256,062 research outputs found
KernSmoothIRT: An R Package for Kernel Smoothing in Item Response Theory
Item response theory (IRT) models are a class of statistical models used to
describe the response behaviors of individuals to a set of items having a
certain number of options. They are adopted by researchers in social science,
particularly in the analysis of performance or attitudinal data, in psychology,
education, medicine, marketing and other fields where the aim is to measure
latent constructs. Most IRT analyses use parametric models that rely on
assumptions that often are not satisfied. In such cases, a nonparametric
approach might be preferable; nevertheless, there are not many software
applications allowing to use that. To address this gap, this paper presents the
R package KernSmoothIRT. It implements kernel smoothing for the estimation of
option characteristic curves, and adds several plotting and analytical tools to
evaluate the whole test/questionnaire, the items, and the subjects. In order to
show the package's capabilities, two real datasets are used, one employing
multiple-choice responses, and the other scaled responses
How to reduce the number of rating scale items without predictability loss?
Rating scales are used to elicit data about qualitative entities (e.g.,
research collaboration). This study presents an innovative method for reducing
the number of rating scale items without the predictability loss. The "area
under the receiver operator curve method" (AUC ROC) is used. The presented
method has reduced the number of rating scale items (variables) to 28.57\%
(from 21 to 6) making over 70\% of collected data unnecessary.
Results have been verified by two methods of analysis: Graded Response Model
(GRM) and Confirmatory Factor Analysis (CFA). GRM revealed that the new method
differentiates observations of high and middle scores. CFA proved that the
reliability of the rating scale has not deteriorated by the scale item
reduction. Both statistical analysis evidenced usefulness of the AUC ROC
reduction method.Comment: 14 pages, 5 figure
ScratchMaths: evaluation report and executive summary
Since 2014, computing has been part of the primary curriculum. ‘Scratch’ is frequently used by schools, and the EEF funded this trial to test whether the platform could be used to improve pupils’ computational thinking skills, and whether this in turn could have a positive impact on Key Stage 2 maths attainment. Good computational thinking skills mean pupils can use problem solving methods that involve expressing problems and their solutions in ways that a computer could execute – for example, recognising patterns. Previous research has shown that pupils with better computational thinking skills do better in maths.
The study found a positive impact on computational thinking skills at the end of Year 5 – particularly for pupils who have ever been eligible for free school meals. However, there was no evidence of an impact on Key Stage 2 maths attainment when pupils were tested at the end of Year 6.
Many of the schools in the trial did not fully implement ScratchMaths, particularly in Year 6, where teachers expressed concerns about the pressure of Key Stage 2 SATs. But there was no evidence that schools which did implement the programme had better maths results.
Schools may be interested in ScratchMaths as an affordable way to cover aspects of the primary computing curriculum in maths lessons without any adverse effect on core maths outcomes. This trial, however, did not provide evidence that ScratchMaths is an effective way to improve maths outcomes
Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)
We report and fix an important systematic error in prior studies that ranked
classifiers for software analytics. Those studies did not (a) assess
classifiers on multiple criteria and they did not (b) study how variations in
the data affect the results. Hence, this paper applies (a) multi-criteria tests
while (b) fixing the weaker regions of the training data (using SMOTUNED, which
is a self-tuning version of SMOTE). This approach leads to dramatically large
increases in software defect predictions. When applied in a 5*5
cross-validation study for 3,681 JAVA classes (containing over a million lines
of code) from open source systems, SMOTUNED increased AUC and recall by 60% and
20% respectively. These improvements are independent of the classifier used to
predict for quality. Same kind of pattern (improvement) was observed when a
comparative analysis of SMOTE and SMOTUNED was done against the most recent
class imbalance technique. In conclusion, for software analytic tasks like
defect prediction, (1) data pre-processing can be more important than
classifier choice, (2) ranking studies are incomplete without such
pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of
Software Engineering (ICSE), 201
Fuzzy Content Mining for Targeted Advertisement
Content-targeted advertising system is becoming an increasingly important part of the funding source of free web services. Highly efficient content analysis is the pivotal key of such a system. This project aims to establish a content analysis engine involving fuzzy logic that is able to automatically analyze real user-posted Web documents such as blog entries. Based on the analysis result, the system matches and retrieves the most appropriate Web advertisements. The focus and complexity is on how to better estimate and acquire the keywords that represent a given Web document. Fuzzy Web mining concept will be applied to synthetically consider multiple factors of Web content. A Fuzzy Ranking System is established based on certain fuzzy (and some crisp) rules, fuzzy sets, and membership functions to get the best candidate keywords. Once it is has obtained the keywords, the system will retrieve corresponding advertisements from certain providers through Web services as matched advertisements, similarly to retrieving a products list from Amazon.com. In 87% of the cases, the results of this system can match the accuracy of the Google Adwords system. Furthermore, this expandable system will also be a solid base for further research and development on this topic
Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)
We report and fix an important systematic error in prior studies that ranked
classifiers for software analytics. Those studies did not (a) assess
classifiers on multiple criteria and they did not (b) study how variations in
the data affect the results. Hence, this paper applies (a) multi-criteria tests
while (b) fixing the weaker regions of the training data (using SMOTUNED, which
is a self-tuning version of SMOTE). This approach leads to dramatically large
increases in software defect predictions. When applied in a 5*5
cross-validation study for 3,681 JAVA classes (containing over a million lines
of code) from open source systems, SMOTUNED increased AUC and recall by 60% and
20% respectively. These improvements are independent of the classifier used to
predict for quality. Same kind of pattern (improvement) was observed when a
comparative analysis of SMOTE and SMOTUNED was done against the most recent
class imbalance technique. In conclusion, for software analytic tasks like
defect prediction, (1) data pre-processing can be more important than
classifier choice, (2) ranking studies are incomplete without such
pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of
Software Engineering (ICSE), 201
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