Applying Automated Originality Scoring to the Verbal Form of Torrance Tests of Creative Thinking

2021 ◽  
pp. 001698622110618
Author(s):  
Selcuk Acar ◽  
Kelly Berthiaume ◽  
Katalin Grajzel ◽  
Denis Dumas ◽  
Charles “Tedd” Flemister ◽  
...  
Keyword(s):  
Text Mining ◽  
Creative Thinking ◽  
Lower Cost ◽  
Scoring Methods ◽  
Scoring Method ◽  
Verbal Form ◽  
Verbal Responses ◽  
Creativity Assessment ◽  
Word Representation ◽  
Mining Methods

In this study, we applied different text-mining methods to the originality scoring of the Unusual Uses Test (UUT) and Just Suppose Test (JST) from the Torrance Tests of Creative Thinking (TTCT)–Verbal. Responses from 102 and 123 participants who completed Form A and Form B, respectively, were scored using three different text-mining methods. The validity of these scoring methods was tested against TTCT’s manual-based scoring and a subjective snapshot scoring method. Results indicated that text-mining systems are applicable to both UUT and JST items across both forms and students’ performance on those items can predict total originality and creativity scores across all six tasks in the TTCT-Verbal. Comparatively, the text-mining methods worked better for UUT than JST. Of the three text-mining models we tested, the Global Vectors for Word Representation (GLoVe) model produced the most reliable and valid scores. These findings indicate that creativity assessment can be done quickly and at a lower cost using text-mining approaches.

The Relation Between Linguistic Awareness Skills and Spelling in Adults: A Comparison Among Scoring Procedures

2020 ◽  
Vol 63 (4) ◽  
pp. 1240-1253
Author(s):  
Victoria S. Henbest ◽  
Lisa Fitton ◽  
Krystal L. Werfel ◽  
Kenn Apel
Keyword(s):  
Phonemic Awareness ◽  
Concurrent Validity ◽  
Word Reading ◽  
Continuous Measure ◽  
Scoring Methods ◽  
Syntactic Awareness ◽  
Scoring Method ◽  
Spelling Performance ◽  
Linguistic Awareness ◽  
Average Readers

Purpose Spelling is a skill that relies on an individual's linguistic awareness, the ability to overtly manipulate language. The ability to accurately spell is important for academic and career success into adulthood. The spelling skills of adults have received some attention in the literature, but there is limited information regarding which approach for analyzing adults' spelling is optimal for guiding instruction or intervention for those who struggle. Thus, we aimed to examine the concurrent validity of four different scoring methods for measuring adults' spellings (a dichotomous scoring method and three continuous methods) and to determine whether adults' linguistic awareness skills differentially predict spelling outcomes based on the scoring method employed. Method Sixty undergraduate college students who were determined to be average readers as measured by a word reading and contextual word reading task were administered a spelling task as well as morphological, orthographic, phonemic, and syntactic awareness tasks. Results All four scoring methods were highly correlated suggesting high concurrent validity among the measures. Two linguistic awareness skills, morphological awareness and syntactic awareness, predicted spelling performance on both the dichotomous and continuous scoring methods. Contrastively, phonemic awareness and orthographic awareness predicted spelling performance only when spelling was scored using a continuous measure error analysis. Conclusions The results of this study confirm that multiple linguistic awareness skills are important for spelling in adults who are average readers. The results also highlight the need for using continuous measures of spelling when planning intervention or instruction, particularly in the areas of orthographic and phonemic awareness.

scholarly journals Systematic evaluation of scoring methods for Ki67 as a surrogate for 21-gene recurrence score

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Soonmyung Paik ◽  
Youngmee Kwon ◽  
Moo Hyun Lee ◽  
Ji Ye Kim ◽  
Da Kyung Lee ◽  
...  
Keyword(s):  
Clinical Utility ◽  
Recurrence Score ◽  
Predictive Marker ◽  
Labeling Index ◽  
Systematic Evaluation ◽  
Scoring Methods ◽  
Scoring Method ◽  
Ki67 Labeling Index ◽  
Risk Patients ◽  
Potential Clinical Utility

AbstractAlthough Ki67 labeling index is a potential predictive marker for chemotherapy benefit, its clinical utility has been limited by the lack of a standard scoring method resulting in poor interobserver reproducibility. Especially, there is no consensus on the use of average versus hotspot score for reporting. In order to determine the best method for Ki67 scoring and validate manual scoring method proposed by the International Ki67 Working Group (IKWG), we systematically compared average versus hotspot score in 240 cases with a public domain image analysis program QuPath. We used OncotypeDx Recurrence Score (RS) as a benchmark to compare the potential clinical utility of each scoring methods. Both average and hotspot scores showed statistically significant but only modest correlation with OncotypeDx RS. Only hotspot score could meaningfully distinguish RS low-risk versus high-risk patients. However, hotspot score was less reproducible limiting its clinical utility. In summary, our data demonstrate that utility of the Ki67 labeling index is influenced by the choice of scoring method.

scholarly journals Evaluation of Environmental Information Disclosure of Listed Companies in China’s Heavy Pollution Industries: A Text Mining-Based Methodology

2021 ◽  
Vol 13 (10) ◽  
pp. 5415
Author(s):  
Rongjiang Cai ◽  
Tao Lv ◽  
Xu Deng
Keyword(s):  
Text Mining ◽  
Information Disclosure ◽  
Listed Companies ◽  
Environmental Information ◽  
Evaluation Framework ◽  
Scoring Method ◽  
Study Results ◽  
Environmental Information Disclosure ◽  
Heavy Pollution

Environmental information disclosure (EID) of listed companies is a significant and essential reference for assessing their environmental protection commitment. However, the content and form of EID are complex, and previous assessment studies involved manual scoring mainly by the experts in this field. It is subjective and has low timeliness. Therefore, this paper proposes an automatic evaluation framework of EID quality based on text mining (TM), including the EID index system’s construction, automatic scoring of environmental information disclosure quality, and EID index calculation. Furthermore, based on the EID of 801 listed companies in China’s heavy pollution industry from 2013 to 2017, case studies are conducted. The case study results show that the overall quality of the EID of listed companies in China’s heavily polluting industries is low, and there is a gap differentiation between the 16 industries. Compared with the subjective manual scoring method, TM evaluation can evaluate the quality of EID more effectively and accurately. It has great potential and can become an essential tool for the sustainable development of society and listed companies.

