Matching Items (4)
Description
This research contributes to emergent body of knowledge regarding the understanding of relationship between visual elements and E-learning outcomes. Visual images and texts are the main visual elements within the study.
A literature review was conducted on E-learning situations, and a discussion on the role of visual elements in E-learning. Data collection was also conducted by way of a test, which randomly placed participants into three groups and assigned them to three different E-learning courses. The texts for the three courses were the same font, but the first course had text only, the second course had text and "bad" images, and the third one had text and "good" images. Every time participants finished a short course, they were requested to do a short quiz based on what they had learned. In addition, every participant needed to do a survey based on his or her E-learning experience. Research data was finally collected through the test scores and surveys.
Key findings of this research are: (1) The combination of text and "good" image materials in E-learning can greatly enhance the learning outcomes; (2) the "good" images in learning materials can add to the value of the text content as well as improve the satisfactory level of learners in E-learning; (3) "bad" images do not enhance E-learning outcomes; and (4) E-learners will spend a longer time to complete learning materials containing images, no matter how good or "bad" the images are.
A literature review was conducted on E-learning situations, and a discussion on the role of visual elements in E-learning. Data collection was also conducted by way of a test, which randomly placed participants into three groups and assigned them to three different E-learning courses. The texts for the three courses were the same font, but the first course had text only, the second course had text and "bad" images, and the third one had text and "good" images. Every time participants finished a short course, they were requested to do a short quiz based on what they had learned. In addition, every participant needed to do a survey based on his or her E-learning experience. Research data was finally collected through the test scores and surveys.
Key findings of this research are: (1) The combination of text and "good" image materials in E-learning can greatly enhance the learning outcomes; (2) the "good" images in learning materials can add to the value of the text content as well as improve the satisfactory level of learners in E-learning; (3) "bad" images do not enhance E-learning outcomes; and (4) E-learners will spend a longer time to complete learning materials containing images, no matter how good or "bad" the images are.
ContributorsWang, Yanfei (Author) / Giard, Jacques (Thesis advisor) / Fehler, Michelle (Committee member) / Faria, Rowan De (Committee member) / Arizona State University (Publisher)
Created2015
Description
Computational visual aesthetics has recently become an active research area. Existing state-of-art methods formulate this as a binary classification task where a given image is predicted to be beautiful or not. In many applications such as image retrieval and enhancement, it is more important to rank images based on their aesthetic quality instead of binary-categorizing them. Furthermore, in such applications, it may be possible that all images belong to the same category. Hence determining the aesthetic ranking of the images is more appropriate. To this end, a novel problem of ranking images with respect to their aesthetic quality is formulated in this work. A new data-set of image pairs with relative labels is constructed by carefully selecting images from the popular AVA data-set. Unlike in aesthetics classification, there is no single threshold which would determine the ranking order of the images across the entire data-set.
This problem is attempted using a deep neural network based approach that is trained on image pairs by incorporating principles from relative learning. Results show that such relative training procedure allows the network to rank the images with a higher accuracy than a state-of-art network trained on the same set of images using binary labels. Further analyzing the results show that training a model using the image pairs learnt better aesthetic features than training on same number of individual binary labelled images.
Additionally, an attempt is made at enhancing the performance of the system by incorporating saliency related information. Given an image, humans might fixate their vision on particular parts of the image, which they might be subconsciously intrigued to. I therefore tried to utilize the saliency information both stand-alone as well as in combination with the global and local aesthetic features by performing two separate sets of experiments. In both the cases, a standard saliency model is chosen and the generated saliency maps are convoluted with the images prior to passing them to the network, thus giving higher importance to the salient regions as compared to the remaining. Thus generated saliency-images are either used independently or along with the global and the local features to train the network. Empirical results show that the saliency related aesthetic features might already be learnt by the network as a sub-set of the global features from automatic feature extraction, thus proving the redundancy of the additional saliency module.
This problem is attempted using a deep neural network based approach that is trained on image pairs by incorporating principles from relative learning. Results show that such relative training procedure allows the network to rank the images with a higher accuracy than a state-of-art network trained on the same set of images using binary labels. Further analyzing the results show that training a model using the image pairs learnt better aesthetic features than training on same number of individual binary labelled images.
Additionally, an attempt is made at enhancing the performance of the system by incorporating saliency related information. Given an image, humans might fixate their vision on particular parts of the image, which they might be subconsciously intrigued to. I therefore tried to utilize the saliency information both stand-alone as well as in combination with the global and local aesthetic features by performing two separate sets of experiments. In both the cases, a standard saliency model is chosen and the generated saliency maps are convoluted with the images prior to passing them to the network, thus giving higher importance to the salient regions as compared to the remaining. Thus generated saliency-images are either used independently or along with the global and the local features to train the network. Empirical results show that the saliency related aesthetic features might already be learnt by the network as a sub-set of the global features from automatic feature extraction, thus proving the redundancy of the additional saliency module.
ContributorsGattupalli, Jaya Vijetha (Author) / Li, Baoxin (Thesis advisor) / Davulcu, Hasan (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2016
Description
Powerpoint slides from Spindler's presentation at the 56th annual Arizona History Convention in Tucson, Arizona, April 24th, 2015. Details of the 1993-1995 U.S. District Court orders directing the corporate archives to Arizona State University and ASU's efforts to recover information from an obsolete digital imaging system are presented.
ContributorsSpindler, Rob (Author)
Created2015-04-24
Description
Until 2013, the Embryo Project Encyclopedia had not presented images in articles due to the fact that there was no formal protocol for developing images at that time. I have created an image style guide that outlines the basic steps of creating and submitting an image that can complement an Embryo Project article and can enhance a reader’s understanding of the discussed concept. In creating this style guide, I investigate similar protocols used by other scientific journals and medical professionals. I also use different programs and base my style guide on the procedures that I had used in Adobe Illustrator CS6.
ContributorsHamidi, Neekta (Author) / Tuoti, Whitney Alexandra (Editor) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)
Created2020-11-27