Models and Algorithms for Understanding and Supporting Learning Goals in Information Retrieval

While search technology is widely used for learning-oriented information needs, the results provided by popular services such as Web search engines are optimized primarily for generic relevance, not effective learning outcomes. As a result, the typical information trail that a user must follow while searching to achieve a learning goal may be an inefficient one, possibly involving unnecessarily difficult content, or material that is irrelevant to actual learning progress relative to a user's existing knowledge. My work addresses these problems through multiple studies where various models and frameworks are developed and tested to support particular dimensions of search as learning. Empirical analysis of these studies through user studies demonstrate promising results and provide a solid foundation for further work. The earliest work we focused on centered on developing a framework and algorithms to support vocabulary learning objectives in a Web document context. The proposed framework incorporates user information, topic information and effort constraints to provide a desirable combination of personalized and efficient (by word length) learning experience. Our user studies demonstrate the effectiveness of our framework against a strong commercial baseline's (Google search) results in both short- and long-term assessment. While topic-specific content features (such as frequency of subtopic occurrences) naturally play a role in influencing learning outcomes, stylistic and structural features of the documents themselves may also play a role. Using such features we construct robust regression models that show strong predictive strength for multiple measures of learning outcomes. We also show early evidence that regression models trained on one dataset of search as learning can show strong test-set predictions on an independent dataset of search as learning, suggesting a certain degree of generalizability of stylistic content features. The models developed in my work are designed to be as generalizable, scalable and efficient as possible to make it easier for practitioners in the field to improve how people use search engines for learning. Finally, we investigate how gaze-tracking and automatic question generation could be used to scale a form of active learning to arbitrary text material. Our results show promising potential for incorporating interactive learning experiences in arbitrary text documents on the Web. A major theme in these studies centers on understanding and improving how people learn when using Web search engines. We also put specific emphasis on long-term learning outcomes and demonstrate that our models and frameworks actually yield sustainable knowledge gains, both for passive and interactive learning. Taken together, these research studies provide a solid foundation for multiple promising directions in exploring search as learning.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155065/1/rmsyed_1.pd

Eye Tracking to Support eLearning

Online eLearning environments to support student learning are of growing importance. Students are increasingly turning to online resources for education; sometimes in place of face-to-face tuition. Online eLearning extends teaching and learning from the classroom to a wider audience with different needs, backgrounds, and motivations. The one-size-fits-all approach predominately used is not effective for catering to the needs of all students. An area of the increasing diversity is the linguistic background of readers. More students are reading in their non-native language. It has previously been established that first English language (L1) students read differently to second English language (L2) students. One way of analysing this difference is by tracking the eyes of readers, which is an effective way of investigating the reading process. In this thesis we investigate the question of whether eye tracking can be used to make learning via reading more effective in eLearning environments. This question is approached from two directions; first by investigating how eye tracking can be used to adapt to individual student’s understanding and perceptions of text. The second approach is analysing a cohort’s reading behaviour to provide information to the author of the text and any related comprehension questions regarding their suitability and difficulty. To investigate these questions, two user studies were carried out to collect eye gaze data from both L1 and L2 readers. The first user study focussed on how different presentation methods of text and related questions affected not only comprehension performance but also reading behaviour and student perceptions of performance. The data from this study was used to make predictions of reading comprehension that can be used to make eLearning environments adaptive, in addition to providing implicit feedback about the difficulty of text and questions. In the second study we investigate the effects of text readability and conceptual difficulty on eye gaze, prediction of reading comprehension, and perceptions. This study showed that readability affected the eye gaze of L1 readers and conceptual difficulty affected the eye gaze of L2 readers. The prediction accuracy of comprehension was consequently increased for the L1 group by increased difficulty in readability, whereas increased difficulty in conceptual level corresponded to increased accuracy for the L2 group. Analysis of participants’ perceptions of complexity revealed that readability and conceptual difficulty interact making the two variables hard for the reader to disentangle. Further analysis of participants’ eye gaze revealed that both the predefined and perceived text complexity affected eye gaze. We therefore propose using eye gaze measures to provide feedback about the implicit reading difficulty of texts read. The results from both studies indicate that there is enormous potential in using eye tracking to make learning via reading more effective in eLearning environments. We conclude with a discussion of how these findings can be applied to improve reading within eLearning environments. We propose an adaptive eLearning architecture that dynamically presents text to students and provides information to authors to improve the quality of texts and questions

Framing digital image credibility: image manipulation problems, perceptions and solutions

Image manipulation is subverting the credibility of photographs as a whole. Currently there is no practical solution for asserting the authenticity of a photograph. People express their concern about this when asked but continue to operate in a ‘business as usual’ fashion. While a range of digital forensic technologies has been developed to address falsification of digital photographs, such technologies begin with ‘sourceless’ images and conclude with results in equivocal terms of probability, while not addressing the meaning and content contained within the image. It is interesting that there is extensive research into computer-based image forgery detection, but very little research into how we as humans perceive, or fail to perceive, these forgeries when we view them. The survey, eye-gaze tracking experiments and neural network analysis undertaken in this research contribute to this limited pool of knowledge. The research described in this thesis investigates human perceptions of images that are manipulated and, by comparison, images that are not manipulated. The data collected, and their analyses, demonstrate that humans are poor at identifying that an image has been manipulated. I consider some of the implications of digital image manipulation, explore current approaches to image credibility, and present a potential digital image authentication framework that uses technology and tools that exploit social factors such as reputation and trust to create a framework for technologically packaging/wrapping images with social assertions of authenticity, and surfaced metadata information. The thesis is organised into 6 chapters. Chapter 1: Introduction I briefly introduce the history of photography, highlighting its importance as reportage, and discuss how it has changed from its introduction in the early 19th century to today. I discuss photo manipulation and consider how it has changed along with photography. I describe the relevant literature on the subject of image authentication and the use of eye gaze tracking and neural nets in identifying the role of human vision in image manipulation detection, and I describe my area of research within this context. Chapter 2: Literature review I describe the various types of image manipulation, giving examples, and then canvas the literature to describe the landscape of image manipulation problems and extant solutions, namely: • the nature of image manipulation, • investigations of human perceptions of image manipulation, • eye gaze tracking and manipulated images, • known efforts to create solutions to the problem of preserving unadulterated photographic representations and the meanings they hold. Finally, I position my research activities within the context of the literature. Chapter 3: The research I describe the survey and experiments I undertook to investigate attitudes toward image manipulation, research human perceptions of manipulated and unmanipulated images, and to trial elements of a new wrapper-style file format that I call .msci (mobile self-contained image), designed to address image authenticity issues. Methods, results and discussion for each element are presented in both explanatory text and by presentation of papers resulting from the experiments. Chapter 4: Analysis of eye gaze data using classification neural networks I describe pattern classifying neural network analysis applied to selected data obtained from the experiments and the insights this analysis provided into the opaque realm of cognitive perception as seen through the lens of eye gaze. Chapter 5: Discussion I synthesise and discuss the outcomes of the survey and experiments. I discuss the outcomes of this research, and consider the need for a distinction between photographs and photo art. I offer a theoretical formula within which the overall authenticity of an image can be assessed. In addition I present a potential image authentication framework built around the .msci file format, designed in consideration of my investigation of the requirements of the image manipulation problem space and the experimental work undertaken in this research. Chapter 6: Conclusions and future work This thesis concludes with a summary of the outcomes of my research, and I consider the need for future experimentation to expand on the insights gained to date. I also note some ways forward to develop an image authentication framework to address the ongoing problem of image authenticity