Service-Learning Faculty Assessment: Report of Results, 2018

In Spring 2018, the VCU Service-Learning Office sponsored an evaluation process that gathered feedback from faculty members who teach service-learning classes. The goal was to deepen understanding of the barriers faced by VCU’s service-learning faculty instructors and to solicit feedback about key strategies for overcoming these barriers. An independent research consultant conducted the evaluation in two phases: an online anonymous survey and a 30-minute phone interview. Eighty service-learning instructors completed the online survey, and a stratified sample of 18 instructors completed the telephone interviews. Findings indicated that both the online survey respondents and phone interview participants experienced similar supports and barriers to teaching their service-learning classes. Key findings and recommendations are outlined in the full report

A Theoretical Analysis of Two-Stage Recommendation for Cold-Start Collaborative Filtering

In this paper, we present a theoretical framework for tackling the cold-start collaborative filtering problem, where unknown targets (items or users) keep coming to the system, and there is a limited number of resources (users or items) that can be allocated and related to them. The solution requires a trade-off between exploitation and exploration as with the limited recommendation opportunities, we need to, on one hand, allocate the most relevant resources right away, but, on the other hand, it is also necessary to allocate resources that are useful for learning the target's properties in order to recommend more relevant ones in the future. In this paper, we study a simple two-stage recommendation combining a sequential and a batch solution together. We first model the problem with the partially observable Markov decision process (POMDP) and provide an exact solution. Then, through an in-depth analysis over the POMDP value iteration solution, we identify that an exact solution can be abstracted as selecting resources that are not only highly relevant to the target according to the initial-stage information, but also highly correlated, either positively or negatively, with other potential resources for the next stage. With this finding, we propose an approximate solution to ease the intractability of the exact solution. Our initial results on synthetic data and the Movie Lens 100K dataset confirm the performance gains of our theoretical development and analysis

Service-Learning Community Partner Impact Assessment Report

In the summer of 2017, VCU’s Office of Service-Learning conducted an evaluation of the impact of service-learning on community partner organizations. This assessment aimed to collect actionable feedback from partners and to inform improvements to service-learning courses at VCU that successfully address partners’ concerns. An external researcher conducted phone interviews with a representative sample of 22 community partners. Partners were asked how a specific service-learning course impacted their organization in three areas: organizational capacity, economically, and socially. Partners were also asked about faculty interactions and the likelihood of recommending the service-learning course to other organizations like their own. This report presents the findings of this community partner impact assessment, outlines an assessment model for a three-year continuous improvement cycle, and offers key recommendations and next steps that emerged from this assessment

Controlling Fairness and Bias in Dynamic Learning-to-Rank

Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users, as done by virtually all learning-to-rank algorithms, can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist). In particular, we propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility, dynamically adapting both as more data becomes available. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.Comment: First two authors contributed equally. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval 202

Effectiveness of the Travelers Summer Research Fellowship Program in Preparing Premedical Students for a Career in Medicine

This study measured the effectiveness of the Travelers Summer Research Fellowship (T-SRF) Program for Premedical Students. No in-depth study has been conducted on the impact of its activities. A program-oriented qualitative summative evaluation approach and a logic model design were used to analyze survey responses for participants from four program years randomly chosen from 2000 to 2015, medical school enrollment records for participants from 1969 to 2015, physician practice locations for participants from 1969 to 2009, and interviews with a purposeful random sample of 10 physicians who were program participants from 2004 to 2008. Narrative inquiry consisted of audio recording, transcription, and analysis of individual accounts and participant experiences. The study revealed that participants valued interactions with physicians from backgrounds underrepresented in medicine. Talks on careers in medicine increased participants’ knowledge, and research projects helped develop skills. Cardiovascular physiology lectures introduced participants to the medical school learning experience and increased their confidence to apply to medical school successfully. T-SRF enhanced participants’ medical school applications and sharpened interviewing skills; 83% matriculated into medical school, 90% graduated, and 45% practice in HPSAs, MUAs/Ps, and rural areas. Recommendations included improving program orientation, making the cardiovascular physiology lectures and examinations more valuable experiences, re-evaluating the study skills curriculum, providing more clinical experiences, increasing the weekly stipend, improving maintenance of the alumni database, formally partnering admissions with the T-SRF program, helping alumni return to Weill Cornell as residents or fellows, and considering other ways to measure social concern. Further studies of T-SRF should be undertaken

Guide for third and fourth year students

Advice compiled by Boston University School of Medicine students for incoming first year students and third or fourth year students preparing for clinical rotations