2000-03-15seq presentation, f. farance, ©1999 edutool.com 1 sequencing information (seq) 2000-03-15...

2000-03-15 SEQ Presentation, F. Farance, ©1999 Edutool.Com

1

Sequencing Information (SEQ)2000-03-15 Presentation

Frank Farance, +1 212 486 4700, [email protected]

Edutool.Com, a division of Farance Inc.Slides and Document: http://edutool.com/seqPresentation to LTSC Course Sequencing WG(IEEE 1484.6) http://ltsc.ieee.org/wg6

http://edutool.com/seq
















2

Overview

• Review PAR Scope• Problem definition• Industry approaches• Solution framework• Paradigm list• Applications• Summary and conclusions


3

Review PAR Scope [1/2]

• This standard will specify a specification language and environment for managing sessions in learning technology systems, e.g., computer-aided instruction, intelligent learning environments, intelligent tutoring systems. The standard will


4

Review PAR Scope [2/2]

– (1) define the specification language, its conceptual model, semantics, and syntax,

– (2) define the control transfer mechanisms and their encodings, e.g., how learning sessions are controlled and conducted,

– (3) define the data transfer mechanisms and their encodings, e.g., how student assessments and lesson plans are exchanged,

– (4) define an encoding method for storing and transferring session management “programs”, i.e., interactive lesson plans.


5

Problem Definition

• Export/import of courseware across tools/systems

• Preserve investment of existing courseware• Compatibility of “object models”• Sample scenario: content developed with tool

A is later modified with tool B


6

Industry/Technical Approach:Common Question “Objects” [1/2]

• Main focus: common abstractions of question types and interactions

• Examples: multiple choice, fill in blank, match columns, true/false

• Attempts:– Asymetrix question objects. Problem: Too many

question types, poor abstraction, hard to implement– Flightsafety/Boeing 737/777 libraries. Problem:

Solutions for 737 were different than 777; no common abstraction; author still needs modifications


7

Industry/Technical Approach:Common Question “Objects” [2/2]

• Attempts (cont’d):– Empower assessment/question objects. Problem:

Incomplete definition; direction is not clear

– Question Markup Language (QML). Problem: Simplistic XML coding of simple cases; poor semantics

• Current state: a “sense” of common features, yet no common abstraction, definition, specification

• Activity in: IEEE 1484.6, IEEE 1484.10, IMS


8

Industry/Technical Approach:Common Launch Methods [1/2]

• Main focus: launching content from management systems

• Attempts:– Web-based content. Problem: Requires internet

access and web servers; requires user activation in browser

– Java-based content. Problem: Requires all content must be Java or Java callable

– Javascript content: Problem: Same problems as web-based content


9

Industry/Technical Approach:Common Launch Methods [2/2]

• Attempts (cont’d):– Invoking desktop executables (or Remote

Procedure Calls). Problem: Security and administration issues; not portable; outside of browser

– IMS launch methods. Problem: Yet to be defined• Current state: commonality only within a realm

(e.g., Java), but no generic common methods• Activity in: JTC1/SC22/JSG, IMS, IEEE

1484.6, IEEE 1484.10, IMS


10

Industry/Technical Approach:Common Packaging Methods [1/2]

• Main focus: common delivery methods for bundles of content

• Attempts:– ZIP files. Problem: Only archive info, no manifest– Java archives (JAR files). Problem: Only

addresses Java features, not educational metadata

– POSIX TAR (PAX) files. Problem: Not widely adopted outside of POSIX (UNIX) systems


11

Industry/Technical Approach:Common Packaging Methods [2/2]

• Attempts (cont’d):– Unbundled data. Problem: Need to interpret

content to locate all content in bundle– IMS packaging methods. Problem: Yet to be

defined

• Current state: ZIP and JAR files are most popular, but lack features necessary for learning technology

• Activity in: IMS, IEEE 1484.17


12

Industry/Technical Approach:Text Dump of Data [1/2]

• Main focus: text-based interchange format• Attempts:

– RTF (Rich Text Format). Problem: Only useful for word processing

– HTML. Problem: Difficult to extend because browser compatibility is necessary

– XML. Problem: Scaling problems because of weakness in XML specification; XML binding yet to be defined; opposition by vendors to protect “turf”


13

Industry/Technical Approach:Text Dump of Data [2/2]

