// Educational psychology experiment design and analysis covering IRT, growth modeling, A/B testing, and learning curve estimation for research.
| name | learning-experiment |
| description | Educational psychology experiment design and analysis covering IRT, growth modeling, A/B testing, and learning curve estimation for research. |
| tags | ["education-research","item-response-theory","learning-analytics","experimental-design","growth-modeling"] |
| version | 1.0.0 |
| authors | ["@xjtulyc"] |
| license | MIT |
| platforms | ["claude-code","codex","gemini-cli","cursor"] |
| dependencies | {"python":["numpy>=1.24","pandas>=2.0","scipy>=1.11","statsmodels>=0.14","scikit-learn>=1.3","matplotlib>=3.7"]} |
| last_updated | 2026-03-17 |
| status | stable |
Use this skill when you need to:
Trigger keywords: item response theory, Rasch model, 2PL, 3PL, student growth, learning curve, forgetting curve, spaced repetition, educational A/B test, mastery learning, knowledge tracing, Bayesian knowledge tracing, DIF analysis, NAEP, PISA, CAT, adaptive testing, effect size, pre-post design.
The 3-parameter logistic model (3PL) gives the probability that student $\theta$ answers item $i$ correctly:
$$P(X_i=1|\theta) = c_i + (1-c_i) \cdot \frac{e^{a_i(\theta - b_i)}}{1 + e^{a_i(\theta - b_i)}}$$
The 2PL sets $c_i=0$; the 1PL (Rasch) additionally fixes $a_i=1$.
$$I_i(\theta) = a_i^2 \cdot \frac{(P_i(\theta) - c_i)^2}{(1-c_i)^2} \cdot \frac{(1-P_i(\theta))}{P_i(\theta)}$$
Total test information: $I(\theta) = \sum_i I_i(\theta)$. Standard error: $SE(\theta) = 1/\sqrt{I(\theta)}$.
$$T_n = T_1 \cdot n^{-\alpha}$$
where $T_n$ is the time (or error rate) on trial $n$ and $\alpha$ is the learning rate. Log-linearized: $\ln T_n = \ln T_1 - \alpha \ln n$.
Hidden Markov model with states $K \in {0,1}$ (unlearned/learned):
$$P(K_{n+1}=1 | K_n=0) = p_T \quad \text{(transit)}$$ $$P(X=1 | K=1) = 1-p_S,\quad P(X=1|K=0) = p_G$$
pip install numpy>=1.24 pandas>=2.0 scipy>=1.11 statsmodels>=0.14 \
scikit-learn>=1.3 matplotlib>=3.7
import numpy as np
import pandas as pd
import scipy.optimize as opt
import statsmodels.api as sm
import matplotlib.pyplot as plt
print("Learning experiment analysis environment ready")
import numpy as np
import pandas as pd
import scipy.optimize as opt
import matplotlib.pyplot as plt
# -----------------------------------------------------------------
# Simulate a 2PL item response dataset
# 500 students, 20 items
# -----------------------------------------------------------------
np.random.seed(42)
n_students = 500
n_items = 20
# True parameters
theta_true = np.random.normal(0, 1, n_students) # student abilities
a_true = np.random.uniform(0.5, 2.5, n_items) # discriminations
b_true = np.random.linspace(-2.5, 2.5, n_items) # difficulties
# 2PL response probability
def p2pl(theta, a, b):
"""2PL probability of correct response."""
return 1.0 / (1.0 + np.exp(-a * (theta[:, None] - b[None, :])))
P_matrix = p2pl(theta_true, a_true, b_true)
responses = (np.random.uniform(size=P_matrix.shape) < P_matrix).astype(int)
print(f"Response matrix shape: {responses.shape}")
print(f"Mean item difficulty (p-values): {responses.mean(axis=0).round(2)}")
print(f"Overall correct rate: {responses.mean():.3f}")
# -----------------------------------------------------------------
# Marginal Maximum Likelihood (simplified via EM-like approach)
# For a production system use mirt, TAM, or irtpy packages
# -----------------------------------------------------------------
def neg_log_likelihood_2pl(params, responses):
"""Compute -2LL for 2PL model with fixed quadrature points for theta.
