Grouping Effect for Bar Graph Summarization for People with Visual Impairments

Introduction The present study evaluated the feasibility of using an iPad application or “app” for algebra-readiness mathematics, with accompanying braille materials and accessible graphics, when used in authentic educational settings. Methods Twenty-nine students with visual impairments in grades 4–11 used the materials under the direction of their teachers of students with visual impairments ( N = 19). Twenty percent of the mathematics problems included graphics such as maps, line graphs, and bar graphs. Students and teachers of students with visual impairments provided feedback about the feasibility of using the app with supplemental materials, and made suggestions for additional features to improve the instructional package. Results Students completed 984 word problems and solved 80% of them correctly within three attempts. The use of some features such as hints and “worked-example” videos was low. Participants provided suggestions for improvement of the app features and accessible graphics. Discussion Students and teachers of students with visual impairments were able to use the materials successfully, and comments were generally positive. Participants’ comments and suggestions were used to revise the app and materials. Implications for practitioners The study results contribute to the growing body of knowledge about the potential value of tablet-type devices for use by visually impaired students.

The UN Convention on the Rights of Persons with Disabilities Article 24 states that “States Parties shall ensure inclusive education at all levels of education and life long learning.” This article focuses on the inclusion of people with visual impairments in learning processes including complex table-based data. Gaining insight into and understanding of complex data is a highly demanding task for people with visual impairments. Especially in the case of table-based data, the classic approaches of braille-based output devices and printing concepts are limited. Haptic perception requires sequential information processing rather than the parallel processing used by the visual system, which hinders haptic perception to gather a fast overview of and deeper insight into the data. Nevertheless, neuroscientific research has identified great dependencies between haptic perception and the cognitive processing of visual sensing. Based on these findings, we developed a haptic 3D surface representation of classic diagrams and charts, such as bar graphs and pie charts. In a qualitative evaluation study, we identified certain advantages of our relief-type 3D chart approach. Finally, we present an education model for German schools that includes a 3D printing approach to help integrate students with visual impairments.

Background The potential of haptic touchscreens in accessing digital graphical information is argued in recent research. With a growing demand for graph accessibility, particularly for individuals with visual impairments, haptic touchscreens emerge as a viable solution. Methods In this study, 19 adults with visual impairments participated in an experiment aimed at evaluating their perception of five graphs “touched” via an electrostatic Tanvas touchscreen. The objectives of the research are to examine: (1) whether the responses provided by the participants for the graph values are accurate; i.e., how much these responses deviate from the actual graph values; (2) whether the individual characteristics of the participants, such as severity of the visual impairment and age of onset of the visual impairment, affect their performance; and (3) what are the parameters (i.e., the characteristics of the tasks) that negatively or positively affect the performance of participants; The study comprised a familiarization test and five test-graphs, namely two bar graphs, one pie chart, one scatter plot, and one line graph. Results Results indicate promising outcomes, with participants demonstrating satisfactory graph perception through the device. Demographic and personal factors exerted no statistically significant influence on performance. Participants encountered challenges in tasks involving a horizontal axis with densely arranged segments, especially when tasked with counting a large number of segments. Conclusions Overall, there seems to be potential for providing access to digital graphical information for individuals with visual impairments through the Tanvas haptic device. However, further investigation with audio integration would be beneficial.

Introduction: The ability of students to engage with graphical materials supports learning in science, technology, engineering, arts, and mathematics areas. For students with visual impairments, understanding the factors that contribute to the effective interpretation of graphics can promote meaningful access to the curricula. Methods: Forty students with visual impairments completed multiple-choice question tasks for five types of graphics presented in their medium of choice and provided difficulty ratings. The teachers of students with visual impairments rated the students on several factors. Statistical analyses investigated the relationship between performance differences and teacher-rated factors. Results: Significant differences in performance between print and tactile graphics users were found for bar graph, map, and total correct responses on all tasks. For some tasks, perceived difficulty by tactile graphic users did not align with actual performance. Teachers’ ratings of students who had Individualized Education Program goals for graphics, independence in using graphics, problem-solving ability, mathematics ability, and frequency of engaging with graphics contributed to significant differences in performance across total correct and most individual graphic results. Discussion: Although medium type was a significant contributor across graphic types, some teacher-rated variables appeared to mitigate the importance of medium on student performance. Depending on the graphic type, experience, content knowledge, skills with graphics, and confidence and motivation can all affect student performance when interpreting graphics. Implications for practitioners: Teachers should provide students with early and frequent opportunities to engage with graphics and support their problem-solving abilities regarding how to engage with different graphic types to enhance their independent use of graphics.

