Enhance exam review summary to include dominant score for top submitted answers

anatomy/templates/anatomy/partials/exam_review_question_summary_fragment.html

@@ -67,9 +67,20 @@
             {% if top_answers %}
                 <ul class="list-group">
                     {% for a in top_answers %}
-                        <li class="list-group-item d-flex justify-content-between align-items-start">
+                        <li class="list-group-item d-flex justify-content-between align-items-center">
-                            <div>{{ a.answer_compare }}</div>
+                            <div class="flex-grow-1">{{ a.answer_compare }}</div>
-                            <div class="small text-muted">{{ a.count }}</div>
+                            <div class="d-flex align-items-center">
+                                {% if a.dominant_score == '2' %}
+                                    <span class="badge bg-success">Correct</span>
+                                {% elif a.dominant_score == '1' %}
+                                    <span class="badge bg-warning text-dark">Half</span>
+                                {% elif a.dominant_score == '0' %}
+                                    <span class="badge bg-danger">Incorrect</span>
+                                {% else %}
+                                    <span class="badge bg-secondary">Unmarked</span>
+                                {% endif %}
+                                <div class="small text-muted me-2">{{ a.count }}</div>
+                            </div>
                         </li>
                     {% endfor %}
                 </ul>

anatomy/views.py

@@ -316,8 +316,37 @@ def exam_review_question_summary(request, pk: int, q_index: int):
     # simpler: compute via values and normalise unmarked -> 'empty'
     score_counts = {str(row["score"]) if row["score"] is not None else "empty": row["count"] for row in ua_qs.values("score").annotate(count=Count("id"))}
 
-    # top submitted answers (by answer_compare)
+    # top submitted answers (by answer_compare) with dominant score per answer
-    top_answers = list(ua_qs.values("answer_compare").annotate(count=Count("id")).order_by("-count")[:10])
+    # First, get counts grouped by answer_compare and score
+    grouped = ua_qs.values("answer_compare", "score").annotate(count=Count("id"))
+
+    # Aggregate totals and score breakdown per answer_compare
+    answer_map = {}
+    for row in grouped:
+        ans = row["answer_compare"]
+        sc = str(row["score"]) if row["score"] is not None else "empty"
+        cnt = row["count"]
+        if ans not in answer_map:
+            answer_map[ans] = {"total": 0, "score_counts": {}}
+        answer_map[ans]["total"] += cnt
+        answer_map[ans]["score_counts"][sc] = answer_map[ans]["score_counts"].get(sc, 0) + cnt
+
+    # Build list sorted by total count desc, and choose a dominant score per answer
+    top_answers = []
+    for ans, meta in answer_map.items():
+        # pick dominant score by highest count; tie-break prefer higher numeric score
+        score_counts = meta["score_counts"]
+        # sort by (count desc, score desc) where 'empty' maps to -1
+        def score_key(item):
+            s, c = item
+            s_val = -1 if s == "empty" else int(s)
+            return (c, s_val)
+
+        dominant = max(score_counts.items(), key=score_key)[0]
+        top_answers.append({"answer_compare": ans, "count": meta["total"], "dominant_score": dominant})
+
+    top_answers.sort(key=lambda x: x["count"], reverse=True)
+    top_answers = top_answers[:10]
 
     correct_count = None
     correct_pct = None