NEW COURSE PROPOSAL
College: [Humanities ]
Department: [ Linguistics/TESL
]
Note: Use this form to request a single course that can be offered independently of any other course, lab
or activity.
1. Course information for Catalog Entry
Subject Abbreviation and Number: [ LING 455]
Course Title: [Computational Linguistics]
Units: [ 3 ] units
Course Prerequisites: [
] (if any)
Course Corequisites: [
] (if any)
Recommended Preparatory Courses: [
] (if any)
2. Course Description for Printed Catalog: Notes: If grading is NC/CR only, please state in course description.
If a
course numbered less than 500 is available for graduate credit, please state “Available for graduate credit in the catalog description.”
[ This course is designed as an intensive workshop for computational data processing in linguistics.
While computers have no minds of their own, computers are increasingly able to interface with
natural, human language, which is often `messy' and unpredictable. How do researchers harness
computing power to deal with natural language in order to answer research questions in linguistics?
Relevant topics include both formal properties of language as well as language use in the social
world. This course also serves as an overview of natural language processing (NLP), which
provides a set of tools for researchers and data scientists to understand natural language in real
world environments. Prior experience with computer programming is not necessary to take this
course. This course is available for graduate credit.
3. Date of Proposed Implementation: (Semester/Year): [ Spring ] / [ 2017] Comments
4. Course Level
[]Undergraduate Only
[]Graduate Only
[x]Graduate/Undergraduate
5. Course Abbreviation “Short title” (maximum of 17 characters and spaces)
Short Title: [Computation Ling]
6. Basis of Grading:
[]Credit/No Credit Only
[X]Letter Grade Only
[]CR/NC or Letter Grade
7. Number of times a course may be taken:
[ X ] May be taken for credit for a total of [1] times, or for a maximum of [3] units
[ ] Multiple enrollments are allowed within a semester
8. C-Classification: (e.g., Lecture-discussion (C-4).)
[ 3] units @ [C] [4]
9. Replaces Current Experimental Course?
[ ] YES
[X] NO
Replaces Course Number/Suffix:[
]
Previously offered [
] times.
10. Proposed Course Uses: (Check all that apply)
[ X ]Own Program:
[ ]Major
[ ]Minor
[ ]Masters
[ ] Requirement or Elective in another Program
[ ] General Elective
[ ] General Education, Section [
]
[ ] Meets GE Information Competence (IC) Requirement
[ ] Meets GE Writing Intensive (WI) Requirement
[ ] Community Service Learning (CS)
[ ] Cross-listed with: (List courses) [
]
[ ]Credential
11. Justification for Request: Course use in program, level, use in General Education, Credential, or other.
Include information on overlap/duplication of courses within and outside of department or program. (Attach)
12. Estimate of Impact on Resources within the Department, for other Departments and
the University. (Attach)
(See Resource List)
13. Course Outline and Syllabus (Attach) Include methods of evaluation, suggested texts, and selected
bibliography. Describe the difference in expectations of graduates and undergraduates for all 400 level courses
that are offered to both.
14. Indicate which of the PROGRAM’S measurable Student Learning Outcomes are
addressed in this course. (Attach)
15. Assessment of COURSE objectives (Attach)
A. Identify each of the course objectives and describe how the student performance will
be assessed
(For numbers 14 and 15, see Course Alignment Matrix and the Course Objectives Chart)
16. If this is a General Education course, indicate how the General Education Measurable
Student Learning Outcomes (from the appropriate section) are addressed in this
course. (Attach)
17. Methods of Assessment for Measurable Student Learning Outcomes (Attach)
A. Assessment tools
B. Describe the procedure dept/program will use to ensure the faculty teaching the
course will be involved in the assessment process (refer to the university’s policy on
assessment.)
[ X ]Other
18. Record of Consultation: (Normally all consultation should be with a department chair or program
coordinator.) If more space is needed attach statement and supporting memoranda.
Department Chair/ Program
Concur
Date:
Dept/College:
Coordinator
(Y/N)
[Computer Science]
[Rick Covington]
[
]
[ ]
[Digital
Humanities
[Scott
Kleinman]
[9-21[Y]
Initiative]
15 ]
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Consultation with the Oviatt Library is needed to ensure the availability of appropriate
resources to support proposed course curriculum.
Collection Development Coordinator
Please send an email to: collection.development@csun.edu
Date
[9-17
15 ]
19. Approvals:
Department Chair/Program Coordinator:
Evelyn McClave, Chair
Date:
College (Dean or Associate Dean):
Date:
Educational Policies Committee:
Date:
Graduate Studies Committee:
Date:
Provost:
Date:
[9-11-15
[
[
[
[
]
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]
]
11. Justification for Request
The Linguistics/TESL Department has for years considered it a gap in our curriculum that we did
not offer a course in computational linguistics, one of the most robust subfields of linguistics.
