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While the natural expectation is that students seek greater depth of learning as they develop intellectually during their studies, some research calls this into question and even suggests that student learning can become shallower from year to year. The present study aims to investigate the relative depth of students' learning at different stages of their undergraduate studies by comparing second‐year with third‐year students in two statistics units.

A survey was conducted using Biggs's Study Process Questionnaire. The survey results were used to compare second‐ and third‐year groups, as well as to investigate other variables by comparing the performances of: international and domestic students, male and female students, students who worked and those who did not work, and students who intended to register for a higher degree and those who did not.

Significant differences in approaches were found between male and female students; and between students who intended to enrol in a higher degree and those who did not.

Characteristics of the learning and teaching environment, including quality of teaching, were not investigated in this study. These and the possibility of students' mixed approaches to learning depending on the unit of study might have significant impact on the results. Additionally, this study is specific to one Sydney university; therefore the results might not be generalisable.

The findings from this study provide evidence that there is no significant difference between second and third year; or in international and local students' approaches to learning in statistics.

The purpose of this paper is to reflect upon and describe the introduction of an articulated engagement learning and teaching approach to a diverse cohort in a postgraduate management accounting unit of study.

A case study, based on the authors' experiences teaching diverse cohorts applies Bandura's social learning theory. Observation and modelling (which shaped earlier educational experiences and dominate student behaviour and classroom engagement) were used to overcome passive learning behaviour in diverse cohorts.

Better preparation for class by students is engendered by showing how study is applied. High engagement during class time is a combination of careful team construction and a task that has work relevance. Diversity need not be a constraint on teaching nor a barrier to learning. Diversity can be harnessed to facilitate learning. Reflection of the experience of students indicates high engagement, more confident, flexible and non‐scripted student responses and awareness of the value of diversity in the team.

An articulated engagement learning and teaching approach is described which caters for diversity, using resourcing problems with alternative recommendation choices, requiring justification, critique and defence.

The purpose of this papers is to provide an overview of how students and teachers in Taiwan conceptualize learning, especially in technology-enhanced learning environments. Their conceptions of learning reveal the extent to which the prevalence of technological use in education has facilitated students to cultivate a more advanced conception of learning and develop a deeper learning approach.

It reviews a total of nine relevant case studies, covering the contexts of conventional schools (from elementary schools to college, and cram schools) as well as technology-enhanced environments (internet-assisted learning and mobile learning); and participants from Grade 2 students to adult learners as well as teachers. Their conceptions of learning and preferred learning approaches are summarized.

Results of the studies show the Taiwanese students’ and teachers’ conceptions of learning in general and of technology-enhanced learning in particular. The students tended to be passive learners to receive instructions and considered examinations as a short-term goal for their study, with surface learning approaches commonly adopted. Despite technology may help to promote their cultivation of a more sophisticated conception of learning, many of them still opted for rote memorization and practice as the major ways to study. The potentials of technology in enhancing learning thus have not been fully realized.

The results shed light on an Asian-specific educational culture which is exam oriented. They reveal the challenges regarding the use of technology in education, which hinder the promotion of students’ advanced conceptions of learning. They also highlight the directions of future work to create a more accessible and gratifying technology-enhanced environment.

In this chapter, a general introduction on artificial intelligence (AI) is given as well as an overview of the advances of AI in different engineering disciplines, including its effectiveness in driving the United Nations Sustainable Development Goals (UN SDGs). This chapter begins with some fundamental definitions and concepts on AI and machine learning (ML) followed by a classification of the different categories of ML algorithms. After that, a general overview of the impact which different engineering disciplines such as Civil, Chemical, Mechanical, Electrical and Telecommunications Engineering have on the UN SDGs is given. The application of AI and ML to enhance the processes in these different engineering disciplines is also briefly explained. This chapter concludes with a brief description of the UN SDGs and how AI can positively impact the attainment of these goals by the target year of 2030.

This paper aims to apply a methodology that is capable to classify and localize mechanical, electrical and plumbing (MEP) elements to assist facility managers. Furthermore, it assists in decreasing the technical complexity and sophistication of different systems to the facility management (FM) team.

This research exploits artificial intelligence (AI) in FM operations through proposing a new system that uses a deep learning pre-trained model for transfer learning. The model can identify new MEP elements through image classification with a deep convolutional neural network using a support vector machine (SVM) technique under supervised learning. Also, an expert system is developed and integrated with an Android application to the proposed system to identify the required maintenance for the identified elements. FM team can reach the identified assets with bluetooth tracker devices to perform the required maintenance.

