School Crisis Response Team Helps Students Cope With Grief

U.S. News

Team members worked with students in groups or individually, helping them process their grief. Pinter said the most important thing is for students to be able to express themselves without further upsetting other students. He said this is difficult because everyone's response to a death is different, and for older students, the social pressure of peers' responses can influence their reactions.


Analysis of Optimization Techniques to Improve User Response Time of Web Applications and Their Implementation for MOODLE

arXiv.org Artificial Intelligence

Analysis of seven optimization techniques grouped under three categories (hardware, back-end, and front-end) is done to study the reduction in average user response time for Modular Object Oriented Dynamic Learning Environment (Moodle), a Learning Management System which is scripted in PHP5, runs on Apache web server and utilizes MySQL database software. Before the implementation of these techniques, performance analysis of Moodle is performed for varying number of concurrent users. The results obtained for each optimization technique are then reported in a tabular format. The maximum reduction in end user response time was achieved for hardware optimization which requires Moodle server and database to be installed on solid state disk.


This NBA coach had an inspiring response to a question about winning championships

Mashable

Gregg Popovich has five NBA Championship rings to his name, but the San Antonio Spurs head coach considers something else even more important than winning. Popovich, along with Harvard professor and civil-rights activist Dr. Cornel West, took questions about social issues from 250 high school students in San Antonio last month, per The Nation. One of those students asked if the Spurs would win a championship this year, a feat they accomplished in 2014. "Win the championship?" he said. "I don't know, but it's not a priority in my life.


Contextual Collaborative Filtering for Student Response Prediction in Mixed-Format Tests

AAAI Conferences

The purpose of this study is to design a machine learning approach to predict the student response in mixed-format tests. Particularly, a novel contextual collaborative filtering model is proposed to extract latent factors for students and test items, by exploiting the item information. Empirical results from a simulation study validate the effectiveness of the proposed method.


Data-Mining Textual Responses to Uncover Misconception Patterns

arXiv.org Machine Learning

An important, yet largely unstudied, problem in student data analysis is to detect misconceptions from students' responses to open-response questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality of instruction. In this paper, we propose a new natural language processing-based framework to detect the common misconceptions among students' textual responses to short-answer questions. We propose a probabilistic model for students' textual responses involving misconceptions and experimentally validate it on a real-world student-response dataset. Experimental results show that our proposed framework excels at classifying whether a response exhibits one or more misconceptions. More importantly, it can also automatically detect the common misconceptions exhibited across responses from multiple students to multiple questions; this property is especially important at large scale, since instructors will no longer need to manually specify all possible misconceptions that students might exhibit.