The success of persuasive systems in changing people's attitudes and behaviours has been established in various domains. Specifically, research has shown that personalized persuasive technology is more effective at achieving the desired goal than the one-size-fits-all approach. However, in the education domain, there are limited studies on the personalization of persuasive strategies to students. To advance persuasive technology research in this area, we investigated the susceptibility of undergraduate students (n = 243) to four persuasive strategies (Reward, Competition, Social Comparison and Social Learning) in order to provide a guideline for designing and personalizing persuasive systems in education. These four strategies were chosen because research on persuasion has established their effectiveness in changing behaviour and/or attitude. The results of our analysis reveal that students are more susceptible to Reward, followed by Competition and Social Comparison (both of which come in the second place) and Social Learning (the least persuasive). Moreover, there is no gender difference in the persuasiveness of the strategies. Therefore, in choosing persuasive strategies to motivate student's learning and success, among the strategies we investigated, Reward should be given priority, followed by Competition and Social Comparison, while Social Learning should be least favoured.

Institutional implementation of learning analytics calls for thoughtful management of cultural change. This interactive halfday workshop responds to the LA literature describing the benefits and challenges of institutional LA implementation by offering participants an opportunity to learn about and begin planning for a program to actively engage faculty as leaders of data exploration around the theme of 'student success'. This session will share experiences from five institutions actively engaged in fostering Learning Analytics Communities (LAC) by identifying key issues, sharing lessons learned, and considering structural frameworks that are transferable to other institutional contexts. Structured discussion and activities will engage participants in developing an action plan for establishing an LAC on their own campus.

This workshop on Learning Analytics for Curriculum and Program Quality Improvement investigates how LAK can drive improvements in teaching practices, instructional and curricular design, and academic program delivery. This workshop brings forward research and examples of how LAK can help build the case for instructional, curricular, or programmatic change and further how LAK can be used to foster acceptance of change processes by teachers, administrators, and other stakeholders in the educational enterprise.

This paper describes a method we have developed to convert statistical predictive models into visual narratives which explain student classifications. Building off of the work done within the user experience community, we apply the concept of personas to predictive models. These personas provide familiar and memorable descriptions of the learners identified by data mining activities, and bridge the gap between the data scientist and the learning specialist.

This paper presents the current state of our research project which aims to give users a simple, easy to use, and computationally light way of creating mashups of lecture content within the Opencast Matter horn lecture capture system. The system modifies the playback components of Matter horn to deliver thin and light video clipping functionality without requiring installation of any additional software. We plan to make use of the extensive logging framework built into Matter horn to examine the effects of this tool on learner engagement.

This paper considers the issue of the locus of control in adaptive e-learning environments from the perspective of a new stakeholder; the instructional expert. With an ever increasing ability to gain insight into learners based on their online activities, instructors and instructional designers are poised to add value to the process of adaptation, a process normally reserved for either systems designers or the end user. This work describes the design of an e-learning system which provides automated analytics information to these experts for consideration, and then leverages the insights these experts have made as the basis for content and feature adaptation.

Video lecture capture is rapidly being deploying in higher-education institutions as a means of increasing student learning, outreach, and experience. Understanding how learners use these systems and relating this use back to pedagogical and institutional goals is a hard issue that has largely been unexplored. This work describes a novel web-based lecture presentation system which contains fine-grained user tracking features. These features, along with student surveys, have been used to help analyse the behaviour of hundreds of students over an academic term, quantifying both the learning approaches of students and their perceptions on learning with lecture capture.

Role- and relationship-based identity management (RRIM) appears to be a useful identity management paradigm, which contributes to protecting users' privacy. This paper presents an implementation of RRIM in iHelp Discussion, the discussion forum component of an e-learning environment. We include an evaluation of the effectiveness of RRIM in facilitating a privacy-enhanced discussion forum.

This paper describes work we are doing to identify significant events in video captures of academic lectures. Unlike other approaches which tend to define per-image comparison threshold values based on intuition or empirically derived results, we use supervised machine learning techniques to automatically determine appropriate image characteristics based on end-users understanding of what constitutes an important event. This makes our approach more adaptable to different kinds of content, and still provides a substantial level of agreement with human experts.

This paper describes a low-cost distributed computing approach for the post-processing of videos. It has been implemented to support the creation of composite videos from various video sources, and supports image manipulation on individual frames of the video. The principle deployment scenario for this work has been to support the automatic creation of output video based on traditional face-to-face lectures. Experimental results suggest that this approach can lead to significant speedups with a reduction in operating costs by leveraging existing underutilized equipment.

Web-based learning involves provision of personal on-line spaces to learners and teachers. Yet due to a lack of proper privacy policies and technical frameworks to implement them, seemingly innocent data transactions can carry risks to privacy in e-learning environments. We investigate privacy issues in e-learning and make recommendations for building environments that enhance privacy but allow features like content personalization and peer- collaboration. iHelp, a learning environment that supports both learners and instructors, implements many of these recommendations.

This research investigates the use of patterns in designing adaptable, flexible hypermedia applications. While patterns are particularly applicable to software design, they can also be used to assist designers of other types of applications. We have developed a method (APHID) that guides a hypermedia creator through the analysis and design process. The method ensures that good design principles are followed, both for the hypermedia application and for the interface that presents the hypermedia application. Our method uses a concept map, constraints, and patterns (instructional and presentation) to support partial automation for creating hypermedia applications. We also present a prototype software system that uses the APHID method to create instructional hypermedia applications semi-automatically. The applications created using APHID are tailored to specific types of learners. We conclude with a claim that this approach is applicable not just to instructional hypermedia, but to the larger problem of generating adaptable interfaces.

Object bases underlying knowledge based applications tend to be complex and require management. This research aims at improving the performance of object bases underlying a class of large knowledge based systems that utilize object oriented technology to engineer the knowledge base. A hybrid clustering strategy that beneficially combines semantic clustering and iterative graph partitioning techniques has been developed and evaluated for use in knowledge bases storing information in the form of object graphs. It is demonstrated via experimentation that such a technique is useful and feasible in realistic object bases. A semantic specification mechanism similar to placement trees has been developed for specifying the clustering. The workload and the nature of object graphs in knowledge bases differ significantly from those present in conventional object oriented databases. Therefore, the evaluation has been performed by building a new benchmark called the Granularity Benchmark. A segmented storage scheme for the knowledge base using large object storage mechanisms of existing storage managers is also examined.

A knowledge-based system for tutoring student physicians to diagnose bronchial asthma is presented. The system uses a case-based instruction methodology where the student learns how to diagnose bronchial asthma through practice on carefully selected cases. The tutoring system presents the case to the student as the student requests information, and is able to provide help if the student needs it. The dialogue is completely controlled by the student, who can use his/her own language in conversing with the system. Embedded in the tutoring system is an expert diagnostician for bronchial asthma, which is called upon to evaluate the student's attempted diagnosis and is used to provide a summary of the reasoning behind the expert's diagnosis. The system is fully implemented, has been tested with actual physicians, and is currently being field tested with physicians and residents in internal medicine. The system is robust and seems to be effective for the domain of bronchial asthma. Initial indications imply that the methodologies developed for this knowledge-based tutoring system will apply beyond the bronchial asthma domains as well. >