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Cabrera, I., Villalon, J., & Chavez, J. (2017). Blending Communities and Team-Based Learning in a Programming Course. IEEE Trans. Educ., 60(4), 288–295.
Abstract: In recent years, engineering education teachers have needed to incorporate technology-supported collaboration to enhance learning. Implementing these activities requires course redesign, which must be meticulous for their full potential to be reached. This can require a lot of work for first time users, which can be a barrier to implementation. Educational design patterns alleviate this burden by facilitating new course design with practices demonstrated to promote student engagement. This paper reports on the redesign of an introductory programming course and its experimental evaluation. The redesign was based on the community of inquiry learning framework (CoL), using design patterns from online Web communities and team-based learning (TBL). The evaluation included 562 students, 117 of them randomly assigned to two different experimental groups. One group used a CoL approach, and the other a blended TBL and CoL methodology. The remaining students were assigned to control groups. Results showed that students in the experimental groups outperformed those in the control group by the end of the semester, while the experimental CoL and TBL methodology helped students achieve a higher level of understanding in a shorter period of time due to increased participation rates. These data provide empirical evidence of the learning gains offered by online learning communities, and the way in which educational design patterns can facilitate course redesign.
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Villalon, J., & Calvo, R. A. (2011). Concept Maps as Cognitive Visualizations of Writing Assignments. Educ. Technol. Soc., 14(3), 16–27.
Abstract: Writing assignments are ubiquitous in higher education. Writing develops not only communication skills, but also higher-level cognitive processes that facilitate deep learning. Cognitive visualizations, such as concept maps, can also be used as part of learning activities including as a form of scaffolding, or to trigger reflection by making conceptual understanding visible at different stages of the learning process. We present Concept Map Miner (CMM), a tool that automatically generates Concept Maps from students' compositions, and discuss its design and implementation, its integration to a writing support environment and its evaluation on a manually annotated corpora of university essays (N=43). Results show that complete CM, with concepts and labeled relationships, are possible and its precision depends the level of summarization (number of concepts) chosen.
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Villalon, J., & Calvo, R. A. (2013). A Decoupled Architecture for Scalability in Text Mining Applications. J. Univers. Comput. Sci., 19(3), 406–427.
Abstract: Sophisticated Text Mining features such as visualization, summarization, and clustering are becoming increasingly common in software applications. In Text Mining, documents are processed using techniques from different areas which can be very expensive in computation cost. This poses a scalability challenge for real-life applications in which users behavior can not be entirely predicted. This paper proposes a decoupled architecture for document processing in Text Mining applications, that allows applications to be scalable for large corpora and real-time processing. It contributes a software architecture designed around these requirements and presents TML, a Text Mining Library that implements the architecture. An experimental evaluation on its scalability using a standard corpus is also presented, and empirical evidence on its performance as part of an automated feedback system for writing tasks used by real students.
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