Args:
params: vector of length 2*n_items (a1..an, b1..bn)
responses: (n_students, n_items) binary matrix
Returns:
negative log-likelihood
"""
n_i = responses.shape[1]
a = np.abs(params[:n_i]) + 0.2 # ensure a > 0
b = params[n_i:]
# Gaussian quadrature over theta
n_quad = 15
quad_pts, quad_wts = np.polynomial.hermite.hermgauss(n_quad)
theta_q = quad_pts * np.sqrt(2) # transform to N(0,1)
wts = quad_wts / np.sqrt(np.pi)
total_ll = 0.0
for s in range(len(responses)):
# Likelihood for student s at each quadrature point
# P_iq = 2PL probability
p_iq = 1.0 / (1.0 + np.exp(-a[None, :] * (theta_q[:, None] - b[None, :])))
# Response likelihood at each quadrature point
r = responses[s]
log_lik_q = (r * np.log(np.clip(p_iq, 1e-9, 1 - 1e-9)) +
(1 - r) * np.log(np.clip(1 - p_iq, 1e-9, 1 - 1e-9))).sum(axis=1)
lik_q = np.exp(log_lik_q)
marginal_lik = (lik_q * wts).sum()
total_ll += np.log(max(marginal_lik, 1e-300))
return -total_ll
# Initialize with reasonable starting values
a_init = np.ones(n_items)
b_init = np.zeros(n_items)
params_init = np.concatenate([a_init, b_init])
print("\nFitting 2PL model (simplified MML)...")
# Use a subset for speed demonstration
subset_students = responses[:100]
result = opt.minimize(neg_log_likelihood_2pl, params_init,
args=(subset_students,),
method="L-BFGS-B",
options={"maxiter": 50, "ftol": 1e-4})
a_est = np.abs(result.x[:n_items]) + 0.2
b_est = result.x[n_items:]
print(f"Optimization converged: {result.success}")
print(f"Correlation(a_true, a_est): {np.corrcoef(a_true, a_est)[0,1]:.3f}")
print(f"Correlation(b_true, b_est): {np.corrcoef(b_true, b_est)[0,1]:.3f}")
# -----------------------------------------------------------------
# Item Characteristic Curves (ICC) visualization
# -----------------------------------------------------------------
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
theta_plot = np.linspace(-4, 4, 200)
# Plot 4 ICCs
colors = ["steelblue", "orange", "green", "red"]
for idx, item_idx in enumerate([0, 5, 10, 15]):
p_true = 1 / (1 + np.exp(-a_true[item_idx] * (theta_plot - b_true[item_idx])))
axes[0].plot(theta_plot, p_true, color=colors[idx],
label=f"Item {item_idx+1} (b={b_true[item_idx]:.1f})")
axes[0].set_xlabel("θ (Ability)"); axes[0].set_ylabel("P(correct)")
axes[0].set_title("Item Characteristic Curves")
axes[0].legend(fontsize=8)
axes[0].axhline(0.5, color="gray", ls="--", lw=0.8)
# Item Information Functions
for idx, item_idx in enumerate([0, 5, 10, 15]):
a, b = a_true[item_idx], b_true[item_idx]
p = 1 / (1 + np.exp(-a * (theta_plot - b)))
info = a**2 * p * (1 - p)
axes[1].plot(theta_plot, info, color=colors[idx],
label=f"Item {item_idx+1} (a={a:.2f})")
axes[1].set_xlabel("θ"); axes[1].set_ylabel("Information")
axes[1].set_title("Item Information Functions")
axes[1].legend(fontsize=8)
# Test information function (total)
P_all = 1 / (1 + np.exp(-a_true[:, None] * (theta_plot[None, :] - b_true[:, None])))
test_info = (a_true[:, None]**2 * P_all * (1 - P_all)).sum(axis=0)
se_theta = 1 / np.sqrt(np.maximum(test_info, 0.01))
axes[2].plot(theta_plot, test_info, "steelblue", lw=2, label="Test Information")
ax2r = axes[2].twinx()
ax2r.plot(theta_plot, se_theta, "r--", lw=1.5, label="SE(θ)")
ax2r.set_ylabel("Standard Error", color="red")
axes[2].set_xlabel("θ"); axes[2].set_ylabel("Information")
axes[2].set_title("Test Information & SE")
axes[2].legend(loc="upper left")
plt.tight_layout()
plt.savefig("irt_analysis.png", dpi=150, bbox_inches="tight")
plt.close()
print("\nFigure saved: irt_analysis.png")
import numpy as np
import pandas as pd
import scipy.optimize as opt
import matplotlib.pyplot as plt
# -----------------------------------------------------------------
# Simulate learning experiment: 30 students, 20 practice trials
# -----------------------------------------------------------------
np.random.seed(42)
n_students = 30
n_trials = 20
trials = np.arange(1, n_trials + 1, dtype=float)
# True power law parameters (student-specific)
T1_true = np.random.uniform(20, 40, n_students) # initial response time (seconds)
alpha_true = np.random.uniform(0.2, 0.6, n_students) # learning rate
# Observed response times with noise
times = np.zeros((n_students, n_trials))
for s in range(n_students):
times[s] = T1_true[s] * trials**(-alpha_true[s]) * np.exp(
np.random.normal(0, 0.05, n_trials))
# -----------------------------------------------------------------
# Power law of learning: T_n = T1 * n^(-alpha)
# -----------------------------------------------------------------
def power_law(n, T1, alpha):
"""Power law of practice."""
