Information augmented museum visit device

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2002.Includes bibliographical references (leaves 44-46).This thesis tries to develop a new museum guide device with the combination functions of digital cameras and palms as a tool that maps information onto digital images to support a real-time contextualized learning that goes beyond separate out-of-task-context learning and existing museum guide modes. In order to perform a self-directed, interest-triggering learning process, one needs to gain information from both personal experiences and museum databases. By keeping a continuous personal experience between different journeys, an individual could bring his own knowledge and history into relation with museum databases to support a dynamic information access during museum visits and after the visits. However, existing guide devices and their applications do not fully exploit the potential of real-time learning generated by wireless and mobile technology. This study proposes a tool, which encourages personal-controlled learning during museum visits by mapping dynamic information layer into physical space. The visitor " gets object-oriented knowledge and a coherent experience through the exploration into the information space with the movement in the physical space both real-time and after the visit.by Xingchen Wang.S.M

Interactive digital textbooks and engagement: A learning strategies framework

This mixed-methods study explored non-native English speaking students’ learning processes and engagement as they used a customized interactive digital textbook housed on a mobile device. Think aloud protocols, surveys of anticipated and actual engagement with the digital textbook, reflective journals, and member checking constituted data collection. Participants included 13 students in a large U.S. university Business English class. This study responds to the call for further research on how interacting with digital textbooks and mobile devices may affect student reading behaviors and the learning process, using the cultures-of-use conceptual framework by Thorne (2003) as a lens for analysis. Results of a paired Wilcoxon signed-rank test found that participants entered the course with high expectations for the digital textbook and ratings remained high over the term, with only one area showing a significant decrease in engagement. Analysis of think aloud protocol and reflective journal data resulted in the creation of the Framework for Learning with Digital Resources. This framework of learning processes and strategies can be used by materials designers in creating digital textbooks and resources and by educators in supporting students as they move from using digital devices mainly for personal use to utilizing them effectively in their learning

GeoCamera: Telling Stories in Geographic Visualizations with Camera Movements

In geographic data videos, camera movements are frequently used and combined to present information from multiple perspectives. However, creating and editing camera movements requires significant time and professional skills. This work aims to lower the barrier of crafting diverse camera movements for geographic data videos. First, we analyze a corpus of 66 geographic data videos and derive a design space of camera movements with a dimension for geospatial targets and one for narrative purposes. Based on the design space, we propose a set of adaptive camera shots and further develop an interactive tool called GeoCamera. This interactive tool allows users to flexibly design camera movements for geographic visualizations. We verify the expressiveness of our tool through case studies and evaluate its usability with a user study. The participants find that the tool facilitates the design of camera movements.Comment: 15 pages. Published as a conference paper at the ACM Conference on Human Factors in Computing Systems (CHI) 202

Novel Datasets, User Interfaces and Learner Models to Improve Learner Engagement Prediction on Educational Videos

With the emergence of Open Education Resources (OERs), educational content creation has rapidly scaled up, making a large collection of new materials made available. Among these, we find educational videos, the most popular modality for transferring knowledge in the technology-enhanced learning paradigm. Rapid creation of learning resources opens up opportunities in facilitating sustainable education, as the potential to personalise and recommend specific materials that align with individual users’ interests, goals, knowledge level, language and stylistic preferences increases. However, the quality and topical coverage of these materials could vary significantly, posing significant challenges in managing this large collection, including the risk of negative user experience and engagement with these materials. The scarcity of support resources such as public datasets is another challenge that slows down the development of tools in this research area. This thesis develops a set of novel tools that improve the recommendation of educational videos. Two novel datasets and an e-learning platform with a novel user interface are developed to support the offline and online testing of recommendation models for educational videos. Furthermore, a set of learner models that accounts for the learner interests, knowledge, novelty and popularity of content is developed through this thesis. The different models are integrated together to propose a novel learner model that accounts for the different factors simultaneously. The user studies conducted on the novel user interface show that the new interface encourages users to explore the topical content more rigorously before making relevance judgements about educational videos. Offline experiments on the newly constructed datasets show that the newly proposed learner models outperform their relevant baselines significantly

The use of process data to examine reading strategies

Researchers are increasingly interested in the cognitive behaviors students display during tests. This interest has led researchers to look for innovative ways to collect this type of data. Due to the proliferation of computer-based assessments, process data has become popular for its ability to help show what students know, what students don’t know, and how students interact during assessments. Aim: The aims of the current study are 1) to use process data to identify potential reading strategies and 2) to examine if reading strategy is associated with gender, race/ethnicity, and differences in performance. Methods: Apply latent profile analysis (LPA) to extracted process data variables collected from US examinees who participated in the literacy section of the Program for the International Assessment of Adult Competencies (PIAAC). The variables are item response time and number of highlight events per item. Results: A two-class solution provided the best fit for the data in each testlet of the literacy section of the PIAAC. Class one progressed through items in each testlet faster than class two. Class one most closely resembled a skimming strategy while class two most closely resembled a full-reading strategy. However, there was not conclusive evidence to suggest that the classes were reminiscent of skimming and full-reading. Class assignment had no significant relationship with gender nor race/ethnicity, and there was no significant difference in literacy performance between the two classes, except in one case. Even then, both classes performed at a level two on the PIAAC literacy achievement scale. Discussion: Response time was found to be the only discriminating variable in the identification of patterns related to reading strategies. While there was some separation between classes, it was minimal in some cases. Response time was found to be useful but not enough to identify conclusive reading strategies. Further research is needed to identify process data variables with explanatory power other than response time to aid in the identification of reading strategies

Visual analytics for relationships in scientific data

Domain scientists hope to address grand scientific challenges by exploring the abundance of data generated and made available through modern high-throughput techniques. Typical scientific investigations can make use of novel visualization tools that enable dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These general tools should be applicable to many disciplines: allowing biologists to develop an intuitive understanding of the structure of coexpression networks and discover genes that reside in critical positions of biological pathways, intelligence analysts to decompose social networks, and climate scientists to model extrapolate future climate conditions. By using a graph as a universal data representation of correlation, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool integrates techniques such as graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized B-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using real-world workflows from several large-scale studies. Parallel coordinates has proven to be a scalable visualization and navigation framework for multivariate data. However, when data with thousands of variables are at hand, we do not have a comprehensive solution to select the right set of variables and order them to uncover important or potentially insightful patterns. We present algorithms to rank axes based upon the importance of bivariate relationships among the variables and showcase the efficacy of the proposed system by demonstrating autonomous detection of patterns in a modern large-scale dataset of time-varying climate simulation