Adopting Learning Analytics to Inform Postgraduate Curriculum Design: Recommendations and Research Agenda

2023 | Information Systems Frontiers | Citations: 0

Authors: Dennehy, Denis; Conboy, Kieran; Babu, Jaganath

Abstract: Understanding student sentiment plays a vital role in understanding the changes ... Expand

Abstract: Understanding student sentiment plays a vital role in understanding the changes that could or should be made in curriculum design at university. Learning Analytics (LA) has shown potential for improving student learning experiences and supporting teacher inquiry. Yet, there is limited research that reports on the adoption and actual use of LA to support teacher inquiry. This four-year longitudinal study captures sentiment of postgraduate students at a university in Ireland, by integrating LA with the steps of teacher inquiry. This study makes three important contributions to teaching and learning literature. First, it reports on the use of LA to support teacher inquiry over four one-year cycles of a Master of Science in Business Analytics programme between 2016 and 2020. Second, it provides evidence-based recommendations on how to optimise LA to support teacher inquiry, with specific attention as to how these can improve the assimilation of LA into the curriculum design and delivery. Third, the paper concludes with a research agenda to help improve the adoption and integration of LA in the future. Collapse

Topics: innovation management Tableau software analytical information system interactive data visualization Python

Methods: qualitative interview Cross-Industry Standard Process for Data Mining sentiment analysis case study design framework

Theories: assimilation theory of learning

Digital nudging for technical debt management at Credit Suisse

2023 | European Journal of Information Systems | Citations: 0

Authors: Haki, Kazem; Rieder, Annamina; Buchmann, Lorena; W.Schneider, Alexander

Abstract: Technical debt (TD) is a technical compromise wherein the ability to maintain in ... Expand

Abstract: Technical debt (TD) is a technical compromise wherein the ability to maintain information technology (IT) applications over the long term is sacrificed for short-term goals. TD occurs when software development teams undergo constant pressure to release applications swiftly, on a tight schedule. The accumulation of TD, which often leads to a significant cost surplus, presents a ubiquitous challenge in technology-driven organisations. To keep TD levels under control, many organisations implement top-down mechanisms that impose enterprise-wide principles on software development teams. This clinical research presents a complementary but distinct approach to managing TD. A digital nudge was introduced at Credit Suisse, a global financial services company, to help raise awareness and understanding, and stimulate actions related to TD decision-making in software development teams. This paper reports on the nudge’s clinical design, implementation, impact, and evaluation. As the nudge was effective in reducing TD in IT applications after one year of use, we demonstrate that digital nudges are viable means for guiding collective decisions in complex decision environments like that of TD management. Our findings have several implications for research and practice. Collapse

Topics: systems development decision making software developer IT manager agile software development

Methods: survey qualitative interview focus group personal interview design methodology

Theories: boundary objects theory nudge theory

Laboratory Forensics for Open Science Readiness: an Investigative Approach to Research Data Management

2023 | Information Systems Frontiers | Citations: 0

Authors: Lefebvre, Armel; Spruit, Marco

Abstract: Recently, the topic of research data management has appeared at the forefront of ... Expand

Abstract: Recently, the topic of research data management has appeared at the forefront of Open Science as a prerequisite for preserving and disseminating research data efficiently. At the same time, scientific laboratories still rely upon digital files that are processed by experimenters to analyze and communicate laboratory results. In this study, we first apply a forensic process to investigate the information quality of digital evidence underlying published results. Furthermore, we use semiotics to describe the quality of information recovered from storage systems with laboratory forensics techniques. Next, we formulate laboratory analytics capabilities based on the results of the forensics analysis. Laboratory forensics and analytics form the basis of research data management. Finally, we propose a conceptual overview of open science readiness, which combines laboratory forensics techniques and laboratory analytics capabilities to help overcome research data management challenges in the near future. Collapse

Topics: data management data quality digital forensics digital dashboard digital transformation

Methods: experiment digital trace data case study design artifact exploratory data analysis

Theories: semiotic theory theory of information information systems theory

They Work with Data and Do Some Science: How Identity Conflict Turns Data Professionals away from Data Science

2023 | International Conference on Information Systems | Citations: 0

Authors: Rosa, Marta; Aikath, Devdeep; Mayer, Anne

Abstract: Data science is widely perceived as an attractive, lucrative, and prestigious e ... Expand

Abstract: Data science is widely perceived as an attractive, lucrative, and prestigious emerging occupation. Research so far has focused on understanding data scientists' practices and identity work associated with establishing and legitimizing this new occupation. This work, however, is not sufficient to explain a phenomenon we observed whereby professionals rejected the opportunity to adopt this new occupational identity. To understand why professionals may not want to be labeled data scientists, we analyzed 43 interviews with data professionals at an educational measurement company in the U.S. Despite a clear steer from management towards the data science label, many interviewees stuck to their established professional identities. In our preliminary findings, we use the literature on identity conflict as a lens to make sense of our observations. By identifying three types of conflicts: 1) task conflict, 2) role conflict, 3) tool conflict, we begin to explain what turns professionals away from data science. Collapse

Topics: database system structured query language Tableau software programming language relational database

Methods: qualitative interview grounded theory machine learning personal interview qualitative coding

Open Data Explorer: An End-to-end Tool for Data Storytelling using Open Data

2022 | Americas Conference on Information Systems | Citations: 0

Authors: Chokki, Abiola Paterne; Frenay, Benoit; Vanderose, Benoît

Abstract: Enabling users to better understand government actions is one reason why govern ... Expand

