-based concept mapping tool providing visual, textual and remedial feedback (ex. videos, website links and applets) on the concept relations. This information is then placed on the expert concept map, but not on the students concept map. Additionally, students are asked to note what they understand from given feedback, and given the opportunity to revise their knowledge maps after receiving various types of feedback.

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