Latent Feature Extraction for Process Data via Multidimensional Scaling
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2019
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| _version_ | 1866912178627411968 |
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| author | Tang, Xueying Wang, Zhi He, Qiwei Liu, Jingchen Ying, Zhiliang |
| author_facet | Tang, Xueying Wang, Zhi He, Qiwei Liu, Jingchen Ying, Zhiliang |
| contents | Computer-based interactive items have become prevalent in recent educational assessments. In such items, the entire human-computer interactive process is recorded in a log file and is known as the response process. This paper aims at extracting useful information from response processes. In particular, we consider an exploratory latent variable analysis for process data. Latent variables are extracted through a multidimensional scaling framework and can be empirically proved to contain more information than classic binary responses in terms of out-of-sample prediction of many variables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1904_09699 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Latent Feature Extraction for Process Data via Multidimensional Scaling Tang, Xueying Wang, Zhi He, Qiwei Liu, Jingchen Ying, Zhiliang Applications Computer-based interactive items have become prevalent in recent educational assessments. In such items, the entire human-computer interactive process is recorded in a log file and is known as the response process. This paper aims at extracting useful information from response processes. In particular, we consider an exploratory latent variable analysis for process data. Latent variables are extracted through a multidimensional scaling framework and can be empirically proved to contain more information than classic binary responses in terms of out-of-sample prediction of many variables. |
| title | Latent Feature Extraction for Process Data via Multidimensional Scaling |
| topic | Applications |
| url | https://arxiv.org/abs/1904.09699 |