Refine
Has Fulltext
- no (2)
Year of publication
- 2018 (2) (remove)
Document Type
- Article (2)
Language
- English (2)
Is part of the Bibliography
- yes (2)
Institute
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.