return T1 * n**(-alpha)
# Fit to mean learning curve
mean_times = times.mean(axis=0)
popt, pcov = opt.curve_fit(power_law, trials, mean_times,
p0=[30, 0.3], bounds=([1, 0.01], [200, 2.0]))
T1_fit, alpha_fit = popt
print(f"Power law fit: T1 = {T1_fit:.2f}s, α = {alpha_fit:.3f}")
print(f"95% CI for α: [{alpha_fit - 1.96*np.sqrt(pcov[1,1]):.3f}, "
f"{alpha_fit + 1.96*np.sqrt(pcov[1,1]):.3f}]")
# Exponential fit for comparison: T_n = T_inf + (T1 - T_inf)*exp(-k*(n-1))
def exponential_learning(n, T_inf, T1, k):
"""Exponential learning model."""
return T_inf + (T1 - T_inf) * np.exp(-k * (n - 1))
popt_exp, _ = opt.curve_fit(exponential_learning, trials, mean_times,
p0=[5, 35, 0.1], bounds=([0, 1, 0], [50, 200, 5]))
print(f"Exponential fit: T_inf = {popt_exp[0]:.2f}s, T1 = {popt_exp[1]:.2f}s, "
f"k = {popt_exp[2]:.4f}")
# AIC comparison
def aic(n_data, k_params, sse):
"""Compute AIC."""
sigma2 = sse / n_data
return n_data * np.log(sigma2) + 2 * k_params
sse_power = ((mean_times - power_law(trials, *popt))**2).sum()
sse_exp = ((mean_times - exponential_learning(trials, *popt_exp))**2).sum()
print(f"\nAIC (power law): {aic(n_trials, 2, sse_power):.2f}")
print(f"AIC (exponential): {aic(n_trials, 3, sse_exp):.2f}")
# -----------------------------------------------------------------
# Forgetting curve: Ebbinghaus retention function
# R(t) = exp(-t/S) where S is stability
# -----------------------------------------------------------------
def ebbinghaus_retention(t, S):
"""Ebbinghaus forgetting curve: R(t) = e^{-t/S}."""
