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Modularization describes the transformation of MOOCs from a comprehensive academic course format into smaller, more manageable learning offerings. It can be seen as one of the prerequisites for the successful implementation of MOOC-based micro-credentials in professional education and training. This short paper reports on the development and application of a modularization framework for Open Online Courses. Using the example of eGov-Campus, a German MOOC provider for the public sector linked to both academia and formal professional development, the structural specifications for modularized MOOC offerings and a methodology for course transformation as well as associated challenges in technology, organization and educational design are outlined. Following on from this, future prospects are discussed under the headings of individualization, certification and integration.
EMOOCs 2023
(2023)
From June 14 to June 16, 2023, Hasso Plattner Institute, Potsdam, hosted the eighth European MOOC Stakeholder Summit (EMOOCs 2023).
The pandemic is fortunately over. It has once again shown how important digital education is. How well-prepared a country was could be seen in our schools, universities, and companies. In different countries, the problems manifested themselves differently. The measures and approaches to solving the problems varied accordingly. Digital education, whether micro-credentials, MOOCs, blended learning formats, or other e-learning tools, received a major boost.
EMOOCs 2023 focusses on the effects of this emergency situation. How has it affected the development and delivery of MOOCs and other e-learning offerings all over Europe? Which projects can serve as models for successful digital learning and teaching? Which roles can MOOCs and micro-credentials bear in the current business transformation? Is there a backlash to the routine we knew from pre-Corona times? Or have many things become firmly established in the meantime, e.g. remote work, hybrid conferences, etc.?
Furthermore, EMOOCs 2023 has a closer look at the development and formalization of digital learning. Micro-credentials are just the starting point. Further steps in this direction would be complete online study programs or full online universities.
Another main topic is the networking of learning offers and the standardization of formats and metadata. Examples of fruitful cooperations are the MOOChub, the European MOOC Consortium, and the Common Micro-Credential Framework.
The learnings, derived from practical experience and research, are explored in EMOOCs 2023 in four tracks and additional workshops, covering various aspects of this field. In this publication, we present papers from the conference’s Research & Experience Track, the Business Track and the International Track.
Purpose:
The purpose of this paper is to examine the expectations, perceptions and role understanding of academic staff using a model of academic competencies (i.e. time management, learning skills, technology proficiency, self-monitoring and research skills).
Design/methodology/approach:
Semi-structured interviews were conducted with ten members of academic staff at a German university. Participants’ responses to the open-ended questions were coded inductively, while responses concerning the proposed model of academic competencies were coded deductively using a priori categories.
Findings:
Participating academic staff expected first-year students to be most competent in time management and in learning skills; they perceived students’ technology proficiency to be rather high but their research skills as low. Interviews indicated a mismatch between academic staff expectations and perceptions.
Practical implications:
These findings may enable universities to provide support services for first-year students to help them to adjust to the demands of higher education. They may also serve as a platform to discuss how academic staff can support students to develop the required academic competencies, as well as a broader conversation about higher education pedagogy and competency assessment.
Originality/value:
Little research has investigated the perspectives of academic staff concerning the academic competencies they expect of first-year students. Understanding their perspectives is crucial for improving the quality of institutions; their input into the design of effective support services is essential, as is a constructive dialogue to identify strategies to enhance student retention.
Students often enter higher education academically unprepared and with unrealistic perceptions and expectations regarding academic competencies for their studies. However, preparedness and realistic perceptions are important factors for student retention. With regard to a proposed model of five academic competencies(time management, learning skills, technology proficiency, self-monitoring, and research skills), incoming students’ perceptions concerning academic staff support and students’ selfreported confidence at a German university were examined. Using quantitative data, an initial exploratory study was conducted (N = 155), which revealed first-year students’ perceptions of the role of academic staff in supporting their development, especially in
research skills, as well as low self-reported confidence in this competence. Thus, a follow up study (N = 717) was conducted to confirm these findings as well as to provide an indepth understanding of research skills. Understanding students’ perceptions is crucial if higher education institutions are to meet students’ needs and provide adequate support services in the challenging first year. Thus, in order to increase student retention, it is
suggested that universities assist first-year students in developing academic competencies through personalised competence-based programs and with the help of emerging
research fields and educational technologies such as learning analytics and digital badges.
Learning analytics design
(2018)
Instructional designers use learning analytics information to evaluate designs of learning environments, learning materials, and tasks, and adjust difficulty levels, as well as measure the impact of interventions and feedback. Integrating real-time educational data and analysis into the design of learning environments, referred to as learning analytics design (LAD), seems to be a promising approach. Valid pedagogical recommendations may be suggested on the fly as learning analytics methodologies and visualizations evolve and as reliable tools become available and ready for classroom practice. This chapter aims to offer an overview on design and analytics of learning environments before reviewing opportunities of learning analytics design for optimizing learning environments in near real time. Learning analytics (LA) use static and dynamic information about learners and learning environments—assessing, eliciting, and analyzing them—for real-time modeling, prediction, and optimization of learning processes, learning environments, and educational decision-making.
EMOOCs 2021
(2021)
From June 22 to June 24, 2021, Hasso Plattner Institute, Potsdam, hosted the seventh European MOOC Stakeholder Summit (EMOOCs 2021) together with the eighth ACM Learning@Scale Conference.
Due to the COVID-19 situation, the conference was held fully online.
The boost in digital education worldwide as a result of the pandemic was also one of the main topics of this year’s EMOOCs. All institutions of learning have been forced to transform and redesign their educational methods, moving from traditional models to hybrid or completely online models at scale. The learnings, derived from practical experience and research, have been explored in EMOOCs 2021 in six tracks and additional workshops, covering various aspects of this field. In this publication, we present papers from the conference’s Experience Track, the Policy Track, the Business Track, the International Track, and the Workshops.
The purpose of this paper is to analyse data on first-year students’ needs regarding academic support services and reasons for their intention to leave the institution prior to degree completion. On the basis of the findings, a digital badge outline is proposed which could contribute to improved communication of academic requirements in order to help students to better adapt to higher education demands. Digital badges might also serve as an indicator for students’ needing additional academic support services.
Recently, interest in collecting and mining large sets of educational data on student background and performance to conduct research on learning and instruction has developed as an area generally referred to as learning analytics. Higher education leaders are recognising the value of learning analytics for improving not only learning and teaching but also the entire educational arena. However, theoretical concepts and empirical evidence need to be generated within the fast evolving field of learning analytics. In this paper, we introduce a holistic learning analytics framework. Based on this framework, student, learning, and curriculum profiles have been developed which include relevant static and dynamic parameters for facilitating the learning analytics framework. Based on the theoretical model, an empirical study was conducted to empirically validate the parameters included in the student profile. The paper concludes with practical implications and issues for future research.