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Nagy, Gabriel; Ulitzsch, Esther – Educational and Psychological Measurement, 2022
Disengaged item responses pose a threat to the validity of the results provided by large-scale assessments. Several procedures for identifying disengaged responses on the basis of observed response times have been suggested, and item response theory (IRT) models for response engagement have been proposed. We outline that response time-based…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Predictor Variables, Classification
von Davier, Matthias; Tyack, Lillian; Khorramdel, Lale – Educational and Psychological Measurement, 2023
Automated scoring of free drawings or images as responses has yet to be used in large-scale assessments of student achievement. In this study, we propose artificial neural networks to classify these types of graphical responses from a TIMSS 2019 item. We are comparing classification accuracy of convolutional and feed-forward approaches. Our…
Descriptors: Scoring, Networks, Artificial Intelligence, Elementary Secondary Education

Kazen, Joseph K.; Otani, Hajime – Educational and Psychological Measurement, 1997
A Statistical Package for the Social Sciences (SPSS) program is described that computes four common clustering measures used to analyze free recall of information from categorized lists. Computed are a ratio of repetition, a modified ratio of repetition, an adjusted ratio of clustering, and a deviation score for stimulus lists. (SLD)
Descriptors: Classification, Cluster Analysis, Computer Software, Recall (Psychology)

Koslowsky, Meni – Educational and Psychological Measurement, 1985
The technique of generalizing sample results in a classification study to large subpopulations of unequal sizes was examined. The usual output from the discriminant analysis routine in the Statistical Package for the Social Sciences was extended to handle the present statistical problems. Advantages of the technique were discussed. (Author/DWH)
Descriptors: Classification, Computer Software, Discriminant Analysis, Generalization