Showing convergence in probability of sample variance to population variance Problem 6.7.2 of Resnick's A Probability Path says: Let $\{X_{n}\}$ be iid, with $EX_{n}=\mu$, $\mathrm{Var}(X_{n})=\sigma^2$.  Set $\overline{X}=\sum_{i=1}^{n} X_{i}/n$. Show:
$$
\frac{1}{n}\sum_{i=1}^{n}(X_{i}-\overline{X})^2\to \sigma^{2}
$$
where the $\to$ means convergence in probability.  There is no hypothesis on higher moments of $X_{n}$, so I believe that one cannot apply Chebyshev's theorem.  Furthermore this precedes any discussion of characteristic functions or the strong law of large numbers. How does one get this result without such tools?
[This is probably the same question as this one : Convergence in probability of sample variance but the answers there apparently invoke the Strong Law which isn't proved until Chapter 7 of Resnick.]
 A: 
Tools: Borel-Cantelli lemma, Tschebysheff inequality, dominated convergence theorem

The first step is to show the weak law of large numbers.

Theorem 1 Let $(Y_n)_{n \in \mathbb{N}}$ be a sequence of independent identically distributed random variables and suppose that $\mathbb{E}(|Y_1|)<\infty$. Then $$S_n := \frac{1}{n} \sum_{i=1}^n Y_i$$ converges in probability to $\mu := \mathbb{E}(Y_1)$ as $n \to \infty$.

For the proof we use the following auxiliary statement.

Lemma 1 Let $(Y_n)_{n \in \mathbb{N}}$ and $(Z_n)_{n \in \mathbb{N}}$ be two sequences of random variables. If $\sum_{n \geq 1} \mathbb{P}(Y_n \neq Z_n)<\infty$, then $\sum_{n \geq 1} (Y_n-Z_n)$ converges almost surely.

Proof of Lemma 1: It follows from the Borel Cantelli lemma that $\mathbb{P}(Z_n \neq Y_n$ infinitely often$)=0$, and therefore there exists a null set $N$ such that for any $\omega \notin N$, we have $Z_n(\omega) = Y_n(\omega)$ for $n$ sufficiently large. Obviously, this implies the (almost sure) convergence of the series.
Proof of Theorem 1: Recall that $\mathbb{E}(|Y_1|)$ implies that $\sum_{n \geq 1} \mathbb{P}(|Y_1|>n)<\infty$. If we define truncated random variables $Z_n := Y_n 1_{\{|Y_n| \leq n\}}$, then $$\sum_{n \geq 1} \mathbb{P}(Z_n \neq Y_n) = \sum_{n \geq 1} \mathbb{P}(|Y_n|>n) = \sum_{n \geq 1} \mathbb{P}(|Y_1|>n)<\infty.$$ Applying Lemma 1, we find that $S_n = n^{-1} \sum_{i=1}^n Y_i$ converges in probability to $\mu$ if (, and only if,) $T_n := n^{-1} \sum_{i=1}^n Z_i$ converges in probability to $\mu$. To show that $T_n \stackrel{\mathbb{P}}{\to} \mu$, we first calculate the variance of $T_n$. Since the random variables $Z_n$, $n \in \mathbb{N}$, are independent and bounded, we have
$$\text{var}(T_n) = \frac{1}{n^2} \sum_{i=1}^n \text{var}(Z_i) \leq \frac{1}{n^2} \sum_{i=1}^n \mathbb{E}(Z_i^2) \tag{1} $$
where we have used that
$$\text{var}(Z_i) = \mathbb{E}(Z_i^2)-[\mathbb{E}(Z_i)]^2 \leq \mathbb{E}(Z_i^2).$$
Now choose some sequence $(a_n)_{n \in \mathbb{N}} \subseteq \mathbb{N}$ such that $a_n \to \infty$ and $a_n/n \to 0$ as $n \to \infty$. Using that $|Z_i| \leq i$, we find
$$\begin{align*} \sum_{i=1}^n \mathbb{E}(Z_i^2) &= \sum_{i=1}^{a_n} \mathbb{E}(Z_i^2) + \sum_{i=a_{n}+1}^n \mathbb{E}(Z_i^2) \\ &\leq a_n \sum_{i=1}^{a_n} \mathbb{E}(|Z_i|) + \sum_{i=a_n+1}^n \mathbb{E}(Z_i^2 1_{|Z_i| \leq a_n}) + \sum_{i=a_n+1}^n \mathbb{E}(Z_i^2 1_{|Z_i| > a_n}) \\ &\leq a_n \sum_{i=1}^{a_n} \mathbb{E}(|Z_i|) + a_n \sum_{i=a_n+1}^n\mathbb{E}(|Z_i|) + n \sum_{i=a_n+1}^n \mathbb{E}(|Z_i| 1_{|Z_i|>a_n})\\ &\leq a_n \sum_{i=1}^n \mathbb{E}(|Y_i|) + n \sum_{i=a_{n}+1}^n \mathbb{E}(|Y_i| 1_{|Y_i|>a_n}) \\ &\leq a_n n \mathbb{E}(|Y_1|) +n^2 \mathbb{E}(|Y_1| 1_{|Y_1|>a_n}).  \end{align*}$$
Since $Y_1 \in L^1$, $a_n/n \to 0$ and $a_n \to \infty$, the dominated convergence theorem gives
$$\lim_{n \to \infty} \frac{1}{n^2} \text{var}(T_n) \stackrel{(1)}{\leq} \lim_{n \to \infty} \frac{1}{n^2} \sum_{i=1}^n \mathbb{E}(Z_i^2) =0.$$
Applying Tschebysheff's inequality, it follows easily that $T_n$ converges in probability to $\mu$. This finishes the proof.

Theorem 2: Let $(X_n)_{n \in \mathbb{N}}$ be a sequence of independent identically distributed random variables with $\mathbb{E}(X_n) = \mu$ and $\text{var}(X_n) = \sigma^2<\infty$. Set $\bar{X}_n = n^{-1} \sum_{i=1}^n X_i$ and $S_n := n^{-1} \sum_{i=1}^n (X_i-\bar{X}_n)^2$. Then $S_n \to \sigma^2$ in probability.

Proof: Since $$(X_i-\mu+(\mu-\bar{X}))^2 = (X_i-\mu)^2 + 2 (X_i-\mu) \cdot (\mu-\bar{X})+(\mu-\bar{X})^2 $$
we have $$\begin{align} S_n= \frac{1}{n} \sum_{i=1}^n (X_i-\bar{X})^2 &= \frac{1}{n} \sum_{i=1}^n (X_i-\mu)^2 + 2 (\mu-\bar{X}) \underbrace{\frac{1}{n} \cdot \sum_{i=1}^n (X_i-\mu)}_{\left(\frac{1}{n} \sum_{i=1}^n X_i\right)-\mu =(\bar{X}-\mu)} + (\mu-\bar{X})^2 \\ &=  \frac{1}{n} \sum_{i=1}^n (X_i-\mu)^2  - (\bar{X}-\mu)^2. \end{align}$$
By the weak law of large numbers, Theorem 1, we have
$$\bar{X} = \frac{1}{n} \sum_{i=1}^n X_i \to \mu \quad \text{in probability} \\ \frac{1}{n} \sum_{i=1}^n (X_i-\mu)^2 \to \mathbb{E} \left( (X_i-\mu)^2 \right)=\sigma^2 \quad \text{in probability}. $$
Hence $S_n \to \sigma^2$ in probability.
Remark: The idea for this proof is taken from Chung's book A Course in Probability Theory.
