Comparison of Clustering Algorithms for Learning Analytics with Educational Datasets

Learning Analytics is becoming a key tool for the analysis and improvement of digital education processes, and its potential benefit grows with the size of the student cohorts generating data. In the context of Open Education, the potentially massive student cohorts and the global audience represent a great opportunity for significant analyses and breakthroughs in the field of learning analytics. However, these potentially huge datasets require proper analysis techniques, and different algorithms, tools and approaches may perform better in this specific context. In this work, we compare different clustering algorithms using an educational dataset. We start by identifying the most relevant algorithms in Learning Analytics and benchmark them to determine, according to internal validation and stability measurements, which algorithms perform better. We analyzed seven algorithms, and determined that K-means and PAM were the best performers among partition algorithms, and DIANA was the best performer among hierarchical algorithms

Measuring the impact of learning at the workplace on organisational performance

Purpose: The purpose of this article is to explore the importance of workplace learning in the context of performance measurement on an organisational level. It shows how workplace learning analytics can be grounded on professional identity transformation theory and integrated into performance measurement approaches to understand its organisation-wide impact. Design/methodology/approach: In a conceptual approach, a framework to measure the organisation-wide impact of workplace learning interventions has been developed. As a basis for the description of the framework, related research on relevant concepts in the field of performance measurement approaches, workplace learning, professional identity transformation, workplace and social learning analytics are discussed. A case study in a European Public Employment Service is presented. The framework is validated by qualitative evaluation data from three case studies. Finally, theoretical as well as practical implications are discussed. Findings: Professional identity transformation theory provides a suitable theoretical framework to gain new insights into various dimensions of workplace learning. Workplace learning analytics can reasonably be combined with classical performance management approaches to demonstrate its organisation-wide impact. A holistic and streamlined framework is perceived as beneficial by practitioners from several European Public Employment Services. Research limitations/implications: Empirical data originates from three case studies in the non-profit sector only. The presented framework needs to be further evaluated in different organisations and settings. Practical implications: The presented framework enables non-profit organisations to integrate workplace learning analytics in their organisation-wide performance measurement, which raises awareness for the importance of social learning at the workplace. Originality/value: The paper enriches the scarce research base about workplace learning analytics and its potential links to organisation-wide performance measurement approaches. In contrast to most previous literature, a thorough conceptualisation of workplace learning as a process of professional identity transformation is used

Forward solutions in digital learning transformation: a study in navigating 21st-century organizational learning for learning & development professionals

While there is a substantial volume of information on digital transformation in companies and basic knowledge of the learning functions in organizations, there is little academic research on the skilling required of Learning and Development Practitioners or Professionals (LDPs) and the impact 21st century digital transformation has on their role. This mixed methods study shares lived experiences and the perceptions of LDPs and identifies challenges with which they are faced. Overall, the study explores transformation of LDPs within the construct of organizations and the digital evolution. The study reviews how LDPs are adapting to the rapid changes and the evolution of their learning environments, their input on the support they receive, and how adapting their skills and capabilities are crucial for future success. Furthermore, it identifies the changes that impact the Learning and Development function (L&D) and the effects on LDPs\u27 roles, redefining and reimagining the purpose of organizational learning as it makes up the new ecosystem of learning driven by technology. It aimed to provide insights and answer questions on how LDPs are being supported by their leaders, are leaders removing roadblocks or adding new ones. The study used data, insights, and input from 56 learning practitioners currently impacted by agile organizational practices and the evolution of their role. Guiding the study were several key research questions which focused on the culture and support of learning by LDPs. Do LDPs feel they have opportunities to cultivate new skills and capabilities for the 21st century, and how have the adapted their practices to embrace digital learning. This study revealed 4 key conclusions related to creating a culture of learning for LDPs and providing an ecosystem which will contribute to their success and the broader community of practice. The study concluded with recommendations for future research and obtaining additional input of learning practitioners via interviews to seek out viewpoints which were not easily captured in surveys. Although additional points of view were welcomed, further recommendations identified excluding higher education practitioners to drive to more corporate organization results

