1. Microsoft Access DataBase Automated Grading System
MADBAGS
Microsoft Access DataBase Automated Grading System

2. What does MADBAGS grade?
Table Structure
Field Names
Field Types
Field Sizes
Table Data
Field attributes
Default values
Required fields
Validation rules
Validation text
Indexes
Primary Keys
Relations

3. What does MADBAGS grade? (continued)
DELETE Type
Table Names
Where criteria
Update Type
Table name
Col=expr List
Where critera
Queries
SELECT Type
Fields
Table names plus Join clauses
Where criteria
Total queries

4. What does MADBAGS not grade?
Forms
Reports
Data Access Pages
Macros
Modules

5. How does MADBAGS work? a Microsoft Access application
Attributes are extracted
from student Access databases
into Microsoft Access tables:
_Tables _Indexes _Relations
_Fields _Queries

6. Example Table: Item

7. _Fields Table (Item table structure lines 1-6)

8. Example Access Query

9. _Queries Table

10. How does MADBAGS work? (continued)
After attributes are extracted from both
instructor’s correct database
a student’s database
and placed into tables
The resulting tables are compared
missing attributes are detected
unequal attributes are found

11. Recording Errors
Errors placed in “E R R O R S” table in student database

12. Screenshots

14. Grade Report

15. Class Errors Report

16. Grading Challenges

17. (After table names are recorded as incorrect)
Renaming Tables
(After table names are recorded as incorrect)
Misnamed tables are matched to correct
By comparing field counts
Of non-matched tables in correct DB
With extraneous tables in student DB
Table names are corrected in extracted tables
Fields
Indexes
Relations
Queries

18. (After being recorded as incorrect)
Renaming Fields
(After being recorded as incorrect)
Misnamed fields are matched to correct
By finding minimum Levenshtein distance
Of non-matched fields in correct DB
With extraneous fields in student DB
Levenshtein distance is the number of changes required to convert one string into another string
LD(“tim”,”tom”) = LD(“tim”,”time”) = LD(“tim”,”ti”) = 1
Field names are corrected in extracted tables
Indexes
Relations
Queries

19. MADBAGS grades three query types
Grading Queries
MADBAGS grades three query types
SELECT
DELETE
UPDATE

20. Query Syntax SELECT [ DISTINCT | ALL ] col_expr1, col_expr1, ...
[ FROM from_clause ]
[ WHERE where_expression ]
[ GROUP BY expression1, expression2, .... ]
[ HAVING having_expression ]
[ ORDER BY column1, column2, .... ]
UPDATE table_name
SET col_name1 = expr1, col_name2 = expr2, ....
[ WHERE expression ]
DELETE FROM table_name

21. Grading Queries Query clauses are separated into 3 categories Lists
SELECT col_expressions
GROUP BY expressions
ORDER BY columns
UPDATE table name
UPDATE SET assignments
DELETE table name
Boolean expressions
WHERE clauses
HAVING clause
Unique clauses
FROM clause

22. Grading Queries: List Clauses
Tokenize Correct/Student lists
Place into 2 dictionaries (Red-Black Trees)
For each list element in Correct Dictionary
If Found in Student Dictionary
Delete from Student Dictionary
Else
Generate “Missing Item” error message
For each remaining list element in Student Dictionary
Generate “Extraneous Item” error message

23. Grading Queries: Boolean Expressions
Convert Correct&Student boolean expressions to RPN
For each relational expression (< > =) in Correct RPN
If Found in Student RPN expression
Replace relational expressions in both RPNs with same bool var (e1,e2, …)
Else
Generate “Missing” error message
Replace relational expression in Correct RPN with unique bool var
For any remaining relational expressions in Student Dictionary
Generate “Extraneous” error message
Replace relational expression in Student RPN with unique bool var

24. Grading Queries: Boolean Expressions (continued)
While boolean (AND/OR) expressions exist in Correct RPN expression
Find any boolean expression with two bool var operands (innermost)
If bool expression is found in the Student RPN expression
Replace boolean expressions in both RPNs with same bool var
Else
Generate “Missing” error message with boolean operator
Replace boolean expression in Correct RPN with unique bool var
For each boolean operator in Student Dictionary
Generate “Extraneous” error message with operator

25. How to use MADBAGS

27. Enter number of points deducted for
100 point grading scale
Enter number of points deducted for
Incorrect field names, field type, field size
Mistyped data
Missing queries

28. The amount is deducted for the following errors:
Missing query fields
Missing/incorrect query criteria
Missing/incorrect joins

29. The same amount is deducted for the following errors:
Missing primary keys
Missing indices
Missing relations
Missing/incorrect validation rules and/or validation text

30. Options: Only Grade Tables
Only grades table structure and table data
Useful with first class assignments
Avoids grading more advanced features (for example, queries, indexing, relations) before being covered in class
Avoids “error overload”; hides details that might be more easily handled manually

31. Options: Deduct Extraneous
Extra tables, fields, records, queries, etc. are counted against the student

32. Error Analysis Helps discover possible plagiarism
Considers a pair of students at a time
How many errors do they have in common?
More errors, more evidence of plagiarism
Input is minimum # errors to be included in report
What percent of a student A’s errors were also made by student B?

33. Student Pairs Common Errors