Asking for help (help-seeking) is a recognized and effective problem-solving strategy. This study investigates students' interaction with on-demand hints (automated hints requested by students) and assesses their impact on learning progress. We conducted an A/B experiment in a third-year computer science database course, offering hints for selected SQL problems with different hint designs. We collected data on students' code submissions, grades, and hint requests, and we administered a survey to gather feedback and gauge student perception of the hints. Many students accessed hints immediately without attempting the problem first, often requesting multiple hints in quick succession. While students perceived the hints to be valuable, we did not detect an impact on student problem-solving. These insights could inform future studies on the possible impact of students' attitudes toward hints, and how different types of hints might impact uptake and perception of hints.

This study explores links between attributes of computing students, such as prior programming experience (PE) and gender, with expectations for success and the perception of challenges. Using Expectancy-Value Theory (EVT), we investigate their major intentions and the impact of these factors post-CS1. Data was gathered using surveys at the beginning and end of an introductory programming course, focusing on demographics, expectations of success, and perceptions of challenges. Application status for the computing major was also recorded. Our results revealed that men and students with PE generally perceived greater potential for success and reported facing fewer challenges. In contrast, women and students without PE more often indicated concerns about intellectual ability and perceived challenges less positively. Notably, while gender appears in the preceding results, an intersectional analysis indicates that PE is the central factor. PE is also linked to persistence in the field of computing. Our results further highlight the importance of providing students with opportunities to develop experience, as it can help shape their expectations, perceived challenges, and retention in computing.

Previous work has reported on the advantageous effects of prior experience in CS1, but it remains unclear whether these effects fade over a sequence of introductory programming courses. Furthermore, while student perceptions suggest that prior experience remains important, studies have reported that a student's expectation of their performance is a more accurate predictor of outcome. We aim to confirm if prior experience (formal or informal) provides short-term and long-term advantages in computing courses or if the advantage fades. Furthermore, we explore whether the expectation of performance is a more accurate predictor of student success than informal and formal prior experience. To explore these questions, we deployed surveys in a CS1 course to gauge students' level of prior experience in programming, prediction of final exam grades, and self-efficacy to succeed in university. Grades from CS1 and CS2 were also collected. We observed a persistent (1-letter grade) gap between the performance of students with no prior experience and those with any experience, but we did not observe a noteworthy gap when comparing student performance based on formal or informal experience. We also observed differences in self-efficacy and retention rates between different levels of prior experience. Lastly, we confirm that success in CS1 can be better reflected and predicted by some controllable factors, such as students' perceptions of ability.

Many students are first exposed to computing in a programming course such as CS1. This course affects their understanding of computing and may affect their intention to major in the program. We investigate the intention to major in computing in relation to demographic factors and factors related to academic success. We deployed surveys at the beginning and end of a CS1 course to gauge students' level of prior experience in programming, elicit demographic factors such as gender and parental education level, and identify their intention to major in computing. Grades from CS1 and CS2 were also collected. Our results suggest that most students do not change their intention to major in computing after taking CS1. Students who were more likely to intend to major in programming at the beginning of the course were those with prior experience, those who identified as men, or students who had a parent with a bachelor's or post-grad degree. We also find that students' grades correlate to their change in program intention. This reinforces the need to change perceptions about computing early, prior to CS1.

Rock detachment events along roadways pose public safety concerns but can be predicted and safely handled using geological measurements of discontinuities. With modern sensing technology, these measurements can be taken on 3-D point clouds and 2-D optical images that provide a high level of structural accuracy and visual detail. Doing so allows engineers to obtain the needed data with relative ease while eliminating the biases and hazards inherent in taking manual measurements. This letter presents an approach for fusing the 2-D and 3-D data in natural and unstructured scenes. This includes a novel method for visualizing imagery obtained with very different sensors to maximize their visual similarity making registration a more tangible task. To show the effectiveness of our registration methodology, we evaluate measurements taken manually and digitally on rock facet and cut discontinuity orientations in Rolla, MO. Our method is able to align the 2-D and 3-D data with an accuracy of under 2 cm. The median difference between measurements manually obtained by a geological engineer and those obtained with our proposed software is 3.65.

LIDAR range scans can be used to quickly create accurate 3D models for virtual reality and as a basis to visualize sets of photographs, videos, and virtual objects in a cohesive environment. The number of existing virtual reality programs that use LIDAR data as input has motivated our group to develop methods for fusing images with 3D scans and for augmenting the scans with both dynamic objects present in videos and virtual models. Bringing together as many data sources as possible increases users' abilities to present related information in one, intuitive venue. We demonstrate how to register a variety of 2D imagery with a range scan to construct photo-realistic models and to extract walking people captured in videos and model them in a 3D space. We also present a method for determining the sun position from a set of stitched photographs in order to apply correct lighting to virtual objects placed amongst real world data. Naturally lit objects can be inserted into original photographs using our 2D-3D registration information. These methods are all combined to display and study photos, videos, and virtual objects in a complete 3D environment.

Matching and aligning architectural imagery is an important step for many applications but can be a difficult task due to repetitive elements often present in buildings. Many keypoint descriptor and matching methods will fail to produce distinctive descriptors for each region of man-made structures, which causes ambiguity when attempting to match areas between images. In this paper, we outline a technique for reducing the search space for matching by taking a two-step approach, aligning pairs one dimension at a time and by abstracting images that originally contain many repetitive elements into a set of distinct, representative patches. We also present a simple, but very effective method for computing the intra-image saliency for a single image that allows us to directly identify unique areas in an image without machine learning. We use this information to find distinctive keypoint matches across image pairs. We show that our pipeline is able to overcome many of the pitfalls encountered when using traditional keypoint and regional matching techniques on commonly encountered images of urban scenes.

In the event of natural or man-made disasters, many videos may be collected by civilians and surveillance cameras that can be extremely useful for first responders trying to ascertain the extent of the damage. However, watching and analyzing numerous videos on separate screens can be a cumbersome task. Registering a set of 2D videos with a 3D model can provide an intuitive venue for viewing multiple videos simultaneously. In such a setup, it is likely that the user will want to work with the dynamic 3D environment from a remote location, requiring that videos be transferred over a network to be registered with a 3D model. In this paper, we propose combining the fields of computer vision, cloud computing, and high-speed networking to create a system that takes in HD videos, streams the data to a server where a dynamic 3D model is constructed, and provides a virtual scene navigation program for viewing the videos in a 3D scene from a mobile device. We test transferring the data of interest over different types of networks and processing the videos on various server configurations to determine the capabilities of such a system and the necessary requirements for it to provide a high-quality user experience.

We propose a contextual framework for 2D image matching and registration using an ensemble feature. Our system is beneficial for registering image pairs that have captured the same scene but have large visual discrepancies between them. It is common to encounter challenging visual variations in image sets with artistic rendering differences or in those collected over a period of time during which the lighting conditions and scene content may have changed. Differences between images may also be caused using a variety of cameras with different sensors, focal lengths, and exposure values. Local feature matching techniques cannot always handle these difficulties, so we have developed an approach that builds on traditional methods to consider linear and histogram of gradient information over a larger, more stable region. We also present a technique for using linear features to estimate corner keypoints, or pseudo corners, that can be used for matching. Our pipeline follows this unique matching stage with homography refinement methods using edge and gradient information. Our goal is to increase the size of accurate keypoint match sets and align photographs containing a combination of man-made and natural imagery. We show that incorporating contextual information can provide complimentary information for scale invariant feature transform and boost local keypoint matching performance, as well as be used to describe corner feature points.