Ernst Denert Award for Software Engineering 2020

This open access book provides an overview of the dissertations of the eleven nominees for the Ernst Denert Award for Software Engineering in 2020. The prize, kindly sponsored by the Gerlind & Ernst Denert Stiftung, is awarded for excellent work within the discipline of Software Engineering, which includes methods, tools and procedures for better and efficient development of high quality software. An essential requirement for the nominated work is its applicability and usability in industrial practice. The book contains eleven papers that describe the works by Jonathan Brachthäuser (EPFL Lausanne) entitled What You See Is What You Get: Practical Effect Handlers in Capability-Passing Style, Mojdeh Golagha’s (Fortiss, Munich) thesis How to Effectively Reduce Failure Analysis Time?, Nikolay Harutyunyan’s (FAU Erlangen-Nürnberg) work on Open Source Software Governance, Dominic Henze’s (TU Munich) research about Dynamically Scalable Fog Architectures, Anne Hess’s (Fraunhofer IESE, Kaiserslautern) work on Crossing Disciplinary Borders to Improve Requirements Communication, Istvan Koren’s (RWTH Aachen U) thesis DevOpsUse: A Community-Oriented Methodology for Societal Software Engineering, Yannic Noller’s (NU Singapore) work on Hybrid Differential Software Testing, Dominic Steinhofel’s (TU Darmstadt) thesis entitled Ever Change a Running System: Structured Software Reengineering Using Automatically Proven-Correct Transformation Rules, Peter Wägemann’s (FAU Erlangen-Nürnberg) work Static Worst-Case Analyses and Their Validation Techniques for Safety-Critical Systems, Michael von Wenckstern’s (RWTH Aachen U) research on Improving the Model-Based Systems Engineering Process, and Franz Zieris’s (FU Berlin) thesis on Understanding How Pair Programming Actually Works in Industry: Mechanisms, Patterns, and Dynamics – which actually won the award. The chapters describe key findings of the respective works, show their relevance and applicability to practice and industrial software engineering projects, and provide additional information and findings that have only been discovered afterwards, e.g. when applying the results in industry. This way, the book is not only interesting to other researchers, but also to industrial software professionals who would like to learn about the application of state-of-the-art methods in their daily work

Developing Learning Analytics for Epistemic Commitments in a Collaborative Information Seeking Environment

Learning analytics sits at the confluence of learning, information, and computer sciences. Using a distinctive account of learning analytics as a form of assessment, I first argue for its potential in pedagogically motivated learning design, suggesting a particular construct – epistemic cognition in literacy contexts – to probe using learning analytics. I argue for a recasting of epistemic cognition as ‘epistemic commitments’ in collaborative information tasks drawing a novel alignment between information seeking and multiple document processing (MDP) models, with empirical and theoretical grounding given for a focus on collaboration and dialogue in such activities. Thus, epistemic commitments are seen in the ways students seek, select, and integrate claims from multiple sources, and the ways in which their collaborative dialogue is brought to bear in this activity. Accordingly, the empirical element of the thesis develops two pedagogically grounded literacy based tasks: a MDP task, in which pre-selected documents were provided to students; and a collaborative information seeking task (CIS), in which students could search the web. These tasks were deployed at scale (n > 500) and involved writing an evaluative review, followed by a pedagogically supported peer assessment task. Assessment outcomes were analysed in the context of a new epistemic commitments-oriented set of trace data, and psychometric data regarding the participants’ epistemic cognition. Demonstrating the value of the methodological and conceptual approach taken, qualitative analyses indicate clear epistemic activity, and stark differences in behaviour between groups, the complexity of which is challenging to model computationally. Despite this complexity, quantitative analyses indicate that up to 30% of variance in output scores can be modelled using behavioural indicators. The explanatory potential of behaviourally-oriented models of epistemic commitments grounded in tool-interaction and collaborative dialogue is demonstrated. The thesis provides an exemplification of theoretically positioned analytic development, drawing on interdisciplinary literatures in addressing complex learning contexts

Ernst Denert Award for Software Engineering 2020

This open access book provides an overview of the dissertations of the eleven nominees for the Ernst Denert Award for Software Engineering in 2020. The prize, kindly sponsored by the Gerlind & Ernst Denert Stiftung, is awarded for excellent work within the discipline of Software Engineering, which includes methods, tools and procedures for better and efficient development of high quality software. An essential requirement for the nominated work is its applicability and usability in industrial practice. The book contains eleven papers that describe the works by Jonathan Brachthäuser (EPFL Lausanne) entitled What You See Is What You Get: Practical Effect Handlers in Capability-Passing Style, Mojdeh Golagha’s (Fortiss, Munich) thesis How to Effectively Reduce Failure Analysis Time?, Nikolay Harutyunyan’s (FAU Erlangen-Nürnberg) work on Open Source Software Governance, Dominic Henze’s (TU Munich) research about Dynamically Scalable Fog Architectures, Anne Hess’s (Fraunhofer IESE, Kaiserslautern) work on Crossing Disciplinary Borders to Improve Requirements Communication, Istvan Koren’s (RWTH Aachen U) thesis DevOpsUse: A Community-Oriented Methodology for Societal Software Engineering, Yannic Noller’s (NU Singapore) work on Hybrid Differential Software Testing, Dominic Steinhofel’s (TU Darmstadt) thesis entitled Ever Change a Running System: Structured Software Reengineering Using Automatically Proven-Correct Transformation Rules, Peter Wägemann’s (FAU Erlangen-Nürnberg) work Static Worst-Case Analyses and Their Validation Techniques for Safety-Critical Systems, Michael von Wenckstern’s (RWTH Aachen U) research on Improving the Model-Based Systems Engineering Process, and Franz Zieris’s (FU Berlin) thesis on Understanding How Pair Programming Actually Works in Industry: Mechanisms, Patterns, and Dynamics – which actually won the award. The chapters describe key findings of the respective works, show their relevance and applicability to practice and industrial software engineering projects, and provide additional information and findings that have only been discovered afterwards, e.g. when applying the results in industry. This way, the book is not only interesting to other researchers, but also to industrial software professionals who would like to learn about the application of state-of-the-art methods in their daily work