• Attempts (cont’d):– Vendor-based text dump. Problem: May have

some use, but not interoperable because each vendor defines own text dump format

– Tcl-based coding. Problem: Still in experiment and definition phases (i.e., this presentation)

• Current state: Tcl-based approach is being developed

• Activity in: IEEE 1484.6, IEEE 1484.10


14

Industry/OrganizationApproaches [1/4]

• IEEE 1484 (Learning Technology Standards Committee) Working Groups:– 1484.1: Learning Technology Systems

Architecture– 1484.6: Course Sequencing (library, environment)– 1484.10: CBT Data Interchange (Tcl specification)– 1484.11: Computer Managed Instruction– 1484.12: Learning Objects Metadata– 1484.17: Content Packaging


15


• IMS– Content Objects (overall framework)– Launch Methods– Content Packaging (develop in IMS, standardize in

IEEE 1484.17)


16


• AICC– AGR006: Computer Managed Instruction

(developed in AICC, standardized in IEEE 1484.11)

– AGR007: Courseware Interchange (collaborate with IEEE, standardized in IEEE 1484.6 and IEEE 1484.10)


17


• Others– ADL: Content definition– QML: Question markup language– SATML


18

IEEE 1484.6, 1484.10

• Close collaboration• 1484.6: Environment issues, access to

assessment, performance, leaning styles, metadata, content libraries, launching content

• 1484.10: Tcl syntax, control transfer (invoke)


19

Other IEEE 1484 WGs

• 1484.1: Architecture definition• Common language that describes the

features: 1484.6, 1484.10, 1484.11• How student progresses from one “lesson” to

another -- both 1484.6 and 1484.11:– 1484.6: implies the use of control logic– 1484.11: is table driven

• Step, “page turning”, within assignable unit: 1484.10


20

Other IEEE 1484 WGs

• Accessing environment, e.g., student records and available content: 1484.6

• Launching applications or lessons, control transfer:1484.6 and 1484.11.

• Events (e.g., timeouts) and actions in courseware: 1484.6.

• 1484.12: Metadata definition• 1484.14: XML may be used for coding• 1484.17: Will define content packaging


21

LearningContent

(history)

A s s e s s m e n t

IEEE 1484.6:Course Sequencing WG

• Primary design issues: coach, performance info, assessment info, query, catalog info (metadata), locator index (e.g., URLs), invocation of learning content

• Secondary design issues: learning preferences, learner records, learning resources, delivery, learning content

Delivery Evaluation

CoachLearningResources

Query (new)

Multimedia Behavior

LearnerRecords

Performance (current)

LearnerEntity

Performance/Preferences

LearningPreferences

L o c a t o r

Catalog Info

Locator

Interaction Context


22

IEEE 1484.6 Focus

• Environment specification -- related to 1484.10 language

• Access to student records: get, put, scan• Access to content indexes (metadata): what

learning content is available• Uses logic features of 1484.10 to choose the

next “lesson”, i.e., “assignable unit”.• Launches “lessons”


23

IEEE 1484.10 Focus

• Language specification -- related to 1484.6 libraries and environment

• Defines programming logic features, useful for creating sequence programs, e.g., choosing the next “lesson”, i.e., “assignable unit”.

• Syntax for launching “lessons”


24

Other Activity:IMS Content Workshop

• Several meetings, work has stalled in past year• Content interoperability issues

– Other participants– TCL used as example implementation of sequencer

• 1998-06: Investigate TCL as a scripting language for 1484.10 and bind 1484.6 libraries and environment to 1484.10 language


25

IMS Content Issues

• What is content?– Anything that has a locator index (URL)?– Anything that has metadata?– Anything that can be sequenced?– Interoperability issues– Examples


26

IEEE 1484.6:Pedagogical Sequencing

• Sequencing systems, examples:– Type #1: Content is linked via pre-requisites and co-requisites– Type #2: Simple navigation algorithm– Type #3: Rule-based– Type #4: Programming/scripting language

• Sequencing may be internal (embedded) or external (attached)• For internal sequencing, method to extract sequencing component

SequencerSystem

Learning Resources

Content Retrieve(metadata)

Content Search

History

A s s e s s m e n tL a u n c h C o n t e n t

Content InternalSequencing

ExternalSequencingContent


27

IEEE 1484.6: Sampling OfCommon Sequencing Features

• Control structure, animations

• Page turner

• Linear sequence

• Motion algorithms

• Prerequisites

• Embedded code

• Object-based (hidden imp)

