BlogSum: A Query-based Summarization Approach to Make Sense of Social Media

With the rapid growth of the Social Web, a large amount of informal opinionated texts are available on numerous topics. However, people can be overwhelmed with this vast amount of information and they need help to find the information of their interests. Natural language tools for automatically analyzing these opinions become necessary to help individuals, organizations, and governments in making timely decisions. To address this need, I proposed a summarization approach for opinionated texts. To validate my approach, BlogSum is developed and evaluated experimentally using current benchmarks. Users can ask BlogSum any question (e.g. Why do people like Chrome better than Firefox?). To answer user's question, BlogSum first retrieves relevant blogs, reviews from the web then generates a concise summary that represents people opinions expressed towards the topic. Since blog summarization is a more recent endeavor, an error analysis was conducted by manually analyzing blog summaries to find there is any information processing difference needed for blogs compared to factual data. This analysis shows that question irrelevance and discourse incoherence, which decrease the overall quality of a summary and reduces the summary coherence, are two major issues for blog summaries. To address question irrelevance and discourse incoherence, in this work a domain-independent schema-based summarization approach is developed that utilizes discourse structures. This approach is based on the automatic identification of discourse relations within candidate sentences in order to instantiate the most appropriate discourse schema and filter and order candidate sentences in the most effective way. BlogSum also needs to deal with opinions, emotions effectively to be successful. BlogSum's overall performance as well as performance for question relevance and coherence was evaluated using various dataset. These results show that the proposed approach can effectively reduce question irrelevance and discourse incoherence and satisfy user's information need

Design and Evaluate the Factors for Flipped Classrooms for Data Management Courses

This Research to Practice Full Paper presents a framework to evaluate and design flipped classroom activities for data science and management courses. Variants of flipped classrooms have been employed in STEM fields with great success in students' learning outcomes. Research shows that flipped classrooms would improve students' learning if it is implemented following rigorous procedures of an efficient instructional design. As a result, one of the critical focus of current flipped classroom research is what factors educators need to consider when designing a flipped learning environment. Currently, educators incorporate various factors such as "pre-recorded video lecture", "group activity" as a trial and error basis and adjust these factors based on their own experience and students' feedback. On the other hand, the emergence of big data expects a new graduate to demonstrate mastery of concepts and skills for data acquisition, management, and analysis of inference from data when they enter the workforce. Currently, there is no systematic approach available to design a flipped classroom that is for the data science and management courses. In this research, we develop a framework first to investigate and evaluate the flipped classroom factors mentioned in the literature and identify a few that are most relevant to the two data management courses at our institute. Then, we classify each course topics into broader categories. So that the flipped classroom model can be developed for each category. For the flipped classroom for each category, we identify the pre-class and in-class activities to meet a certain learning objective for that topic category for each course. To evaluate the effectiveness of different factors as well as our flipped classroom models, students' performance data, interviews, and surveys are conducted. This process is transformative and can be employed by other STEM disciplines to find the most influential factors to design effective flipped learning classrooms

Incorporate Cross-Course Knowledge Integration into Computing Education

This research to practice full paper describes our cross-course knowledge integration approach, which uses a project-based learning environment. The structure and instructional pedagogies of some of our data management concentration courses in our department have not changed in a long time. Most of our data management courses cover theoretical knowledge without showing their practical applications. As a result, students don't find those courses interesting enough. In addition, none of our data management courses require cross-course interactions, which is the current instructional pedagogical direction. With the goal of addressing these issues and improving student learning, faculty employed a cross-course knowledge integration model to teach the advanced database design course CIT 44400. We redesigned this advanced database course as part of our data management curriculum enhancement also to align with the trends of the IT industry and to enable students to correlate knowledge learned in various courses. In this redesign process, we integrated a project that aligns with industrial projects in this higher-level course curriculum, so that students can integrate and apply theoretical knowledge gained in lower-level courses through participating in a project-based learning approach, using current technology of the field. Evaluation results show crosscourse knowledge integration-based pedagogy gives students comprehensive knowledge that improves students' performance and helps them link and apply knowledge learned in various courses