Quiz And Survey Master stores every attempt in the wp_mlw_results table, with related per-question rows in wp_mlw_question_answers. Each result row carries a calculated points score and a correct_score percentage, and free-text or essay questions push the attempt into a manual grading state until an editor reviews it. The QSM admin gives you a Results screen which is a long paginated list but no board where each attempt sits visually in a column matching its grading state.

SleekView Kanban joins wp_mlw_results with the parent quiz post and the optional respondent user, then surfaces every attempt as a card. The natural column to group by is the attempt status derived from correct_score against the pass threshold combined with the manual-grading flag, which gives you Pending Review, Passed, Failed, and Spam stages. Card fronts show respondent name, quiz title, total score, percent correct, and the date the attempt was submitted.

Drag an attempt from Pending Review to Passed and SleekView updates the result row through QSM's filter API so any post-grade email, certificate add-on, or webhook integration fires correctly. Spam attempts and abandoned drafts stay in their own columns, which keeps the data the team needs for cleanup work, follow-up emails, and grader feedback visible.

Quiz And Survey Master gives you a Results screen that lists every attempt in chronological order. That works for searching one record. It fails for grading queues.

A grading queue is a flow. Attempts arrive, an editor reviews each one, and the attempt either passes or fails or gets flagged as spam. The default Results screen forces graders to open each attempt page individually, scroll down to the essay section, enter a score, save, and go back to the list.

Multiply that by fifty attempts a day and the team loses an hour to context-switching. A kanban view changes the experience. Pending Review sits in one column.

Graders work through it visually from oldest first. Each drag to Passed or Failed records the grade through QSM's official filters so emails and certificates fire correctly. Spam moves to its own column with one drag too.

The result is a queue that flows instead of a list that stalls, and the team gets through the same grading load in roughly half the time it used to take.