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CONTACT PERSON FOR THE FOLLOWING COURSE INFORMATION:
Mark Goor, Graduate School of Education, 3-2080, mgoor@gmu.edu

Modified courses for approval

Request change of grading type from GR to GT to allow for use of IP grading for the following courses:

• EDCI 516 Bilingualism and Language Acquisition Research
• EDCI 519 Methods of Teaching in Bilingual/ESL Settings
• EDCI 520 Assessment in Bilingual/ESL Settings
• EDCI 521 Curriculum Development in Bilingual/ESL Settings
• EDCI 777 Research to Practice
• EDRD 615 Teaching Reading/Writing in Multicultural/Multilingual Settings

In 1998 the Grad Council approved FAST TRAIN's request for IP grades for its intensive summer coursework in its elementary education program. Students come to campus from all over the world, take 3 courses and then must complete the classes by engaging in field experiences throughout the academic year. Since the field experience requirement is extensive (up to 120 hours), it cannot be completed in the normal 9-week period allowed for Incomplete grades. Thus, the Grad Council approved granting IP grades only for the intensive summer coursework.

FAST TRAIN has just initiated an alternative program in Teaching English as a Second Language. While the courses are the same as those required for the "regular" curriculum, they are offered in the same intensive summer format.

FAST TRAIN will use IP grades for the sections of courses listed above offered during the full-time intensive summer session only. No other section will use the IP grade.

Modified program

New Major Code for FAST TRAIN ESL Program (EDFM) FAST TRAIN requests the assignment of a Major Code (EDFM) for its ESL program. Currently, the FAST TRAIN ESL program falls under the same Major Code as the MME ESL program. This creates difficulties in advising and admissions. As mentioned, FAST TRAIN's ESL program offers the same courses but through a different schedule. At present, students who apply to FAST TRAIN ESL cannot designate on their application. Thus, applications are often sent to the wrong advisor, leading to misunderstandings (and, in one case, withdrawal of the application).

In addition, since FAST TRAIN is a contract-course program, its tuition is collected by the university and directed to a dedicated account for program operation. The program is self-supporting, and depends on tuition for survival. Without its own Major Code there is a problem in the direction of tuition funds. There is a real danger that tuition will not be directed to the correct account, thereby leading to lost revenue for FAST TRAIN.

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CONTACT PERSON FOR THE FOLLOWING COURSE INFORMATION:
Peter Becker, School of Computational Sciences, 3-3619, pbecker@gmu.edu

New courses for approval

BINF 702. Research Methods (3:3:0)
Prerequisites: Admission to the Ph.D. program in bioinformatics or biosciences
This course trains students in research methodologies for the life sciences. The course will cover the three phases of biological research projects: experimental design, data collection, and data analysis.

BINF 703. Bioinformatics Lab Rotation (1:0:1)
Prerequisites: Permission of instructor.
Short-term introductory research on a specific topic in computational sciences and informatics under the direction of a faculty member. May be repeated as necessary.

BINF 732. Genomics (3:3:0)
Prerequisites: General biology, programming experience, CSI 700 or equivalent, CSI 731, or permission of instructor.
A survey of computational tools and techniques used to study whole genomes. The biological basis of genome analysis algorithms will be explored. Lecture topics include genome mapping, comparitive genomics, and functional genomics.

BINF 733. Gene Expression Analysis (3:3:0)
Prerequisites: Consent of the instructor, ability to program in a high-level language and a course in molecular biology; S-Plus or Matlab experience may also be helpful.
This course will focus on the analysis of gene expression data. Particular topics include: cluster analysis and visualization of expression data, inference of genetic regulatory networks, and theoretical models of genetic networks.

CSI 873. Computational Learning and Discovery (3:3:0)
Prerequisites: CS 580 or equivalent, or permission of instructor.
Presents modern ideas, theories, and methods for computational learning and discovery. Topics include an elucidation of fundamental concepts in computational learning, addressing in particular relationships, discovery of equations, theory of causality, comparison with biological and cognitive models, and applications in computational sciences. Students will have an opportunity to make presentations on topics of their research interest, and to work on projects involving state-of-the art systems.

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