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    Data Quality Challenges in Educational Process Mining: Building Process-Oriented Event Logs from Process-Unaware Online Learning Systems
    (Inderscience, 2022-05-04) Umer R; Susnjak T; Mathrani A; Suriadi S
    Educational process mining utilizes process-oriented event logs to enable discovery of learning practices that can be used for the learner’s advantage. However, learning platforms are often process-unaware, therefore do not accurately reflect ongoing learner interactions. We demonstrate how contextually relevant process models can be constructed from process-unaware systems. Using a popular learning management system (Moodle), we have extracted stand-alone activities from the underlying database and formatted it to link the learners’ data explicitly to process instances (cases). With a running example that describes quiz-taking activities undertaken by students, we describe how learner interactions can be captured to build process-oriented event logs. This article contributes to the fields of learning analytics and education process mining by providing lessons learned on the extraction and conversion of process-unaware data to event logs for the purpose of analysing online education data.
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    Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data.
    (Elsevier Ltd, 2022-01) Hu R; Gan J; Zhu X; Liu T; Shi X
    In the early diagnosis of the Coronavirus disease (COVID-19), it is of great importance for either distinguishing severe cases from mild cases or predicting the conversion time that mild cases would possibly convert to severe cases. This study investigates both of them in a unified framework by exploring the problems such as slight appearance difference between mild cases and severe cases, the interpretability, the High Dimension and Low Sample Size (HDLSS) data, and the class imbalance. To this end, the proposed framework includes three steps: (1) feature extraction which first conducts the hierarchical segmentation on the chest Computed Tomography (CT) image data and then extracts multi-modality handcrafted features for each segment, aiming at capturing the slight appearance difference from different perspectives; (2) data augmentation which employs the over-sampling technique to augment the number of samples corresponding to the minority classes, aiming at investigating the class imbalance problem; and (3) joint construction of classification and regression by proposing a novel Multi-task Multi-modality Support Vector Machine (MM-SVM) method to solve the issue of the HDLSS data and achieve the interpretability. Experimental analysis on two synthetic and one real COVID-19 data set demonstrated that our proposed framework outperformed six state-of-the-art methods in terms of binary classification and regression performance.
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    Social outcome expectations and women's intentions to return to IT employment
    (Australasian Association for Information Systems and Australian Computer Society, 27/05/2023) Tretiakov A; Bensemann J; Jurado T
    Women leaving IT employment for childcare or other reasons, and never returning, is a phenomenon that contributes to the underrepresentation of women in IT. However, potential women returners, women who have recently left IT employment and may or may not return, remain an under-researched group. We studied the effects of social outcome expectations on the intention to return to IT employment for 182 potential women returners from New Zealand, Australia, and the United States. The data were obtained via a survey questionnaire. Expectations of friendly co-workers, work-life balance, and family proximity were included; and the expectations of friendly co-workers had a statistically significant effect on the intentions of potential women returners to return to IT employment. The results highlight the difficulty of creating an environment that encourages potential women returners to return to IT because, unlike work-life balance or family proximity, friendly co-workers is a factor that is difficult to control via managerial interventions. For practice, the results suggest that organisations should promote an environment friendly to women, which in part may be achievable by implementing agile approaches to organizing IT work.
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    Investigating the effects of online communication apprehension and digital technology anxiety on organizational dissent in virtual teams
    (Elsevier Ltd, 2023-07) Rahmani D; Zeng C; Chen MH; Fletcher P; Goke R
    Working in virtual teams has become increasingly common in contemporary workplaces with technology that allows teams to collaborate online without being present in the same physical space. For some employees, communicating via virtual technologies such as email, phone, video conferences or applications to work in teams can cause anxiety, which in turn may influence their decision to engage in organizational dissent. This study examines the impact of two forms of online anxiety on employees' virtual organizational dissent: online communication apprehension and digital technology anxiety. The effects of age, technical skills, the portion of workload done virtually, and previous experience in virtual teamwork were included in the study as control variables. Using factorial analysis and structural equation modeling, the results from 321 volunteer employees of various US organizations (males = 135, females = 184, others = 2) were analyzed. The results show that the two forms of online anxiety and technical skills generally increase organizational dissent and aging significantly decreases virtual latent dissent. The study's findings support the social compensation hypothesis of online media use.