• Ontology-based

• Add missing knowledge

• Multiple choice

• Multiple choice, adaptive

• Fill in blank

• Choose M of N

• True-false

• Write sentences

• Submit project

• Randomized content

• Content templates

• Media objects


28

IEEE 1484.6: Sequencing IsIndependent Of Granularity

• Sequencing can be used at any granularity level:– Sequencing among “courses”– Sequencing among “blocks”– Sequencing among “modules”– Sequencing among “lessons”– Sequencing among “assignable units”– Sequencing within “assignable units”– Page turning

• Sequencing can vary among levels, among “units” within a level


29

Example of Sequencing Methods At Various Granularities (Distributed Development)

PrerequisitesPage turner, linear

Low-level template

Motion algorithm

Ontologies (Addmissing knowledge)

Embedded Code(“Are you ready?”)

Granularity Sequencing Types

Coarse

Medium

Fine

Sample Course Sequencing: Coarse granularity (e.g., prerequisites) sequencing for medium granularity components; medium granularity sequencing (e.g., motion algorithms, ontologies, embedded code) for fine granularity components; and so on ...


30

IEEE 1484.6 Course SequencingWhat Is Sequencing?

• Determining “next” based on:– Learning style– Assessment (assessment)– Student history (profile)– Student objectives (profile and content)– Available content (learning content, search, metadata)

• Making choice(s), e.g., a lesson plan• Invoking content (separable metadata: URL

peeled from metadata; embedded: content in hand)


31

LearningContent

(history)

A s s e s s m e n t1

Sequencing: Learning Preferences

• Sends, receives, and “negotiates” Learning Preferences with Learner Entity

Delivery Evaluation


Query (new)

Multimedia Behavior

LearnerRecords


1

LearnerEntity


LearningPreferences

L o c a t o r

Catalog Info

Locator

Interaction Context


32

LearningContent

(history)

A s s e s s m e n t

2

3

Sequencing: Assessment,Student History, Student Objectives

• Receives current Assessment information, Preferences, and Performance information (history and objectives) for future learning experiences

Delivery Evaluation


Query (new)

Multimedia Behavior

LearnerRecords


2 3

LearnerEntity


LearningPreferences

L o c a t o r

Catalog Info

Locator

Interaction Context


33

LearningContent

(history)

A s s e s s m e n t

Sequencing: Available Content

• Queries to the Learning Resources:– Queries sent to search for appropriate material– Catalog Info (learning content metadata) returned as “found” Learning

Content– Extracts the Locators (e.g., URLs) from the returned Catalog Info

(learning content metadata)

Delivery Evaluation


Query (new)

Multimedia Behavior

LearnerRecords


4

4

LearnerEntity


LearningPreferences

L o c a t o r

Catalog Info

Locator

Interaction Context


34

LearningContent

Multimedia

(history)

A s s e s s m e n t5

Sequencing: Making A Choice-- A “Lesson Plan”, Invoking Content

• Locators (similar to web URLs) are sent to Delivery process to identify (but not transfer) Learning Content

• Note: Delivery is responsible for retrieving and Learning Resources is responsible for transferring Learning Content

Delivery Evaluation


Query (new)

Behavior

LearnerRecords


5

LearnerEntity


LearningPreferences

L o c a t o r

Catalog Info

Locator

Interaction Context


35

Example Implementation: Tcl-Based Sequencer for 1484.6, 1484.10

• Current implementation:– Tcl-based– Embedded or separable sequencing code– Embedded or separable metadata

• Future work:– Access methods for assessment and profile– Access methods for metadata– Invoking content


36

Import/Export of Course Structure:

A Sample Course Structure

A

B

C

D

E

F

J

G

H

I


37

Example #1: Page Turning

• Can go forwards or backwards• From A to B, B to C, etc.• At D, learner can go to C or E• Typical use:

– fine granularity sequencing– embedded or external


38


A

B

C

D

E

F

J

G

H

I


39


• Sample implementations:– Browser/viewer capabilities– Forward and backward links– Names (locator indexes) provide ordering rules

e.g., “page-01”, “page-02”, etc.– Sequencing programs:

• Extract metadata to infer ordering• Content directory names (no metadata) infer ordering


40


A: { page_turn \ -next B -content { playimage -pos 0 0 a1.jpg }}B: { page_turn \ -next C -prev A -content { playhtml b-page.html }}

C: { page_turn \ -next D -prev C -content { playmovie c1.avi & playsound c1.wav & }}D: { page_turn \ -next E -prev C -content { runapp d.exe }}


41

Example #2: Linear Sequence

• As learner complete one unit, moves on to next

• Ordering: A, B, C, D, E, F, G, H, I, J• Typical use:

– Fine to medium granularity– Lesson and module sequencing


42


A

B

C

D

E

F

J

G

H

I


43


• Sample implementations:– Browser/viewer capabilities– Start/stop delivery– Forward links– Names (locator indexes) provide ordering rules

e.g., “page-01”, “page-02”, etc.– Sequencing programs:

• Extract metadata to infer ordering• Content directory names (no metadata) infer ordering


44


A: { page_turn \ -next B -content { playimage -pos 0 0 a1.jpg }}B: { page_turn \ -next C -content { playhtml b-page.html }}

C: { page_turn \ -next D -content { playmovie c1.avi & playsound c1.wav & }}D: { page_turn \ -next E -content { runapp d.exe }}


45

Example #3:20-70-10 Motion Algorithm

• Learner gets range of lessons, based on weighted, random distribution (20-70-10 used as example):– 20% of material from “previous” unit (review)– 70% of material from “current” unit– 10% of material from “next” unit (introduction)

• Typical use:– Medium granularity– Sequencing modules within a course


46


A

B

C

D

E

F

J

G

H

I

70% current 10% next20% previous


47


• Sample implementations:– Sequencers operating on “course” rules as applied

to (finer granularity) modules, i.e., separate “sequencer” (program) from “rules” (parameters or inputs)

– Sequencing programs in “modules” that sequence among other modules in a course (coarser granularity)

– Sequencing information can be embedded in content

– Sequencing information can be external to content


48

Example #3: 20-70-10 Motion Algorithm (pseudo-code)

{set prev $thisobj.prevrefset curr $thisobj.refset next $thisobj.nextrefset randval [random 100]if { $randval < 20 } then{ launch $prev }elif { $randval<90 } then{ launch $curr }else{ launch $next }}

X: { \ playmovie c1.avi & playsound c1.wav &}Y: { runapp d.exe }Z: { \playhtml b-page.html}


49

Example #4: Prerequisites

• Content structure determines “partial” ordering, i.e., dependency relationships

• Examples:– After A is completed, any one of B, C, or D can be run– F can only be run after both C and D are completed,

regardless of progress on B or E

• Typical use:– Medium to coarse granularity– Sequence among modules, sequence among courses


50


A

B

C

D

E

F

J

G

H

I


51


• Sample implementations:– Dependency links (not too friendly)– Names (locator indexes) provide ordering rules

e.g., “topic-a-01”, “topic-b-02”, etc.– Metadata can contain ordering– Sequencing programs that infer ordering– Learner profile (performance) information can be

used– Assessment information can be used


52

Example #5: Embedded Code

• Sequencing code is embedded in content or available in metadata

• Content asked: “Am I ready to run this content?”

• Typical use:– Medium to coarse granularity– Sequencing courses– Sequencing specialized standalone “lessons”

incorporated into many courses


53


A

B

C

D

E

F

J

G

H

I

SequencerProfiles

Other ServicesSequencer Query“Am I Ready?”

Query Learner History To Support

Answering Question“Am I Ready?”


54


• Sample implementations:– CGI scripts, Perl, Java, possibly Javascript– Tcl programs– Executables

• Separate “Are you ready to run?” from “Start running” messages

• Can be embedded or attached to content


55

Example #6: Add MissingKnowledge (Ontologies)

• Sequencing based on learner’s mastery of identifiable competencies

• In this example: list of “missing knowledge” is extracted from profile information as “learner’s objectives” (future performance information)

• Typical use:– Any granularity– Course comprised of knowledge objectives– Objectives may be in profile, not it content


