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Development of Representational Competence was judged by assessing how learners understand semantics of each representation, how they understand which parts of the domain are represented, how they relate representations to each other, and how they translate between representations (Ainsworth 1999).
Our series of benchmarks show when each representation is best.
The advantages of each representation is given in Table 6.
The PAR, IPV, and CM are calculated for each representation.
\(\alpha\) and \ \beta\) represent the relative dimming of each representation (Weiss 1928: 77).
For each representation a hierarchy of clusterings is produced via the complete link algorithm.
Each representation of a motion can be normalized to improve computational cost and storage utilization.
A preliminary experimental session was aimed to find the best LSTM parameter configuration for each representation.
Each representation in MERs can show specific aspects of the domain to be learnt.
Based on these observations, we design algorithms for integrating the individual classifiers to capture the significant aspects of each representation.
Each representation is used to train a classifier, and the results are compared by means of a dissimilarity metric.
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