scholarly journals Personas Design for Conversational Systems in Education

Informatics ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 46 ◽  
Author(s):  
Fatima Ali Amer Jid Almahri ◽  
David Bell ◽  
Mahir Arzoky
Keyword(s):  
Machine Learning ◽  
Student Engagement ◽  
Interaction Analysis ◽  
Design Science ◽  
Science Research ◽  
Data Driven ◽  
Gap Statistic ◽  
Optimal Value ◽  
Persona Development ◽  
Future Work

This research aims to explore how to enhance student engagement in higher education institutions (HEIs) while using a novel conversational system (chatbots). The principal research methodology for this study is design science research (DSR), which is executed in three iterations: personas elicitation, a survey and development of student engagement factor models (SEFMs), and chatbot interaction analysis. This paper focuses on the first iteration, personas elicitation, which proposes a data-driven persona development method (DDPDM) that utilises machine learning, specifically the K-means clustering technique. Data analysis is conducted using two datasets. Three methods are used to find the K-values: the elbow, gap statistic, and silhouette methods. Subsequently, the silhouette coefficient is used to find the optimal value of K. Eight personas are produced from the two data analyses. The pragmatic findings from this study make two contributions to the current literature. Firstly, the proposed DDPDM uses machine learning, specifically K-means clustering, to build data-driven personas. Secondly, the persona template is designed for university students, which supports the construction of data-driven personas. Future work will cover the second and third iterations. It will cover building SEFMs, building tailored interaction models for these personas and then evaluating them using chatbot technology.

Applications of Machine Learning in Education

2021 ◽  
pp. 72-113
Author(s):  
Fatima Ali Amer Jid Almahri ◽  
David Bell ◽  
Mahir Arzoky
Keyword(s):  
Machine Learning ◽  
Student Engagement ◽  
Interaction Analysis ◽  
Design Science ◽  
Science Research ◽  
Data Driven ◽  
Persona Development ◽  
Applications Of Machine Learning ◽  
Future Work ◽  
Is Design

This research aims to explore how to enhance student engagement in higher education institutions using novel chatbots. This study's principal research methodology is design science research, which is executed in three iterations: personas elicitation, a survey and development of student engagement factor models (SEFMs), and chatbot interaction analysis. This chapter focuses on the first iteration, personas elicitation, which proposes a data-driven persona development method (DDPDM) that utilises machine learning, precisely a k-means clustering technique. Data analysis is conducted using two datasets. Eight personas are produced from the two data analyses. The pragmatic findings from this study make two contributions to the current literature. Firstly, the proposed DDPDM uses machine learning, specifically k-means clustering, to build data-driven personas. Secondly, the persona template is designed for university students, which supports the construction of data-driven personas. Future work will cover the second and third iterations.

scholarly journals Doclass: open-source software to support document labeling and classification

2020 ◽  
Author(s):  
Marcelo Inuzuka ◽  
Hugo Do Nascimento ◽  
Fernando Almeida ◽  
Bruno Barros ◽  
Walid Jradi
Keyword(s):  
Active Learning ◽  
Open Source ◽  
Open Source Software ◽  
Design Science ◽  
Text Processing ◽  
Science Research ◽  
Development Stage ◽  
Large Sets ◽  
Rest Api ◽  
Future Work

This article introduces Doclass, a free and open-source software for the Web that aims to assist in labeling and classifying large sets of documents. The research involved a design science research methodology, guided by the real demands of a legal text processing company. The architecture, several design decisions and the current development stage of the software are presented. Preliminary user experiments for evaluating interactive document labeling are described. As a result, the first version of a system with an architecture composed of a mobile frontend that communicates with a backend through a REST API was published, with satisfactory performance evaluation by the applicant. Other results involve the use of active learning techniques to reduce human effort when performing the classification of documents, as well as the Uncertainty strategy to choose the document to be labeled. The effectiveness of the stop criterion for the active learning technique based on confidence level was tested and proved unsatisfactory, remaining as a future work.

scholarly journals Self-Service Data Science in Healthcare with Automated Machine Learning

2020 ◽  
Vol 10 (9) ◽  
pp. 2992
Author(s):  
Richard Ooms ◽  
Marco Spruit
Keyword(s):  
Machine Learning ◽  
User Interfaces ◽  
Web Application ◽  
Data Science ◽  
Design Science ◽  
Science Research ◽  
Research Approach ◽  
Automated Machine Learning ◽  
Self Service ◽  
Service Data

