Bayesian statistics & modelling

The book contains seven Chapters. Chapter 1 deals with the nature of statistics.  In Chapter 2, we discuss how to describe data, using graphical and summary statistics.  Chapter 3 covers probability while Chapter 4 covers probability distributions.  Chapters 5 and 6 present basic tools of statistical inference; point estimation, interval estimation and hypothesis testing. Our presentation is distinctly applications-oriented.  Chapter 7 presents linear regression and correlation.
A prominent feature of the book is the inclusion of many examples.  Each example is carefully selected to illustrate the application of a particular statistical technique and or interpretation of results.  Another feature is that each chapter has an extensive collection of exercises.  Many of these exercises are from published sources, including past examination questions from King Saud University (Saudi Arabia) and Methodist University College Ghana.  Answers to all the exercises are given at the end of the book.

Traditionally, intimate-partner violence has been considered a special type of crime that occurs behind closed doors, with different characteristics from street-level crime. The aim of this study is to analyze the spatial overlap of police calls reporting street-level and behind-closed-doors crime. We analyzed geocoded police calls in the 552 census-block groups of the city of Valencia, Spain, related to street-level crime (N = 26,624) and to intimate-partner violence against women (N = 11,673). A Bayesian joint model was run to analyze the spatial overlap. In addition, two Bayesian hierarchical models controlled for different neighborhood characteristics to analyze the relative risks. Results showed that 66.5% of the total between-area variation in risk of reporting street-level crime was captured by a shared spatial component, while for reporting IPVAW the shared component was 91.1%. The log relative risks showed a correlation of 0.53, with 73.6% of the census-block groups having ...

A simply supported bridge is modelled to investigate the effect of a strain rate dependent modulus of elasticity on the dynamic response of the structure to a moving load. The bridge is modelled as a one-dimensional discretized finite element beam and the moving load is represented by a point force. A constant modulus of elasticity is traditionally employed when simulating the dynamic response of structures under moving loads. In this paper, a time-variant modulus is used to calculate strains and displacements and compare them to the traditional approach for different speeds and bridge spans. The time-variant modulus is obtained from the strain rate of the structure which is used in turn to update the strain. The results show significant changes in the modulus and in the resulting load effect as load magnitude and speed increase.

Understanding drivers of temporal variation in demographic parameters is a central goal of mark-recapture analysis. To estimate the survival of migrating animal populations in migration corridors, space-for-time mark-recapture models employ discrete sampling locations in space to monitor marked populations as they move past monitoring sites, rather than the standard practice of using fixed sampling points in time. Because these models focus on estimating survival over discrete spatial segments, model parameters are implicitly integrated over the temporal dimension. Furthermore, modeling the effect of time-varying covariates on model parameters is complicated by unknown passage times for individuals that are not detected at monitoring sites. To overcome these limitations, we extended the Cormack-Jolly-Seber (CJS) framework to estimate temporally stratified survival and capture probabilities by including a dis-cretized arrival time process in a Bayesian framework. We allow for flexibility in the model form by including temporally stratified covariates and hierarchical structures. In addition, we provide tools for assessing model fit and comparing among alternative structural models for the parameters. We demonstrate our framework by fitting three competing models to estimate daily survival, capture, and arrival probabilities at four hydroelectric dams for over 200 000 individually tagged migratory juvenile salmon released into the Snake River, USA. K E Y W O R D S capture

En este documento se presenta la versión inicial 1.0, aún en desarrollo, de un sistema abierto escrito en TOL1 para la simulación e inferencia bayesianas de tipo MonteCarlo-Markov Chain (MCMC) mediante el algoritmo de Gibbs, sobre modelos de regresión lineal sparse con estructura arbitraria (jerárquicos, redes bayesianas, ...) con restricciones de desigualdad lineal, con tratamiento de omitidos y filtros no lineales, ambas tanto en el input como en el output, así como con estructuras ARIMA.

