Modeling Individual Differences among Writers Using ReaderBench

The current study builds upon a previous study, which examined the degree to which the lexical properties of students’ essays could predict their vocabulary scores. We expand on this previous research by incorporating new natural language processing indices related to both the surface- and discourse-levels of students’ essays. Additionally, we investigate the degree to which these NLP indices can be used to account for variance in students’ reading comprehension skills. We calculated linguistic essay features using our framework, ReaderBench, which is an automated text analysis tools that calculates indices related to linguistic and rhetorical features of text. University students (n = 108) produced timed (25 minutes), argumentative essays, which were then analyzed by ReaderBench. Additionally, they completed the Gates-MacGinitie Vocabulary and Reading comprehension tests. The results of this study indicated that two indices were able to account for 32.4% of the variance in vocabulary scores and 31.6% of the variance in reading comprehension scores. Follow-up analyses revealed that these models further improved when only considering essays that contained multiple paragraph (R2 values = .61 and .49, respectively). Overall, the results of the current study suggest that natural language processing techniques can help to inform models of individual differences among student writers.

Chronotype, sleep quality and sleep duration in adult distance education: Not related to study progress

Research in traditional education shows chronotype, sleep duration and sleep quality to be related to learning performance. Research in adult students participating in distance education (DE) is scarce. This study aims to provide knowledge on these relationships in this educational setting. In an observational longitudinal study, chronotype, sleep duration (i.e., for work and free days separately) and sleep quality of 894 students were analyzed in a multiple regression analyses. Students provided information on sleep-relatedmeasures and important covariates at the start of their study and study progress was evaluated after 14 months (i.e., the number of successfully completed modules). In linewith previous research, chronotype did not predict study progress. Further, sleep duration did not predict study progress, neither as a linear nor as a polynomial term. Third, sleep quality did not predict study progress. Concluding, these results are in linewith previous research that DE provides a solution to the asynchrony problem. Findings regarding sleep duration and sleep quality are new and unexpected, asking for attention and further research. Despite the study's observational nature, findings suggest that students participating in DE may benefit from this type of education as the asynchrony problem appears not to apply here, as students can choose their own study schedule.

Sedentary behavior and not physical activity predicts study progress in distance education`

For the official publication, see: http://dx.doi.org/10.1016/j.lindif.2016.06.021