@conference{5404867cc8c840b0a0c4ba69aaf1a56b,
title = "Linking language to math success in an on-line course",
abstract = "This study takes a novel approach toward understanding success in a math course by examining the linguistic features and affect of students’ language production within a blended (with both on-line and traditional face to face instruction) undergraduate course (n=158) on discrete mathematics. Three linear effects models were compared: (a) a baseline linear model including non-linguistic fixed effects, (b) a model including only linguistic factors, (c) a model including both linguistic and non-linguistic effects. The best model (c) explained 16{\%} of the variance of final course scores, revealing significant effects for one non-linguistic feature (days on the system) and two linguistic features (Number of dependents per prepositional object nominal and Sentence linking connectives). One non-linguistic factor (Is a peer tutor) and two linguistic variables (Words related to self and Words related to tool use) demonstrated marginal significance. The findings indicate that language proficiency is strongly linked to math performance such that more complex syntactic structures and fewer explicit cohesion devices equate to higher course performance. The linguistic model also indicated that less self-centered students and students using words related to tool use were more successful. In addition, the results indicate that students that are more active in on-line discussion forums are more likely to be successful.",
keywords = "Math, NLP, On-line learning, Student success",
author = "Scott Crossley and Tiffany Barnes and Collin Lynch and McNamara, {Danielle S.}",
year = "2017",
month = "1",
day = "1",
language = "English (US)",
pages = "180--185",
note = "10th International Conference on Educational Data Mining, EDM 2017 ; Conference date: 25-06-2017 Through 28-06-2017",
}