Recommender systems aim to personalize the shopping experience of a user by suggesting related products, or products that are found to be in the general interests of the user. The information available for users and products is heterogenous, and many systems use one or some of the information. The information available include the user's interactions history with the products and categories, textual information of the products, a hierarchical classification of the products into a taxonomy, user interests based on a questionnaire, the demographics of a user, inferred interests based on product reviews given by a user, interests based on the physical location of a user and so on. Taxonomy discovery for personalized recommendation is work published in 2014 which uses the first three information sources { the user's interaction history, textual information of the products and optionally, an existing taxonomy of the products. In this paper, we describe a parallel implementation o...

Given the reproducibility crisis (or replication crisis), more psychologists and social-cultural scientists are getting involved with Bayesian inference. Therefore, the current article provides a brief overview of programs (or software) and steps to conduct Bayesian data analysis in social sciences.

The theory of dual numerical means of random and experienced variables is briefly described in the framework of the new theory of experience and the chance that arises as an axiomatic synthesis of two dual theories — the Kolmogorov theory of probability and the theory of believability. A new term is introduced for the numerical mean of the experienced variable — mathematical reflection, which is dual to the mathematical expectation of a random variable within the framework of the new theory. The basic properties and examples of dual numerical means are considered.

This chapter compares frequentist (maximum likelihood) and Bayesian approaches to mixedeffect logistic regression, which is de facto the standard method for modelling linguistic variation. The main conceptual differences between frequentist and Bayesian inference are discussed, and the key components of Bayesian inferencethe priors, data (evidence) and posteriorsare explained using a simple example. We also discuss the epistemological and practical advantages of Bayesian inference, such as the ability of testing the research hypothesis directly, instead of trying to reject the null hypotheses, and the use of informative priors, which helps to fit adequate models in difficult cases. The Bayesian approach to generalized mixed-effect models is illustrated by a multifactorial case study of help + (to-)infinitive in U.S. magazines. Statistical analyses are performed with the help of brms, a popular R package for Bayesian regression The paper shows also how to recycle the results of previous analyses as informative priors in a new Bayesian model. This enables the linguist to use small samples, building on previous research.

Why Study Statistics?
Modern Statistics
Statistics and Engineering
The Role of the Scientist and Engineer in Quality Improvement
A Case Study : Visually Inspecting Data to Improve Product Quality
Two Basic Concepts - Population and Sample
Treatment of Data
Pareto Diagrams and Dot Diagrams
Frequency Distributions
Graphs of Frequency Distributions
Stem-and-leaf Displays
Descriptive Measures
Quartiles and Percentiles
The Calculation of x and s
A Case Study: Problems with Aggregating
Data

Bayesci istatistik, özellikle psikoloji alanında, tekrarlanabilirlik krizi denilen, gerçekleştirilen çoğu araştırmanın sonuçlarının tekrarlanabilir olmaması nedeniyle Frekansçı yaklaşıma alternatif olarak sık kullanılır hale gelmiştir. Bilgi teknolojilerinin gelişmesi ile gerekli hesaplamaların kısa sürede yapılabilir hale gelmesi, Bayesci istatistiğin uygulamalarının yaygınlaşmasını sağlamıştır. Bu çalışma, Akdeniz Üniversitesi Veri Analitiği ve Yönetimi Tezsiz Yüksek Lisans Dönem Projesi çalışmanın bir kısmını oluşturmakta ve Bayesci yöntemleri araştırmacılara ve bu konuya meraklı/ilgili kişilere tanıtmayı amaçlamaktadır.

The aim of this paper is to study and develop Bayesian models for the analysis of volleyball match outcomes as recorded by the set-difference. Due to the peculiarity of the outcome variable (set-difference) which takes discrete values from $-3$ to $3$, we cannot consider standard models based on the usual Poisson or binomial assumptions used for other sports such as football/soccer. Hence, the first and foremost challenge was to build models appropriate for the set-differences of each volleyball match. Here we consider two major approaches: a) an ordered multinomial logistic regression model and b) a model based on a truncated version of the Skellam distribution. For the first model, we consider the set-difference as an ordinal response variable within the framework of multinomial logistic regression models. Concerning the second model, we adjust the Skellam distribution in order to account for the volleyball rules. We fit and compare both models with the same covariate structure as...