56


A

B

C

D

E

F

J

G

H

I

Knowledge units, perlearner objectives

ProfilesKnowledge dependenciesoutside of pre-requisites

Knowledge objectivesretrieved fromlearner’s profile


57


• Sample implementations:– Tcl scripts comparing learner models with

knowledge libraries– Expert systems

• Highly dependent on knowledge structure, learner model


58

Low Level PresentationAnd Question Sequencing

• Based on low-level, widely used paradigm• Based on work by Claude Ostyn (Asymetrix)

and Bill McDonald (Flightsafety/Boeing)• Simplified abstractions, parameterized

abstractions• Starting point for common interaction

templates


59

Example #7: Multiple Choice

• Presentation “text”• Question “text”• Choices

– Labels– Choice text

• Flag: randomization• Correct answer


60

Multiple Choice(Tcl-based CBT Data Interchange)

multiple_choice \-P type \ presentation type, e.g., text, text-file, MIME type-p value \ presentation, e.g., string, filename, URL-Q type \ question type ... same as pres. type-q value \ question value ... same a pres.-L type \ label type ... same as pres. type, multiple option-l value \ label name ... same as pres., multiple option-C type \ choice type ... same as pres. type, multiple option-c value \ choice value ... same as pres., multiple option-A type \ answer type ... same as pres. type, usually "string”-a value \ answer value ... same as pres., usually match string-H type \ hint type ... same as pres. type-h value \ hint value ... same as pres.-D type \ repository type-d loc \ location in repositorycontent_id content ID associated with interaction


61

Multiple Choice Content Example

multiple-choice \ -P text-file -p content-1-p \ -Q text-file -q content-1-q \ -C text-file -c content-1-1 \ -C text-file -c content-1-2 \ -C text-file -c content-1-3 \ -C text-file -c content-1-4 \ -A string -a Friday \ content-1


62

Example #8: Fill In Blank

• Presentation “text”• Question “text”, including blank• Correct answer• Flag: case insensitive• Flag: ignore leading, multiple, trailing spaces• Spelling fuzziness


63

Fill In Blank(Tcl-based CBT Data Interchange)

fill_in_blank \-P type \ presentation type, e.g., text, text-file, MIME type-p value \ presentation, e.g., string, filename, URL-Q type \ question type ... same as pres. type-q value \ question value ... same a pres. (replace “____”)-L type \ label type ... same as pres. type, multiple option-l value \ label name ... same as pres., multiple option-C type \ choice type ... same as pres. type, multiple option-c value \ choice value ... same as pres., multiple option-A type \ answer type ... same as pres. type, usually "string”-a value \ answer value ... same as pres., usually match string-H type \ hint type ... same as pres. type-h value \ hint value ... same as pres.-D type \ repository type-d loc \ location in repositorycontent_id content ID associated with interaction


64

Fill-In-Blank Content Example

fill_in_blank \ -P text-file -p content-2-p \ -Q text-file -q content-2-q \ -A string -a Friday \ content-2


65

Example #9: Choose M of N

• Presentation “text”• Left column of choices• Right column of choices• Flag: ordered vs. unordered• Flag: randomization• Answer list


66

Choose M of N(Tcl-based CBT Data Interchange)

selection_choice \-P type \ presentation type, e.g., text, text-file, MIME type-p value \ presentation, e.g., string, filename, URL-Q type \ question type ... same as pres. type-q value \ question value ... same a pres.-L type \ label type ... same as pres. type, multiple option-l value \ label name ... same as pres., multiple option-C type \ choice type ... same as pres. type, multiple option-c value \ choice value ... same as pres., multiple option-A type \ answer type ... same as pres. type, usually "string”-a value \ answer value ... same as pres., usually match string-H type \ hint type ... same as pres. type-h value \ hint value ... same as pres.-D type \ repository type-d loc \ location in repositorycontent_id content ID associated with interaction


67

Example #10: True-False

• Presentation “text”• Correct answer


68

True-False(Tcl-based CBT Data Interchange)

true_false \-P type \ presentation type, e.g., text, text-file, MIME type-p value \ presentation, e.g., string, filename, URL-Q type \ question type ... same as pres. type-q value \ question value ... same a pres.-L type \ label type ... same as pres. type, multiple option-l value \ label name ... same as pres., multiple option-C type \ choice type ... same as pres. type, multiple option-c value \ choice value ... same as pres., multiple option-A type \ answer type ... same as pres. type, usually "string”-a value \ answer value ... must be “true” or “false”-H type \ hint type ... same as pres. type-h value \ hint value ... same as pres.-D type \ repository type-d loc \ location in repositorycontent_id content ID associated with interaction


