Fix an integer $n\geq 2$. Let $X_1,X_2,\ldots,X_n$ be independent and identically distributed random variable with distribution function $F$ and probability density $f$. Write $Y_1,Y_2,\ldots,Y_n$ for a rearrangement of $X_1,X_2,\ldots,X_n$ so that $Y_1\leq Y_2\leq \ldots\leq Y_n$. Then, prove that the probability density $g_k$ of $Y_k$ is given by
$$g_k(y)=n\binom{n-1}{k-1}\,F(y)^{k-1}\,\big(1-F(y)\big)^{n-k}\,f(y)\text{ for all }y\in\mathbb{R}\,.$$
In particular, if $X_1,X_2,\ldots,X_n$ are uniformly distributed on $[0,L]$, then we have
$$f(x)=\frac{1}{L}\text{ and }F(x)=\frac{x}{L}\text{ for }x\in[0,L]\,.$$
Therefore,
$$g_k(y)=n\binom{n-1}{k-1}\,\left(\frac{y}{L}\right)^{k-1}\,\left(1-\frac{y}{L}\right)^{n-k}\,\frac{1}{L}\text{ for }y\in[0,L]\,.$$
This means
$$\mathbb{E}[Y_k]=n\binom{n-1}{k-1}\,\int_0^L\,\left(\frac{y}{L}\right)^{k-1}\,\left(1-\frac{y}{L}\right)^{n-k}\,\frac{y}{L}\,\text{d}y\,.$$
Letting $t:=\dfrac{y}{L}$, we get
$$\mathbb{E}[Y_k]=n\binom{n-1}{k-1}\,L\,\int_0^1\,t^{k}\,(1-t)^{n-k}\,\text{d}t=n\binom{n-1}{k-1}\,L\,\frac{k!\,(n-k)!}{(n+1)!}=\frac{kL}{n+1}\,.$$
(This integral is a beta integral. See the definition of the beta function if you need to.) Thus,
$$\mathbb{E}[Y_{k+1}-Y_k]=\mathbb{E}[Y_{k+1}]-\mathbb{E}[Y_k]=\frac{(k+1)L}{n+1}-\frac{kL}{n+1}=\frac{L}{n+1}\,.$$
Taking the average for $k=1,2,\ldots,n-1$, we get
$$\frac{1}{n-1}\,\sum_{k=1}^{n-1}\,\mathbb{E}[Y_{k+1}-Y_k]=\frac{L}{n+1}\,.$$