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马来西亚开放大学（OUM）90％以上的学生为在职人员。这些成年学生离开学校至少已经5年,而且大多数人的数学基础不好。因此,仅是在线学习加少量面授辅导的教学模式对他们来说还不能完全解决学习困难。学校试用了“课前指导工作室”与“补充指导”的教学方法,探讨了这一方法对学员的在线参与程度及考试结果的影响,运用源于探究团体模式的34项量表对在线论坛的内容进行了分析,结果表明,学员参加工作室与否同他们的期末考试成绩有很大关系。参加指导老师工作室及补充指导的学员与那些参加指导老师常规指导的学员相比,前者的在线参与程度及期末考试的平均分都存在着很大的差异。两组学员的数学探究团体模式平均分表明,两组学员在教学存在和认知存在方面有差异,但在社会存在方面基本相同。 相似文献
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To ensure the success of the e-learning initiatives, OUM has developed its own e-learning management system, known as myLMS. Since its introduction, many modifications and improvements have been introduced to increase its effectiveness. It is now timely that OUM take stock of its students＇ attitudes towards e-learning. Thus, a survey was conducted on about 1,000 students at one of OUM＇s own learning centres, that is, the Kelantan Regional Centrel. The study indicated that generally the teacher cohort had a somewhat neutral attitude towards e-learning. The use of e-learning was more specifically aimed at achieving short term goals of obtaining good coursework and examination grades by capitalizing on the use of the Discassion Board and Courseware. A closer examination reveals that the females prefer the Discussion Board while the males prefer the Courseware. Learners in the Engineering and English programmes had more positive attitudes towards e-learning compared to learners in the Mathematics and Science programmes. Learners with CGPA 〉 3.0 who are categorized as high achievers are more positive towards e-learning as compared to the low achievers （ CGPA 〈 3.0）. Age difference, learners＇ income per month, learners＇ Internet and e-learning habits were also found to be predictors of attitude towards e-learning. 相似文献
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Thin-walled spread foundations are used in coastal projects where the soil strength is relatively low. Developing a predictive model of bearing capacity for this kind of foundation is of interest due to the fact that the famous bearing capacity equations are proposed for conventional footings. Many studies underlined the applicability of artificial neural networks (ANNs) in predicting the bearing capacity of foundations. However, the majority of these models are built using conventional ANNs, which suffer from slow rate of learning as well as getting trapped in local minima. Moreover, they are mainly developed for conventional footings. The prime objective of this study is to propose an improved ANN-based predictive model of bearing capacity for thin-walled shallow foundations. In this regard, a relatively large dataset comprising 145 recorded cases of related footing load tests was compiled from the literature. The dataset includes bearing capacity (Qu), friction angle, unit weight of sand, footing width, and thin-wall length to footing width ratio (Lw/B). Apart from Qu, other parameters were set as model inputs. To enhance the diversity of the data, four more related laboratory footing load tests were conducted on the Johor Bahru sand, and results were added to the dataset. Experimental findings suggest an almost 0.5 times increase in the bearing capacity in loose and dense sands when Lw/B is increased from 0.5 to 1.12. Overall, findings show the feasibility of the ANN-based predictive model improved with particle swarm optimization (PSO). The correlation coefficient was 0.98 for testing data, suggesting that the model serves as a reliable tool in predicting the bearing capacity. 相似文献
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