The Ethical Matrix in Digital Innovation Projects in Higher Education

Higher educational institutions incorporate projects into their curricula, in which students, together with educators, researchers and professionals from practice, try to find solutions for real, societal problems, to develop relevant skills. Because such solutions are increasingly digital with high impact on society, ethical responsibility is an important part of these skills. In this study, we analyze two cases of digital innovation projects in higher education in which the concept of the Ethical Matrix is adapted and integrated in a Value Sensitive Design approach and applied by educators (case 1) and by students (case 2). We find that an adapted version of the Ethical Matrix supports educators and students in taking values of different types of stakeholders into account which leads to different design choices

Exploring Design Principles for Technology-Enhanced Workplace Learning

Technology-enhanced learning can be used to replicate existing teaching practices, supplement existing teaching or transform teaching and/or learning processes and outcomes. Enhancing workplace learning, which is integrated into higher professional education, with technology, calls for designing such transformations. Although research is carried out into different kinds of technological solutions to enhance workplace learning, we do not know which principles should guide such designs. Therefore, we carried out an explorative, qualitative study and found two such design principles for the design of technology-enhanced workplace learning in higher professional education. In this research, we focused on the students’ perspective, since they are the main users of such technology when they are learning at the workplace, as part of their study in becoming lifelong learning, competent professionals

The Potential Impact of Gamification Elements on the Acceptance of Technology in the Context of Education: A Literature Review

Innovative new digital technologies arise within the field of education every day. There seems to be a large potential impact in using gamification for improving acceptance and use of new technologies in education. This study aims to gain better and new insights on how to improve the acceptance of new educational technology by applying gamification elements. To this aim, we performed a systematic literature review of 1271 publications, yielding 56 relevant studies. We positioned these studies based on which gamification element(s) and which educational technology acceptance constructs were discussed. Our results show that few studies focus on individual gamification elements and that most studies focus on the same elements and constructs, i.e. Learning Expectancy, Social Influence and Hedonic Motivation are the most discussed constructs related to increasing the acceptance of educational technology when applying gamification, while Points, Badges, Leaderboards and Social Games & Teamwork are the most discussed gamification elements. The impact of gamifying educational technology is mixed – both negative and positive results are being reported – and thus we conclude that the knowledge of how to successfully gamify educational technology is still limited

From dirty data to multiple versions of truth: How different choices in data cleaning lead to different learning analytics outcomes

Learning analytics is the analysis of student data with the purpose of improving learning. However, the process of data cleaning remains underexposed within learning analytics literature. In this paper, we elaborate on choices made in the cleaning process of student data and their consequences. We illustrate this with a case where data was gathered during six courses taught via Moodle. In this data set, only 21% of the logged activities were linked to a specific course. We illustrate possible choices in dealing with missing data by applying the cleaning process twelve times with different choices on copies of the raw data. Consequently, the analysis of the data shows varying outcomes. As the purpose of learning analytics is to intervene based on analysis and visualizations, it is of utmost importance to be aware of choices made during data cleaning. This paper\u27s main goal is to make stakeholders of (learning) analytics activities aware of the fact that choices are made during data cleaning have consequences on the outcomes. We believe that there should be transparency to the users of these outcomes and give them a detailed report of the decisions made

Refining the Learning Analytics Capability Model: A Single Case Study

Learning analytics can help higher educational institutions improve learning. Its adoption, however, is a complex undertaking. The Learning Analytics Capability Model describes what 34 organizational capabilities must be developed to support the successful adoption of learning analytics. This paper described the first iteration to evaluate and refine the current, theoretical model. During a case study, we conducted four semi-structured interviews and collected (internal) documentation at a Dutch university that is mature in the use of student data to improve learning. Based on the empirical data, we merged seven capabilities, renamed three capabilities, and improved the definitions of all others. Six capabilities absent in extant learning analytics models are present at the case organization, implying that they are important to learning analytics adoption. As a result, the new, refined Learning Analytics Capability Model comprises 31 capabilities. Finally, some challenges were identified, showing that even mature organizations still have issues to overcome

Programmaboekje Seminar Hacking

Tijdens deze door studenten gegeven seminar kwamen de volgende onderwerpen aan bod: DDoS, Clickjacking, Social engineering, SQL injectie, XSS, Brute forcing en Man in the middle

Workplace learning analytics in higher engineering education

Learning in the workplace is crucial in higher engineering education, since it allows students to transfer knowledge and skills from university to professional engineering practice. Learning analytics endeavors in higher education have primarily focused on classroom-based learning. Recently, workplace learning analytics has become an emergent research area, with target users being workers, students and trainers. We propose technology for workplace learning analytics that allows program managers of higher engineering education programs to get insight into the workplace learning of their students, while ensuring privacy of students' personal data by design. Using a design-based agile methodology, we designed and developed a customizable workplace learning dashboard. From the evaluation with program managers in the computing domain, we can conclude that such technology is feasible and promising. The proposed technology was designed to be generalizable to other (engineering) domains. A next logical step would be to evaluate and improve the proposed technology within other engineering domains

Blended Learning Crossing Borders, Creating an Online Platform for a Joint International Course

Presented at the Essence International Conference, Alcoi – Sept 22 2015. The link refers to a youtube video recording of the presentation. This presentation describes a project for online and blended learning

A Capability Model for Learning Analytics Adoption: Identifying Organizational Capabilities from Literature on Learning Analytics, Big Data Analytics, and Business Analytics

Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?” Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – Data, Management, People, Technology, and Privacy & Ethics. Capabilities presently absent from existing learning analytics frameworks concern sourcing and integration, market, knowledge, training, automation, and connectivity. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption

A First Step Towards Learning Analytics: Implementing an Experimental Learning Analytics Tool

The educational domain is momentarily witnessing the emergence of learning analytics – a form of data analytics within educational institutes. Implementation of learning analytics tools, however, is not a trivial process. This research-in-progress focuses on the experimental implementation of a learning analytics tool in the virtual learning environment and educational processes of a case organization – a major Dutch university of applied sciences. The experiment is performed in two phases: the first phase led to insights in the dynamics associated with implementing such tool in a practical setting. The second – yet to be conducted – phase will provide insights in the use of pedagogical interventions based on learning analytics. In the first phase, several technical issues emerged, as well as the need to include more data (sources) in order to get a more complete picture of actual learning behavior. Moreover, self-selection bias is identified as a potential threat to future learning analytics endeavors when data collection and analysis requires learners to opt in