return np.exp(-t / S)
# Simulate retention test at 1, 2, 7, 14, 30 days post-learning
retention_days = np.array([1, 2, 7, 14, 30], dtype=float)
true_S = 12.0 # stability (days)
retention_obs = ebbinghaus_retention(retention_days, true_S) + np.random.normal(0, 0.03, 5)
retention_obs = np.clip(retention_obs, 0, 1)
S_fit, _ = opt.curve_fit(ebbinghaus_retention, retention_days, retention_obs,
p0=[10], bounds=([1], [100]))
print(f"\nEbbinghaus S (stability) = {S_fit[0]:.1f} days")
print(f"Predicted 50% retention at: {S_fit[0] * np.log(2):.1f} days")
# -----------------------------------------------------------------
# Visualization
# -----------------------------------------------------------------
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
# Individual learning curves + mean
for s in range(min(10, n_students)):
axes[0].plot(trials, times[s], color="lightblue", alpha=0.5, lw=0.8)
axes[0].plot(trials, mean_times, "k-", lw=2, label="Mean")
axes[0].plot(trials, power_law(trials, *popt), "r--", lw=2,
label=f"Power Law (α={alpha_fit:.2f})")
axes[0].set_xlabel("Trial"); axes[0].set_ylabel("Response Time (s)")
axes[0].set_title("Learning Curves")
axes[0].legend()
# Log-log plot for power law verification
axes[1].plot(np.log(trials), np.log(mean_times), "ko", label="Data")
axes[1].plot(np.log(trials), np.log(power_law(trials, *popt)), "r-",
label=f"Fit (slope={-alpha_fit:.2f})")
axes[1].set_xlabel("ln(Trial)"); axes[1].set_ylabel("ln(Time)")
axes[1].set_title("Power Law Verification (Log-Log)")
axes[1].legend()
# Forgetting curve
t_cont = np.linspace(0, 35, 300)
axes[2].scatter(retention_days, retention_obs, color="red", s=80, zorder=5,
label="Observed")
axes[2].plot(t_cont, ebbinghaus_retention(t_cont, S_fit[0]), "steelblue", lw=2,
label=f"Ebbinghaus (S={S_fit[0]:.1f}d)")
axes[2].set_xlabel("Days Since Learning"); axes[2].set_ylabel("Retention (fraction)")
axes[2].set_title("Forgetting Curve")
axes[2].set_ylim(0, 1.1)
axes[2].legend()
plt.tight_layout()
plt.savefig("learning_curves.png", dpi=150, bbox_inches="tight")
plt.close()
print("\nFigure saved: learning_curves.png")
import numpy as np
import pandas as pd
import scipy.stats as stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
# -----------------------------------------------------------------
# Simulate pre-post RCT: 200 students randomized to treatment/control
# Treatment: intelligent tutoring system vs. conventional instruction
# Outcome: post-test score (0-100)
# -----------------------------------------------------------------
np.random.seed(42)
n = 200
n_t = n // 2 # treatment group size
# Pre-test scores (baseline, shared before randomization)
pre_test = np.random.normal(55, 15, n)
pre_test = np.clip(pre_test, 0, 100)
# Treatment assignment (completely randomized)
treat = np.concatenate([np.ones(n_t), np.zeros(n_t)])
np.random.shuffle(treat)
# Post-test: treatment effect = 8 points, with heterogeneous effects
te_individual = 8 + np.random.normal(0, 3, n) # ATE = 8
post_test = (pre_test * 0.7
+ treat * te_individual
+ np.random.normal(0, 10, n))
post_test = np.clip(post_test, 0, 100)
df = pd.DataFrame({"student_id": range(n), "treat": treat,
"pre_test": pre_test, "post_test": post_test})
df["gain"] = df["post_test"] - df["pre_test"]
# -----------------------------------------------------------------
# Analysis 1: Simple t-test on gains
# -----------------------------------------------------------------
t_gains = df[df["treat"] == 1]["gain"]
c_gains = df[df["treat"] == 0]["gain"]
t_stat, p_val = stats.ttest_ind(t_gains, c_gains)
