As providers of higher education begin to harness the power of big data analytics, one very fitting application for these new techniques is that of predicting student attrition. The ability to pinpoint students who might soon decide to drop out, or who may be following a suboptimal path to success, allows those in charge not only to understand the causes for this undesired outcome, but provides room for the development of early intervention systems. While making such inferences based on academic performance data alone is certainly possible, we claim that in many cases there is no substantial correlation between how well a student performs and his/her decision to withdraw. This is especially true when the overall set of students has a relatively similar academic performance. To address this issue, we derive measurements of engagement from students' electronic portfolios and show how these features can be used effectively to augment the quality of predictions.

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As more and more college classrooms utilize online platforms to facilitate teaching and learning activities, analyzing student online behaviors becomes increasingly important for instructors to effectively monitor and manage student progress and performance. In this paper, we present CCVis, a visual analytics tool for analyzing the course clickstream data and exploring student online learning behaviors. Targeting a large college introductory course with over two thousand student enrollments, our goal is to investigate student behavior patterns and discover the possible relationships between student clickstream behaviors and their course performance. We employ higher-order network and structural identity classification to enable visual analytics of behavior patterns from the massive clickstream data. CCVis includes four coordinated views (the behavior pattern, behavior breakdown, clickstream comparative, and grade distribution views) for user interaction and exploration. We demonstrate the effectiveness of CCVis through case studies along with an ad-hoc expert evaluation. Finally, we discuss the limitation and extension of this work.

The purpose of this study was to examine the effectiveness of an intervention in which teacher-led instruction was combined with computerized writing software to improve paragraph writing for three middle school students with intellectual disability. A multiple probe across participants design was used to evaluate the effectiveness of the intervention. During each 30 to 45-min intervention session, the teacher provided instruction using a graphic organizer to remind students about grammar rules and proper paragraph structure. Then participants wrote paragraphs using three of the four software components of SOLO Literacy Suite (Write: OutLoud, Co:Writer and Draft:Builder). Data indicated that the intervention was effective in improving writing quality (topic adherence and mechanics measured on a rubric), percentage of words spelled correctly and percentage of correct word sequences for all participants. Implications for educators and future research are discussed.