Is Oversampling a Signal Same as Discretizing the Signal?
Oversampling is the case the rate the data is sampled is higher than required by the data Bandwidth and Nyquist Shannon Sampling Theorem.
It has many good properties regarding the processing yet it also generate some issues (Like coloring the quantization noise).

Regarding your questions:


In BPSK the symbol is the phase range of modulated signal (Sign). By using repmat() you indeed oversmapled the data (Symbols) yet not the modulated signal. What you needed to do is creating a grid where you sample the modulated signal. So if the the modulated signal has a frequency $ f $ than for 100 oversampling rate you should sample at $ {F}_{s} = 100 \cdot 2 \cdot f $.
What you did is replication of the symbol. Not higher sampling rate unless you imitate the effect of oversampling after Symbol inference.
In order to see the effect of oversmapling by factor of $ 100 $ generate the modulated signal over a grid where $ \Delta T = \frac{1}{100 \cdot 2 \cdot f} $ with $ f $ being the modulating signal frequency.


Regarding your question in the title.
The process of sampling data is basically discretizing data over 2 grids:


The Time Grid
Basically sampling the continuous data in specific times. Oversampling refers to the grid interval.  
The Value Grid
Process we usually call quantization which means the value we sample isn't continuous but has discrete property of being a value withing a pre defined set of values (Quantization Grid).