print("=== A/B Test: Treatment vs. Control ===")
print(f"Mean gain (treatment): {t_gains.mean():.2f}")
print(f"Mean gain (control): {c_gains.mean():.2f}")
print(f"Difference: {t_gains.mean() - c_gains.mean():.2f}")
print(f"t-statistic: {t_stat:.3f}, p-value: {p_val:.4f}")
# Effect sizes
pooled_sd = np.sqrt((t_gains.var() + c_gains.var()) / 2)
cohens_d = (t_gains.mean() - c_gains.mean()) / pooled_sd
# Hedges' g correction
j = 1 - 3 / (4 * (n - 2) - 1)
hedges_g = cohens_d * j
print(f"Cohen's d: {cohens_d:.3f}")
print(f"Hedges' g: {hedges_g:.3f}")
# -----------------------------------------------------------------
# Analysis 2: ANCOVA (controls for pre-test score)
# -----------------------------------------------------------------
X_ancova = sm.add_constant(df[["treat", "pre_test"]])
ancova = sm.OLS(df["post_test"], X_ancova).fit(cov_type="HC3")
print("\n=== ANCOVA (post ~ treat + pre) ===")
print(ancova.summary().tables[1])
print(f"\nANCOVA treatment effect: {ancova.params['treat']:.3f} "
f"(SE={ancova.bse['treat']:.3f})")
# Power analysis (post-hoc)
from scipy.stats import norm
alpha_level = 0.05
observed_d = cohens_d
se_d = np.sqrt(2/n + observed_d**2 / (2*n))
power_obs = 1 - stats.norm.cdf(stats.norm.ppf(1 - alpha_level/2) - observed_d / se_d)
print(f"\nPost-hoc power: {power_obs:.3f}")
# -----------------------------------------------------------------
# Analysis 3: Subgroup analysis (high vs. low baseline)
# -----------------------------------------------------------------
df["baseline_group"] = pd.cut(df["pre_test"], bins=[0, 50, 100],
labels=["Low", "High"])
subgroup = df.groupby(["baseline_group", "treat"])["gain"].agg(["mean", "std", "count"])
print("\n=== Subgroup Analysis (by baseline) ===")
print(subgroup.round(2))
# -----------------------------------------------------------------
# Visualization
# -----------------------------------------------------------------
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
# Pre-post scatter by treatment
colors = {1: "steelblue", 0: "orange"}
labels = {1: "Treatment (n=100)", 0: "Control (n=100)"}
for t_val in [0, 1]:
mask = df["treat"] == t_val
axes[0].scatter(df.loc[mask, "pre_test"], df.loc[mask, "post_test"],
color=colors[t_val], alpha=0.5, s=20, label=labels[t_val])
axes[0].plot([0, 100], [0, 100], "k--", lw=0.8, label="No change")
axes[0].set_xlabel("Pre-test Score"); axes[0].set_ylabel("Post-test Score")
axes[0].set_title("Pre-Post Scores by Treatment")
axes[0].legend(fontsize=8)
# Distribution of gains
axes[1].hist(c_gains, bins=20, alpha=0.6, color="orange", label="Control", density=True)
axes[1].hist(t_gains, bins=20, alpha=0.6, color="steelblue", label="Treatment", density=True)
axes[1].axvline(c_gains.mean(), color="orange", ls="--", lw=2)
axes[1].axvline(t_gains.mean(), color="steelblue", ls="--", lw=2)
axes[1].set_xlabel("Score Gain"); axes[1].set_ylabel("Density")
axes[1].set_title(f"Distribution of Gains\n(d={cohens_d:.2f})")
axes[1].legend()
# ANCOVA fitted vs. actual
axes[2].scatter(ancova.fittedvalues, df["post_test"], alpha=0.4, s=15,
c=df["treat"].map({0: "orange", 1: "steelblue"}))
axes[2].plot([20, 100], [20, 100], "k--")
axes[2].set_xlabel("ANCOVA Fitted"); axes[2].set_ylabel("Observed")
axes[2].set_title(f"ANCOVA Fit (R²={ancova.rsquared:.3f})")
plt.tight_layout()
plt.savefig("educational_ab_test.png", dpi=150, bbox_inches="tight")
plt.close()
print("\nFigure saved: educational_ab_test.png")
import numpy as np
from scipy.optimize import minimize
def bkt_forward(responses, p_L0, p_T, p_S, p_G):
"""Forward pass of BKT (single skill, single student).