Mathematical analysis is becoming ever more useful when dealing with large amounts of archaeological data, due to the precision and certainty with which results can be produced. This article will discuss the use of a relatively new tool in deciphering and dealing with archaeological data, the Naïve Bayes Classifier. The ‘Bayesian’ approach was first proposed in the early 1990s, by archaeological statisticians, Clive Orton (see Orton 1992:139; Buck et al. 1996:1), though at that time the lack of computational power available made use of the Classifier prohibitively difficult. Today a Naïve Bayes Classifier can be employed by anyone with a computer, without any need for particularly specialised computer skills. Programs such as Orange use a graphical interface as a way to circumvent the need for specific mathematical knowledge of the process, and the use of this program is detailed in the paper. The Naïve Bayes Classifier is most useful in attempting to identify unseen patterns in a large amount of data, such as a database with thousands of entries, and potential uses will be illustrated here. This paper presents a case study using a Naïve Bayes Classifier in an attempt to date the rune-stones of Viking-age rune-stones of Sweden which remain undated through conventional methods Two variables were identified as showing some small trace of temporal evolution, the Christian crosses and runic inscriptions on the stones, and the Classifier was utilized to explore their further use.

From the bestselling author of Blink and The Tipping Point , Malcolm Gladwell's Outliers: The Story of Success overturns conventional wisdom about genius to show us what makes an ordinary person an extreme overachiever. Why do some people achieve so much more than others? Can they lie so far out of the ordinary? In this provocative and inspiring book, Malcolm Gladwell looks at everyone from rock stars to professional athletes, software billionaires to scientific geniuses, to show that the story of success is far more surprising, and far more fascinating, than we could ever have imagined. He reveals that it's as much about where we're from and what we do, as who we are-and that no one, not even a genius, ever makes it alone. Outliers will change the way you think about your own life story, and about what makes us all unique.

When formulating scientific hypotheses which fulfill the rejectability criterion, the archaeologist is faced with the challenge of choosing a representative sample from often highly fragmentary data. The natural scientific approach of random and repeated sampling is possible only in the case of highly abundant archaeological records mostly of natural origin, such as coal or pollen samples in 14C dating. To achieve scientific rigor in evaluating cultural artifacts, an approach of modelling based on exploratory statistics and subsequent statistical testing of these models using primary archaeological sources as validation criteria seems more plausible.

The talk is included in the pdf as annotations.

In this work we present Bayesian hypothesis tests for the independence between two categorical variables in contingency tables. Initially we implement conjugate analysis based on the Multinomial-Dirichlet setup. We compute the Bayes factor and assess the sensitivity of the results to the prior distribution. Then we focus on log-linear models. We compare the saturated and the model of independence with the utilization of Bayes factor compared with several model selection criteria. Finally, we focus on Bayesian inference in association models, a class of models describing the structure of association between two categorical variables. MCMC procedures are facilitated with the help of WinBUGS in order to estimate the posterior distributions of model parameters. Laplace approximation methods are applied so as to approximately compute the marginal likelihood. We illustrate all methods using real datasets.

Con questo studio si vuole fornire una valutazione sull’attendibilità intrinseca del testimone analizzando in particolare le differenze delle strategie argomentative che le due parti processuali, accusa e difesa, utilizzano al fine di modellare il convincimento del giudice avvalendosi di specifici consulenti esperti in materia. A tal scopo, si partirà da un inquadramento teorico e giuridico della testimonianza e da un’analisi sulla letteratura in tema di memoria con particolare riferimento alla memoria autobiografica e alle memorie di eventi traumatici prendendo in considerazione le convinzioni e gli errori più diffusi tra giudici e giurati nella valutazione della credibilità del testimone.
Si mostrerà quindi come il ricordo e la testimonianza possono essere influenzati da diverse fonti di distorsione che necessitano di esser prese in considerazione in ambito forense.

