Making Sense of Knowledge Integration Maps

Abstract

Digital knowledge maps are rich sources of information to track students' learning. However, making sense of concept maps has been found challenging. Abstract Digital knowledge maps are rich sources of information to track students' learning. However, making sense of concept maps has been found challenging.Using multiple quantitative and qualitative methods in combination allows triangulating of changes in students' understanding. This chapter introduces a novel form of concept map, called knowledge integration map (KIM), and uses KIMs as examples for an overview of concept map analysis methods. KIMs are a form of digital knowledge maps. KIMs have been implemented in high school science classrooms to facilitate and assess complex science topics, such as evolution. KIM analysis aims to triangulate changes in learners' conceptual understanding through a multilevel analysis strategy, combining quantitative and qualitative methodologies. Quantitative analysis included overall, selected, and weighted propositional analysis using a knowledge integration rubric and network analysis describing changes in network density and prominence of selected concepts. Research suggests that scoring only selected propositions can be more sensitive to measuring conceptual change because it focuses on key concepts of the map. Qualitative analysis of KIMs included topographical analysis methods to describe the overall geometric structure of the map and qualitative analysis of link types. This chapter suggests that a combination of quantitative and qualitative analysis methods can capture different aspects of KIMs and can provide a rich description of changes in students' understanding of complex topics.

Pre-Service Teachers’ Knowledge of Relational Structure of Physics Concepts: Finding Key Concepts of Electricity and Magnetism

“…A recurrent theme in science education is the discussion of how the structure of novices' knowledge differs from experts' knowledge and how to facilitate the transformation of novices' knowledge to a more expert-like knowledge. It has been repeatedly noted that the effectiveness of experts' knowledge is derived from its organized structure, which allows easy, fast and accurate retrieval of knowledge [1,2]. The interest towards the structure of students' knowledge is well documented in many research reports, and thus its importance hardly needs reiteration.…”

“…This method, while is well motivated for link-counting based detection of local structures, derives the global hierarchy-based structure from interpretative analysis rather than from purely structural analysis. To remedy the limitations based on local link-counting, some researchers have suggested that in analysing pre-service teacher students' representations of their knowledge in the form of concept maps, researchers should pay proper attention to the interlinked structure as a whole, not only to local link-counting (see [2] and references therein).…”

“…The focus of interest is on students' knowledge in general, and in exploring the elements of high global contiguity and local cohesion, and if such elements are of diverse kinds, as suggested by recent research on students' abstractness knowledge [19,20]. To explore pre-service teacher students' relational knowledge as it is available from the student concept map, we utilize recently suggested network analysis methods for cartography of students' knowledge [2,18,20,24]. These methods are based on finding and quantifying intrinsic local and global connections between the knowledge elements that students explicate in their concept maps [18,24].…”

On Necessity of Semantic Analysis when Evaluating Concept Maps

“…Difficulties of using CMs result primarily from years of rote-mode learning practice in school settings [2]; thus, the use of CCM tasks would also foster meaningful learning instead of rote learning and allow differentiating between those two. Less constrained tasks also provide more insight into students' partial knowledge [6].…”

“…Those measures that can be calculated with little effort, such as a number of concepts and relationships, provide little insight into student's understanding [6], but those regarding the quality of CM elements ask for natural language processing. There is still no widely accepted method for scoring concept maps despite numerous attempts to create one (see, for example, [4], [7] or [8]).…”