This study seeks to examine university students’ attitudes and their perceptions on calculus education. Lecturers’ suggestions to remedy the current situation of calculus learning were also investigated. The instruments of this study consisted of three sets of questionnaires, aimed at collecting data regarding students’ attitudes and perceptions on calculus and lecturers’ suggestions. The respondents consisted of 278 full-time diploma students in a public university in Malaysia. The findings indicate that the students’ attitudes towards the Course of calculus according to gender were insignificant. However, there was a significant difference in the students’ attitudes towards the Field of calculus between female and male students. Students of Pre-Calculus and Calculus I were found to be feeling, thinking, and behaving similarly about calculus. Their attitudes towards the Field of calculus among the four engineering and science programmes were significant. After attempting 39.27% of the given questions, students’ perceptions on the difficulty of the questions remained the same, which was neither easier nor harder than they expected. Nevertheless, students’ perceptions changed positively (questions more difficult than expected) in 19.45% of the questions and negatively (questions easier than expected) in 41.28% of the questions. The implications from these findings provided inputs to improve calculus teaching and learning. Information regarding students’ attitudes toward calculus could help lecturers to design comprehensive calculus lessons that suit all kinds of students. Students also need to change their attitudes towards calculus, for example by having a closer inspection of the exact nature of the calculus questions before attempting them.

Fitting a time series model to the process data before applying a control chart to the residuals is essential to fulfill the basic assumptions of statistical process control (SPC). Autoregressive integrated moving average (ARIMA) model has been one of the well-established time series modeling approaches that is extensively used for this purpose and is widely recognized for its accuracy and efficiency. Nevertheless, the research community commented that its iterative stages are laborious and time-consuming. In addressing this gap, a novel time series modeling technique with its conceptual assumptions of attributes that was derived from the geometric Brownian motion (GBM) law was developed in this study. It was termed as the logarithmic return (LR) model. Then, the model was employed and tested on a real-world autocorrelated data, whereby the results were assessed and benchmarked with the ARIMA model. The findings for LR model reported a mean average percentage error that ranged between 1.5851% and 3.3793% (less than 10%), which were as accurate as the ARIMA model. The running time (in second of CPU time) taken by the LR model was at least 96.2% faster than the ARIMA model. Interestingly, the corresponding multivariate control chart constructed from the LR model also portrayed a similar general conclusion as that of its counterpart. The LR model was obviously parsimonious and easier to compute and took a shorter running time than the ARIMA model. Therefore, it possessed the potential as an alternative time series modeling methodology for the ARIMA model in the procedures of SPC.

No abstract

No abstract