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As a high-impact educational practice, service-learning has demonstrated success in positively influencing students’ overall development, and much work has been done on investigating student learning outcomes from service-learning. A particular direction is to model students’ learning outcomes in the context of their learning experience, i.e., the various student, course, and pedagogical elements. It contributes to a better understanding of the learning process, a more accurate prediction of students’ attainments on the learning outcomes, and improvements in the design of learning activities to maximize student learning. However, most of the existing work in this area relies on statistical analysis that makes assumptions about attribute independence or simple linear dependence, which may not accurately reflect real-life scenarios. In contrast, the study described in this paper adopted a neural network-based approach to investigate the impact of students’ learning experience on different service-learning outcomes. A neural network with attention mechanisms was constructed to predict students’ service-learning outcomes by modeling the contextual information from their various learning experiences. In-depth evaluation experiments on a large-scale dataset collected from more than 10,000 students showed that this proposed model achieved better accuracy on predicting service-learning outcomes. More importantly, it could capture the interdependence between different aspects of student learning experience and the learning outcomes. We believe that this framework can be extended to student modeling for other types of learning activities.

Human-centered computing is rapidly becoming a major research direction as new developments in sensor technology make it increasingly feasible to obtain signals from human beings. At the same time, the pervasiveness of computing devices is also encouraging more research in human---computer interaction, especially in the direction of personalized and adaptive user interfaces. Among the various research issues, affective computing, or the ability of computers to understand and react according to what a user "feels," has been gaining in importance. In order to recognize the human affect (feeling), computers rely on the analysis of signal inputs captured by a multitude of means. This paper proposes the use of human physiological signals as a new form of modality in determining human affects, in a non-intrusive manner. The principle of non-invasiveness is very important, since it imposes no extra burden on the user, which improves user accessibility and encourages user adoption. This goal is realized via the physiological mouse, as a first step toward the support of affective computing. The conventional mouse is converted with a small optical component for capturing user photoplethysmographic (PPG) signal. With the PPG signal, it is possible to compute and derive human physiological signals. A prototype of the physiological mouse was built and raw PPG readings measured. The accuracy of the approach was evaluated through empirical studies to determine human physiological signals from the mouse PPG data. Finally, pilot experiments to correlate human physiological signals with various modes of human---computer interaction, namely gaming and video watching, were conducted. The trend in physiological signals could be used as feedback to the computer system which in turn adapts to the needs or the mood of the user, for instance change the volume and the light intensity when watching a video or playing a game based on current user emotion. The authors argue that this research will provide a new dimension for multimodal affective computing research, and the pilot study has already shed some light toward this research goal.

The rapid development of social media technologies enables travellers to share travel experiences and opinions online by posting reviews, which then serve as information source for other travellers. However, the explosive growth of reviews and the proliferation of uninformative, biased or even false information make it very challenging for travellers to find credible information. To help travellers seek credible information, most current work apply mainly qualitative approaches to investigate the credibility of reviews or reviewers. This paper adopts an Impact Index to quantify the credibility of reviewers by simultaneously evaluating the expertise and trustworthiness of reviewers based on the number of reviews posted by them and the number of helpful votes received by those reviews. Furthermore, the Impact Index is enhanced into the Exposure-Impact Index by considering in addition reviewers' breadth of expertise in the form of the number of destinations on which reviewers posted reviews. To examine the effectiveness and applicability of Impact Index and Exposure-Impact Index, this paper evaluates them on several data sets collected from two rather different online travel communities: TripAdvisor, the world's largest travel community, and Qunar, one of the most popular travel communities in China. Experimental results show that both Impact Index and Exposure-Impact Index lead to more consistent results with human judgments than the state-of-the-art method in measuring the credibility of reviewers from diverse communities, manifesting their effectiveness and applicability.

In a mobile computing environment, database servers disseminate information to multiple mobile clients via wireless channels. Due to the low bandwidth and low reliability of wireless channels, it is important for a mobile client to cache its frequently accessed database items into its local storage. This improves performance of database queries and improves availability of database items for query processing during disconnection. In this paper, we investigate issues on caching granularity, coherence strategy, and replacement policy of caching mechanisms for a mobile environment utilizing point-to-point communication paradigm. We first illustrate that page-based caching is not suitable in the mobile context due to the lack of locality among database items. We propose three different levels of caching granularity: attribute caching, object caching, and hybrid caching, a hybrid approach of attribute and object caching. Next, we show that existing coherence strategies are inappropriate due to frequent disconnection in a mobile environment, and propose a cache coherence strategy, based on the update patterns of database items. Via a detail simulation model, we examine the performance of various levels of caching granularity with our cache coherence strategy. We observe, in general, that hybrid caching could achieve a better performance. Finally, we propose several cache replacement policies that can adapt to the access patterns of database items. For each given caching granularity, we discover that our replacement policies outperform conventional ones in most situations.

In a mobile environment, querying a database at a stationary server from a mobile client is expensive due to the limited bandwidth of a wireless channel and the instability of the wireless network. We address this problem by maintaining a materialized view in a mobile client's local storage. Such a materialized view can be considered as a data warehouse. The materialized view contains results of common queries in which the mobile client is interested. In this paper, we address the view update problem for maintaining the consistency between a materialized view at a mobile client and the database server. The content of a materialized view could become incoherent with that at the database server when the content of the database server and/or when the location of the client is changed. Existing view update mechanisms are ‘push-based’. The server is responsible for notifying all clients whose views might be affected by the changes in database or the mobility of the client. This is not appropriate in a mobile environment due to the frequent wireless channel disconnection. Furthermore, it is not easy for a server to keep track of client movements to update individual client location-dependent views. We propose a ‘pull-based’ approach that allows a materialized view to be updated at a client in an incremental manner, requiring a client to request changes to its view from the server. We demonstrate the feasibility of our approach with experimental results.

An inherent limitation in mobile data access is due to the unreliable and low bandwidth wireless communication channel. Caching of useful database items from database server in local storage of mobile clients is effective in reducing data access latency and wireless bandwidth consumption. In the event of disconnection, cached data can also serve the purpose of partial query processing. In this paper, we present the implementation and evaluate a new caching mechanism for object-oriented database systems in a mobile environment called MODEC. MODEC possesses the capabilities of performing caching at multiple granularities and adapting to changes in data access pattern, providing improved performance through tolerating limited inconsistency to read-only transactions. This caching capabilities is supported via standard ODMG modeling constructs. The prototype of MODEC is implemented using ODE database. Empirical system performance results are obtained from experiments on the prototype with data from a real-life database. The results are validated against results obtained via detailed simulation studies on MODEC. Both sets of results are found to be consistent and are in favor of our MODEC mechanism in providing a feasible solution to the mobile data access problem under the constraints in a mobile environment.

The mobile computing environment is receiving increasing attention recently. We consider a mobile environment in which a collection of mobile clients accesses a stationary database server via a wireless channel. Due to the limited bandwidth of a wireless channel and the instability of the wireless network, caching of frequently accessed data items in a client's local storage becomes especially important for improving the performance and data availability of data access queries. In this paper, we discuss the limitations of existing caching mechanisms in a mobile environment and investigate issues that need to be addressed. We propose an adaptive caching model that could cope with the nature of a mobile environment and the low-bandwidth wireless media, supporting fast data access. We describe the adaptive cache replacement and refresh mechanisms; explain the implementation in the context of object-oriented databases; and illustrate the results of some exploratory experiments to demonstrate the feasibility of the mechanisms.