Automated Grading of Short Literal Comprehension Questions

2015 ◽  
pp. 251-262 ◽  
Author(s):  
Andrew Kwok-Fai Lui ◽  
Lap-Kei Lee ◽  
Hiu-Wai Lau
Keyword(s):  
Automated Grading ◽  
Literal Comprehension

scholarly journals An AI-Based System for Formative and Summative Assessment in Data Science Courses

2020 ◽  
Author(s):  
Pierpaolo Vittorini ◽  
Stefano Menini ◽  
Sara Tonelli
Keyword(s):  
Data Science ◽  
Ad Hoc ◽  
Online Courses ◽  
Assessment Tools ◽  
Science Courses ◽  
Summative Assessments ◽  
Assessment Activity ◽  
Automated Grading ◽  
Automated Feedback

AbstractMassive open online courses (MOOCs) provide hundreds of students with teaching materials, assessment tools, and collaborative instruments. The assessment activity, in particular, is demanding in terms of both time and effort; thus, the use of artificial intelligence can be useful to address and reduce the time and effort required. This paper reports on a system and related experiments finalised to improve both the performance and quality of formative and summative assessments in specific data science courses. The system is developed to automatically grade assignments composed of R commands commented with short sentences written in natural language. In our opinion, the use of the system can (i) shorten the correction times and reduce the possibility of errors and (ii) support the students while solving the exercises assigned during the course through automated feedback. To investigate these aims, an ad-hoc experiment was conducted in three courses containing the specific topic of statistical analysis of health data. Our evaluation demonstrated that automated grading has an acceptable correlation with human grading. Furthermore, the students who used the tool did not report usability issues, and those that used it for more than half of the exercises obtained (on average) higher grades in the exam. Finally, the use of the system reduced the correction time and assisted the professor in identifying correction errors.

Automated grading assistance for student programs

1994 ◽  
Vol 26 (1) ◽  
pp. 381-382 ◽  
Author(s):  
David G. Kay ◽  
Terry Scott ◽  
Peter Isaacson ◽  
Kenneth A. Reek
Keyword(s):  
Automated Grading

0-67. Automated grading in a prognostic index

The Breast ◽  
1997 ◽  
Vol 6 (4) ◽  
pp. 243
Author(s):  
S.R. Kohlhardt ◽  
R.W. Blarney ◽  
S.E. Pinder ◽  
I.O. Ellis ◽  
C..W Elston
Keyword(s):  
Prognostic Index ◽  
Automated Grading

Listening Ears or Reading Eyes: A Meta-Analysis of Reading and Listening Comprehension Comparisons

2021 ◽  
pp. 003465432110608
Author(s):  
Virginia Clinton-Lisell
Keyword(s):  
Listening Comprehension ◽  
Variance Estimation ◽  
Meta Analysis ◽  
Age Groups ◽  
Robust Variance ◽  
Reading Condition ◽  
P 16 ◽  
Robust Variance Estimation ◽  
Literal Comprehension

In this study, a meta-analysis of reading and listening comprehension comparisons across age groups was conducted. Based on robust variance estimation (46 studies; N = 4,687), the overall difference between reading and listening comprehension was not reliably different (g = 0.07, p = .23). Reading was beneficial over listening when the reading condition was self-paced (g = 0.13, p = .049) rather than experimenter-paced (g = −0.32, p = .16). Reading also had a benefit when inferential and general comprehension rather than literal comprehension was assessed (g = 0.36, p = .02; g = .15, p = .05; g = −0.01, p = .93, respectively). There was some indication that reading and listening were more similar in languages with transparent orthographies than opaque orthographies (g = 0.001, p = .99; g = 0.10, p = .19, respectively). The findings may be used to inform theories of comprehension about modality influences in that both lower-level skill and affordances vary comparisons of reading and listening comprehension. Moreover, the findings may guide choices of modality; however, both audio and written options are needed for accessible instruction.

Automated Grading Operation for Hydraulic Excavators

2014 ◽  
Author(s):  
Jiaqi Xu ◽  
Bradley Thompson ◽  
Hwan-Sik Yoon
Keyword(s):  
Hydraulic Excavators ◽  
Automated Grading

scholarly journals Automated grading of mangoes based on surface defect detection using a combined approach of image segmentation

2020 ◽  
pp. 17-23
Author(s):  
Neeraj Kumari ◽  
Ashutosh Kumar Bhatt ◽  
Rakesh Kumar Dwivedi ◽  
Rajendra Belwal
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Image Segmentation ◽  
Surface Defect ◽  
Input Image ◽  
Segmentation Technique ◽  
Segmentation Accuracy ◽  
Surface Defect Detection ◽  
Automated Grading ◽  
Processing Accuracy

Image segmentation is an essential and critical step in huge number of applications of image processing. Accuracy of image segmentation influence retrieved information for further processing in classification and other task. In image segmentation algorithms, a single segmentation technique is not sufficient in providing accurate segmentation results in many cases. In this paper we are proposing a combining approach of image segmentation techniques for improving segmentation accuracy. As a case study fruit mango is selected for classification based on surface defect. This classification method consists of three steps: (a) image pre-processing, (b) feature extraction and feature selection and (c) classification of mango. Feature extraction phase is performed on an enhanced input image. In feature selection PCA methodology is used. In classification three classifiers BPNN, Naïve bayes and LDA are used. Proposed image segmentation technique is tested on online dataset and our own collected images database. Proposed segmentation technique performance is compared with existing segmentation techniques. Classification results of BPNN in training and testing phase are acceptable for proposed segmentation technique.

scholarly journals Measuring Shear Wave Splitting Using the Silver and Chan Method

2021 ◽  
Author(s):  
◽  
Ernestynne Walsh
Keyword(s):  
Shear Wave ◽  
Statistical Tests ◽  
Tectonic Stress ◽  
Shear Wave Splitting ◽  
Parameter Estimates ◽  
Transverse Waves ◽  
Seismic Shear ◽  
Wave Splitting ◽  
Orthogonal Components ◽  
Automated Grading

<p>Seismic shear waves emitted by earthquakes can be modelled as plane (transverse) waves. When entering an anisotropic medium they can be split into two orthogonal components moving at different speeds. This splitting occurs along an axis, the fast direction, that is determined by the ambient tectonic stress. Shear wave splitting is thus a commonly used tool for examining tectonic stress in the Earth’s interior. A common technique used to measure shear wave splitting is the Silver and Chan (1991) method. However, there is little literature assessing the robustness of this method, particularly for its use with local earthquakes, and the quality of results can vary. We present here a comprehensive analysis of the Silver and Chan method comprising theoretical derivations and statistical tests of the assumptions behind this method. We then produce an automated grading system calibrated against an expert manual grader using multiple linear regression. We find that there are errors in the derivation of certain equations in the Silver and Chan method and that it produces biased estimates of the errors. Further, the assumptions used to generate the errors do not hold. However, for high quality results (earthquake events where the signal is strong and the earthquake geometry is optimal), the standard errors are representative of the spread in the parameter estimates. Also, we find that our automated grading method produces grades that match the manual grades, and is able to identify mistakes in the manual grades by detecting substantial inconsistencies with the automated grades.</p>

Sign in / Sign up

Export Citation Format

Share Document