The Machines Take Over (Record no. 45024)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02248nam a22002057a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20231229172120.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 231228b ||||| |||| 00| 0 eng d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER | |
| ISSN | 0022-0175 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Buczak, Philip |
| 245 ## - TITLE STATEMENT | |
| Title | The Machines Take Over |
| Remainder of title | : A Comparison of Various Supervised Learning Approaches for Automated Scoring of Divergent Thinking Tasks (Journal Article) |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Hoboken,NJ |
| Name of publisher | : Wiley Subscription Services Inc. |
| Year of publication | , 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 17-36p. |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE | |
| Title | The Journal of Creative Behaviour |
| Volume number/sequential designation | , Volume 57: Number 1, First Quarter 2023 |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | ***______{For Hard Copy, Please visit Library.}________***<br/><br/> |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Abstract: Traditionally, researchers employ human raters for scoring responses to creative thinking tasks. Apart from the associated costs this approach entails two potential risks. First, human raters can be subjective in their scoring behavior (inter-rater-variance). Second, individual raters are prone to inconsistent scoring patterns (intra-rater-variance). In light of these issues, we present an approach for automated scoring of Divergent Thinking (DT) Tasks. We implemented a pipeline aiming to generate accurate rating predictions for DT responses using text mining and machine learning methods. Based on two existing data sets from two different laboratories, we constructed several prediction models incorporating features representing meta information of the response or features engineered from the response’s word embeddings that were obtained using pre-trained GloVe and Word2Vec word vector spaces. Out of these features, word embeddings and features derived from them proved to be particularly effective. Overall, longer responses tended to achieve higher ratings as well as responses that were semantically distant from the stimulus object. In our comparison of three state-of-the-art machine learning algorithms, Random Forest and XGBoost tended to slightly outperform the Support Vector Regression. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Divergent Thinking| Creative Quality| Human Ratings| Supervised Learning| Random Forest| Gradient Boosting| Vector Regression |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Huang, He | Forthmann, Boris | Doebler, Philipp |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1002/jocb.559 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Periodicals |
| Lost status | Damaged status | Home library | Current library | Date acquired | Koha item type |
|---|---|---|---|---|---|
| RIE BPL Library | RIE BPL Library | 29.12.2023 | Periodicals |