I agree. The "Sequential Explanatory Mixed Methods Design" usually begins with a quantitative phase to test hypotheses, look at relationships, or spot trends. A qualitative phase then follows to assist interpret and explain the quantitative results. The students work you mention, may not fully meet the objectives of a Sequential Explanatory design if it solely employs descriptive quantitative methods without looking at linkages, causal ties, or performing inferential analysis. In Sequential Explanatory, the quantitative phase's goal is usually to produce quantifiable, objective findings that direct the subsequent qualitative investigation. Since descriptive analysis only offers a picture and does not always guide qualitative follow-up targeted at deeper insights or explanations, it may not be sufficient for this exploratory link. A sequential exploratory approach might be more appropriate in situations where the first quantitative phase is solely descriptive and the goal is to use qualitative data to expand on an initial overview or to further develop a theory. As an alternative, the quantitative phase might be made more compatible with a Sequential Explanatory framework by include inferential tests.

The type of quantitative analysis does not have to be a specific type, such as correlation. Descriptive statistics can be quite powerful and useful, as pointed out and championed by Stanley Pogrow. Everything depends on your research purposes and aims. Admittedly, most journals prize sophistication, but simplicity has a lot to offer.

The whole point of a sequential explanatory design is that there has to be something to explain. In a descriptive quantitative analysis, this might be why is a distribution so skewed or bimodal. Or why are there outliers from a distribution? But the research question for the qualitative follow-up would have to be carefully described and justified.