The proposed system aids facility managers in their tasks and decreases the maintenance costs of facilities by maintaining, upgrading, operating assets cost-effectively using the proposed system.

The paper considers three fire protection systems for proactive maintenance, where other structural or architectural systems can also significantly affect the level of service and cost expensive repairs and maintenance. Also, the proposed system relies on different platforms that required to be consolidated for facility technicians and managers end-users. Therefore, the authors will consider these limitations and expand the study as a case study in future work.

This paper assists in a proactive manner to decrease the lack of knowledge of the required maintenance to MEP elements that leads to a lower life cycle cost. These MEP elements have a big share in the operation and maintenance costs of building facilities.

Parkinson's disease (PD) is a well-known complex neurodegenerative disease. Typically, its identification is based on motor disorders, while the computer estimation of its main symptoms with computational machine learning (ML) has a high exposure which is supported by researches conducted. Nevertheless, ML approaches required first to refine their parameters and then to work with the best model generated. This process often requires an expert user to oversee the performance of the algorithm. Therefore, an attention is required towards new approaches for better forecasting accuracy.

To provide an available identification model for Parkinson disease as an auxiliary function for clinicians, the authors suggest a new evolutionary classification model. The core of the prediction model is a fast learning network (FLN) optimized by a genetic algorithm (GA). To get a better subset of features and parameters, a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.

The proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets. The very popular wrappers induction models such as support vector machine (SVM), K-nearest neighbors (KNN) have been tested in the same condition. The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.

A novel efficient PD detection model is proposed, which is called A-W-FLN. The A-W-FLN utilizes FLN as the base classifier; in order to take its higher generalization ability, and identification capability is also embedded to discover the most suitable feature model in the detection process. Moreover, the proposed method automatically optimizes the FLN's architecture to a smaller number of hidden nodes and solid connecting weights. This helps the network to train on complex PD datasets with non-linear features and yields superior result.

In organizations, individual user’s compliance with business processes is important from a regulatory and efficiency point of view. The restriction of users’ choices by implementing a restrictive information system is a typical approach in many organizations. However, restrictions and mandated compliance may affect employees’ performance negatively. Especially when users need a certain degree of flexibility in completing their work activity. The purpose of this paper is to introduce the concept of directive explanations (DEs). DEs provide context-dependent feedback to users, but do not force users to comply.

The experimental study used in this paper aims at investigating how DEs influence users’ process compliance. The authors used a laboratory experiment to test the proposed hypotheses. Every participant underwent four trials for which business process compliance was measured. Two trial blocks were used to cluster the four trials. Diagrammatic DEs were provided in one of the trial blocks, while textual DEs were provided in the other. Trial blocks were counterbalanced.

The results of the experiment show that DEs influence a user’s compliance, but the effect varies for different types of DEs. The authors believe this study is significant as it empirically examines design characteristics of explanations from knowledge-based systems in the context of business processes.

This study is certainly not without limitations. The sample used for this study was drawn from undergraduate information systems management students. The sample is thus not representative of the general population of organizations’ IT users. However, a student sample adequately represents novice IT users, who are not very familiar with a business process. They are particularly suitable to study how users react to first-time contact with a DE.

The findings of this study are important to designers and implementers of systems that guide users to follow business processes. As the authors have illustrated with a real-world scenario, an ERP system’s explanation can lack details on how a user can resolve a blocked activity. In situations in which users bypass restricted systems, DEs can guide them to comply with a business process. Particularly diagrammatic explanations, which depict actors, activities, and constraints for a business process, have been found to increase the probability that users’ behavior is business process compliant. Less time may be needed to resolve a situation, which can result in very efficient user-system cooperation.

This study makes several important contributions to research on explanations, which are provided by knowledge-based systems. First, the authors conceptualized, designed, and investigated a novel type of explanations, namely, DEs. The results of this study show how dramatic the difference in process compliance performance is when exposed to certain types of DEs (in one group from 57 percent on the initial trial to 82 percent on the fourth trial). This insight is important to derive design guidelines for DE, particularly when multimedia material is used.

Discusses the nature of Raymond Irwin's notion of the “compleat librarian” and develops this theme into the present day and the current stat of the library profession.

The article is a literary essay.

The “compleat librarian” concept is useful as a tool to view the current state of the profession.

This article would be of interest to anyone interested in the perceptions of librarians of old vs today.

Novel view of an old concept.