Args:
responses: binary list of responses (1=correct, 0=incorrect)
p_L0: prior probability of knowing skill at trial 1
p_T: probability of transitioning to learned state
p_S: slip probability (knows but answers wrong)
p_G: guess probability (doesn't know but answers correctly)
Returns:
p_Ln: posterior P(learned) at each trial
log_likelihood
"""
p_Ln = np.zeros(len(responses) + 1)
p_Ln[0] = p_L0
log_lik = 0.0
for n, r in enumerate(responses):
p_L = p_Ln[n]
# Probability of correct response at trial n+1
p_correct = p_L * (1 - p_S) + (1 - p_L) * p_G
# Accumulate log-likelihood
log_lik += np.log(p_correct + 1e-10) if r == 1 else np.log(1 - p_correct + 1e-10)
# Bayesian update: P(L|response)
if r == 1:
p_L_given_r = p_L * (1 - p_S) / (p_correct + 1e-10)
else:
p_L_given_r = p_L * p_S / (1 - p_correct + 1e-10)
# Transition
p_Ln[n + 1] = p_L_given_r + (1 - p_L_given_r) * p_T
return p_Ln[1:], log_lik
# Simulate a student mastering a skill
np.random.seed(42)
true_params = {"p_L0": 0.2, "p_T": 0.15, "p_S": 0.08, "p_G": 0.20}
n_trials = 25
p_L_true = np.zeros(n_trials + 1)
p_L_true[0] = true_params["p_L0"]
for i in range(n_trials):
if np.random.random() > p_L_true[i]:
p_L_true[i+1] = p_L_true[i] + (1 - p_L_true[i]) * true_params["p_T"]
else:
p_L_true[i+1] = p_L_true[i]
learned = p_L_true[1:] > 0.5
sim_responses = []
for is_learned in learned:
if is_learned:
sim_responses.append(1 if np.random.random() > true_params["p_S"] else 0)
else:
sim_responses.append(1 if np.random.random() < true_params["p_G"] else 0)
# Fit BKT via MLE
def neg_ll(params):
p_L0, p_T, p_S, p_G = params
if not all(0.01 < p < 0.99 for p in params):
return 1e10
_, ll = bkt_forward(sim_responses, p_L0, p_T, p_S, p_G)
return -ll
result = minimize(neg_ll, x0=[0.3, 0.2, 0.1, 0.25], method="Nelder-Mead")
p_L0_f, p_T_f, p_S_f, p_G_f = result.x
print(f"BKT MLE: L0={p_L0_f:.3f}, T={p_T_f:.3f}, S={p_S_f:.3f}, G={p_G_f:.3f}")
p_mastery, _ = bkt_forward(sim_responses, p_L0_f, p_T_f, p_S_f, p_G_f)
print(f"Final mastery probability: {p_mastery[-1]:.3f}")
print(f"Trial when mastery > 0.95: "
f"{np.argmax(p_mastery > 0.95) + 1 if (p_mastery > 0.95).any() else 'Not reached'}")
import numpy as np
import pandas as pd
from scipy.stats import chi2_contingency
def mantel_haenszel_dif(responses, group, ability_strata=5):
"""Mantel-Haenszel DIF statistic for each item.
Args:
responses: (n_students, n_items) binary matrix
group: (n_students,) binary array — 0=reference, 1=focal
ability_strata: number of ability level strata
Returns:
DataFrame with item DIF statistics
"""
n_items = responses.shape[1]
total_score = responses.sum(axis=1)
strata = pd.qcut(total_score, ability_strata, labels=False, duplicates="drop")
results = []
for item in range(n_items):
A = B = C = D = 0 # MH 2x2 table cells aggregated across strata
for s in range(ability_strata):
mask_s = strata == s
if mask_s.sum() < 4:
continue
ref_mask = mask_s & (group == 0)
foc_mask = mask_s & (group == 1)
n_s = mask_s.sum()
a = (responses[foc_mask, item]).sum()
b = (responses[ref_mask, item]).sum()
c = foc_mask.sum() - a
d = ref_mask.sum() - b
A += a * d / n_s
B += b * c / n_s
C += (a + d) * (a + c) * (b + c) * (b + d) / (n_s**2 * (n_s - 1) + 1e-10)
alpha_mh = A / (B + 1e-10) # odds ratio
# Delta MH (ETS scale)
delta_mh = -2.35 * np.log(alpha_mh + 1e-10)
results.append({"item": item, "MH_alpha": alpha_mh, "delta_MH": delta_mh})
return pd.DataFrame(results)
np.random.seed(42)
n_s = 400
n_i = 15
theta = np.random.normal(0, 1, n_s)
group = np.random.binomial(1, 0.4, n_s)
# Focal group has lower mean ability
theta[group == 1] -= 0.5
a_dif = np.ones(n_i)
b_dif = np.linspace(-2, 2, n_i)
b_dif[5] += 0.8 # Item 6 has DIF against focal group
P_dif = 1 / (1 + np.exp(-a_dif[None, :] * (theta[:, None] - b_dif[None, :])))
resp_dif = (np.random.uniform(size=P_dif.shape) < P_dif).astype(int)
dif_results = mantel_haenszel_dif(resp_dif, group)
print("=== DIF Analysis (Mantel-Haenszel) ===")
print(dif_results.round(3).to_string())
flagged = dif_results[dif_results["delta_MH"].abs() > 1.0]
print(f"\nFlagged items (|ΔMH| > 1.0): {flagged['item'].tolist()}")
| Problem | Cause | Fix |
|---|---|---|
| IRT estimation diverges | Extreme item p-values (< 0.05 or > 0.95) | Remove extreme items; increase sample |
| Power law fit R² < 0.80 | Plateau or ceiling effects | Check for ceiling; use exponential model |
| BKT mastery oscillates | Slip > 0.5 or guess > 0.5 | Constrain params: p_S < 0.4, p_G < 0.4 |
| Negative Cohen's d | Score direction | Verify higher score = better; check treatment coding |
| ANCOVA violation | Non-parallel slopes | Test treatment × pre interaction; use moderated regression |
| Negative DIF delta | Focal group advantaged | Expected; classify DIF direction (A favors reference, B favors focal) |
import numpy as np
import pandas as pd
def cronbach_alpha(responses):
"""Compute Cronbach's alpha reliability coefficient.
