Predicting students' graduation outcomes through support vector machines - Pang, Yulei; Judd, Nicolas; O'Brien, Joseph; Ben-Avie, Michael (2017 IEEE Frontiers in Education Conference (FIE), 2017)
Low graduation rate is a significant and growing problem in U.S. higher education systems. Although previous studies have demonstrated the usefulness of building statistical models for predicting students' graduation outcomes, advanced machine learning models promise to improve the effectiveness of these models, and hone in on the “difference that makes a difference” not only on the group level, but also on the level of the individual student. In this paper we propose an ensemble support vector machines based model for predicting students' graduation. Up to about 100 features, including a set of psychological-educational factors, were employed to construct the predicting model. We evaluated the proposed model using data taken from a state university's longitudinal, cohort data sets from the incoming classes of students from 2011-2012 (n=350). The experimental results demonstrated the effectiveness of the model, with considerable accuracy, precision, and recall. This paper presents the results of analysis that were conducted in order to gauge the predictive capability of a machine learning algorithm to predict on-time graduation that took into consideration students' learning and development.
Keywords: Artificial intelligence; Education computing; Forecasting; Graduation outcome; Graduation rates; Group level; Higher education; Higher education system; Kernel; Learning algorithms; Learning systems; Machine learning; Machine learning algorithms; Machine learning models; Measurement; Predicting models; Predictive capabilities; Predictive models; Seminars; Students; Support vector machine; Support vector machines; advanced machine learning models; dept.mat; ensemble support vector machines; further education; graduation outcome; higher education; higher education systems; learning (artificial intelligence); low graduation rate; machine learning; on-time graduation; psychological-educational factors; statistical analysis; statistical models; support vector machine; support vector machines

Improving students' academic learning by helping them access text - Ben-Avie, Michael; Randall, Régine; Dunne, Diane Weaver; Kelly, Chris (Recent Advances in Assistive Technologies to Support Children with Developmental Disorders, 2015)
Conventional methods of addressing the needs of students with print disabilities include text-to-speech services. One major drawback of text-to-speech technologies is that computerized speech simply articulates the same words in a text whereas human voice can convey emotions such as excitement, sadness, fear, or joy. Audiobooks have human narration, but are designed for entertainment and not for teaching word identification, fluency, vocabulary, and comprehension to students. This chapter focuses on the 3-year pilot of CRISKids; all CRIS recordings feature human narration. The pilot demonstrated that students who feel competent in their reading and class work tend to be more engaged in classroom routines, spend more time on task and demonstrate greater comprehension of written materials. When more demonstrate these behaviors and skills, teachers are better able to provide meaningful instruction, since less time is spent on issues of classroom management and redirection. Thus, CRISKids impacts not only the students with print disabilities, but all of the students in the classroom.
Keywords: dept.cur

School Leadership for Learning: Learning Theory to Improve Professional Practice - Gregory, Jess L.; Ben-Avie, Michael; Billups, Felice; Grassetti, Mary; Hesser, Tiffany; Lawlor, Jennifer Lynne; Martone, Drey; Maykel, Cheryl; Minkos, Marlena L.; Paslov, Linda; Trombly, Christopher; Tucker, Lauren; et al. (Jess L. Gregory, 2021)
Keywords: Educational leadership; School administrators; Handbooks, manuals, etc; In-service training; School administrators; In-service training; Teachers; dept.edl; dept.sed; faculty_collection