The Validity of the 12-Item General Health Questionnaire in Australia: A Comparison Between Three Scoring Methods

2001 ◽  
Vol 35 (2) ◽  
pp. 231-235 ◽  
Author(s):  
Susan Donath
Keyword(s):  
Mental Illness ◽  
General Health ◽  
General Health Questionnaire ◽  
Composite International Diagnostic Interview ◽  
Health Practitioner ◽  
Health Questionnaire ◽  
Scoring Methods ◽  
Scoring Method ◽  
Specificity And Sensitivity ◽  
Threshold Score

Objective: To investigate the specificity and sensitivity of three different scoring methods of the 12-item General Health Questionnaire (GHQ-12) and hence to determine the best GHQ-12 threshold score for the detection of mental illness in community settings in Australia. Method: Secondary data analysis of the 1997 Australian National Survey of Health and Wellbeing (n = 10 641), using the Composite International Diagnostic Interview as the gold standard for diagnosis of mental illness. Results: The area under the Receiver Operating Characteristic (ROC) curve for the C-GHQ scoring method was 0.84 (95% CI = 0.83–0.86) compared with the area for the standard scoring method of 0.78 (95% CI = 0.76–0.80). The best threshold with C-GHQ was 3/4, with sensitivity 82.9% (95% CI = 80.2–85.5%) and specificity 69.0% (95% CI = 68.6–69.4%). The best threshold score with the standard scoring method was 0/1, with sensitivity 75.4% (95% CI = 72.5–78.4%) and specificity 69.9% (95% CI = 69.5–70.3%). These were also the best thresholds for a subsample of the population who had consulted a health practitioner in the previous 4 weeks. Conclusion: In the Australian setting, the C-GHQ scoring method is preferable to the standard method of scoring the GHQ-12. In Australia the GHQ-12 appears to be a less useful instrument for detecting mental illness than in many other countries.

Automated Scoring of Figural Creativity using a Convolutional Neural Network

2021 ◽  
Author(s):  
David H Cropley ◽  
Rebecca L Marrone
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Creative Thinking ◽  
Divergent Thinking ◽  
Reliability And Validity ◽  
Semantic Distance ◽  
Self Report ◽  
Creativity Assessment ◽  
Creativity Research ◽  
Creative Self

One of the abiding challenges in creativity research is assessment. Objectively scored tests of creativity such as the Torrance Tests of Creativity (TTCT) and the Test of Creative Thinking - Drawing Production (TCT-DP) offer high levels of reliability and validity but are slow and expensive to administer and score. As a result, many creativity researchers default to simpler and faster self-report measures of creativity and related constructs (e.g., creative self-efficacy, openness). Recent research, however, has begun to explore the use of computational approaches to address these limitations. Examples include the Divergent Association Task (DAT) that uses computational methods to rapidly assess the semantic distance of words, as a proxy for divergent thinking. To date, however, no research appears to have emerged that uses methods drawn from the field of artificial intelligence to assess existing objective, figural (i.e., drawing) tests of creativity. This paper describes the application of machine learning, in the form of a convolutional neural network, to the assessment of a figural creativity test – the TCT-DP. The approach shows excellent accuracy and speed, eliminating traditional barriers to the use of these objective, figural creativity tests and opening new avenues for automated creativity assessment.

Applications of Pattern Discovery Using Sequential Data Mining

2012 ◽  
pp. 1-23 ◽  
Author(s):  
Manish Gupta ◽  
Jiawei Han
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Intrusion Detection ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Sequential Pattern Mining ◽  
Web Usage Mining ◽  
Sequential Pattern ◽  
Sequential Data ◽  
Mining Methods

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.

Conclusion

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Text Mining ◽  
High Throughput ◽  
Relevant Literature ◽  
Critical Role ◽  
The Body ◽  
Data Sets ◽  
Data Set ◽  
New Techniques ◽  
Scientific Text ◽  
Mining Methods

The genomics era has presented many new high throughput experimental modalities that are capable of producing large amounts of data on comprehensive sets of genes. In time there will certainly be many more new techniques that explore new avenues in biology. In any case, textual analysis will be an important aspect of the analysis. The body of the peer-reviewed scientific text represents all of our accomplishments in biology, and it plays a critical role in hypothesizing and interpreting any data set. To altogether ignore it is tantamount to reinventing the wheel with each analysis. The volume of relevant literature approaches proportions where it is all but impossible to manually search through all of it. Instead we must often rely on automated text mining methods to access the literature efficiently and effectively. The methods we present in this book provide an introduction to the avenues that one can employ to include text in a meaningful way in the analysis of these functional genomics data sets. They serve as a complement to the statistical methods such as classification and clustering that are commonly employed to analyze data sets. We are hopeful that this book will serve to encourage the reader to utilize and further develop text mining in their own analyses.

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