(1) Background: This work investigates whether and how researcher-physicians can be supported in their knowledge discovery process by employing Automated Machine Learning (AutoML). (2) Methods: We take a design science research approach and select the Tree-based Pipeline Optimization Tool (TPOT) as the AutoML method based on a benchmark test and requirements from researcher-physicians. We then integrate TPOT into two artefacts: a web application and a notebook. We evaluate these artefacts with researcher-physicians to examine which approach suits researcher-physicians best. Both artefacts have a similar workflow, but different user interfaces because of a conflict in requirements. (3) Results: Artefact A, a web application, was perceived as better for uploading a dataset and comparing results. Artefact B, a Jupyter notebook, was perceived as better regarding the workflow and being in control of model construction. (4) Conclusions: Thus, a hybrid artefact would be best for researcher-physicians. However, both artefacts missed model explainability and an explanation of variable importance for their created models. Hence, deployment of AutoML technologies in healthcare remains currently limited to the exploratory data analysis phase.

Towards data-driven decision support for organizational IT security audits

2018 ◽  
Vol 60 (4) ◽  
pp. 207-217 ◽  
Author(s):  
Michael Brunner ◽  
Christian Sillaber ◽  
Lukas Demetz ◽  
Markus Manhart ◽  
Ruth Breu
Keyword(s):  
Decision Support ◽  
Design Science ◽  
Science Research ◽  
Data Driven ◽  
It Security ◽  
Related Data ◽  
Security Controls ◽  
Audit Data ◽  
Data Driven Approach ◽  
Security Audits

Abstract As the IT landscape of organizations increasingly needs to comply with various laws and regulations, organizations manage a plethora of security-related data and have to verify the adequacy and effectiveness of their security controls through internal and external audits. Existing Governance, Risk and Compliance (GRC) approaches provide little support for auditors or are tailored to the needs of auditors and do not fully support required management activities of the auditee. To address this gap and move towards a holistic solution, a data-driven approach is proposed. Following the design science research paradigm, a data-driven approach for audit data management and analytics that addresses organizational needs as well as requirements for audit data analytics was developed. We contribute workflow support and associated data models to support auditing and security decision making processes. The evaluation shows the viability of the proposed IT artifact and its potential to reduce costs and complexity of security management processes and IT security audits. By developing a model and associated decision support workflows for the entire IT security audit lifecycle, we present a solution for both the auditee and the auditor. This is useful to developers of GRC tools, vendors, auditors and organizational decision makers.

scholarly journals A data driven methodology for social science research with left-behind children as a case study

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242483
Author(s):  
Chao Wu ◽  
Guolong Wang ◽  
Simon Hu ◽  
Yue Liu ◽  
Hong Mi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Science ◽  
Social Issues ◽  
Social Science Research ◽  
Science Research ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Left Behind ◽  
Learning Techniques ◽  
The Social

For decades, traditional correlation analysis and regression models have been used in social science research. However, the development of machine learning algorithms makes it possible to apply machine learning techniques for social science research and social issues, which may outperform standard regression methods in some cases. Under the circumstances, this article proposes a methodological workflow for data analysis by machine learning techniques that have the possibility to be widely applied in social issues. Specifically, the workflow tries to uncover the natural mechanisms behind the social issues through a data-driven perspective from feature selection to model building. The advantage of data-driven techniques in feature selection is that the workflow can be built without so much restriction of related knowledge and theory in social science. The advantage of using machine learning techniques in modelling is to uncover non-linear and complex relationships behind social issues. The main purpose of our methodological workflow is to find important fields relevant to the target and provide appropriate predictions. However, to explain the result still needs theory and knowledge from social science. In this paper, we trained a methodological workflow with left-behind children as the social issue case, and all steps and full results are included.

IT Governance Maturity Patterns in Portuguese Healthcare

2017 ◽  
pp. 24-52
Author(s):  
Ruben Pereira ◽  
Miguel Mira da Silva ◽  
Luís Velez Lapão
Keyword(s):  
Design Science ◽  
It Governance ◽  
Science Research ◽  
Design Science Research ◽  
Healthcare Services ◽  
Exploratory Research ◽  
Financial Industry ◽  
Use Of Technology ◽  
The Right ◽  
Future Work

The pervasive use of technology in organizations to address the increased services complexity has created a critical dependency on Information Technology (IT) that calls to a specific focus on IT Governance (ITG). However, determining the right ITG mechanisms remains a complex endeavor. This paper uses Design Science Research and proposes an exploratory research by analyzing ITG case studies to elicit possible ITG mechanisms patterns. Six interviews were performed in Portuguese healthcare services organizations to assess the ITG practices. Our goal is to build some theories (ITG mechanisms patterns), which we believe will guide healthcare services organizations about the advisable ITG mechanisms given their specific context. We also intend to elicit conclusions regarding the most relevant ITG mechanisms for Portuguese healthcare services organizations. Additionally, a comparison is made with the financial industry to identify improvement opportunities. We finish our work with limitations, contribution and future work.

