Engineering students face challenges of on-time successful degree completion. Universities incorporate academic advising as a solution to these challenges. Decision support systems enhance the effectivity of academic advising. Combined with machine learning, it can predict future student performance providing useful information. Compared to common ‘black box’ models, linguistic rules provide better interpretation and insight discovery. However, existing models often use positive predictors of academic excellence, with limited consideration on factors of negative effect. This work, therefore, generates linguistic rules for academic advising based on three predictors using rough set theory (RST) and then compared with artificial neural network (ANN) for benchmarking. Forty-eight samples of mechanical engineering students taking up machine design were considered. RST attained accuracy of 72.92% while ANN attained 66.66%. The model generated 13 linguistic rules, having reflected unrealized insights. The findings from this study may be utilized by academic advisers for pattern recognition, in identifying ‘at-risk’ students.

Decision-making in choosing a degree in engineering, especially for women, involves trade-offs between numerous conflicting objectives. To increase the number of female students enrolling in the Mechanical Engineering Department of Far Eastern University, a survey was conducted among the 30 female engineering students of the Mechanical Engineering Department of Far Eastern University, Institute of Technology. In this study, the objective is to determine the factor that affects female students in choosing engineering field. A multi-criteria decision analytical process was done using a pairwise comparison matrix wherein the goal is to rank the n alternatives. The results of the study revealed that 39.48% of their decision is based on their own choice. It is followed by the influence of the economy with 19%, while the influence of family, available opportunity and the impact of the latest trend have close relative weights, with value within 10% to 12%. Lastly, peer pressure had the least influence with relative weights of 7%. Since personal choice is based on individual's skills, it is recommended that universities should invest in various activities and workshops during their promotional campaign. This will help incoming college students to familiarized and be confident to include engineering in their degree of choice.

In this paper, the analysis of metal and plastic straw choice of individuals residing in Metro Manila is conducted using discrete choice modelling. The mathematical model was correlated with the key factors determined in the study. Using the RStudio software as tool, a discrete choice model is generated. The key factors for straw choice of plastic and metal were hygiene, comfortability, trend, habituation, value, and willingness to buy their choice of straw.

This research works focuses on the co-pyrolysis of coconut coir fiber combined with PET in order to increase its heating value, in addition to solid mass reduction for prolonged shell life and storage issues. Co-pyrolysis is a process of efficiently producing high-quality biofuel from two or more materials. Parameters combinations were identified using the Taguchi optimization methodology model in MINITAB19. Nine samples with three replications were evaluated. Results revealed that changing the temperature, duration, and feedstock blends show a significant effect on solid mass yield and heating value. The biochar with 75:25 (coconut coir fiber: PET) shows that duration and temperature directly affect the solid yield. For biochar, with 25:75 (coconut coir fiber: PET), pyrolysis duration contributed largely to the output. The highest solid mass reduction with an average of 55% solid yield was obtained. Despite a high solid mass reduction, the heating value measured is only 13 MJ/kg. Feedstock blend with PET to coconut coir ratios of 75:25, 25:75, and 50:50 resulted to an average solid yield of 70%, 65%, and 83% respectively. In terms of heating value, for all three replications, the biochar sample subjected to 200°C, 30 minutes, and PET to coconut coir ratio of 75:25, with an average solid yield of 67%, had the highest value with 20.94 MJ/kg, 24.42 MJ/kg, and 23.55 MJ/kg for Trial A, B, and C, respectively. The result shows that the incorporation of PET effectively increases the heating value of the coconut coir fiber from 10 MJ/kg to 24.42 MJ/kg.

Attaining high retention rates among engineering institutions is a predominant is-sue. A significant portion of engineering students face challenges of retention. Academic advising was implemented to resolve the issue. Decision support sys-tems were developed to support the endeavor. Machine learning have been inte-grated among such systems in predicting student performance accurately. Most works, however, rely on a black box model approach. Rule induction generates simpler if-then rules, exhibiting clearer understanding. As most research works considered attributes for positive academic performance, there is the need to con-sider ‘negative’ attributes. ‘Negative’ attributes are critical indicators to possibility of failure. This work applied rule induction techniques for course grade predic-tion using ‘negative’ attributes. The dataset is the academic performance of 48 mechanical engineering students taking a machine design course. Students’ at-tributes on workload, course repetition, and incurred absences are the predictors. This work implemented two rule induction techniques, rough set theory (RST) and adaptive neuro fuzzy inference system (FIS). Both models attained a classifi-cation accuracy of 70.83% with better performance for course grades of ‘Pass’ and ‘High’. RST generated 16 crisp rules while ANFIS generated 27 fuzzy rules, yielding significant insights. Results of this study can be used for comparative analysis of student traits between institutions. The illustrated framework can be used in formulating linguistic rules of other institutions.