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To promote interest in computer science (CS) among minority high school students from low-income families, we offered a one-month Summer Academy followed by a semester-long online group project. A group of 31 minority students from Alabama’s Black Belt areas participated in the program. The purpose of this study was two-fold. First, we examined factors affecting students’ motivation for learning CS. Second, we explored the program’s impact on student's future interest in learning CS. We conducted an individual Zoom interview with all participants, and they were also invited to complete a pre-and post-survey. We also observed classroom teaching during the Summer Academy and facilitated online Zoom meetings in the fall semester on a monthly basis. The findings revealed that early exposure to CS and family encouragement motivated students to explore the field of CS. Students found MIT App Inventor learning, meeting with guest speakers, and group projects most valuable, which positively affected students’ motivation to continue to learn CS.
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Measuring university students’ interest in biology: evaluation of an instrument targeting Hidi and Renninger’s individual interest
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Point-of-interest (POI) recommendation is essential to a variety of services for both users and business. An extensive number of models have been developed to improve the recommendation performance by exploiting various characteristics and relations among POIs (e.g., spatio-temporal, social, etc.). However, very few studies closely look into the underlying mechanism accounting for why users prefer certain POIs to others. In this work, we initiate the first attempt to learn the distribution of user latent preference by proposing an Adversarial POI Recommendation (APOIR) model, consisting of two major components: (1) the recommender (R) which suggests POIs based on the learned distribution by maximizing the probabilities that these POIs are predicted as unvisited and potentially interested; and (2) the discriminator (D) which distinguishes the recommended POIs from the true check-ins and provides gradients as the guidance to improve R in a rewarding framework. Two components are co-trained by playing a minimax game towards improving itself while pushing the other to the boundary. By further integrating geographical and social relations among POIs into the reward function as well as optimizing R in a reinforcement learning manner, APOIR obtains significant performance improvement in four standard metrics compared to the state of the art methods.more » « less
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Kasneci, Enkelejda (Ed.)Many eye-tracking data analyses rely on the Area-of-Interest (AOI) methodology, which utilizes AOIs to analyze metrics such as fixations. However, AOI-based methods have some inherent limitations including variability and subjectivity in shape, size, and location of AOIs. In this article, we propose an alternative approach to the traditional AOI dwell time analysis: Weighted Sum Durations (WSD). This approach decreases the subjectivity of AOI definitions by using Points-of-Interest (POI) while maintaining interpretability. In WSD, the durations of fixations toward each POI is weighted by the distance from the POI and summed together to generate a metric comparable to AOI dwell time. To validate WSD, we reanalyzed data from a previously published eye-tracking study (n = 90). The re-analysis replicated the original findings that people gaze less towards faces and more toward points of contact when viewing violent social interactions.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1186/s40594-020-00217-4
