This paper describes a methodology for controller and communication scheduling co-design in control systems operating over wirelessHART networks. Data collection and dissemination operations are identified and scheduled to minimize the nominal communication latency. Techniques for improving the reliability of the network when link transmissions are unreliable are discussed, and a Markov-chain model for computing the latency distribution of data collection operations for a given schedule is proposed. The resulting latency models allow to represent the networked control loop as a jump-linear system, whose performance can be analyzed using techniques from stochastic control. We demonstrate how this framework can be used to co-design a networked LQG controller for a five-by-five MIMO control loop.
The collection and selection of the data used in learning analytics applications deserve more attention. Optimally, selection of data should be guided by pedagogical purposes instead of data availability. Using design science research methodology, we designed an artifact to collect time-series data on students' self-regulated learning and conceptual thinking. Our artifact combines curriculum data, concept mapping, and structured learning diaries. We evaluated the artifact in a case study, verifying that it provides relevant data, requires a limited amount of effort from students, and works in different educational contexts. Combined with learning analytics applications and interventions, our artifact provides possibilities to add value for students, teachers, and academic leaders.
Notes for Practice• Data about constructs related to self-regulated learning, such as motivation, emotion, and metacognitive experiences, is highly relevant in learning analytics applications.• While this data is difficult to capture automatically, learning diaries can gather process data about students' internal states.• The main contribution of this article is to present a methodology in which concept maps are used as learning diaries to gather meaningful data for learning analytics applications.• In the future, this method of data gathering can be combined with different kinds of learning analytics interventions, including personalized feedback at scale.
Chatbots show promise as a novel way to provide support to students. However, a central issue with new technologies such as chatbots is whether students trust the technology. In the present study, we use a chatbot to proactively offer academic and non-academic support to students (N = 274) in a Finnish vocational education and training (VET) organization. Students responded to the chatbot with a very high response rate (86%), and almost one-fifth (19%) of the respondents disclosed a need for support. Survey with a subset of participants (N = 49) showed satisfactory trust (total trust score 71% as measured by a human-computer trust scale) and satisfaction (average of 3.83 as measured by a five-point customer satisfaction instrument) with the chatbot. Trust was positively correlated with satisfaction as well as students’ likelihood to respond to the chatbot. Our results show that this kind of approach is applicable for recognizing students’ latent needs for support. Future studies should target the formation of trust in more detail and cultural differences in trusting chatbots.
Understanding students’ learning is crucial to the development of teaching. The most common systematic approach to understanding students’ learning has been summative course feedback, gathered after each course. However, students do not seem to be motivated to give this summative feedback after courses, with answer rates as low as 30 percent at Aalto University. Based on our observations, this is partly due to the timing and quality of current methods: students do not themselves benefit from any possible developments to the course. We soon realised that we merely need to develop a more dynamic culture of giving feedback during a course and started a project called Dynamic Course and Programme Level Feedback. We use a concept mapping tool to collect data from students during a course. This data is then visualised as dashboards that serve as feedback for teachers and enable adaptive teaching.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.