Améliorer la douleur et le sommeil chez les aînés souffrant de douleur chronique à l’aide de la stimulation transcrânienne par courant direct (tDCS)

Résumé : Introduction: La douleur chronique et les problèmes de sommeil ont un impact significatif sur la qualité de vie. La prévalence de ces deux conditions augmente substantiellement avec l’âge. L’objectif de cette étude était d’évaluer la faisabilité d’une étude randomisée, contrôlée par placebo et de recueillir des données sur l’efficacité de la stimulation transcrânienne par courant direct (tDCS) pour réduire la douleur et améliorer le sommeil chez des aînés souffrant de douleur chronique d’origines musculo-squelettique. Méthodes: Quatorze patients souffrant de douleur chronique et de problèmes de sommeil ont reçu cinq séances quotidiennes consécutives de tDCS anodique appliquée au cortex moteur primaire (2 mA, 20 minutes) ou cinq séances de tDCS simulée de manière randomisée. L’intensité de la douleur était mesurée avec une échelle visuelle analogue et les paramètres de sommeil avec l’actigraphie. Pendant toute la durée de l’étude, des journaux de bord de douleur et de sommeil étaient aussi utilisés afin de mesurer l'effet de la tDCS sur la douleur et le sommeil du quotidien des participants. Résultats: Les résultats indiquent que la tDCS réelle engendre une analgésie de 59 %, alors que la tDCS simulée ne réduit pas la douleur (p < 0,05). Par contre, aucun changement n’a été observé au niveau des paramètres de sommeil (tous les p ≥ 0,18). Conclusion: Il appert que cinq séances de tDCS anodique appliquée au niveau du cortex moteur primaire seraient efficaces pour réduire la douleur des aînés souffrant de douleur chronique, mais pas pour améliorer leur sommeil. De futures études seront nécessaires afin de déterminer si d’autres paramètres de stimulation pourraient avoir un impact sur le sommeil et si ces résultats peuvent être reproduits en utilisant un plus grand nombre de patients.

The Effect of using online homework exercises on student achievement in a Quantitative methods course

Abstract: Quantitative Methods (QM) is a compulsory course in the Social Science program in CEGEP. Many QM instructors assign a number of homework exercises to give students the opportunity to practice the statistical methods, which enhances their learning. However, traditional written exercises have two significant disadvantages. The first is that the feedback process is often very slow. The second disadvantage is that written exercises can generate a large amount of correcting for the instructor. WeBWorK is an open-source system that allows instructors to write exercises which students answer online. Although originally designed to write exercises for math and science students, WeBWorK programming allows for the creation of a variety of questions which can be used in the Quantitative Methods course. Because many statistical exercises generate objective and quantitative answers, the system is able to instantly assess students’ responses and tell them whether they are right or wrong. This immediate feedback has been shown to be theoretically conducive to positive learning outcomes. In addition, the system can be set up to allow students to re-try the problem if they got it wrong. This has benefits both in terms of student motivation and reinforcing learning. Through the use of a quasi-experiment, this research project measured and analysed the effects of using WeBWorK exercises in the Quantitative Methods course at Vanier College. Three specific research questions were addressed. First, we looked at whether students who did the WeBWorK exercises got better grades than students who did written exercises. Second, we looked at whether students who completed more of the WeBWorK exercises got better grades than students who completed fewer of the WeBWorK exercises. Finally, we used a self-report survey to find out what students’ perceptions and opinions were of the WeBWorK and the written exercises. For the first research question, a crossover design was used in order to compare whether the group that did WeBWorK problems during one unit would score significantly higher on that unit test than the other group that did the written problems. We found no significant difference in grades between students who did the WeBWorK exercises and students who did the written exercises. The second research question looked at whether students who completed more of the WeBWorK exercises would get significantly higher grades than students who completed fewer of the WeBWorK exercises. The straight-line relationship between number of WeBWorK exercises completed and grades was positive in both groups. However, the correlation coefficients for these two variables showed no real pattern. Our third research question was investigated by using a survey to elicit students’ perceptions and opinions regarding the WeBWorK and written exercises. Students reported no difference in the amount of effort put into completing each type of exercise. Students were also asked to rate each type of exercise along six dimensions and a composite score was calculated. Overall, students gave a significantly higher score to the written exercises, and reported that they found the written exercises were better for understanding the basic statistical concepts and for learning the basic statistical methods. However, when presented with the choice of having only written or only WeBWorK exercises, slightly more students preferred or strongly preferred having only WeBWorK exercises. The results of this research suggest that the advantages of using WeBWorK to teach Quantitative Methods are variable. The WeBWorK system offers immediate feedback, which often seems to motivate students to try again if they do not have the correct answer. However, this does not necessarily translate into better performance on the written tests and on the final exam. What has been learned is that the WeBWorK system can be used by interested instructors to enhance student learning in the Quantitative Methods course. Further research may examine more specifically how this system can be used more effectively.