Sentence examples for multiple unusual patterns from inspiring English sources

Non-random, unusual patterns, consistent with contract cheating, were found at the academic discipline-, unit-, and student-level, with approximately 2.1% of students producing multiple unusual patterns.

Significant variation was also observed within the student performances analysed here, with a small percentage of students observed to produce multiple unusual patterns of performance on supervised and unsupervised assessment items across units they took (paralleling previous findings relating to repeat offending for contract cheating, e.g., Curtis & Clare, 2017).

By testing a series of difference rules designed to identify 'unusual' patterns of difference, this analysis looks to identify repeat offenders (students with multiple unusual patterns across units they have taken) and repeat victimisation (with certain units producing disproportionately large numbers of unusual patterns of difference).

Fig. 1 Frequency of unusual patterns (UP) and probability of multiple violations per student for (a) UP1, (b) UP2, (c) UP3, (d) UP4, and (e) UP5 Fig. 2 Frequency of multiple unusual patterns across 31 students (inclusion criteria: produced UP2 at least twice) as a percentage of all units completed.

Second, looking for motivated offenders, it is anticipated a small percentage of individual students will produce multiple unusual patterns of performance on supervised and unsupervised assessment items across units they have taken (paralleling previous findings relating to repeat offending in criminal contexts).

As explained above, it was anticipated a small percentage of individual students would produce multiple unusual difference score patterns across units they took.

To test this expectation, the frequency of rule unusual patterns and probability of multiple occurrences per student are displayed in Fig. 1.

This is designed to pick up unusual patterns of expenditure.

Combined techniques of removal are sometimes required for unusual patterns of nails breakage.

Next, the difference scores and unusual patterns were analysed at the individual student level.

Algorithms monitor the data automatically on a daily basis to detect unusual patterns of sales.