02248nam a22002057a 4500 20231229172120.0 231228b ||||| |||| 00| 0 eng d 0022-0175 Buczak, Philip The Machines Take Over : A Comparison of Various Supervised Learning Approaches for Automated Scoring of Divergent Thinking Tasks (Journal Article) Hoboken,NJ : Wiley Subscription Services Inc. , 2022 17-36p. The Journal of Creative Behaviour , Volume 57: Number 1, First Quarter 2023 ***______{For Hard Copy, Please visit Library.}________*** 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. Divergent Thinking| Creative Quality| Human Ratings| Supervised Learning| Random Forest| Gradient Boosting| Vector Regression Huang, He | Forthmann, Boris | Doebler, Philipp https://doi.org/10.1002/jocb.559 PER 0 0 0 0 RIEBPL RIEBPL 2023-12-29 0 2023-12-29 00:00:00 2023-12-29 PER 45024 45023