Learning academic words through writing sentences and compositions: Any signs of an increase in cognitive load?

The involvement load hypothesis (ILH), which predicts the lexical learning potential of tasks, assumes that writing sentences (SW) and compositions (CW) using novel target words (TWs) lead to similar lexical gains. However, research on the issue is scarce and contradictory. One possibility is that the higher cognitive load of CW hinders learning relative to SW. To verify the learning potential of SW and CW, we selected 20 English academic TWs and conducted a pretest–posttest quasi-experiment with Polish advanced learners of English. First, all participants wrote a control essay (without TWs), then SW participants wrote sentences and CW participants wrote two essays, each with 10 TWs. Generalized linear mixed models revealed higher gains in breadth and depth of knowledge for SW than for CW, which contradicts the predictions of the ILH. Furthermore, to detect signs of cognitive load, we derived three task-based performance measurements from the compositions: holistic scores, number of errors, and words per minute. The measurements found that the control essay and essays with TWs were of similar quality (holistic scores), but that the control essay was written faster and with fewer errors than the other two. Concluding, using TWs in essays probably increased learners’ cognitive load, slowing down their writing, generating more errors, and ultimately, decreasing learning of the TWs.

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l2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time

Statistical Methods in Medical Research ◽

10.1177/09622802211017583 ◽

2021 ◽

pp. 096228022110175

Author(s):

Jan P Burgard ◽

Joscha Krause ◽

Ralf Münnich ◽

Domingo Morales

Keyword(s):

Parameter Estimation ◽

Medical Treatment ◽

Mixed Models ◽

Generalized Linear Mixed Models ◽

Linear Mixed Models ◽

Obesity Prevalence ◽

Model Parameter ◽

Model Parameter Estimation ◽

Public Health Reporting ◽

Over Time

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

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