In prior literature on using clickstream data to capture student behavior in virtual learning environments, procrastination is typically measured by the extent to which students delay their coursework. However, students may delay coursework under personal and environmental contexts and not all delays should be considered procrastination. Thus, this study aims to identify different types of delayers and examine how they differ in academic engagement and performance. We utilized learning management system (LMS) data from three online undergraduate courses. Specifically, using data from the first three weeks of the course, we classified delayers into three subgroups – high-achieving, low-achieving, and sporadic delayers – based on the timing of their coursework access and submission, the consistency of these behaviors, and their short-term course performance. Our findings reveal that the subgroups significantly differ in course engagement and long-term performance. Low-achieving delayers exhibited the lowest levels of engagement and performance. While sporadic delayers and high-achieving delayers demonstrated comparable levels of engagement, the latter received higher course grades. These findings challenge commonly used LMS measures for procrastination, highlight the complexity of academic delays, and reveal nuanced patterns of student behavior. The results contribute to discussions on future interventions and research related to distinct forms of delays.

The enthusiasm for student-facing analytics as tools for supporting student self-regulation is overshadowed by uncertainties about their actual impact on student outcomes. This study aims to fill the gap in experimental evidence concerning student-facing analytics by implementing a randomized control trial. Specifically, we investigated the effects of data visualizations that display student level of content mastery in comparison to their peers, alongside recommendations for learning strategies. The preliminary results reveal that the intervention impacts student attribution and motivation in varying ways, based on their self-evaluation of their current course performance. Further analysis, including coding students' interpretation of the data visualization, will be conducted to uncover the diverse ways students might interpret the analytics.

This study addresses the gap in knowledge about differences in how instructors use analytics to inform teaching by examining the ways that thirteen college instructors engaged with a set of university-provided analytics. Using multiple walk-through interviews with the instructors and qualitative inductive coding, two profiles of instructor analytics use were identified that were distinct from each other in terms of the goals of analytics use, how instructors made sense of and took actions upon the analytics, and the ways that ethical concerns were conceived. Specifically, one group of instructors used analytics to help students get aligned to and engaged in the course, whereas the other group used analytics to align the course to meet students’ needs. Instructors in both profiles saw ethical questions as central to their learning analytics use, with instructors in one profile focusing on transparency and the other on student privacy and agency. These findings suggest the need to view analytics use as an integrated component of instructor teaching practices and envision complementary sets of technical and pedagogical support that can best facilitate the distinct activities aligned with each profile.

Despite growing implementation of teacher-facing analytics in higher education, relatively little is known about the detailed processes through which instructors make sense of analytics in their teaching practices beyond their initial encounters with tools. This study unpacked the sensemaking process of thirteen instructors with analytic experience, using interviews that included walkthroughs of their analytics use. Qualitative inductive analysis was used to identify themes related to (1) the questions they asked of the analytics, (2) the techniques they used to interpret them, and (3) the challenges they encountered. Findings indicated that instructors went beyond a general curiosity to develop three types of questions of the analytics (goal-oriented, problem-oriented, and instruction modification questions). Instructors also used specific techniques to read and explain data by (a) developing expectations about the answers the analytics would provide, and (b) making comparisons to reveal student diversity, identify effects of instructional revision and diagnose issues. The study found instructors faced an initial learning curve when seeking and making use of relevant information, but also continued to revisit these challenges when they were not able to develop a routine of analytics use. These findings both contribute to a conceptual understanding of instructor analytic sensemaking and have practical implications for its systematic support.

We systematically study the design of image recognition CAPTCHAs (IRCs) in this paper. We first review and examine all existing IRCs schemes and evaluate each scheme against the practical requirements in CAPTCHA applications, particularly in large-scale real-life applications such as Gmail and Hotmail. Then we present a security analysis of the representative schemes we have identified. For the schemes that remain unbroken, we present our novel attacks. For the schemes for which known attacks are available, we propose a theoretical explanation why those schemes have failed. Next, we provide a simple but novel framework for guiding the design of robust IRCs. Then we propose an innovative IRC called Cortcha that is scalable to meet the requirements of large-scale applications. It relies on recognizing objects by exploiting the surrounding context, a task that humans can perform well but computers cannot. An infinite number of types of objects can be used to generate challenges, which can effectively disable the learning process in machine learning attacks. Cortcha does not require the images in its image database to be labeled. Image collection and CAPTCHA generation can be fully automated. Our usability studies indicate that, compared with Google's text CAPTCHA, Cortcha allows a slightly higher human accuracy rate but on average takes more time to solve a challenge.

Image orientation detection is a useful, yet challenging research topic in intelligent image processing. Existing methods generally train a detector on ensemble data-set which is little scalability when new image samples with novel scenes come. This paper proposes a data-scalable algorithm for image orientation detection using bagging, a method aggregates several classifiers trained independently on non-intersecting sub data sets. By the proposed algorithm, when new classifiers trained on novel data sets are added, the prediction accuracy increases. In the paper, more representative feature set and more efficient learning algorithm are adopted to remedy the possible decrease of detection accuracy caused by the curtailment of the training data for single classifiers. Compared with previous work, the proposed algorithm has great competitiveness in terms of data-scalable ability, prediction accuracy, training and detection complexity.

This paper presents a robust real-time method for general detection of abandoned and removed objects from surveillance videos. The system introduces a unique combination of a new pixel-wise static region detector and a novel abandoned/removed object classifier based on color richness. In the static region detection phase, two backgrounds are constructed respectively to build foreground and stationary masks which are then used to update a static region confidence map. Static regions are thus extracted from the confidence map and further classified into abandoned or removed items by comparing color richness between the background and current frame. Our algorithm is easy to implement, robust to small repetitive motions, illumination change and can handle object occlusion. Experimental results on two public video databases which are shot in different scenarios demonstrate the robustness and practicability of the proposed method in real-time video surveillance.