End-User Programming of Mobile Services: Empowering Domain Experts to Implement Mobile Data Collection Applications

The widespread use of smart mobile devices (e.g., in clinical trials or online surveys) offers promising perspectives with respect to the controlled collection of high-quality data. The design, implementation and deployment of such mobile data collection applications, however, is challenging in several respects. First, various mobile operating systems need to be supported, taking the short release cycles of vendors into account as well. Second, domain-specific requirements need to be flexibly aligned with mobile application development. Third, usability styleguides need to be obeyed. Altogether, this turns both programming and maintaining mobile applications into a costly, time-consuming, and error-prone endeavor. To remedy these drawbacks, a model-driven framework empowering domain experts to implement robust mobile data collection applications in an intuitive way was realized. The design of this end-user programming framework is based on experiences gathered in real-life mobile data collection projects. Facets of various stakeholders involved in such projects are discussed and an overall architecture as well as its components are presented. In particular, it is shown how the framework enables domain experts (i.e., end users) to flexibly implement mobile data collection applications on their own. Overall, the framework allows for the effective support of mobile services in a multitude of application domains

Mixing the reactive with the personal: Opportunities for end-user programming in personal information management

The transition of personal information management (PIM) tools off the desktop to the Web presents an opportunity to augment these tools with capabilities provided by the wealth of real-time information readily available. In this chapter, we describe a personal information assistance engine that lets end-users delegate to it various simple context- and activity-reactive tasks and reminders. Our system, Atomate, treats RSS/ATOM feeds from social networking and life-tracking sites as sensor streams, integrating information from such feeds into a simple unified RDF world model representing people, places and things and their time-varying states and activities. Combined with other information sources on the web, including the user's online calendar, web-based e-mail client, news feeds and messaging services, Atomate can be made to automatically carry out a variety of simple tasks for the user, ranging from context-aware filtering and messaging, to sharing and social coordination actions. Atomate's open architecture and world model easily accommodate new information sources and actions via the addition of feeds and web services. To make routine use of the system easy for non-programmers, Atomate provides a constrained-input natural language interface (CNLI) for behavior specification, and a direct-manipulation interface for inspecting and updating its world model

Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on

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

Personalised video retrieval: application of implicit feedback and semantic user profiles

A challenging problem in the user profiling domain is to create profiles of users of retrieval systems. This problem even exacerbates in the multimedia domain. Due to the Semantic Gap, the difference between low-level data representation of videos and the higher concepts users associate with videos, it is not trivial to understand the content of multimedia documents and to find other documents that the users might be interested in. A promising approach to ease this problem is to set multimedia documents into their semantic contexts. The semantic context can lead to a better understanding of the personal interests. Knowing the context of a video is useful for recommending users videos that match their information need. By exploiting these contexts, videos can also be linked to other, contextually related videos. From a user profiling point of view, these links can be of high value to recommend semantically related videos, hence creating a semantic-based user profile. This thesis introduces a semantic user profiling approach for news video retrieval, which exploits a generic ontology to put news stories into its context. Major challenges which inhibit the creation of such semantic user profiles are the identification of user's long-term interests and the adaptation of retrieval results based on these personal interests. Most personalisation services rely on users explicitly specifying preferences, a common approach in the text retrieval domain. By giving explicit feedback, users are forced to update their need, which can be problematic when their information need is vague. Furthermore, users tend not to provide enough feedback on which to base an adaptive retrieval algorithm. Deviating from the method of explicitly asking the user to rate the relevance of retrieval results, the use of implicit feedback techniques helps by learning user interests unobtrusively. The main advantage is that users are relieved from providing feedback. A disadvantage is that information gathered using implicit techniques is less accurate than information based on explicit feedback. In this thesis, we focus on three main research questions. First of all, we study whether implicit relevance feedback, which is provided while interacting with a video retrieval system, can be employed to bridge the Semantic Gap. We therefore first identify implicit indicators of relevance by analysing representative video retrieval interfaces. Studying whether these indicators can be exploited as implicit feedback within short retrieval sessions, we recommend video documents based on implicit actions performed by a community of users. Secondly, implicit relevance feedback is studied as potential source to build user profiles and hence to identify users' long-term interests in specific topics. This includes studying the identification of different aspects of interests and storing these interests in dynamic user profiles. Finally, we study how this feedback can be exploited to adapt retrieval results or to recommend related videos that match the users' interests. We analyse our research questions by performing both simulation-based and user-centred evaluation studies. The results suggest that implicit relevance feedback can be employed in the video domain and that semantic-based user profiles have the potential to improve video exploration

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

The Assistment Builder: A tool for rapid tutor development

Intelligent Tutoring Systems are notoriously costly to construct, and require PhD level experience in cognitive science and rule based programming. The purpose of this research was to ease the development process for building pseudo-tutors. Pseudo-tutors are ITS constructs that mimic cognitive tutors but are limited in that they only apply to a single problem. The Assistment Builder is a tool designed to rapidly create, test, and deploy simple pseudo-tutors. These tutors provide a simplified cognitive model based upon a state graph designed for a specific problem. These tutors offer many of the features of rule-based tutors, but with shorter creation time. The system simplifies the process of tutor creation to allow users with little or no ITS experience to develop content. The system provides a web-based interface as a means to build and store these simple tutors we have called Assistments. This paper describes our attempt to make the process of developing, testing, and deploying content easy for teachers. We present data to suggest that users can develop a tutor that can be released to students in approximately an hour