Exploratory Advising Impact Report: Spring 2016 to Spring 2019

Academic advising performs a pivotal contribution to student success by providing information about univeristy expectations and avenues towards graduation. Exploratory student advising has the additional task of supporting students in major selection. This analysis investigated the relationship between academic advising and student persistence for exploratory students to better understand the impact of current advising practices. METHODS: Exploratory academic advisors met with an average 53% of exporatory students each semester. Students with a record of meeting with an academic advisor were compared to similar exploratory students who did not. Students were compared using prediction-based propensity score matching. Students who met with an advisor were matched with students who did not based on their persistence predication and their propensity to participate. The groups were compared using difference-in-difference testing (DID). FINDINGS: Students were 99% similar following matching. Students who met with an academic advisor were significantly more likely to persist at USU than similar students who did not (DID = 0.099, p \u3c .001). The unstandardized effect size can be estimated through student impact. It is estimated that academic advising assisted in retaining 91 (CI: 74 to 107) exploratory students each year who were otherwise not expected to persist

University Academic Advising: Impact Analysis

abstract: Academic advising performs a pivotal contribution to student success by providing information about university expectations and avenues towards graduation. The impact of academic advising is routinely assessed to explore its influence on student persistence. This report explores the impact of academic advising between 2016 to 2019 on student persistence to the next term. METHODS: Academic advising met with nearly 40% of students at USU each semester. Students who had a record of meeting with an academic advisor were compared to similar students who did not. Students were compared using prediction-based propensity score matching. Students who met with an advisor were matched with students who did not based on their persistence predication and their propensity to participate. The groups were compared using difference-in-difference testing (DID). FINDINGS: Students were 99% similar following matching. Students who met with an academic advisor were significantly more likely to persist at USU than similar students who did not (DID = 0.052, p \u3c .001). The unstandardized effect size can be estimated through student impact. It is estimated that academic advising assisted in retaining 667 (CI: 618 – 715) students each year who were otherwise not expected to persist

The Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes

The measurement of student performance during their progress through university study provides academic leadership with critical information on each student’s likelihood of success. Academics have traditionally used their interactions with individual students through class activities and interim assessments to identify those “at risk” of failure/withdrawal. However, modern university environments, offering easy on-line availability of course material, may see reduced lecture/tutorial attendance, making such identification more challenging. Modern data mining and machine learning techniques provide increasingly accurate predictions of student examination assessment marks, although these approaches have focussed upon large student populations and wide ranges of data attributes per student. However, many university modules comprise relatively small student cohorts, with institutional protocols limiting the student attributes available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. We describe an experiment conducted on a final-year university module student cohort of 23, where individual student data are limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. We found potential for predicting individual student interim and final assessment marks in small student cohorts with very limited attributes and that these predictions could be useful to support module leaders in identifying students potentially “at risk.”.Peer reviewe

Dropout Distillation for Efficiently Estimating Model Confidence

We propose an efficient way to output better calibrated uncertainty scores from neural networks. The Distilled Dropout Network (DDN) makes standard (non-Bayesian) neural networks more introspective by adding a new training loss which prevents them from being overconfident. Our method is more efficient than Bayesian neural networks or model ensembles which, despite providing more reliable uncertainty scores, are more cumbersome to train and slower to test. We evaluate DDN on the the task of image classification on the CIFAR-10 dataset and show that our calibration results are competitive even when compared to 100 Monte Carlo samples from a dropout network while they also increase the classification accuracy. We also propose better calibration within the state of the art Faster R-CNN object detection framework and show, using the COCO dataset, that DDN helps train better calibrated object detectors

The efficacy of using data mining techniques in predicting academic performance of architecture students.

In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. The input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. The developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities

How to Ask for a Favor: A Case Study on the Success of Altruistic Requests

Requests are at the core of many social media systems such as question & answer sites and online philanthropy communities. While the success of such requests is critical to the success of the community, the factors that lead community members to satisfy a request are largely unknown. Success of a request depends on factors like who is asking, how they are asking, when are they asking, and most critically what is being requested, ranging from small favors to substantial monetary donations. We present a case study of altruistic requests in an online community where all requests ask for the very same contribution and do not offer anything tangible in return, allowing us to disentangle what is requested from textual and social factors. Drawing from social psychology literature, we extract high-level social features from text that operationalize social relations between recipient and donor and demonstrate that these extracted relations are predictive of success. More specifically, we find that clearly communicating need through the narrative is essential and that that linguistic indications of gratitude, evidentiality, and generalized reciprocity, as well as high status of the asker further increase the likelihood of success. Building on this understanding, we develop a model that can predict the success of unseen requests, significantly improving over several baselines. We link these findings to research in psychology on helping behavior, providing a basis for further analysis of success in social media systems.Comment: To appear at ICWSM 2014. 10pp, 3 fig. Data and other info available at http://www.mpi-sws.org/~cristian/How_to_Ask_for_a_Favor.htm