Diagnosing genetic neuromuscular disorder such as muscular dystrophy is complicated when the imperfection occurs while splicing. This paper aims in predicting the type of muscular dystrophy from the gene sequences by extracting the well-defined descriptors related to splicing mutations. An automatic model is built to classify the disease through pattern recognition techniques coded in python using scikit-learn framework.

In this paper, the cloned gene sequences are synthesized based on the mutation position and its location on the chromosome by using the positional cloning approach. For instance, in the human gene mutational database (HGMD), the mutational information for splicing mutation is specified as IVS1-5 T > G indicates (IVS - intervening sequence or introns), first intron and five nucleotides before the consensus intron site AG, where the variant occurs in nucleotide G altered to T. IVS (+ve) denotes forward strand 3′– positive numbers from G of donor site invariant and IVS (−ve) denotes backward strand 5′ – negative numbers starting from G of acceptor site. The key idea in this paper is to spot out discriminative descriptors from diseased gene sequences based on splicing variants and to provide an effective machine learning solution for predicting the type of muscular dystrophy disease with the splicing mutations. Multi-class classification is worked out through data modeling of gene sequences. The synthetic mutational gene sequences are created, as the diseased gene sequences are not readily obtainable for this intricate disease. Positional cloning approach supports in generating disease gene sequences based on mutational information acquired from HGMD. SNP-, gene- and exon-based discriminative features are identified and used to train the model. An eminent muscular dystrophy disease prediction model is built using supervised learning techniques in scikit-learn environment. The data frame is built with the extracted features as numpy array. The data are normalized by transforming the feature values into the range between 0 and 1 aid in scaling the input attributes for a model. Naïve Bayes, decision tree, K-nearest neighbor and SVM learned models are developed using python library framework in scikit-learn.

To the best knowledge of authors, this is the foremost pattern recognition model, to classify muscular dystrophy disease pertaining to splicing mutations. Certain essential SNP-, gene- and exon-based descriptors related to splicing mutations are proposed and extracted from the cloned gene sequences. An eminent model is built using statistical learning technique through scikit-learn in the anaconda framework. This paper also deliberates the results of statistical learning carried out with the same set of gene sequences with synonymous and non-synonymous mutational descriptors.

The data frame is built with the Numpy array. Normalizing the data by transforming the feature values into the range between 0 and 1 aid in scaling the input attributes for a model. Naïve Bayes, decision tree, K-nearest neighbor and SVM learned models are developed using python library framework in scikit-learn. While learning the SVM model, the cost, gamma and kernel parameters are tuned to attain good results. Scoring parameters of the classifiers are evaluated using tenfold cross-validation using metric functions of scikit-learn library. Results of the disease identification model based on non-synonymous, synonymous and splicing mutations were analyzed.

Certain essential SNP-, gene- and exon-based descriptors related to splicing mutations are proposed and extracted from the cloned gene sequences. An eminent model is built using statistical learning technique through scikit-learn in the anaconda framework. The performance of the classifiers are increased by using different estimators from the scikit-learn library. Several types of mutations such as missense, non-sense and silent mutations are also considered to build models through statistical learning technique and their results are analyzed.

To the best knowledge of authors, this is the foremost pattern recognition model, to classify muscular dystrophy disease pertaining to splicing mutations.

Construction action recognition is essential to efficiently manage productivity, health and safety risks. These can be achieved by tracking and monitoring construction work. This study aims to examine the performance of a variant of deep convolutional neural networks (CNNs) for recognizing actions of construction workers from images of signals of time-series data.

This paper adopts Inception v1 to classify actions involved in carpentry and painting activities from images of motion data. Augmented time-series data from wearable sensors attached to worker's lower arms are converted to signal images to train an Inception v1 network. Performance of Inception v1 is compared with the highest performing supervised learning classifier, k-nearest neighbor (KNN).

Results show that the performance of Inception v1 network improved when trained with signal images of the augmented data but at a high computational cost. Inception v1 network and KNN achieved an accuracy of 95.2% and 99.8%, respectively when trained with 50-fold augmented carpentry dataset. The accuracy of Inception v1 and KNN with 10-fold painting augmented dataset is 95.3% and 97.1%, respectively.

Only acceleration data of the lower arm of the two trades were used for action recognition. Each signal image comprises 20 datasets.

Little has been reported on recognizing construction workers' actions from signal images. This study adds value to the existing literature, in particular by providing insights into the extent to which a deep CNN can classify subtasks from patterns in signal images compared to a traditional best performing shallow network.