scholarly journals Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system

2021 ◽  
Vol 7 ◽  
pp. e532
Author(s):  
Saurabh Pal ◽  
Pijush Kanti Dutta Pramanik ◽  
Aranyak Maity ◽  
Prasenjit Choudhury
Keyword(s):  
Online Learning ◽  
Real Time ◽  
User Experience ◽  
Language Model ◽  
Correction Method ◽  
Learning System ◽  
Learner Satisfaction ◽  
Study Materials ◽  
Question Formation ◽  
Novel Method

In an interactive online learning system (OLS), it is crucial for the learners to form the questions correctly in order to be provided or recommended appropriate learning materials. The incorrect question formation may lead the OLS to be confused, resulting in providing or recommending inappropriate study materials, which, in turn, affects the learning quality and experience and learner satisfaction. In this paper, we propose a novel method to assess the correctness of the learner's question in terms of syntax and semantics. Assessing the learner’s query precisely will improve the performance of the recommendation. A tri-gram language model is built, and trained and tested on corpora of 2,533 and 634 questions on Java, respectively, collected from books, blogs, websites, and university exam papers. The proposed method has exhibited 92% accuracy in identifying a question as correct or incorrect. Furthermore, in case the learner's input question is not correct, we propose an additional framework to guide the learner leading to a correct question that closely matches her intended question. For recommending correct questions, soft cosine based similarity is used. The proposed framework is tested on a group of learners' real-time questions and observed to accomplish 85% accuracy.

scholarly journals Online Learning for Statistical Machine Translation

2016 ◽  
Vol 42 (1) ◽  
pp. 121-161 ◽  
Author(s):  
Daniel Ortiz-Martínez
Keyword(s):  
Online Learning ◽  
Real Time ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Learning System ◽  
Training Data ◽  
Time Cost ◽  
Empirical Results ◽  
Learning Techniques ◽  
Batch Learning

We present online learning techniques for statistical machine translation (SMT). The availability of large training data sets that grow constantly over time is becoming more and more frequent in the field of SMT—for example, in the context of translation agencies or the daily translation of government proceedings. When new knowledge is to be incorporated in the SMT models, the use of batch learning techniques require very time-consuming estimation processes over the whole training set that may take days or weeks to be executed. By means of the application of online learning, new training samples can be processed individually in real time. For this purpose, we define a state-of-the-art SMT model composed of a set of submodels, as well as a set of incremental update rules for each of these submodels. To test our techniques, we have studied two well-known SMT applications that can be used in translation agencies: post-editing and interactive machine translation. In both scenarios, the SMT system collaborates with the user to generate high-quality translations. These user-validated translations can be used to extend the SMT models by means of online learning. Empirical results in the two scenarios under consideration show the great impact of frequent updates in the system performance. The time cost of such updates was also measured, comparing the efficiency of a batch learning SMT system with that of an online learning system, showing that online learning is able to work in real time whereas the time cost of batch retraining soon becomes infeasible. Empirical results also showed that the performance of online learning is comparable to that of batch learning. Moreover, the proposed techniques were able to learn from previously estimated models or from scratch. We also propose two new measures to predict the effectiveness of online learning in SMT tasks. The translation system with online learning capabilities presented here is implemented in the open-source Thot toolkit for SMT.

10. Designing an online curriculum supporting risk management and patient safety

2007 ◽  
Vol 30 (4) ◽  
pp. 33
Author(s):  
T. Gondocz ◽  
G. Wallace
Keyword(s):  
Risk Management ◽  
Medical Education ◽  
Patient Safety ◽  
Online Learning ◽  
Online Courses ◽  
Learning System ◽  
Online Curriculum ◽  
For Profit ◽  
Not For Profit ◽  
Managing Risk

