Orthographic Sorting for Second Language Acquisition Using MindSort
| dc.contributor.advisor | Leone, F.T.M. | |
| dc.contributor.advisor | Dijkstra, A.F.J. | |
| dc.contributor.author | Boels, Aline | |
| dc.date.issued | 2022-08-24 | |
| dc.description.abstract | For foreign vocabulary acquisition, similar words are sometimes found to be confusing, even for more advanced learning. MindSort, a brain-inspired application for learning foreign vocabulary, focuses on this challenge of similarity. Hereto, MindSort maps more similar words closer together on the screen. To investigate the best way to map the words on the screen, this study investigates the question How do participants judge orthographic similarity between unknown words in a 2D spatial sorting task? To this end, an experiment was conducted where 20 participants were asked to spatially sort 15 Swahili words on a screen for two vocabulary lists, based on how (dis)similar they judged words to be. The resulting data from this experiment consisted of lists with word pair distances, which were compared to five different orthographic models of word pair distances: Levenshtein distance, Damerau-Levenshtein distance, normalized Levenshtein distance, weighted open bigram, and extended spatial coding. To get the word pair distance per model, the following four steps were taken: (1) implement the algorithm, (2) calculate the distances between words in a distance matrix, (3) map the distances to a 2D map using Multidimensional Scaling (MDS), and (4) calculate the Euclidean distances between word pairs. We calculated the correlation between the experimental data of the word pair distances in the sorting of the participants and the data from the models using Spearman’s rank correlation coefficient. Comparing these correlation for the different models using Friedman’s test and Wilcoxon signed rank test showed that the weighted open bigram model scored significantly better than Damerau- Levenshtein and normalized Levenshtein. Contributing to further developments in the field of psycholinguistics we notice that participants mentioned sound as most important word feature for sorting besides the first letter of the word. Because of the limited scope of this study, we suggest to do further research to see whether our results also hold with more data. Especially the selection of the words for the word list needs further attention for which our research provides several starting points. | |
| dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/15831 | |
| dc.language.iso | en | |
| dc.thesis.faculty | Faculteit der Sociale Wetenschappen | |
| dc.thesis.specialisation | specialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Bachelor Artificial Intelligence | |
| dc.thesis.studyprogramme | studyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence | |
| dc.thesis.type | Bachelor | |
| dc.title | Orthographic Sorting for Second Language Acquisition Using MindSort |
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