This report sets out the main findings and recommendations for using a Bayesian Belief Network (BBN) modelling approach on both qualitative and quantitative data (provided by an anonymous client) with the specification of assessing and managing bacteriological growth risk across a water system.

The thesis deals with spatial structure and chronological development of the early Lengyel Culture (4900-4700 cal BC) settlement at the site Svodín - Busahegy in Nové Zámky district, SW Slovakia. The approach is based on the settlement area theory and more generally on the theory of artefacts. The used evidence consists finds from 1756 features, of which 679 belong to the Lengyel Culture and 184 are burials. The work includes a comprehensive electronic catalogue containing all relevant find documentation, localization plans of the trenches and features, plans of archaeological structures, belonging to different phases of the Lengyel culture settlement. The methodical chapters describe a process for direct digitization of ceramic fragments by laser scanning of profiles and automated reconstruction of ceramic shapes. The elaboration of chronology is based on the observed stratigraphic relations and typological analysis of the finds. Morphometric analysis and multivariate statistical methods were applied for the computerized typological classification of 1992 profiles of ceramic vessels. Find units were assigned to chronological phases based on a stochastic model, which integrates stratigraphic and typo-chronological data. A set of solutions was computed using hybrid particle swarm optimisation and genetic algorithm analysed by Monte Carlo simulation using Markov chains and interpreted in terms of Bayesian Statistics. Based on the spatial distribution of archaeological structures, three residential, three funerary and one rondel component have been identified in the settlement area. The final solution of the chronological model divides the settlement into seven phases, three pre-rondel phases, two phases in which subsequently the smaller and the larger rondels were built and two post-rondel phases. A spatial differentiation of settlement area based on social aspects was observed, and a possible association of the localization of the rondels with previous residential structures. In addition of burials in the vicinity of houses for the entire duration of the settlement, we also observe a separate burial area in the pre-rondel period. The typological composition of pottery shows a strong influence from the area Tisza culture, especially in the pre-rondel period. The usability of seriation and multivariate statistical methods for chronological ordering of find units was critically assessed based on the analysis of typo-chronological development of grave inventories.

Effective management of artisanal fisheries requires understanding fishers, their behaviors, and the drivers that underpin their choices. Behavioral drivers are critical links in understanding the interactions between social and ecological systems and can help inform effective management approaches. A Bayesian Belief Network modeling approach was used to investigate a diverse range of qualitative and quantitative social and ecological drivers of spatial location choice in a multi-species artisanal dive fishery in Costa Rica. Empirical and observer data used to populate the BBN showed the influence of economic factors, environmental conditions as well as social interactions on the decision-making process of spatial location choice. Good governance scenarios represented by Responsible Fisheries Marine Areas Management were analyzed for both hookah and free diving methods to assess the effects of responsible fishing on the fishers and the fishery. Model based-scenario analysis suggests that management interventions should consider the fisher's potential behavioral responses in the context of environmental variability, dependence on cultural assets, and food security. The results show that there is a need to understand fisher's decisions based on broad socio-ecological system understanding and consider the environmental outcomes alongside food security and the cultural significance of different marine species to fishing communities.

Medical Diagnosis and Expert Systems have a particular relationship. Since the birth of the Heuristic program from the Heuristic group headed by Feigenbaum and the beginning of the twentieth one century there were a lot of changes. In the first part of this paper I will introduce the topic of Medical Diagnosis and Expert Systems and how they are related. In the second part I will describe a small part of this story and how those changes can give us a different perspective about the development of Artificial Intelligence programs. The third part will be dedicated to Problem Solving and the structure of how we conceive problems. This part has its importance for explaining the differences of the solving methods for particular problem structures. The fourth part of this paper will characterize the Bayesian Networks and their links in structuring problems.

