Just represent vectors as column vectors from the start, define R^n as R^n,1 (in the notation of the book) and then there's no confusion and you get matrix multiplication of column vectors as students learned in high school: M.x where M is an m x n matrix and x is an n x 1 column vector, with product an m x 1 column vector. Stick to this all the way through a first linear algebra textbook and undergrad tears can be avoided. e.g. example 3.32 in the text: T(1,0) = (1,2,7). Just write these as column vectors and the matrix multiplication is easier to visualize. (Also, T acting on a real number was written T(x) with brackets so T acting on a vector should really be written T((1,0)). Inconsistencies in notation like this lead to pain.)
Then, for example, when you want to show that the rows of a matrix are independent, you can use a row vector to write succinctly x.M = 0 => x = 0 where x is now a row vector. And it's clear we're doing something other than transforming space vectors here as we've used column vectors for vectors representing points in space.
For undergrad textbooks you want to decide on a convention, one that matches what was done in high school preferably, and stick to it.