Sentence examples for questions on comparing from inspiring English sources

Greater insights are needed into how young learners deal with variation in an immersive classroom environment including across disciplines (Watson, 2012), how they represent data in new ways of their own choosing, how they answer questions on comparing variation in different data sets and how they identify and justify the sources of variation they encounter.

While some doctors say the bigger concern may be whether stents of all types are being used too frequently on patients who should be receiving drugs or bypass surgery instead, the panel review and the journal articles focused on the narrower question of comparing the drug-coated stents with the bare metal stents they have largely supplanted.

For example, averaging over 1000 runs, with (alpha = 0.40125) in Fig. 2a, with (gamma = 1.246), we use a total of four rounds on average with 50 questions on average, compared to using seven rounds on average and 49 questions on average with (gamma = 1) (or no redundant questions), and with (gamma = 2.492), only 1 round is used on average, with 53 questions used on average.

The first paper to provide comprehensive evaluation and thorough analysis on comparing machine-generated questions with those produced by humans with several verification methods.

The main contributions of this paper are listed below: Suggesting methods to evaluate vocabulary questions generated by an automatic question generation system from two different perspectives (teacher and student) The first paper to provide comprehensive evaluation and thorough analysis on comparing machine-generated questions with those produced by humans with several verification methods.

In addressing the final research question, we also return to our focus on comparing textbook sections.

Finally, they will use their guiding questions to compare two pieces on the same news event, separating opinion from fact on the sentence level.

We will then discuss methodological challenges pertinent to scoring codes and present important psychometric research questions centered on comparing competing scoring procedures.

We highlight methodological challenges pertinent to scoring software-coded facial emotion expression codes and present important psychometric research questions centered on comparing competing scoring procedures of these codes.

Both control and Celliant™ subjects reported decreased subjective pain after wearing socks for every question based on comparing W1+2 scores to W3+4 scores (see Figures).

Figure  3 shows data on some questions comparing the two universities sampled.