This study is concerned with the analysis of crude oil price using the Box-Jenkins method. The Crude oil price data was taken from 2006-2011.  ARIMA (1,1,0) model was identified to be adequate for the data.  The forecast indicates that the crude oil price will decrease in the year 2012.

Multilevel structural equation modeling (MSEM) is gaining popularity in the social sciences as a framework for estimating latent variable models in the presence of hierarchical data. In addition, we believe that MSEMs are quite helpful to psychological and educational inquiries. Many research papers have been published on technical developments in MSEM; however, we are not aware of any tutorials on how to properly implement these complex models. In addition, applied researchers may be unfamiliar with how to implement a Bayesian estimation approach to MSEM, despite the fact that it has distinct advantages in this modeling context (Depaoli and Clifton, 2015; Hox, van de Schoot, and Matthijsse, 2012). To that end, this paper serves as a nontechnical tutorial on the implementation and application of MSEM using both frequentist and Bayesian estimation methods. We demonstrate the implementation of MSEM with three motivating examples using data from the Program for International Student Assessment (PISA). In addition, we present a small Monte Carlo study to show how the estimation of MSEM is impacted by different types of priors for data structures similar to those presented in the PISA examples. We conclude with recommendations for applied researchers based on findings from the empirical examples and the simulation study. We also outline future methodological considerations in the context of Bayesian MSEM.

This book is intended to encourage an understanding and appreciation of Elective Mathematics at the Senior High School level in West Africa. Providing appropriate solutions to examination problems is of particular importance in the study of mathematics. As a mathematics lecturer, the author has discovered the weaknesses and shortcomings of students in the handling of examination questions. Subsequently, to guide students in answering typical questions in Elective Mathematics as set out in recent examinations, the writer has paid particular attention to those areas of the syllabus, which many students find difficult.

Previous research showed that employing results from meta-analyses of relevant previous fMRI studies can improve the performance of voxelwise Bayesian second-level fMRI analysis. In this process, prior distributions for Bayesian analysis can be determined by information acquired from the meta-analyses. However, only image-based meta-analysis, which is not widely accessible to fMRI researchers due to the lack of shared statistical images, was tested in the previous study, so the applicability of the prior determination method proposed by the previous study might be limited. In the present study, whether determining prior distributions based on coordinate-based meta-analysis, which is widely accessible to researchers, can also improve the performance of Bayesian analysis, was examined. Three different types of coordinate-based meta-analyses, BrainMap and Ginger ALE, and NeuroQuery, were tested as information sources for prior determination. Five different datasets addressing three task conditions, i.e., working memory, speech, and face processing, were analyzed via Bayesian analysis with a meta-analysis informed prior distribution, Bayesian analysis with a default Cauchy prior adjusted for multiple comparisons, and frequentist analysis with familywise error correction. The findings from the aforementioned analyses suggest that use of coordinate-based meta-analysis also significantly enhanced performance of Bayesian analysis as did image-based meta-analysis.

The investigation of the earth's internal structure is of great interest in
areas as diverse as civil engineering, water management and oil
exploration. A common technique looks to estimate the electrical
conductivity of the subsurface, a proxy for the internal structure, from a
grid of surface voltage measurements - an example of an ill-posed
inverse problem. This paper uses a Bayesian modeling approach to
compare different prior model descriptions using Markov chain Monte
Carlo estimation. A range of simulation examples are considered.

A prominent feature of this book is the inclusion of many examples.  Each example is carefully selected to illustrate the application of a particular mathematical technique and interpretation of results.  Another feature is that each chapter has an extensive collection of exercises. It is important that students have several exercises to practice. 

This book is therefore designed to help students to:
1. acquire the basic skills and understanding which is vital to examination success.
2. appreciate the use of mathematics as a tool for analysis and effective thinking.
3. discover order, patterns and relations.
4. communicate their thoughts through symbolic expressions and graphs.
5. develop mathematical abilities useful in commerce, industry and public service.

