In the most general sense, a k-form is an element of the n-dual of a vector space V, i.e., an element of :
(VxVx...xV)*--->F ,
Where F is the base field. To simplify, assume for now that F=Reals.
A 1-form is just any element of the dual space of a vector space.
Example: Say V=Reals over the Reals. Choose a basis {##v_1,..,v_n##} and write ## v=c_1v_1+...c_nv_n ## . Then the map ## v-\in V-> c_1+c_2+...+c_n ##is an element of V*. An n-form is then an n-linear: bilinear, trilinear, etc. map.
Maybe in the most basic sense, in differential geometry, a 1-form w is a linear map from each tangent vector, as an element of T_p M , as elements (usually) to the Reals (most-often the base field of the 1-D tangent plane), i.e., w is an element of (T_pM)*,
the dual of the tangent space, more generally, an n-form is an element of ##(T_pM \times T_p M \times .. \times T_pM )^*-->\mathbb R ##
i.e., a multilinear map in what is called the exterior algebra of ##T_pM ##. A 2-tensor you may be familiar with is the inner-product, which is representable as a matrix.
BUT, a form is a subset (I think actually a subspace) of the n-th dual, consisting of linear maps that are alternating, meaning n-linear maps that invariant under coordinate changes (up to parity of the changes of coordinates). A 1-form is a linear assignment of a Real number at each tangent space, a k-form is an assignment of an alternating n-linear map to each vector in ##(T_p M \times T_p M \times.. \times T_p M) ##.
More formally, a k-form w_k is an element of ##TM^k:= (T_p M)^*:= ( |_| T^*p M) ##, where ##TM^k ## is the k-th power of the cotangent bundle.
Maybe you can start by reading on the Tensor Algebra :
http://en.wikipedia.org/wiki/Tensor_algebra
Like you said, a 1-tensor is just a linear map, a 2-tensor is a quadratic form so that B(x,y): ##x^T MY ##, for a matrix ## M ## ,and 3- and higher- tensors are not (AFAIK) describable with matrices, at least not in a standard form.
The exterior algebrar is a so-cold (specially in winter ;) ) graded algebra, where the n-th grade consists of the n-linear maps, and you can go between different grades to obtain a (k+l)-linear map ( as long as (k+l) is less than the total dimension ) by tensoring maps, or doing a tensoring between graded subspaces.
When you restrict the exterior algebra to alternating forms, you get the tensor algebra.
About tensor products, a tensor product of two ( or more) spaces allow you to have a single space that simplifies your maps in a specific sense: in the case of modules, or vector spaces, the tensor product allows you to construct a new space in which every n-linear map on ##(V_1 \times V_2 \times..\times V_n) ## becomes a linear map on the space ## V_1 \otimes... \otimes V_n ##; so that, e.g., for the case of two vector spaces ( over the same field ) ## V,W## , the tensor space ## V \otimes W ## allows you to describe every bilinear map on ## V \times W \rightarrow \mathbb R## as a linear map on ## V \otimes W \rightarrow \mathbb R ##. This construction (meaning the justification for why it is possible to construct a space where every map factors through) ultimately comes down to properties that allow certain maps to "factor through " other maps, i.e., the map f is said to factor through the map g , if there is a map h with ## f=g \circ h ##, so you see how this seems similar to factoring numbers as n= rs . These properties have to see mostly with kernels and normal subgroups. Maybe you can look up "conditions for maps to factor through ".
This idea of the tensor product can be defined for other types of objects.