The effects of problem-based learning in students reading comprehension for mastering the content and vocabulary acquisition

English reading comprehension is the most crucial language abilities in colleges where English is learned as a second language. A vigorous and powerful reading ability is certainly crucial in primary studies for students to accomplish in advanced education. However, the method in which they are instructed through lecture and paper pencil’s method were less relevant and effective in 21st Century. Students become passive, demotivated, bored, and feeling shy by the conventional teaching and learning. The purpose of this study is to examine the effectiveness of problem-based learning (PBL) on student’s English reading comprehension for mastering the contents and vocabulary acquisition. The study used a quantitative data where using an experimental pre-posttest design and the independent t-test and one-way ANOVA has been performed to test the hypothesis of the studies. The result indicated that there were statistically significant differences between the experimental and control groups for student’s reading comprehension for mastering the content and vocabulary acquisition using PBL. The results showed that the students who received PBL learning approached achieved higher performance outcome than student who is not received the PBL approach

Prediction of grinding machanability when grinds Haynes 242 using water based coolant

Grinding is one of the important finishing machining operations. It is applied at the last stage of manufacturing process so that the high dimensional accuracy and desirable surface finish product can be achieved. However, there is a lot of problem occur in order to achieve the high dimensional accuracy and desirable surface finish. For examples, surface burning due to excessive heat produce and undesirable surface finish outcomes. This report deals with the prediction of grinding machanability when grind Haynes 242 using water based coolant. The aims of this report are to find the optimum parameter of the grinding process which is depth of cut (�m) where the type of surface roughness produces will be investigate and to develop prediction model of surface roughness by using an artificial neural network analysis. This report describes the experimental setups and procedures in finding the optimum value of the depth of cut and thus leading with the development of the prediction model. The material used is Haynes 242 and the depth of cut is independent variables. Calculations of the data were done which had obtained by using Perthometer and analysis was made by using ANN. The grindability results have shown optimum parameter which is gives the lowest surface roughness value. The results from the experiment show that the Al2O3 wheel gives a good surface roughness compare to the SiC at the depth of cut 5μm. By the end of the projects, prediction model had been developed by using ANN and was compared in the graph. The ANN model obtained from predicted value is accurate and effective in predicting the correlation and R-squared which is give 1% of minimum error and 2% of maximum error when using the Al2O3 grinding wheel