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.
| Original language | English (US) |
|---|---|
| Pages | 180-185 |
| Number of pages | 6 |
| State | Published - Jan 1 2017 |
| Externally published | Yes |
| Event | 10th International Conference on Educational Data Mining, EDM 2017 - Wuhan, China Duration: Jun 25 2017 → Jun 28 2017 |
Conference
| Conference | 10th International Conference on Educational Data Mining, EDM 2017 |
|---|---|
| Country | China |
| City | Wuhan |
| Period | 6/25/17 → 6/28/17 |
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Keywords
- Math
- NLP
- On-line learning
- Student success
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
Cite this
Linking language to math success in an on-line course. / Crossley, Scott; Barnes, Tiffany; Lynch, Collin; McNamara, Danielle S.
2017. 180-185 Paper presented at 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, China.Research output: Contribution to conference › Paper
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TY - CONF
T1 - Linking language to math success in an on-line course
AU - Crossley, Scott
AU - Barnes, Tiffany
AU - Lynch, Collin
AU - McNamara, Danielle S.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
KW - Math
KW - NLP
KW - On-line learning
KW - Student success
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