Phase One Foundation

Colleges around the country have adopted technology tools to simplify planning and registration processes and provide interventions to support students’ timely completion. However, there is little evidence of these tools’ efficacy in improving student progression and completion (c.f., Rossman et al., 2021; Velasco et al., 2020). The minimal impact of advising and planning technologies on student success is amplified among institutions supporting the students who make up the new majority of postsecondary learners—those who are Black, Latine, Indigenous, and/or low-income (BLI/LI). One possible reason for this is that such tools were not designed with the needs of this population in mind. Using process map and focus group data from students at two broad-access universities, coupled with interview data from institutional stakeholders, this study interrogates the ways that BLI/LI students engage in academic planning. We find that this process is more cognitively complex and nuanced than is typically acknowledged; program planning tools do not include the information BLI/LI students require; and low-income college students are particularly disadvantaged by these tools. As such, we find support for the hypothesis that current tools’ efficacy is muted because they do not center the needs of BLI/LI students in their design.

Using RAW-images in computer vision problems is surprisingly underexplored considering that converting from RAW to RGB does not introduce any new capture information. In this paper, we show that a sufficiently advanced classifier can yield equivalent results on RAW input compared to RGB and present a new public dataset consisting of RAW images and the corresponding converted RGB images. Classifying images directly from RAW is attractive, as it allows for skipping the conversion to RGB, lowering computation time significantly. Two CNN classifiers are used to classify the images in both formats, confirming that classification performance can indeed be preserved. We furthermore show that the total computation time from RAW image data to classification results for RAW images can be up to 8.46 times faster than RGB. These results contribute to the evidence found in related works, that using RAW images as direct input to computer vision algorithms looks very promising.