An Empirical Study of the Factors Affecting Weblog Success in Higher Education

The use of classroom blogs in higher education serves to engage and motivate students as well as to help them build a professional online profile and connect with fellow classmates. Although many studies have focused on the implementation and benefits of blogging in education, few have investigated best practices in design, which can have a critical impact upon success. In this study, we proposed a success model for classroom blogs considering the impact of system quality, information quality and user aspects to the net benefit, with an emphasis on user experience design. Finally, we evaluated this model by studying a classroom blog used by 146 college students over the course of 3 academic quarters

Using Blogs to Support Constructivist and Social Learning – a Case Study in a University Setting

Recent studies have shown that using social media such as blogs and wikis in classroom can enhance student learning. In this paper, we briefly introduce constructivist and social learning theories, blog technology and the use of blogs in education, and different design architecture of educational blogs. We then present a case study of using blogs in an undergrad level management information systems course in a university setting. The study aims to evaluate the effectiveness of blogmediated learning, knowledge discovery and creation within the scope of information systems education. The study also aims to investigate the design and usability issues of classroom blogs. Relevant data collected over the course of a semester include students\u27 blog posts, classroom presentations and interactions, survey questionnaires (including five-Liker scale and open-ended questions), and instructional materials. Analysis of the data examines student goals, motivations, system usage, and perceived effectiveness and value of using blogs in classroom

Image Sensing and Processing with Convolutional Neural Networks

Convolutional neural networks are a class of deep neural networks that leverage spatial information, and they are therefore well suited to classifying images for a range of applications [...

The Dimensions of Review Comprehensiveness and Its Effect on Review Usefulness: A Latent Dirichlet Allocation Approach

Online review sites like Yelp.com, TripAdvisor.com and AngiesList.com provide values to both business and consumers. A large body of literature investigates drivers of online review usefulness. Review comprehensiveness has been identified as one the most important dimension of review quality and an important predictor of review usefulness. This study contributes to the literature by crafting and operationalizing review comprehensiveness using a text mining approach. We also empirically test the effect of the operationalized review comprehensiveness construct on review usefulness. In practice, online review providers, especially Yelp.com, can benefit from this study by integrating review comprehensiveness in their sorting algorithms

Designing ePortfolio 2.0: Integrating and Coordinating Web 2.0 Services with ePortfolio Systems for Enhancing Users\u27 Learning

An educational ePortfolio usually contains work that a student has collected, reflected, designed, and published to demonstrate personal learning and growth over time. However, previous studies have shown that traditional ePortfolio systems lack flexibility, peer review, and group collaboration. Without these features, ePortfolios do not have the benefits of social learning or Communities of Practice. In this paper, we propose a new design that integrates and coordinates emerging Web 2.0 services into ePortfolio systems to enable community-wide annotation, interaction, and collaboration, with the goal of enhancing the learning experience for individuals as well as the community. We review relevant literatures, theories, and development of traditional ePortfolio systems. We conduct a preliminary survey study to explore users\u27 perceived values in ePortfolio and Web 2.0 services. The survey results show opportunities to design a new generation of ePortfolio systems enabled with Web 2.0. We illustrate and discuss an ePortfolio 2.0 conceptual model, and a system prototype

Cluster Analysis of Musical Attributes for Top Trending Songs

Music streaming services like Spotify have changed the way consumers listen to music. Understanding what attributes make certain songs trendy can help services to create a better customer experience as well as more effective marketing efforts. We performed cluster analysis on Top 100 Trending Spotify Song of 2017, with ten attributes, including danceability, energy, loudness, speechiness, acousticness, instrumentalness, Liveness, valence, tempo, and duration. The results show that music structures with high danceability and low instrumentalness increase the popularity of a song and lead them to chart-topping success

DynaQuadric: Dynamic Quadric SLAM for Quadric Initialization, Mapping, and Tracking

Dynamic SLAM is a key technology for autonomous driving and robotics, and accurate pose estimation of surrounding objects is important for semantic perception tasks. Current quadric SLAM methods are based on the assumption of a static environment and can only reconstruct static quadrics in the scene, which limits their applications in complex dynamic scenarios. In this paper, we propose a visual SLAM system that is capable of reconstructing dynamic objects as quadrics, with a unified framework for jointly optimizing pose estimation, multi-object tracking (MOT), and quadric parameters. We propose a robust object-centric quadric initialization algorithm for both static and moving objects, which decouples the prior estimation of the object pose from the quadric parameters. The object is initialized with a coarse sphere, and quadric parameters are further refined. We design a novel factor graph that tightly optimizes camera pose, object pose, map points and quadric parameters within the sliding window-based optimization. To the best of our knowledge, we are the first to propose a dynamic SLAM that combines quadric representations and MOT in a tightly coupled optimization. We perform qualitative and quantitative experiments on both simulated and real-world datasets, and demonstrate the robustness and accuracy in terms of camera localization, dynamic quadric initialization, mapping and tracking. Our system demonstrates the potential application of object perception with quadric representation in complex dynamic scenes