Courses on computer programming are included in the curricula of almost all engineering disciplines. We surveyed the research literature and identified the techniques that are commonly used by instructors for teaching these courses. We observed that visual programming and game-based learning can enhance computational thinking and problem-solving skills in students and may be used to introduce them to programming. Robot programming may be used to attract students to programming, but the success of this technique is subjected to the availability of robots. Pair and collaborative programming allows students to learn from one another and write efficient programs. Assessment systems help instructors in evaluating programs written by students and provide them with timely feedback. Furthermore, an analysis of citations showed that Scratch is the most researched tool for teaching programming. We discuss how these techniques may be used to teach introductory courses, advanced courses, and massive open online courses on programming.
In this paper, we propose a novel approach for supervised classification of linguistic metaphors in an open domain text using Conditional Random Fields (CRF). We analyze CRF based classification model for metaphor detection using syntactic, conceptual, affective, and word embeddings based features which are extracted from MRC Psycholinguistic Database (MRCPD) and WordNet-Affect. We use word embeddings given by Huang et al. to capture information such as coherence and analogy between words. To tackle the bottleneck of limited coverage of psychological features in MRCPD, we employ synonymy relations from WordNet ®. A comparison of our approach with previous approaches shows the efficacy of CRF classifier in detecting metaphors. The experiments conducted on VU Amsterdam metaphor corpus provides an accuracy of more than 92% and Fmeasure of approximately 78%. Results shows that inclusion of conceptual features improves the recall by 5% whereas affective features do not have any major impact on metaphor detection in open text.
In the last decade, the problem of computational metaphor processing has garnered immense attention from the domains of computational linguistics and cognition. A wide panorama of approaches, ranging from a hand-coded rule system to deep learning techniques, have been proposed to automate different aspects of metaphor processing. In this article, we systematically examine the major theoretical views on metaphor and present their classification. We discuss the existing literature to provide a concise yet representative picture of computational metaphor processing. We conclude the article with possible research directions.
Smartphone apps have lately emerged as a potent instructional aid for teaching engineering courses. Teaching engineering courses often involve explaining complex problems that require creative solutions to students who are typically tech-savvy. This article reviews 10 smartphone apps that have been developed to teach engineering courses. The apps have been used to teach a wide range of engineering courses at undergraduate and graduate levels in classroom and laboratory environments. The apps help students to solve engineering problems by means of simulation and experimentation. They use techniques varying from algorithm visualization to augmented reality to enrich the courses. This article also provides suggestions on how to develop and use smartphone apps for teaching engineering courses. It is recommended that the developers of such apps pay special attention to their content, user interface, dissemination, and integration with the curriculum to get the best result.
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.