Mathematical models for process commonality under quality and resources breakdown in multistage production

It is essential to manage customers’ diverse desires and to keep manufacturing costs as low as possible for survival in competition and eventually in production. Sharing resources in manufacturing for different products is a vital method of accomplishing this goal. The advantages of using a common process in production are stated in the literature. However, the mathematical models as well as simulation or conceptual models are not sufficient. The main objective of this paper is to develop mathematical models for multiproduct and multistage production under quality and breakdown uncertainties. The idea of the process commonality is incorporated in the proposed models. The models are validated by primary data collected from a Malaysian company and comparison of the timely requirement schedules of earlier MRP II and the proposed models under stable and perfect production environments. An appreciable convergence of the outcomes is observed. However, the proposed models are carrying additional information about the available locations of the parts in a time frame. After validation, the effects of process commonality on cost, capacity and the requirement schedule under uncertainties are examined. It is observed that the use of common processes in manufacturing is always better than the non-commonality scenario in terms of production cost. However, the increase in capacity requirement for commonality designs is higher for an ideal system, while it is less when the system suffers from breakdowns and a quality problem.     

American Association for Health,Physical Education,and Recreation

Abstract

Utilizing canonical correlation analysis, the multivariate relationship between selected measures of motor performance and intellectual achievement was studied on 91 high school pupils. The students involved in the study were culturally deprived students who had been achieving well below their expected capacity. A significant relationship was found between the intellectual achievement domain and the motor performance domain. Also, significant relationships were found between the intellectual achievement domain and both of the sub-domains of fitness and coordination. The results in the present investigation were compared with the univariate analyses in a previous study. It was concluded that when the degree of relationship between two domains is desired, canonical correlation analysis may be of more value than the usual univariate procedures.     

Effects of instructional strategies using cross sections on the recognition of anatomical structures in correlated CT and MR images

This research is an effort to best utilize the interactive anatomical images for instructional purposes based on cognitive load theory. Three studies explored the differential effects of three computer‐based instructional strategies that use anatomical cross‐sections to enhance the interpretation of radiological images. These strategies include: (1) cross‐sectional images of the head that can be superimposed on radiological images, (2) transparent highlighting of anatomical structures in radiological images, and (3) cross‐sectional images of the head with radiological images presented side‐by‐side. Data collected included: (1) time spent on instruction and on solving test questions, (2) mental effort during instruction and test, and (3) students' performance to identify anatomical structures in radiological images. Participants were 28 freshmen medical students (15 males and 13 females) and 208 biology students (190 females and 18 males). All studies used posttest‐only control group design, and the collected data were analyzed by either t test or ANOVA. In self‐directed computer‐based environments, the strategies that used cross sections to improve students' ability to recognize anatomic structures in radiological images showed no significant positive effects. However, when increasing the complexity of the instructional materials, cross‐sectional images imposed a higher cognitive load, as indicated by higher investment of mental effort. There is not enough evidence to claim that the simultaneous combination of cross sections and radiological images has no effect on the identification of anatomical structures in radiological images for novices. Further research that control for students' learning and cognitive style is needed to reach an informative conclusion. Anat Sci Ed 1:75–83, 2008. © 2008 American Association of Anatomists.     

Modelling within-person idiographic variance could help explain and individualize learning

Learning analytics is a fast-growing discipline. Institutions and countries alike are racing to harness the power of using data to support students, teachers and stakeholders. Research in the field has proven that predicting and supporting underachieving students is worthwhile. Nonetheless, challenges remain unresolved, for example, lack of generalizability, portability and failure to advance our understanding of students' behaviour. Recently, interest has grown in modelling individual or within-person behaviour, that is, understanding the person-specific changes. This study applies a novel method that combines within-person with between-person variance to better understand how changes unfolding at the individual level can explain students' final grades. By modelling the within-person variance, we directly model where the process takes place, that is the student. Our study finds that combining within- and between-person variance offers a better explanatory power and a better guidance of the variables that could be targeted for intervention at the personal and group levels. Furthermore, using within-person variance opens the door for person-specific idiographic models that work on individual student data and offer students support based on their own insights.

Practitioner notes

What is already known about this topic

• Predicting students' performance has commonly been implemented using cross-sectional data at the group level.
• Predictive models help predict and explain student performance in individual courses but are hard to generalize.
• Heterogeneity has been a major factor in hindering cross-course or context generalization.

What this paper adds

• Intra-individual (within-person) variations can be modelled using repeated measures data.
• Hybrid between–within-person models offer more explanatory and predictive power of students' performance.
• Intra-individual variations do not mirror interindividual variations, and thus, generalization is not warranted.
• Regularity is a robust predictor of student performance at both the individual and the group levels.

Implications for practice

• The study offers a method for teachers to better understand and predict students' performance.
• The study offers a method of identifying what works on a group or personal level.
• Intervention at the personal level can be more effective when using within-person predictors and at the group level when using between-person predictors.