Let me give a geometric explanation in 2D. The first fact that is almost always glossed over in class is that a "linear operator" or a matrix $T$ acting on a space $V \to V$ looks like a combination of rotations, flips, and dilations. To image what I mean, think of a checker-board patterned cloth. If I apply a transform to the space, it stretches (or shrinks) the space by pulling the cloth in different directions, and maybe possibly rotates and flips the cloth too. My point, as I will show, is that the direction in which the space (the cloth) gets pulled is the eigenvectors. Lets start with pictures:
Start by applying $T = \begin{pmatrix} 1 & 0 \\ 1 & 1 \end{pmatrix}$ to the standard grid I discussed above in 2D. The image of this transformation (only showing the 20 by 20 grid's image) is shown below. The bold lines in the second image indicate the eigenvectors of $T.$ Notice that it only has 1 eigenvector (why?). The transform $T$ is such that it "shears" the space, and the only unit vector that doesn't change direction as a result of $T$ is $e_2 = (0,1)^T.$ Draw any other line on this cloth, and every time you apply $T$ it will become more vertical (more aligned with the eigenvector).

Lets look at an example with two eigenvectors:
Here, let $T = \begin{pmatrix} 2 & -1 \\ -1 & 2 \end{pmatrix},$ another common matrix you'll come across. Now we see this has 2 eigenvectors. First, I must apologize about the scales on these images, the eigenvectors are perpendicular (you'll soon learn why must this be so.) Immediately we can see what the action of $T$ is on the standard 20 by 20 grid. Physically, imagine a cloth being held fixed at all 4 corners, then 2 of the opposite corners get stretched in the direction of the bold line. The bold lines are the vectors that do not change direction as $T$ is applied, or one could say they are the characteristic directions of $T$. As you apply $T$ over and over again, any other vector in the space tends toward the direction of an eigenvector.

And last, I decided not to leave a picture, but consider $T = \begin{pmatrix} \cos{x} & -\sin{x} \\ \sin{x} & \cos{x} \end{pmatrix}.$ This is a rotation of the space about the origin, and has no (real) eigenvectors. Could you imagine why a pure rotation would have no real eigenvectors? Hopefully its now clear that because every vector changes direction upon applying $T,$ no real eigenvectors exist.
These concepts can easily be generalized to higher dimensions. My suggestion as a first year student would be to look back at these examples as you learn about geometric multiplicity, symmetric matrices, and orthogonal (unitary) matrices, perhaps this example will give you some physical insight on those important classes of operators too.