The Canadian Medical Protective Association (CMPA) is a not for profit mutual defence organization with a mandate to provide medico-legal assistance to physician members and to educate health professionals on managing risk and enhancing patient safety. To expand the outreach to its 72,000 member physicians, the CMPA built an online learning curriculum of risk management and patient safety materials in 2006. These activities are mapped to the real needs of members ensuring the activities are relevant. Eight major categories were developed containing both online courses and articles. Each course and article is mapped to the RCPSC's CanMEDS roles and the CFPC's Four Principles. This poster shares the CMPA’s experience in designing an online patient safety curriculum within the context of medico-legal risk management and provides an inventory of materials linked to the CanMEDS roles. Our formula for creation of an online curriculum included basing the educational content on real needs of member physicians; using case studies to teach concepts; and, monitoring and evaluating process and outcomes. The objectives are to explain the benefits of curricular approach for course planning across the continuum in medical education; outline the utility of the CanMEDS roles in organizing the risk management and patient safety medical education curriculum; describe the progress of CMPA's online learning system; and, outline the potential for moving the curriculum of online learning materials and resources into medical schools.

scholarly journals Learning As It Happens: A Decade of Analyzing and Shaping a Large-Scale Online Learning System

2018 ◽  
Vol 5 (2) ◽  
Author(s):  
Matthieu J. S. Brinkhuis ◽  
Alexander O. Savi ◽  
Abe D. Hofman ◽  
Frederik Coomans ◽  
Han L. J. Van der Maas ◽  
...  
Keyword(s):  
Online Learning ◽  
Large Scale ◽  
Response Times ◽  
Learning System ◽  
Learning Gains ◽  
Online Experimentation ◽  
Quality Of Fit ◽  
Item Reliability ◽  
Made In

With the advent of computers in education, and the ample availability of online learning and practice environments, enormous amounts of data on learning become available. The purpose of this paper is to present a decade of experience with analyzing and improving an online practice environment for math, which has thus far recorded over a billion responses. We present the methods we use to both steer and analyze this system in real-time, using scoring rules on accuracy and response times, a tailored rating system to provide both learners and items with current ability and difficulty ratings, and an adaptive engine that matches learners to items. Moreover, we explore the quality of fit by means of prediction accuracy and parallel item reliability. Limitations and pitfalls are discussed by diagnosing sources of misfit, like violations of unidimensionality and unforeseen dynamics. Finally, directions for development are discussed, including embedded learning analytics and a focus on online experimentation to evaluate both the system itself and the users’ learning gains. Though many challenges remain open, we believe that large steps have been made in providing methods to efficiently manage and research educational big data from a massive online learning system.

scholarly journals A Novel Method for Detecting Abnormal Energy Data in Building Energy Monitoring System

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Liang Zhao
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Real Time ◽  
Difference Method ◽  
Building Energy ◽  
Energy Monitoring ◽  
Order Difference ◽  
Energy Data ◽  
First Order ◽  
Novel Method

This paper presents a novel abnormal data detecting algorithm based on the first order difference method, which could be used to find out outlier in building energy consumption platform real time. The principle and criterion of methodology are discussed in detail. The results show that outlier in cumulative power consumption could be detected by our method.

Hug@ree: An ARTiVIS Experience for Sustainability

Leonardo ◽  
2014 ◽  
Vol 47 (5) ◽  
pp. 500-501 ◽  
Author(s):  
Mónica Mendes ◽  
Pedro Ângelo ◽  
Nuno Correia
Keyword(s):  
Real Time ◽  
User Experience ◽  
Natural Environment ◽  
Individual Action ◽  
Interactive Installation ◽  
Raising Awareness ◽  
Future Work

Hug@ree is an interactive installation that provides a bond between urban beings and the forest. It is an ARTiVIS (Arts, Real-Time Video and Interactivity for Sustainability) experience that provides interaction with trees and videos of trees in real-time, raising awareness of the natural environment and how individual action can collectively become so relevant. In this paper, the authors present an overview of the Hug@ree concept, related work, implementation, user experience evaluation and future work.

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