Args:
responses: (n_students, n_items) matrix of item scores
Returns:
alpha coefficient
"""
n_items = responses.shape[1]
item_variances = responses.var(axis=0, ddof=1)
total_variance = responses.sum(axis=1).var(ddof=1)
alpha = (n_items / (n_items - 1)) * (1 - item_variances.sum() / total_variance)
return alpha
def item_statistics(responses):
"""Compute classical test theory item statistics."""
total = responses.sum(axis=1)
stats = []
for j in range(responses.shape[1]):
p_val = responses[:, j].mean()
corrected_total = total - responses[:, j]
r_it = np.corrcoef(responses[:, j], corrected_total)[0, 1]
stats.append({"item": j+1, "p_value": p_val, "r_item_total": r_it})
return pd.DataFrame(stats)
np.random.seed(42)
n_s, n_i = 300, 25
theta_r = np.random.normal(0, 1, n_s)
b_r = np.random.normal(0, 1, n_i)
P_r = 1 / (1 + np.exp(-(theta_r[:, None] - b_r[None, :])))
resp_r = (np.random.uniform(size=P_r.shape) < P_r).astype(int)
alpha = cronbach_alpha(resp_r)
print(f"Cronbach's α = {alpha:.3f}")
if alpha >= 0.90: print("Excellent reliability")
elif alpha >= 0.80: print("Good reliability")
elif alpha >= 0.70: print("Acceptable reliability")
else: print("Poor reliability — revise items")
item_stats = item_statistics(resp_r)
print("\nItem Statistics (first 5):")
print(item_stats.head().round(3))
poor_items = item_stats[item_stats["r_item_total"] < 0.20]
print(f"\nPoor discriminating items (r < 0.20): {poor_items['item'].tolist()}")
import numpy as np
import matplotlib.pyplot as plt
def sm2_schedule(initial_easiness=2.5, n_reviews=10, min_ease=1.3):
"""Simulate SM-2 spaced repetition algorithm schedule.
Args:
initial_easiness: starting E-factor (2.5 by default)
n_reviews: number of review sessions
min_ease: minimum E-factor
Returns:
DataFrame with intervals and retention estimates
"""
import pandas as pd
records = []
interval = 1
easiness = initial_easiness
stability = 1.0 # Anki-like
for rev in range(1, n_reviews + 1):
# Assume quality q = 4 (good recall)
q = 4
# Update easiness factor
easiness = max(min_ease, easiness + 0.1 - (5 - q) * (0.08 + (5 - q) * 0.02))
# Next interval
if rev == 1:
next_interval = 1
elif rev == 2:
next_interval = 6
else:
next_interval = round(interval * easiness)
# Forgetting curve: R = e^{-interval/stability}
stability_at_review = stability * easiness
retention = np.exp(-interval / (stability_at_review * 10))
records.append({"review": rev, "interval_days": interval,
"easiness": easiness, "retention": retention})
stability = stability_at_review
interval = next_interval
return pd.DataFrame(records)
schedule = sm2_schedule()
print("SM-2 Spaced Repetition Schedule:")
print(schedule.to_string(index=False))
print(f"\nTotal study sessions to review 10 times: {schedule['interval_days'].sum()} days")
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