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    The University of Google [Book review]
    (LIANZA, 14/07/2008) White BD
    Review of The University of Google by Tara Brabazon
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    Exploring spiral narratives with immediate feedback in immersive virtual reality serious games for earthquake emergency training
    (1/01/2023) Feng Z; González VA; Mutch C; Amor R; Cabrera-Guerrero G
    Various attempts and approaches have been made to teach individuals about the knowledge of best practice for earthquake emergencies. Among them, Immersive Virtual Reality Serious Games (IVR SGs) have been suggested as an effective tool for emergency training. The notion of IVR SGs is consistent with the concept of problem-based gaming (PBG), where trainees interact with games in a loop of forming a playing strategy, applying the strategy, observing consequences, and making reflection. PBG triggers reflection-on-action, enabling trainees to reform perceptions and establish knowledge after making a response to a scenario. However, in the literature of PBG, little effort has been made for trainees to reflect while they are making a response (i.e., reflection-in-action) in a scenario. In addition, trainees do not have the possibility to adjust their responses and reshape their behaviors according to their reflection-in-action. In order to overcome these limitations, this study proposes a game mechanism, which integrates spiral narratives with immediate feedback, to underpin reflection-in-action and reflective redo in PBG. An IVR SG training system suited to earthquake emergency training was developed, incorporating the proposed game mechanism. A controlled experiment with 99 university students and staff was conducted. Participants were divided into three groups, with three interventions tested: a spiral narrated IVR SG, a linear narrated IVR SG, and a leaflet. Both narrated IVR SGs were effective in terms of immediate knowledge gain and self-efficacy improvement. However, challenges and opportunities for future research have been suggested.
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    Critical business intelligence practices to create meta-knowledge
    (Inderscience Publishers Ltd, 14/01/2021) Mathrani S
    In order to successfully implement strategies and respond to business variations in real-time, business intelligence (BI) systems have been deployed by organisations that assist in focused analytical assessments for execution of critical decisions. Although businesses have realised the significance of BI, few studies have explored their analytical decision-enabling capabilities linked to organisational practices. This study investigates the BI practices critical in creating meta-knowledge successfully for strategy-focused analytical decision-making. First, key BI suppliers are interviewed to develop an understanding of their BI capabilities and current deployment practices. Subsequently, two large BI implementation case studies are conducted to examine their practices in data transformation process. Findings reveal that BI practices are highly context-specific in mapping decisions with data assets. Complimentary static and dynamic evaluations provide holistic intelligence in predicting and prescribing a more complete picture of the enterprise. These practices vary across firms in their effectiveness reflecting numerous challenges and improvement opportunities.
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    Towards standardisation of evidence-based clinical care process specifications
    (SAGE Journals, 2020-12) McLachlan S; Kyrimi E; Dube K; Hitman G; Simmonds J; Fenton N
    There is a strong push towards standardisation of treatment approaches, care processes and documentation of clinical practice. However, confusion persists regarding terminology and description of many clinical care process specifications which this research seeks to resolve by developing a taxonomic characterisation of clinical care process specifications. Literature on clinical care process specifications was analysed, creating the starting point for identifying common characteristics and how each is constructed and used in the clinical setting. A taxonomy for clinical care process specifications is presented. The De Bleser approach to limited clinical care process specifications characterisation was extended and each clinical care process specification is successfully characterised in terms of purpose, core elements and relationship to the other clinical care process specification types. A case study on the diagnosis and treatment of Type 2 Diabetes in the United Kingdom was used to evaluate the taxonomy and demonstrate how the characterisation framework applies. Standardising clinical care process specifications ensures that the format and content are consistent with expectations, can be read more quickly and high-quality information can be recorded about the patient. Standardisation also enables computer interpretability, which is important in integrating Learning Health Systems into the modern clinical environment. The approach presented allows terminologies for clinical care process specifications that were widely used interchangeably to be easily distinguished, thus, eliminating the existing confusion.
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    Tempting the Fate of the furious: cyber security and autonomous cars
    (Routledge, 27/05/2022) McLachlan S; Schafer B; Dube K; Kyrimi E; Fenton N
    The United Nations Economic Commission for Europe (UN ECE) has developed new aspects of its WP.29 agreement for harmonising vehicle regulations, focusing on the regulation of vehicle manufacturers’ approaches to ensuring vehicle cyber security by requiring implementation of an approved cyber security management system (CSMS). This paper investigates the background, framework and content of WP.29’s cyber security regulation. We provide an overall description of the processes required to become certified, discuss key gaps, issues and the impacts of implementation on stakeholders, and provide recommendations for manufacturers and the authorities who will oversee the operation. Putting the discussion into a broader theoretical framework on risk certification, we explore to the role of non-academic sources to shape public risk perception and to drive, for better or worse, legislative responses.
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    Initialization-similarity clustering algorithm
    (Springer Science+Business Media, LLC, 2019-12) Liu T; Zhu J; Zhou J; Zhu Y; Zhu X
    Classic k-means clustering algorithm randomly selects centroids for initialization to possibly output unstable clustering results. Moreover, random initialization makes the clustering result hard to reproduce. Spectral clustering algorithm is a two-step strategy, which first generates a similarity matrix and then conducts eigenvalue decomposition on the Laplacian matrix of the similarity matrix to obtain the spectral representation. However, the goal of the first step in the spectral clustering algorithm does not guarantee the best clustering result. To address the above issues, this paper proposes an Initialization-Similarity (IS) algorithm which learns the similarity matrix and the new representation in a unified way and fixes initialization using the sum-of-norms regularization to make the clustering more robust. The experimental results on ten real-world benchmark datasets demonstrate that our IS clustering algorithm outperforms the comparison clustering algorithms in terms of three evaluation metrics for clustering algorithm including accuracy (ACC), normalized mutual information (NMI), and Purity.