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In most academic institutions, students with learning impairments (LIs) are entitled to various accommodations as a means of compensating for their impairment. Ensuring that the appropriate accommodations were selected requires an intelligent support tool to track their effectiveness. In this article, we regard the effectiveness of such accommodations in terms of their quality and reliability. High-quality accommodations allow students with LIs equal access and equal opportunity to demonstrate their knowledge compared to their peers who do not have LIs. Highly reliable accommodations mean a significant performance difference between students with LIs who actually use the accommodations compared with students with LIs who do not use the accommodations, given similar student characteristics. Previous literature is inconclusive regarding the evidence of the effectiveness of such accommodations since different accommodations may have a different effect on different subgroups of exams and students. This article proposes a methodology, based on ordinal interpretable models, that produce practical insights to professionals who are responsible for students with LIs, to address the problem of exploring the effectiveness of learning accommodations. The suggested models use the ordinal information of the target variables for evaluating student performance and yield practical insights for designing the most suitable accommodation for a student with LIs based on their characteristics. The ordinal interpretable models are evaluated using a database of tens of thousands of engineering students. The results demonstrate that the suggested interpretable models perform significantly better than the compared algorithms on all measures.

In ordinal classification problems, the class value exhibits a natural order. Usually, these problems are solved as multiclass classification problems while discarding the ordering form of the class. Recently, several research studies have proposed novel methods for ordinal classification problems while aiming to predict a predefined objective value. These methods are based on modification of the splitting criteria of decision tree-based algorithms. These methods consider the ordinal nature of the data and the magnitude of the potential classification error from a predefined predicted objective. This research aims to consolidate these methods and generalize them for any decision tree-based method while constructing decision trees according to any predefined objective value calculated from the training dataset. Furthermore, to evaluate the proposed method, a comprehensive experimental study is performed based on known ordinal datasets. The proposed method is found to significantly outperform its counterpart nonordinal models. This observation confirms that ordering information benefits ordinal methods and improves their performance. Additionally, the ordinal decision tree-based methods achieved competitive performance compared to state-of-the-art ordinal techniques. The results are validated with various performance measures that are commonly used for ordinal and non-ordinal classification problems. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate how learning disabilities (LDs), in combination with accommodations, affect the performance of a decision-tree to predict the stability of academic behaviour of undergraduate engineering students. Additionally, this study presents several examples to illustrate how a college could use the resultant model to choose the appropriate accommodation to give a student with a learning disability, from among a set of possible accommodations. The findings show that: (1) The models yield superior performance in predicting the stability category for a given student when the LD and accommodation factors are included; (2) Different types of accommodation action have different effects on the stability of academic behaviour, depending on the student pattern. Such a model could be useful for engineering faculties, as it would allow them to predict the stability of academic behaviour and to provide early intervention for students who are likely to need additional support.

In this study, we suggest combining the monitoring of actual examination time used with grades in order to assess examination time extensions in terms of access provision and expected outcome. Using naturally-occurring data collected from a large sample (N = 2315) of undergraduate engineering students, we argue that extended examination time may be regarded as providing equal access when a disabled student actually utilizes "more" examination time than a normally achieving student, regardless of the grade obtained. We further argue that extended examination time may be regarded as resulting in the expected outcome when a disabled student either (a) utilizes "less" or "equal" examination time and achieve grades that are lower than a normally achieving student, or (b) utilizes "more" examination time and achieve grades that are "equal" to a normally achieving student. In our data, equal access was provided in all courses (i.e. disabled students utilized more time than normal achievers), but the expected outcome (i.e. equal grades) was not observed in software and English examinations. The results of this study emphasize the importance of monitoring actual time usage in addition to performance measures when assessing examination time extensions.

We draw on cognitive and behavioural theories and on the artificial intelligence literature in order to propose a framework of future operator – workstation interaction in the 'Industry 4.0' era. We name the proposed framework 'Operator – Workstation Interaction 4.0'. The latter's capabilities permit an adaptive, ongoing interaction that aims to improve operator safety, performance, well-being, and satisfaction as well as the factory's production measures. The framework is composed of three subsystems: (1) the observation subsystem which observes the operator and the processes occurring in the workstation, (2) the analysis subsystem which generates understanding and implications of the observations output, (3) the reaction subsystem which determines if and how to respond. The paper describes these elements and illustrate them using an example of a fatigued worker. The contributions, implications, and limitations of the proposed framework are discussed, and future research directions are presented. [ABSTRACT FROM AUTHOR]

This article considers a problem of optimal production control of a one-product-type production--inventory system. The demand is a discrete-time stochastic process, while production of a limited capacity is continious in time. The dynamics of the inventory over a finite time horizon are investigated and an optimal feedback strategy for production control under a backlog-dependent lost sales effect is developed. This strategy is called a target level strategy since, contrary to the existing production--inventory control strategies, it determines an optimal target inventory level as a function of time period and the initial stock at that period. Numerical examples illustrate the applicability of the developed solutions. Keywords: Inventory control, stochastic modeling, state-co-state dynamics, lost sales, feedback optimal control, perturbation analysis, 1. Introduction Service level is a crucial factor in the success of a firm in competative marketplaces. When potential customers have to wait for their orders to be filled, some [...]

Conventional Statistical Process Control (SPC) schemes fail to monitor nonlinear and finite-state processes that often result from feedback-controlled processes. SPC methods that are designed to monitor autocorrelated processes usually assume a known model (often an ARIMA) that might poorly describe the real process. In this paper, we present a novel SPC methodology based on context modeling of finite-state processes. The method utilizes a series of context-tree models to estimate the conditional distribution of the process output given the context of previous observations. The Kullback-Leibler divergence statistic is derived to indicate significant changes in the trees along the process. The method is implemented in a simulated flexible manufacturing system in order to detect significant changes in its production mix ratio output., 1. Introduction and literature review In many industrial environments process outputs are often adjusted by applying feedback mechanisms and policies. As an example, the temperature in a chemical reactor is [...]

This paper suggests a new technique to facilitate consistent transition between planning and production stages of small lots of complex products (in quantities that do not justify full automation). Despite the usage of modern approaches such as design for assembly and design for manufacturing the typical product planning ends up before production planning begins, instead of having a unified seamless planning process. This paper presents a new three stage technique to fill the gap between product planning and production. The paper shows how to generate a rigorous production plan by: (1) extending the Bill-of-Materials to include additional required information, (2) constructing an assembly ‘method prototype’ and (3) using the ‘method prototype’ to calculate time standards for executing the assembly. A case study illustrates the proposed technique. [ABSTRACT FROM PUBLISHER]