@phdthesis{Staubitz2020,
  author    = {Staubitz, Thomas},
  title     = {Gradable team assignments in large scale learning environments},
  doi       = {10.25932/publishup-47183},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-471830},
  school      = {Universit{\"a}t Potsdam},
  pages     = {122},
  year      = {2020},
  abstract  = {Lifelong learning plays an increasingly important role in many societies. Technology is changing faster than ever and what has been important to learn today, may be obsolete tomorrow. The role of informal programs is becoming increasingly important. Particularly, Massive Open Online Courses have become popular among learners and instructors. In 2008, a group of Canadian education enthusiasts started the first Massive Open Online Courses or MOOCs to prove their cognitive theory of Connectivism. Around 2012, a variety of American start-ups redefined the concept of MOOCs. Instead of following the connectivist doctrine they returned to a more traditional approach. They focussed on video lecturing and combined this with a course forum that allowed the participants to discuss with each other and the teaching team. While this new version of the concept was enormously successful in terms of massiveness—hundreds of thousands of participants from all over the world joined the first of these courses—many educators criticized the re-lapse to the cognitivist model. In the early days, the evolving platforms often did not have more features than a video player, simple multiple-choice quizzes, and the course forum. It soon became a major interest of research to allow the scaling of more modern approaches of learning and teaching for the massiveness of these courses. Hands-on exercises, alternative forms of assessment, collaboration, and teamwork are some of the topics on the agenda. The insights provided by cognitive and pedagogical theories, however, do not necessarily always run in sync with the needs and the preferences of the majority of participants. While the former promote action-learning, hands-on-learning, competence-based-learning, project-based-learning, team-based-learning as the holy grail, many of the latter often rather prefer a more laid-back style of learning, sometimes referred to as edutainment. Obviously, given the large numbers of participants in these courses, there is not just one type of learners. Participants are not a homogeneous mass but a potpourri of individuals with a wildly heterogeneous mix of backgrounds, previous knowledge, familial and professional circumstances, countries of origin, gender, age, and so on. For the majority of participants, a full-time job and/or a family often just does not leave enough room for more time intensive tasks, such as practical exercises or teamwork. Others, however, particularly enjoy these hands-on or collaborative aspects of MOOCs. Furthermore, many subjects particularly require these possibilities and simply cannot be taught or learned in courses that lack collaborative or hands-on features. In this context, the thesis discusses how team assignments have been implemented on the HPI MOOC platform. During the recent years, several experiments have been conducted and a great amount of experience has been gained by employing team assignments in courses in areas, such as Object-Oriented Programming, Design Thinking, and Business Innovation on various instances of this platform: openHPI, openSAP, and mooc.house},
  language  = {en}
}
@article{LaskovGehlKruegeretal.2006,
  author    = {Laskov, Pavel and Gehl, Christian and Kr{\"u}ger, Stefan and M{\"u}ller, Klaus-Robert},
  title     = {Incremental support vector learning: analysis, implementation and applications},
  series = {Journal of machine learning research},
  volume    = {7},
  journal   = {Journal of machine learning research},
  publisher = {MIT Press},
  address   = {Cambridge, Mass.},
  issn      = {1532-4435},
  pages     = {1909 -- 1936},
  year      = {2006},
  abstract  = {Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen.},
  language  = {en}
}
@article{ChenLangeAndjelkovicetal.2022,
  author    = {Chen, Junchao and Lange, Thomas and Andjelkovic, Marko and Simevski, Aleksandar and Lu, Li and Krstic, Milos},
  title     = {Solar particle event and single event upset prediction from SRAM-based monitor and supervised machine learning},
  series = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers},
  volume    = {10},
  journal   = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers},
  number    = {2},
  publisher = {Institute of Electrical and Electronics Engineers},
  address   = {[New York, NY]},
  issn      = {2168-6750},
  doi       = {10.1109/TETC.2022.3147376},
  pages     = {564 -- 580},
  year      = {2022},
  abstract  = {The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.},
  language  = {en}
}