PhD-student (1,0 fte, 4 years) on
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Learning Analytics in blended learning: Translating numbers into learning improvements.
within the Human-Technology Interaction Group of the School of Innovation Sciences (IS), which is part of the Department of Industrial Engineering & Innovation Sciences (IE&IS).
The Department of Industrial Engineering & Innovation Sciences (IE&IS) has several BSc and MSc programs in two separate schools. The School of Innovation Sciences focuses on the development and use of new technologies in a broad societal context. Research is multi-disciplinary, based on fundamental scientific insights and methods. Main areas of interest are economics and sociology of technological developments, sustainable innovation policy, human-technology interaction, and the history and philosophy of technology.
The Human-Technology Interaction (HTI) Group of the School of Innovation Sciences concentrates expertise in both social sciences and engineering, studying technology and its relations to humans and human well-being, within the broader context of a socially and ecologically sustainable society. The HTI group has a strong track record in education at the TU/e, running the BSc program Psychology and Technology and a (international) Master program in Human-Technology Interaction. Current research topics at the Human-Technology Interaction group include data science, affective computing, persuasive technology, virtual environments, digital gaming, recommender systems, online behavior, interactive lighting, robotics, embodied interfaces, and smart environments. Project DescriptionA PhD position (fully funded for four years) is available within the context of the TU/e funded research project 'Learning Analytics in blended learning at schools and universities: Translating numbers into learning improvements'. The project is part of the TU/e 2030 Strategy which aims at leveraging digitization for creating personal learning paths and serving more diverse groups of learners. Learning Analytics considers the use of online traces that users of Learning Management systems (LMSs) create and that are saved automatically. We aim at creating learning analytics models that predict students' learning success or failure, self-regulated learning capabilities, study engagement, and other characteristics that can be used to improve teaching and learning. A sub-question is how early we can build such models in ongoing courses so that early warning and feedback systems can be created. Also, the consequences of planned educational interventions based on the LMS data either in the class or online are relevant. We will use educational theories in field experiments to test whether specific educational designs increase student capabilities and learning outcomes. For a sample of students, we will collect survey data for additional measurement and to create predictive models for (amongst other things) students' learning styles, students' self-regulated learning capabilities, study crafting, study engagement, burnout, and academic performance.
The selected candidate will work within the Human-Technology Interaction Group within the department of Industrial Engineering and Innovation Science at Eindhoven University of Technology under the supervision of Dr. U. Matzat, Dr. Ir. A. Kleingeld (Human Performance Management group), and Prof. dr. C. Snijders. The applicant will access large databases and mine and analyze these data, taking into account the insights of learning theories. In addition, the applicant will administer surveys and perform (online) experiments. Findings will be presented at (international) conferences and written up for publication in scientific journals. The PhD student will be expected to participate in teaching (up to a maximum of 0.15 fte).
Technische Universiteit Eindhoven (TU/e)
We are looking for candidates passionate about improving the way we do science to improve student learning in blended courses, with strong quantitative skills, who enjoy working in an extremely collaborative environment. The applicant should have:A MSc degree, or equivalent, in Psychology, Sociology, Educational Science, Statistics, Economics, Computer Science, or a related field, completed by the start date of the PhD projectA demonstrable interest in the topics studied in the project.Strong programming skills; experience in database programming is appreciatedA good understanding of applied statistics.Experience with performing scientific research in the social & behavioral sciencesFluency in written and spoken English language.
Part-time employment can be negotiated. We strongly encourage applications from members of groups underrepresented in science. The project will start May 1st, but a later starting date can be negotiated. Interviews will start taking place in February and March. At this time screening starts immediately, when we have found a suitable candidate we will close the vacancy so we advise you to apply a.s.a.p.