Abstract: Enabling users to better understand government actions is one reason why governments have sought to open their data to the public. Data storytelling tools help to achieve this goal by facilitating users to turn data into stories. However, existing tools are not able to provide the necessary features to overcome the barriers users face at different data storytelling stages. This paper provides findings regarding the features in the design of these data storytelling tools in the open data context and also presents a generic and endto-end tool called ODE, which helps users through the different data storytelling stages. To achieve the paper's objectives, a literature review was first conducted to collect the features needed for the different data storytelling stages. Then, the identified features were integrated into ODE and its effectiveness in helping users to easily turn data into stories was demonstrated through an evaluation involving 11 users. Collapse

Topics: open data ease of use digital dashboard perceived ease of use data quality

Methods: survey design artifact literature study design science descriptive statistic

Theories: technology acceptance model

A Hybrid Introductory Analytics Course: Lessons Learned During The COVID-19 Pandemic

2022 | Americas Conference on Information Systems | Citations: 0

Authors: Darcy, David P.; Satpathy, Asish

Abstract: We report on the design & development of an introductory analytics course deliv ... Expand

Abstract: We report on the design & development of an introductory analytics course delivered to over 6,000 students undergraduate business students thus far. The course is delivered in a hybrid mode, arguably the best combination of online and face to face delivery modes. The Pandemic reinforced that lesson, among others. We explore those lessons and suggest directions for further research. Collapse

Topics: pandemic video conference online learning system laptop computer analytical information system

Methods: Cross-Industry Standard Process for Data Mining exploratory data analysis machine learning

Visualizing the Maturing Global API Ecosystem

2022 | HICSS | Citations: 1

Authors: Heshmatisafa, Saeid; Huhtamäki, Jukka; Seppänen, Marko

Abstract: Platform boundary resources are prominent digital strategy tools for companies t ... Expand

Abstract: Platform boundary resources are prominent digital strategy tools for companies to disclose business assets to partners and consumers. However, the platform boundary resources of API ecosystems are dependent on API usage and characteristics as well as the social environment around them. This study focuses on the global API ecosystem guided by two assumptions: 1) geographic proximity is distorted, and the global API ecosystem is decentralized in a digitally connected world, and 2) open co-innovation is somewhat prone to geographic nearness and centralized. A data-driven network analysis was utilized to depict the global and regional geolocation of more than 22,000 APIs and 6,000 mashups. First, the global locations of API providers were examined. Next, the connectivity of API mashups at global and regional levels was studied. The findings suggest that APIs are distributed globally, and the API ecosystem has moved from emerging to mature; however, the distribution is skewed towards entrepreneurial regions in North America. Finally, the theoretical and managerial implications are discussed. Collapse

Topics: application programming interface Python productivity missing data Tableau software

Methods: computational algorithm exploratory data analysis cluster analysis network analysis

Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems

2022 | Business & Information Systems Engineering | Citations: 0

Authors: Landwehr, Julius Peter; Kühl, Niklas; Walk, Jannis; Gnädig, Mario

Abstract: With the ever-increasing societal dependence on electricity, one of the critical ... Expand

Abstract: With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. The paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm, two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements are derived from literature and the application field. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact’s capability to capture selected faults (regarding insulators and safety pins) in unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. The paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators’ specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature. Collapse

Topics: decision support system decision making data quality decision support usability

Methods: design principle design science survey qualitative interview image processing

Theories: technology acceptance model

Emotional Regulation in Synchronous Online Collaborative Learning: A Facial Expression Recognition Study

2022 | International Conference on Information Systems | Citations: 0

Authors: Nguyen, Andy; Hong, Yvonne; Dang, Belle; Nguyen, Phuong

Abstract: Emotional regulation in learning has been recognised as a critical factor for c ... Expand

Abstract: Emotional regulation in learning has been recognised as a critical factor for collaborative learning success. However, the "unobservable" processes of emotion and motivation at the core of learning regulation have challenged the methodological progress to examine and support learners' regulation. Artificial intelligence (AI) and learning analytics have recently brought novel opportunities for investigating the learning processes. This multidisciplinary study proposes a novel fine-grained approach to provide empirical evidence on the application of these advanced technologies in assessing emotional regulation in synchronous computer-support collaborative learning (CSCL). The study involved eighteen university students (N=18) working collaboratively in groups of three. The process mining analysis was adopted to explore the patterns of emotional regulation in synchronous CSCL, while AI facial expression recognition was used for examining learners' associated emotions and emotional synchrony in regulatory activities. Our findings establish a foundation for further design of human-centred AI-enhanced support for collaborative learning regulation. Collapse

Topics: artificial intelligence process mining online learning system collaborative authoring programming language

Methods: qualitative coding Kruskal Wallis test nonparametric test chi squared test exploratory data analysis

Skipping class: improving human-driven data exploration and querying through instances

2022 | European Journal of Information Systems | Citations: 0

Authors: Saghafi, Arash; Wand, Yair; Parsons, Jeffrey

Abstract: With the growing focus on business analytics and data-driven decision-making, th ... Expand

Abstract: With the growing focus on business analytics and data-driven decision-making, there is a greater need for humans to interact effectively with data. We propose that presenting data to human users in terms of instances and attributes provides a more flexible and usable structure for querying, exploring, and analysing data. Compared to a traditional representation, an instance-based representation does not impose any predefined classification schema over the data when it is presented to users. This paper examines the potential utility of instance-based data through two laboratory experiments – the first focusing on exploration of data for pattern discovery (open-ended tasks) and the second on retrieval of information (closed-ended tasks). In both cases, participants were able to achieve better results in tasks using instance-based data than using class-based representations. Given the growing need for self-service analytics, as well as using information for purposes not anticipated when it was collected, we show that instance-based representations can be an effective way to satisfy the emerging needs of information users. Collapse

Topics: database system decision making Tableau software business intelligence information retrieval

Methods: experiment parametric test Student's t-test experimental group data modeling

Theories: schema theory