A Theoretical Analysis of How Segmentation of Dynamic Visualizations Optimizes Students' Learning

This article reviews studies investigating segmentation of dynamic visualizations (i.e., showing dynamic visualizations in pieces with pauses in between) and discusses two not mutually exclusive processes that might underlie the effectiveness of segmentation. First, cognitive activities needed for dealing with the transience of dynamic visualizations impose extraneous cognitive load, which may hinder learning. Segmentation may reduce the negative effect of this load by dividing animations into smaller units of information and providing pauses between segments that give students time for the necessary cognitive activities after each of those units of information. Second, event segmentation theory states that people mentally segment dynamic visualizations during perception (i.e., divide the information shown in pieces). Segmentation of dynamic visualisation could cue relevant segments to students, which may aid them in perceiving the structure underlying the process or procedure shown

Attentional Guidance and Media Presentation during Explicit Instruction

While much is known about how certain types of images influence learning in multimedia educational systems, comparatively little work has been done on how different image types compare to each other in terms of the types of knowledge conveyed and transfer of knowledge. Two popular types of media found in many multimedia environments, pictures and concept maps, are capable of blending verbal information (such as in picture labels or node/link labels) and visual information (such as structural information) into a single image, which may result in increased exposure to vocabulary (improving learning) or may create split attention (decreasing learning). Both types can also be presented using animation techniques, although questions remain as to whether animation always improves learning in different kinds of media. This study explores media differences and animation techniques in two experiments, both of which utilize Khan Academy lessons as the basis for the multimedia presentation. In the first experiment, a 2x2 between-subjects design was utilized to examine different media types (labeled pictures vs. concept maps) and animation (animated vs. static). The results of this study indicate that animation improves relational knowledge and free recall scores, but an animation x media type interaction indicates that animated pictures are not very effective for conveying conceptual knowledge. In Study 2, a 2x2 between-subjects experiment dove deeper into the function of labels by examining how animation (animated vs. static) and labels (present vs. absent) interact, as both may be attention directing devises. It was found that animation and prior knowledge both had consistent effects on learning, where those with high prior knowledge did not gain as much from viewing an animated presentation as those with low prior knowledge did, but labels had minimal effects on learning. In all, research indicates that different media should be used depending on the educational goals, animation may be particularly helpful for low prior knowledge students, and labels are not necessarily helpful for learning when the same information is presented orally

Symbolic and Visual Retrieval of Mathematical Notation using Formula Graph Symbol Pair Matching and Structural Alignment

Large data collections containing millions of math formulae in different formats are available on-line. Retrieving math expressions from these collections is challenging. We propose a framework for retrieval of mathematical notation using symbol pairs extracted from visual and semantic representations of mathematical expressions on the symbolic domain for retrieval of text documents. We further adapt our model for retrieval of mathematical notation on images and lecture videos. Graph-based representations are used on each modality to describe math formulas. For symbolic formula retrieval, where the structure is known, we use symbol layout trees and operator trees. For image-based formula retrieval, since the structure is unknown we use a more general Line of Sight graph representation. Paths of these graphs define symbol pairs tuples that are used as the entries for our inverted index of mathematical notation. Our retrieval framework uses a three-stage approach with a fast selection of candidates as the first layer, a more detailed matching algorithm with similarity metric computation in the second stage, and finally when relevance assessments are available, we use an optional third layer with linear regression for estimation of relevance using multiple similarity scores for final re-ranking. Our model has been evaluated using large collections of documents, and preliminary results are presented for videos and cross-modal search. The proposed framework can be adapted for other domains like chemistry or technical diagrams where two visually similar elements from a collection are usually related to each other