Many online learning initiatives have failed to reach beyond the environments in which they were first developed. One exception is the Open Learning Initiative (OLI) at Carnegie Mellon University (CMU). In an attempt to validate the question-based learning methodology implemented in OLI, we developed online material for an introductory course in object-oriented programming, and tested it on two course offerings with a total of 70 students. As our course has been given in the same format for several years, we also had comparable assessment data for two classes prior to our intervention in order to determine that we did not introduce any obvious harm with this methodology. Findings show a reduced teaching and learning time by 25%. No statistically significant differences could be found in the results of the assessment quizzes nor confidence surveys completed by the students. The two teachers (the same who handled the classes before the intervention) took different paths to teaching preparations with this new methodology. One teacher increased preparations, whilst the other reduced them, but both teachers were convinced that using online question-based learning was superior to the previous lecture and textbook-based approach, both for the students and themselves in terms of overall satisfaction. We also gathered time logs from the development to estimate return on investment. CCS CONCEPTS• Human-centered computing → User studies; • Applied computing → Computer-assisted instruction; Interactive learning environments; E-learning; Learning management systems.
This article contributes to the emerging field of research on computational journalism with a practical illustration of an attempt to utilize Machine Learning to generate Search Engine Optimized headlines in a major Swedish newsroom. By using its technical results as a springboard for reflections among internal stakeholders, the experiment serves as a catalyzing innovation revealing deliberations on computational approaches in journalism in general and communicative Artificial Intelligence (AI) in specific. The study concludes with three ideas to support decision makers involved in evaluating potential use cases for communicative AI in journalism.
The AI landscape demands a broad set of legal, ethical, and societal considerations to be accounted for in order to develop ethical AI (eAI) solutions which sustain human values and rights. Currently, a variety of guidelines and a handful of niche tools exist to account for and tackle individual challenges. However, it is also well established that many organizations face practical challenges in navigating these considerations from a risk management perspective within AI governance. Therefore, new methodologies are needed to provide a well-vetted and real-world applicable structure and path through the checks and balances needed for ethically assessing and guiding the development of AI. In this paper, we show that a multidisciplinary research approach, spanning cross-sectional viewpoints, is the foundation of a pragmatic definition of ethical and societal risks faced by organizations using AI. Equally important are the findings of cross-structural governance for implementing eAI successfully. Based on evidence acquired from our multidisciplinary research investigation, we propose a novel data-driven risk assessment methodology, entitled DRESS-eAI. In addition, through the evaluation of our methodological implementation, we demonstrate its state-of-the-art relevance as a tool for sustaining human values in the data-driven AI era.
This empirical study demonstrates that students' learning of computer science takes place in qualitatively different ways. The results consists of categories, where each category describe a certain way, in which the students approach their learning. The paper demonstrates that some of the ways to tackle the learning are better than others in resulting in a good learning outcome, and that they therefore should be encouraged. The data, underlying these results, are collected through interviews with third and fourth year students in two countries, and are further analyzed, using a phenomenographic research approach.
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.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.