Introduction Visually impaired students (that is, those who are blind or have low vision) have difficulty accessing curricular material in mathematical textbooks because many mathematics texts have visual images that contain important content information that are not transcribed or described in digital versions of the texts. However, little is known about the extent to which this issue exists within texts and what sort of information is contained in visual material in mathematics texts. This article describes a process undertaken to classify images in a selection of math textbooks that are currently being used in grades 5,8, and 11. Methods Representative textbooks were chosen that aligned with Common Core Standards. An exhaustive and mutually exclusive set of image categories was developed, researchers became reliable on coding procedures, and then all images in the representative texts were coded. Results The most common images involved student interest, motivation, and organization, but contained little or no math content. The second most common area of image categories often contained large amounts of math content but were difficult to describe succinctly or might have been described without including important mathematical information. This grouping included tables, line graphs, and images specifically related to a single question. The final group of less frequently appearing images included ray or line diagrams, number lines, pie charts, bar graphs, and maps. Discussion To improve access to visual math content, the focus should be on images that appear frequently and contain math content such as tables, scatter or line graphs, shapes, equations, and images specifically related to a single question. Less common image categories such as models, line diagrams, pictures of calculator keys, and number lines lend themselves easily to description. Implications for practitioners Optimization of limited instructional time would be accomplished by having teachers focus on students’ understanding of the most common types of images, such as tables and line graphs.

Introduction The authors examined a tablet computer application (iPad app) for its effectiveness in helping students studying pre-algebra to solve mathematical word problems. Methods Forty-three visually impaired students (that is, those who are blind or have low vision) completed eight alternating mathematics units presented using their traditional literacy medium or an iPad app. Twenty percent of the mathematics problems included graphics such as maps, line graphs, and bar graphs. During each session, teachers of visually impaired students rated the amount of support they provided for students and the student motivation. Results Students answered more mathematics problems correctly when using the iPad app and, overall, teachers reported that their students were more motivated with the app than with their traditional literacy medium. Students often used the hints provided in the app when they did not solve a problem correctly the first time. Discussion Visually impaired students and their teachers found the app and graphics to be easy to use and motivating. The built-in Scratch pad was used by almost all students who were print users. Implications for practitioners The study results contribute to the growing body of knowledge about the potential value of tablet-type devices for use by visually impaired students.

Information graphics, e.g. line or bar graphs, are often displayed in documents and popular media to support an intended message, but for a growing number of people, they are missing the point. The World Health Organization estimates that the number of people with vision impairment could triple in the next thirty years due to population growth and aging. If a graphic is not described, explained in the text, or missing alt tags and other metadata (as is often the case in popular media), the intended message is lost or not adequately conveyed. In this work, we describe a multimodal deep learning approach that supports the communication of the intended message. The multimodal model uses both the pixel data and text data in a single neural network to classify the information graphic into an intention category that has previously been validated as useful for people who are blind or who are visually impaired. Furthermore, we collect a new dataset of information graphics and present qualitative and quantitative results that show our multimodal model exceeds the performance of any one modality alone, and even surpasses the capabilities of the average human annotator.

To summarize documents worths to summationof the main points. A summarization is this kind of summing up. Elementary school book reports are big on summarization. To provide a comprehensible declaration of the significant points is nothing but summarization. In current years, natural language processing (NLP) has stimulated to statistica1l base. Many tribulations in NLP, e.g., parsing, word sense disambiguation, and involuntary paraphrasing. In recent times, robust graph-based methods for NLP is also a lot of scope, e.g., in clustering of words and attachments of prepositional phrase. In proposed paper, we will take in account of graph-based summarization techniques, approaches used for that etc. We will talk about how arbitrary traversing on images of graphs can help in making of question answer based summarization. In current exploration work, question answer based graph summarization system for Bar Graph is shown. The extraction procedure is completely computerized using image processing and text recognition methods. The extracted information can be used to improve the indexing component for bar charts and get better exploration results. After generating questions, questions are rank the according to frequency or priority and answer of the ranked question is summary of given input.