In this paper we introduce the core results of the project on visualisation and analysis of data collected from the vertical transport facilities. The aim of the project was to provide better user experience as well as to help building maintenance staff to increase productivity of their work. We elaborated a web-based system for vertical transportation, to cover the needs of (1) staff working on building maintenance, (2) people who are regularly using the facilities in the corresponding buildings.
Sedelmaier and Landes @cite_13 provided justification from a pedagogical point of view that project-based learning (PBL) allows learners not only to gain technical knowledge in the related area, but also foster soft skills. Dagnino @cite_1 presented a method derived from the collaboration between North Carolina State University and ABB. This method brings diverse techniques to simulate an industrial environment for teaching a senior level Software Engineering course. In contrary to the approach of Dagnino, we do not simulate the industry environment for our projects, but use a real one, based on collaboration with local and overseas industrial partners. In addition to that, project based learning is seen as a contributing factor to graduates' better work preparation, cf. @cite_10 .
@cite_1 @cite_10 @cite_13
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This paper explores what work readiness means for two cohorts of graduate engineers, one from a traditional curriculum, the second from a largely project-based curriculum. Professional bodies and employers have defined a set of attributes for engineering graduates so that graduates will be ‘work ready’. Problem-based learning (PBL) is claimed to be a suitable approach to develop such skills. The graduates were interviewed some months after starting work, along with their managers. All the graduates recognised the benefits of taking PBL subjects as well as vacation work, with success in communication attributed more to PBL. Both cohorts had similar learning outcomes, high skill levels in project management, problem solving, communication skills, research and sustainability. A skills gap in ethics was identified for both cohorts of graduates and their managers. Further work is planned to link skill development with undergraduate learning experience. (Background) Empirical Software Engineering (SE) strives to provide empirical evidence about the pros and cons of SE approaches. This kind of knowledge becomes relevant when the issue is whether to change from a currently employed approach to a new one or not. An informed decision is required and is particularly important in the development of safety-critical systems. For example, for the safety analysis of safety-critical embedded systems, methods such as Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) are used. With the advent of model-based systems and software development, the question arises whether safety engineering methods should also be adopted. New technologies such as Component Integrated Fault Trees (CFT) come into play. Industry demands to know the benefits of these new methods over established ones such as Fault Trees (FT). (Methods) For the purpose of comparing CFT and FT with regard to the capabilities of the safety analysis methods (such as quality of the results) and to the participants' rating of the consistency, clarity, and maintainability of the methods, we designed a comparative study as a controlled experiment using a within-subject design. The experiment was run with seven academic staff members working towards their PhD. The study was replicated with eleven domain experts from industry. (Results) Although the analysis of the tasks' solutions showed that the use of CFT did not yield a significantly different number of correct or incorrect solutions, the participants rated the modeling capacities of CFT higher in terms of model consistency, clarity, and maintainability. (Conclusion) From this first evidence, we conclude that CFT have the potential of being beneficial for companies looking for a safety analysis approachfor projects using model-based development. Context: The global software industry and the software engineering (SE) academia are two large communities. However, unfortunately, the level of joint industry-academia collaborations in SE is still relatively very low, compared to the amount of activity in each of the two communities. It seems that the two 'camps' show only limited interest motivation to collaborate with one other. Many researchers and practitioners have written about the challenges, success patterns (what to do, i.e., how to collaborate) and anti-patterns (what not do do) for industry-academia collaborations.Objective: To identify (a) the challenges to avoid risks to the collaboration by being aware of the challenges, (b) the best practices to provide an inventory of practices (patterns) allowing for an informed choice of practices to use when planning and conducting collaborative projects.Method: A systematic review has been conducted. Synthesis has been done using grounded-theory based coding procedures.Results: Through thematic analysis we identified 10 challenge themes and 17 best practice themes. A key outcome was the inventory of best practices, the most common ones recommended in different contexts were to hold regular workshops and seminars with industry, assure continuous learning from industry and academic sides, ensure management engagement, the need for a champion, basing research on real-world problems, showing explicit benefits to the industry partner, be agile during the collaboration, and the co-location of the researcher on the industry side.Conclusion: Given the importance of industry-academia collaboration to conduct research of high practical relevance we provide a synthesis of challenges and best practices, which can be used by researchers and practitioners to make informed decisions on how to structure their collaborations. We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two algorithms that rely on the empirical estimation of the learning progress, RiARiT that uses information about the difficulty of each exercise and ZPDES that uses much less knowledge about the problem. The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge. The system is evaluated in a scenario where 7-8 year old schoolchildren learn how to decompose numbers while manipulating money. Systematic experiments are presented with simulated students, followed by results of a user study across a population of 400 school children.