A System to Place Incoming Students in Classes
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A System to Place A System to Place Incoming Students Incoming Students in Classesin Classes
Henry M. Walker, Andrew Hirakawa, Russel SteinbachHenry M. Walker, Andrew Hirakawa, Russel SteinbachGrinnell College, Grinnell, IowaGrinnell College, Grinnell, Iowa
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Three specific objectives:Three specific objectives:
1.1.Identify most appropriate courseIdentify most appropriate course
2.2.Communicate placement to students, advisorsCommunicate placement to students, advisors
3.3.Publicize interesting courses, recruitPublicize interesting courses, recruit
Problem: Place Problem: Place incoming students in incoming students in CS, math, statisticsCS, math, statistics
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Placement ApproachesPlacement Approaches1.1. Based on SAT, ACT, similar aptitude testsBased on SAT, ACT, similar aptitude tests
• Often required for admissionOften required for admission
• Tests measure aptitude, not backgroundTests measure aptitude, not background
2.2. Placement testsPlacement tests• Numerous tests commercially availableNumerous tests commercially available
• Logistical issues in administrationLogistical issues in administration
• calibration needed with local courses calibration needed with local courses
3.3. Placement by facultyPlacement by faculty1.1.Can tap faculty expertiseCan tap faculty expertise
• Time consumingTime consuming
• Variation possible from different facultyVariation possible from different faculty
4.4. Use of an expert systemUse of an expert system• Available for incoming, prospective studentsAvailable for incoming, prospective students
• Allows follow-up studies, based on performanceAllows follow-up studies, based on performance
• Needs non-trivial development timeNeeds non-trivial development time3
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Basic Approach for an Basic Approach for an Expert SystemExpert System
• Use available student dataUse available student data• Standardized tests: SAT, ACT, AP, IBStandardized tests: SAT, ACT, AP, IB
• High school transcriptHigh school transcript
• Base placements on rulesBase placements on rules• Initial rules from faculty experienceInitial rules from faculty experience
• Follow-up studies: courses taken, gradesFollow-up studies: courses taken, grades
• Inference engine applies rules to dataInference engine applies rules to data
• Develop placement letters, Web pagesDevelop placement letters, Web pages
• Develop softwareDevelop software• Version 1 (1993) LISP-based, TMYCINVersion 1 (1993) LISP-based, TMYCIN
• Version 2 (2010) PHP, locally developedVersion 2 (2010) PHP, locally developed4
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AcknowledgmentsAcknowledgments1.1. 1993 version (LISP based)1993 version (LISP based)
• TMYCIN Inference Engine: Gordon Novak, AI TMYCIN Inference Engine: Gordon Novak, AI Lab, University of Texas at AustinLab, University of Texas at Austin
• Vikram Subramaniam, Ivan SykesVikram Subramaniam, Ivan Sykes
2.2. ConsultantsConsultants• 1993-2004: Eugene Herman, Charles Jepsen1993-2004: Eugene Herman, Charles Jepsen
• 2004-2009: Emily Moore2004-2009: Emily Moore
• 2009-present: Shonda Kuiper, Chris French, 2009-present: Shonda Kuiper, Chris French, Karen ShumanKaren Shuman
• 2010: Barbara Johnson2010: Barbara Johnson
3.3. Current versionCurrent version• Grinnell CS faculty Grinnell CS faculty
• Dean’s Office for summer “MAP” fundingDean’s Office for summer “MAP” funding5
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Outputs & System Outputs & System OverviewOverview
Our system operates in two primary modes:•Generation of letters to students based on data from the Registrar.
Sample Student LaTeX Letters
2. Web-based, tentative placements to prospective students.
Web Placement Interface
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Technical OverviewTechnical Overview• Data for incoming studentsData for incoming students
• Files from Registrar’s OfficeFiles from Registrar’s Office
• Storage in MySQL databaseStorage in MySQL database
• Web-based materialsWeb-based materials• Data from HTML forms (not stored in MySQL)Data from HTML forms (not stored in MySQL)
• Programming in PHPProgramming in PHP• Connection with MySQLConnection with MySQL
• Web scriptingWeb scripting
• Associative arrays for rule storageAssociative arrays for rule storage
• Inference engineInference engine• TMYCIN (1993) ==> PHP (2010)TMYCIN (1993) ==> PHP (2010)
• Backtracking algorithm for rule processingBacktracking algorithm for rule processing
• NotesNotes• Formal grammar for rule structureFormal grammar for rule structure
• Rules in LISP/PHP format (for scripting)Rules in LISP/PHP format (for scripting)
• Rules printable in English format (faculty)Rules printable in English format (faculty)
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•Rules composed have three partsRules composed have three parts• NameName
• ConclusionConclusion
• Conditions Conditions
•Examples:Examples:
array("statRule120","TSTAT-PLACE = 208",
array("all","stdscores = unknown",
array("some","statsem >= 2",
"apstat >= 1")));
array("satRealRule10", "satmathreal =field satmath",
array("all", "satmath >= sat2math1",
"sat2math2adj < satmath"));
Rule structureRule structure
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Rule ConditionsRule Conditions• Each Condition Array has two partsEach Condition Array has two parts
• Quantifier (e.g., some, all)Quantifier (e.g., some, all)
• Conditions to be satisfiedConditions to be satisfied• Simple conditionsSimple conditions
array("satRealRule10", "satmathreal =field satmath", array("all", "satmath >= sat2math1", "sat2math2adj < satmath"));
• Condition arrayCondition array
array("statRule120","TSTAT-PLACE = 208", array("all","stdscores = unknown", array("some","statsem >= 2", "apstat >= 1")));
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Rule ConclusionsRule Conclusions• Two types of conclusionTwo types of conclusion
• Fixed value conclusionsFixed value conclusions "stdscores = unknown"
• Variable valuesVariable values "satmathreal =field satmath"
• Three partsThree parts• FieldField
• OperatorOperator
• ValueValue
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EfficiencyEfficiency
• Short circuit evaluationShort circuit evaluation
• Rules are markedRules are marked
• Minimize SQL queries.Minimize SQL queries.
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Conclusion & Future WorkConclusion & Future WorkFuture Work:•Ever changing curricula both high school and college demand changes in inference rules.•Occasional re-evaluation of student performance versus generated placements.
Conclusion•Automated placements are a viable alternative.•Two experiments support this claim:•One experiment showed individual placements of students by faculty as reliable as nation-wide placement test.•Second experiment showed rule-based system placements were comparable with placements by faculty.