Fix recency-weighting variable swap in compute_mastery#637
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The zip(recent, weights) call produces (correctness, weight) pairs, but the loop unpacked them as 'for w, c', assigning correctness to w and weight to c. Since weights are always truthy (positive floats), the expression (1.0 if c else 0.0) always evaluated to 1.0, making the numerator a plain correct-count. Recency weights only influenced the denominator, so mastery became order-independent. Fix: swap the unpacking order to 'for c, w' so that c receives the correctness bool and w the recency weight. Now w * (1.0 if c else 0.0) correctly applies the recency weight to each correctness indicator, making recovering ([F,T,T] = 0.6964) score higher than declining ([T,T,F] = 0.6429). Closes HKUDS#618
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Fixes #618. Zip(recent, weights) produces (correctness, weight) pairs, but the generator unpacked them as (w, c), assigning correctness to w and weight to c. Since weights are always truthy, the numerator was always a plain correct-count, disabling recency weighting. Fix: swap unpacking to (c, w).