23,445 research outputs found
Speech Recognition Technology: Improving Speed and Accuracy of Emergency Medical Services Documentation to Protect Patients
Because hospital errors, such as mistakes in documentation, cause one in six deaths each year in the United States, the accuracy of health records in the emergency medical services (EMS) must be improved. One possible solution is to incorporate speech recognition (SR) software into current tools used by EMS first responders. The purpose of this research was to determine if SR software could increase the efficiency and accuracy of EMS documentation to improve the safety of patients of EMS. An initial review of the literature on the performance of current SR software demonstrated that this software was not 99% accurate, and therefore, errors in the medical documentation produced by the software could harm patients. The literature review also identified weaknesses of SR software that could be overcome so that the software would be accurate enough for use in EMS settings. These weaknesses included the inability to differentiate between similar phrases and the inability to filter out background noise. To find a solution, an analysis of natural language processing algorithms showed that the bag-of-words post processing algorithm has the ability to differentiate between similar phrases. This algorithm is best suited for SR applications because it is simple yet effective compared to machine learning algorithms that required a large amount of training data. The findings suggested that if these weaknesses of current SR software are solved, then the software would potentially increase the efficiency and accuracy of EMS documentation. Further studies should integrate the bag-of-words post processing method into SR software and field test its accuracy in EMS settings
Towards an Intelligent Tutor for Mathematical Proofs
Computer-supported learning is an increasingly important form of study since
it allows for independent learning and individualized instruction. In this
paper, we discuss a novel approach to developing an intelligent tutoring system
for teaching textbook-style mathematical proofs. We characterize the
particularities of the domain and discuss common ITS design models. Our
approach is motivated by phenomena found in a corpus of tutorial dialogs that
were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor
for textbook-style mathematical proofs can be built on top of an adapted
assertion-level proof assistant by reusing representations and proof search
strategies originally developed for automated and interactive theorem proving.
The resulting prototype was successfully evaluated on a corpus of tutorial
dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453
An exploratory study into automated précis grading
Automated writing evaluation is a popular research field, but the main focus has been on evaluating argumentative essays. In this paper, we consider a different genre, namely précis texts. A précis is a written text that provides a coherent summary of main points of a spoken or written text. We present a corpus of English précis texts which all received a grade assigned by a highly-experienced English language teacher and were subsequently annotated following an exhaustive error typology. With this corpus we trained a machine learning model which relies on a number of linguistic, automatic summarization and AWE features. Our results reveal that this model is able to predict the grade of précis texts with only a moderate error margin
Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners
In this paper, we suggest a novel method to aid lifelong learners to access
relevant OER based learning content to master skills demanded on the labour
market. Our software prototype 1) applies Text Classification and Text Mining
methods on vacancy announcements to decompose jobs into meaningful skills
components, which lifelong learners should target; and 2) creates a hybrid OER
Recommender System to suggest personalized learning content for learners to
progress towards their skill targets. For the first evaluation of this
prototype we focused on two job areas: Data Scientist, and Mechanical Engineer.
We applied our skill extractor approach and provided OER recommendations for
learners targeting these jobs. We conducted in-depth, semi-structured
interviews with 12 subject matter experts to learn how our prototype performs
in terms of its objectives, logic, and contribution to learning. More than 150
recommendations were generated, and 76.9% of these recommendations were treated
as useful by the interviewees. Interviews revealed that a personalized OER
recommender system, based on skills demanded by labour market, has the
potential to improve the learning experience of lifelong learners.Comment: This paper has been accepted to be published in the proceedings of
CSEDU 2020 by SciTePres
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