Business/IT Alignment through IT Governance Patterns in Portuguese Healthcare

2014 ◽  
Vol 5 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Ruben Pereira ◽  
Miguel Mira da Silva ◽  
Luís Velez Lapão
Keyword(s):  
Design Science ◽  
It Governance ◽  
Science Research ◽  
Healthcare Services ◽  
Exploratory Research ◽  
Financial Industry ◽  
It Alignment ◽  
Use Of Technology ◽  
The Right ◽  
Future Work

The pervasive use of technology in organizations to address the increased services complexity has created a critical dependency on information technology (IT) that calls to a specific focus on IT Governance (ITG). However, determining the right ITG mechanisms remains a complex endeavor. This paper uses Design Science Research and proposes an exploratory research by analyzing ITG case studies to elicit possible ITG mechanisms patterns. Six interviews were performed in Portuguese healthcare services organizations to assess the ITG practices. The goal of the authors is to build some theories (ITG mechanisms patterns), which will guide healthcare services organizations about the advisable ITG mechanisms given their specific context. The authors also intend to elicit conclusions regarding the most relevant ITG mechanisms for Portuguese healthcare services organizations. Additionally, a comparison is made with the financial industry to identify improvement opportunities. The authors finish the paper with limitations, contribution and future work.

scholarly journals WaterBench: A Large-scale Benchmark Dataset for Data-Driven Streamflow Forecasting

2021 ◽  
Author(s):  
Ibrahim Demir ◽  
Zhongrun Xiang ◽  
Bekir Zahit Demiray ◽  
Muhammed Sit
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Earth Science ◽  
Science Research ◽  
Benchmark Dataset ◽  
Data Driven ◽  
Streamflow Forecasting ◽  
Learning Research

This study proposes a comprehensive benchmark dataset for streamflow forecasting, WaterBench, that follows FAIR data principles that is prepared with a focus on convenience for utilizing in data-driven and machine learning studies, and provides benchmark performance for state-of-art deep learning architectures on the dataset for comparative analysis. By aggregating the datasets of streamflow, precipitation, watershed area, slope, soil types, and evapotranspiration from federal agencies and state organizations (i.e., NASA, NOAA, USGS, and Iowa Flood Center), we provided the WaterBench for hourly streamflow forecast studies. This dataset has a high temporal and spatial resolution with rich metadata and relational information, which can be used for varieties of deep learning and machine learning research. We defined a sample streamflow forecasting task for the next 120 hours and provided performance benchmarks on this task with sample linear regression and deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and S2S (Sequence-to-sequence). To some extent, WaterBench makes up for the lack of a unified benchmark in earth science research. We highly encourage researchers to use the WaterBench for deep learning research in hydrology.

scholarly journals Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

2021 ◽  
Vol 4 ◽  
Author(s):  
Matthew S. Shane ◽  
William J. Denomme
Keyword(s):  
Machine Learning ◽  
Substance Use ◽  
Data Driven ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
High Level ◽  
Future Work ◽  
Interacting Features ◽  
Research Domain

Abstract By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.

Standardized but flexible information exchange for networked public administrations

2016 ◽  
Vol 10 (2) ◽  
pp. 239-255 ◽  
Author(s):  
Erwin Folmer ◽  
Martin Matzner ◽  
Michael Räckers ◽  
Hendrik Scholta ◽  
Jörg Becker
Keyword(s):  
Information Exchange ◽  
Design Science ◽  
Science Research ◽  
Information Structures ◽  
Content Type ◽  
Qualitative Feedback ◽  
Novel Method ◽  
Governmental Institutions ◽  
The Many ◽  
Future Work

Purpose Governmental institutions must cooperate with other organizations across institutional boundaries to achieve high-quality service offerings. The required cooperation may lead to complex networks, including several of the thousands of public administrations in the many federal layers of a single country. This paper aims to address the key challenge of the proper management of the information exchange between networked actors, which is generally conducted by means of forms. Design/methodology/approach Following the design science research paradigm, this research develops a method that assists in the design and maintenance of forms in public administrations. Findings Discussions in the project’s focus groups add evidence to the researchers’ expectation that the method developed in this study improves the quality of forms while reducing the effort required for their design and maintenance. Research limitations/implications This paper includes an evaluation of the approach based on qualitative feedback from the project’s stakeholders, although the implementation of the workflows and procedures is subject to future work that evaluates the approach in a variety of practical settings. Practical implications The method developed in this paper allows public administrations and legislative authorities to design and manage forms in a cooperative way. Software developers can assume the existence of information structures. The approach extends the BOMOS standardization framework to the operational level. Originality/value The main contribution of this paper is the development of a novel method that will change how information exchange is managed in public administrations.

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