Computational linguistics includes such areas as machine translation between languages,
corpus linguistics to document language variation and change or stages of child language
acquisition, and predictive text analysis for auto-suggestion on mobile phones. The Linguistics
Department has recently hired Professor David Medeiros, who has expertise in computational
linguistics and is able to create and teach the course, thus strengthening our curriculum overall.
The course will also contribute to the Digital Humanities initiative in the College of Humanities,
as many computational and corpus-based methods are used in interdisciplinary Digital
Humanities work.
The Computer Science Department, while offering courses specific to other disciplines, such as
COMP 106/L Computing in Engineering and Science or COMP 270/L Business Programming, has
no course that focuses specifically on natural language processing from a linguistic perspective.
]
12. Estimate of Impact on Resources within the Department, for other
Departments and the University
a. Faculty and course rotation
This course will be folded into the regular rotation cycle of LING course offerings in alteration
with other course offerings in the program. Dr. David Medeiros has the expertise to teach the
course.
b. Utilization of existing facilities, equipment/software and technical support
We have received permission to teach this course in the Barbara Ann Ward Language Center in
the College of Humanities.
c. Impact on other departments
A course devoted to natural language processing is not taught by the Department of Computer
Science. We envision this course as being of interest to linguistics majors and minors and
computer science majors, as well as increasing numbers of Humanities students with interests
in the Digital Humanities.
13. Course Outline and Syllabus
14. Indicate which of the PROGRAM’S measurable Student Learning
Outcomes are addressed in this course.
LING 455 addresses these B.A. Linguistics SLOs as indicated in the table below:
1. Express what linguists mean by “knowing a human language” by demonstrating
knowledge of such core fields as phonetics, phonology, morphology, syntax, semantics,
and pragmatics.
2. Verbalize what is involved in the acquisition and development of language and discuss
its biological and social foundations.
3. Describe key concepts from such fields as pragmatics, and discourse analysis and relate
them to language data.
4. Verbalize how sociocultural diversity manifests itself in language using methods and
concepts from the field of sociolinguistics.
5. Read, evaluate, and write effectively about linguistic topics.
6. Define the connections between linguistic study and its practical applications.
COURSE ALIGNMENT MATRIX: B.A. in Linguistics
Directions: Assess how well LING 455 contributes to the program’s student learning outcomes by rating each
course objective for that course with an I, P or D.
I
I
I
I
I
I
I
I
I
15. Assessment of COURSE objectives (Attach)
A. Identify each of the course objectives and describe how the student performance will be
assessed:
Student Learning Outcome 7
BA-LING SLO 4
I
BA-LING SLO 6
2. Analyze language data (e.g. text corpora) using Python.
3. Demonstrate how data structures and algorithms are used in
Natural Language Processing (NLP).
4. Develop and answer language-related questions on the basis
of data available on the internet
I
I
I
BA-LING SLO 5
1. Write simple but effective programs in Python.
BA-LING SLO 3
Upon completion of LING 455, students will be able to:
BA-LING SLO 2
Course Objectives
BA-LING SLO 1
I=introduced (basic level of proficiency is expected)
P=practiced (proficient/intermediate level of proficiency is expected)
D=demonstrated (highest level/most advanced level of proficiency is expected)
Course Objectives
Upon completion of LING 455, students will be able to:
1. Write simple but effective programs in Python.
2. Analyze language data (e.g. text corpora) using Python.
3. Demonstrate how data structures and algorithms are used in
Natural Language Processing (NLP).
4. Develop and answer language-related questions on the basis of
data available on the internet
Assessments of Student
Performance
Homework, midterm, final,
final project
Homework, midterm, final,
final project
Homework, midterm, final,
final project
Homework, midterm, final,
final project
16. If this is a General Education course, indicate how the General Education
Measurable Student Learning Outcomes (from the appropriate section) are
addressed in this course. (Attach)
N/A
17. Methods of Assessment for Measurable Student Learning Outcomes (Attach)
A. Assessment tools
This course will be part of the ongoing assessment of the B.A. in Linguistics programs. To
what extent students in the program meet the SLOs of these programs will be assessed
with data in the form of responses to embedded questions and other student work. Results
will be evaluated with the help of rubrics and discussed among the Linguistics/TESL
faculty.
B. Describe the procedure dept./program will use to ensure the faculty teaching the
course will be involved in the assessment process (refer to the university’s
policy on assessment.)
We anticipate that faculty teaching this course will collaborate to discuss and share data
from student written work (exams, data collection through electronic deposits,
presentations, paper) that can be used to evaluate the extent to which students achieve the
course objectives. Revisions of the course will come from faculty experience, knowledge of
the field, and observations of student responses and performance in the course with regard
both to course objectives and program SLOs.