69

True-False Content Example

true_false \ -P text-file -p content-3-p \ -Q text-file -q content-3-q \ -A string -a true \ content-3


70

Example #11: Write Sentences

• Presentation “text”• Correct answer• Comparison script


71

Write Sentences(Tcl-based CBT Data Interchange)

write_sentence \-P type \ presentation type, e.g., text, text-file, MIME type-p value \ presentation, e.g., string, filename, URL-Q type \ question type ... same as pres. type-q value \ question value ... same a pres.-L type \ label type ... same as pres. type, multiple option-l value \ label name ... same as pres., multiple option-C type \ choice type ... same as pres. type, multiple option-c value \ choice value ... same as pres., multiple option-A type \ answer type ... usually ”writing-check”-a value \ answer value ... same as pres., usually match string-H type \ hint type ... same as pres. type-h value \ hint value ... same as pres.-D type \ repository type-d loc \ location in repositorycontent_id content ID associated with interaction


72

Write-Sentences Content Example

write_sentence \ -P text-file -p content-4-p \ -Q text-file -q content-4-q \ -A writing-check \ -a spelling,grammar,\include=Sunday,include=Monday,\include=Tuesday,include=Wednesday,\include=Thursday,include=Friday,\include=Saturday \ content-4


73

Example #12: Submit Project

• Presentation “text”• Requested “pointers” of work• Grading system: E-mail, FTP, posting, script,

CGI


74

Submit-Project(Tcl-based CBT Data Interchange)

submit_project \-P type \ presentation type, e.g., text, text-file, MIME type-p value \ presentation, e.g., string, filename, URL-Q type \ question type ... same as pres. type-q value \ question value ... same a pres.-L type \ label type ... same as pres. type, multiple option-l value \ label name ... same as pres., multiple option-C type \ choice type ... same as pres. type, multiple option-c value \ choice value ... same as pres., multiple option-A type \ answer type ... usually ”human-eval”-a value \ answer value ... same as pres., usually match string-H type \ hint type ... same as pres. type-h value \ hint value ... same as pres.-D type \ repository type-d loc \ location in repositorycontent_id content ID associated with interaction


75

Example #13: Randomized Content

• Presentation “text”• Template “text”• Rendering system• Correct “answer”• Grading system (e.g., CGI)


76

Template Expand(Tcl-based CBT Data Interchange)

template_expand \-P type \ presentation type, e.g., text, text-file, MIME type-p value \ presentation, e.g., string, filename, URL-Q type \ question type ... same as pres. type-q value \ question value ... same a pres.-L type \ label type ... same as pres. type, multiple option-l value \ label name ... same as pres., multiple option-C type \ choice type ... same as pres. type, multiple option-c value \ choice value ... same as pres., multiple option-A type \ answer type ... same as pres. type, usually "string”-a value \ answer value ... same as pres., usually match string-H type \ hint type ... same as pres. type-h value \ hint value ... same as pres.-D type \ repository type-d loc \ location in repository-X loc \ expansion rulescontent_id content ID associated with interaction


77

Example of Sequencing Methods At Various Granularities (Distributed

Development)

PrerequisitesPage turner, linear

Low-level template

Motion algorithm

Ontologies (Addmissing knowledge)

Embedded Code(“Are you ready?”)

Granularity Sequencing Types

Coarse

Medium

Fine

Sample Course Sequencing: Coarse granularity (e.g., prerequisites) sequencing for medium granularity components; medium granularity sequencing (e.g., motion algorithms, ontologies, embedded code) for fine granularity components; and so on ...


78

Environment

• Need access to performance, assessment, preferences, context indexes ==> CBT Data Interchange work

• Launch ==> Work with IMS


79

Open Issues

• Means of navigation• User actions/behavior• Should subdivide list between navigation

actions/behavior• Focus on some grouping “level” to identify the

triggers that can be passed to affect the sequence

• Should refer to low-level micro operations• State transition diagrams


80

Future Work (2000-03)

• Revised SEQ data interchange specification• Collaboration among: IEEE LTSC, IMS,

AICC, others• Demonstration of practical examples

2000-03-15seq presentation, f. farance, ©1999 edutool.com 1 sequencing information (seq) 2000-03-15...

Documents