This thesis considers applications of machine learning techniques in hospital emergency readmission and comorbidity risk problems, using healthcare administrative data. The aim is to introduce generic and robust solution approaches that can be applied to different healthcare settings. Existing solution methods and techniques of predictive risk modelling of hospital emergency readmission and comorbidity risk modelling are reviewed. Several modelling approaches, including Logistic Regression, Bayes Point Machine, Random Forest and Deep Neural Network are considered. Firstly, a framework is proposed for pre-processing hospital administrative data, including data preparation, feature generation and feature selection. Then, the Ensemble Risk Modelling of Hospital Readmission (ERMER) is presented, which is a generative ensemble risk model of hospital readmission model. After that, the Temporal-Comorbidity Adjusted Risk of Emergency Readmission (T-CARER) is presented for identifying very s...

For a long time, one of my dreams was to describe the nature of uncertainty axiomatically, and it looks like I've finally done it in my co∼eventum mechanics! Now it remains for me to explain to everyone the co∼eventum mechanics in the most approachable way. This is what I'm trying to do in this work. The co∼eventum mechanics is another name for the co∼event theory, i.e., for the theory of experience and chance which I axiomatized in 2016 [1, 2]. In my opinion, this name best reflects the co∼event-based idea of the new dual theory of uncertainty, which combines the probability theory as a theory of chance, with its dual half, the believability theory as a theory of experience. In addition, I like this new name indicates a direct connection between the co∼event theory and quantum mechanics, which is intended for the physical explanation and description of the conict between quantum observers and quantum observations [4]. Since my theory of uncertainty satises the Kolmogorov axioms of probability theory, to explain this co∼eventum mechanics I will use a way analogous to the already tested one, which explains the theory of probability as a theory of chance describing the results of a random experiment. The simplest example of a random experiment in probability theory is the " tossing a coin ". Therefore, I decided to use this the simplest random experiment itself, as well as the two its analogies: the " "flipping a coin " and the " spinning a coin " to explain the co∼eventum mechanics, which describes the results of a combined experienced random experiment. I would like to resort to the usual for the probability theory " coin-based " analogy to explain (and first of all for myself) the logic of the co∼eventum mechanics as a logic of experience and chance. Of course, this analogy one may seem strange if not crazy. But I did not come up with a better way of tying the explanations of the logic of the co∼eventum mechanics to the coin-based explanations that are commonly used in probability theory to explain at least for myself the logic of the chance through a simple visual " coin-based " model that clarifies what occurs as a result of a combined experienced random experiment in which the experience of observer faces the chance of observation. I hope this analogy can be useful not only for me in understanding the co∼eventum mechanics.

This book is intended to encourage an understanding and appreciation of core mathematics at the Senior High School level in West Africa. Providing appropriate solutions to examination problems is of particular importance in the study of mathematics. As a mathematics lecturer, the author has discovered the weaknesses and shortcomings of students in the handling of examination questions. Subsequently, to guide students in answering typical questions in core mathematics as set out in recent examinations, the writer has paid particular attention to those areas of the syllabus, which many students find difficult.

Petroleum production and export play a dominant role in Nigeria's economy and account for about 90% of her gross earnings. This dominant role has pushed agriculture, the traditional mainstay of the economy, from the early fifties and sixties, to the background. In this research work we fitted a univariate Seasonal Autoregressive Integrated Moving Average model (SARIMA) to the monthly crude oil production in Nigeria between 2002 and 2016. Different Box-Jenskin Autoregressive Integrated Moving Average (ARIMA) models are fitted and diagnosed. However, ARIMA (2,1,0)(2,1,1) 12 was the best model for the data. The model was further validated and it was discovered that autocorrelation between residuals at different lag times was not significant. Finally, the time plot of the in-sample forecast errors shows that the variance of the forecast errors seems to be roughly constant over time and the histogram of the time series shows that the forecast errors are roughly normally distributed and the mean seems to be close to zero, it seems plausible that the forecast errors are normally distributed with mean zero and constant variance.

The book is intended as a beginning text in stochastic processes for students familiar with elementary probability theory. The objectives of the book are threefold:
1. To introduce students to use standard concepts and methods of stochastic process.
2. To illustrate the diversity of applications of stochastic processes.
3. To provide exercises in the application of simple stochastic analysis to appropriate problems.

Bayesowskie podejście rzadko omawiane jest w polskojęzycznej literaturze statystycznej (zob. Grzenda, 2012; Niemiro, 2013), mimo iż stanowi ważną gałąź statystyki. Możemy je stosować jako podstawową metodę estymacji, uzupełnienie metod częstościowych lub jako metodę używaną równolegle. (...) głównym zastosowaniem tego podejścia jest modelowanie statystyczne. Podejście Bayesowskie pozwala na bezpośrednie określenie modelu zgodnie z terminologią relacji stochastycznych między poszczególnymi zmiennymi ujętymi w modelu. Estymacja w tym ujęciu najczęściej opiera się na stosowaniu metod symulacyjnych, dzięki czemu w mniejszym stopniu ograniczeni jesteśmy wielkością próby w porównaniu do metod częstościowych. Z drugiej strony, badacz stosujący to podejście musi zaakceptować fakt, że uzyskany w ten sposób wynik może być obciążony założeniami a priori, jakie przyjął, definiując model statystyczny interesującego go zjawiska. (...)

The book is written with the realization that concepts of probability and probability distributions – even though they often appear deceptively simple – are in fact difficult to comprehend. Every basic concept and method is therefore explained in full, in a language that is easily understood.
A prominent feature of the book is the inclusion of many examples.  Each example is carefully selected to illustrate the application of a particular statistical technique and or interpretation of results.  Another feature is that each chapter has an extensive collection of exercises.  Many of these exercises are from published sources, including past examination questions from King Saud University in Saudi Arabia, and Methodist University College Ghana.  Answers to all the exercises are given at the end of the book.

Abstract: 
CONTEXT
This position paper extends a previous study which found engineering practice is based on distributed knowledge and often problems in engineering practice stem from engineers’ lack of professional/interpersonal (such as collaboration and communication) skills. While most university engineering programs produce technically skilled engineers, it is becoming increasingly important that engineers develop their professional skills and are capable of collaborating across disciplinary boundaries. This will require additional learning resources that do not overcrowd the curriculum.
PURPOSE
This paper explores combining learning and gaming to provide situated, experiential, and constructive learning for student engineers to prepare them for transition into the workplace. The paper asks if serious games (SGs) can assist in helping engineering students to acquire the necessary professional skills needed to improve their performance in the workplace.
RESULT
This paper examines the potential of game-based learning in the virtual world in particular implementing realistic virtual environments and simulations, where students can acquire, practice, and verify knowledge and collaborate and communicate with other disciplines. These educational games are also known as SGs. Students can use SGs to immerse themselves in the virtual world and interact with various stakeholders of a virtual project. For example, an individual student could engage in dialogue with disciplinary stakeholders in a virtual construction site, such as engineers, architects, clients, and constructors. These interactions may develop student comprehension of the nature of work and the perspectives and concerns of other project stakeholders. These forms of virtual exposure may also enable students to organise and apply new knowledge and skills. Future studies will explore how universities and industry could collaborate on developing appropriate SGs for engineers.
DISCUSSION
The literature suggests that SGs in the virtual environment may allow for role playing and the development and practice of skills through simulated learning that is accurate, engaging, and applicable to real-world situations. These SGs may provide an engaging, self-reinforcing context in which to motivate, educate, and train engineering students.
KEYWORDS
Distributed knowledge; learning resources; serious games (SGs); collaboration and communication skills.