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Generative Computer Assisted Instruction: an Application of Artificial Intelligence to CAI

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Generative Computer Assisted Instruction: an Application of Artificial Intelligence to CAI DOCUMENT RESUME ED 078 648 EM 011 178 AUTHOR Koffman, Elliot B. TITLE Generative Computer Assisted Instruction: An Application of Artificial Intelligence to CAI. INSTITUTION Connecticut Univ., Storrs. Dept. of Electrical Engineering. SPONS*AGENCY National Center for Educational Resei,,:cb and Development (DHEW/OE), Washington, D.C.; National ScienCe Foundation, Washington, D.C. Office of Computl!ng Activities. PUB DATE 72 GRANT oEG-0-40895 NOTE 8p.; USA-Japan Computer Conference, 1972. EDRS PRICE MF7A0.651HC-$3.29 DESCRIPTORS Algorithms; *Arkificial Intelligence; *Computer Assisted Instruction; *Computers; Problem Solving; Programed Texts; State of the Art Reviews IDENTIFIERS Generative Computer Assisted Instruction; Natural, Language Communication ABSTRACT Frame-oriented computer- assisted instruction (CAI) systems dominate the field, but these mechanized programed texts utilize the computational power of the computer to a minimal degree and are difficult to modify..Newer, generative CAI systems which are supplied with a knowledge'of subject matter can generate theirown problems and solutions, can provide extensive drill and tutoring, and can diagnose learner problems and prescribe remedial feedback. These systems reduce the amount of-instructor effort required to teach new material and encourage students to assume the initiative in studying new topics. Information,-Structure Oriented (ISO) systems have capabilities for natural language communication and are useful in the -humanities-and social sciences...Quantitative systems rely on algorithms and are oriented toward providing students with practice in problem-solving. While the existing generative systems are still experimental it is-expected that their use will become more widespread as-research continues and,as CAI facilities become more available and economical. (PB) .1 First USAJAPAN Computer Conference, -1972 1 GENERATIVE COMPUTER ASSISTED INSTRUCTION: ANAPPLICATION OF ARTIFICIAL INTELLIGENCE 'TO CAI Elliot B. Koffman U S. DEPARTMENT OFHEALTH. EDUCATION A WELFARE NATIONAL INSTITUTE OF (University of Connecticut, Storrs, Connecticut) EDUCATION BEEN REPRO THIS DOCUMENT HASRECEIVED FROM DUCED EXACTLY AS ORIGIN THE PERSON OR ORGANIZATIOU ATING IT POINTS OF VIEWOR OPINIONS REPRE STATED DO NOT NECESSARILY INSTITUTE OF SENT OEFICIAL NAIONAL EDUCATION POSITION ORPOLICY In an ISO system, there must be a capability I. INTRODUCTION for natural language communication between teacher Limited progress has been made in software for and student. The system must be able to determine by computer-assisted instruction:- Frame-oriented CAI the degree of correctness of a student's recionse systems have dominated the field. These systems are comparing it with information stored in its data -e- classically mechanized programmed texts and utilize base. The system:Would be able to understand and student. the computational power of the computer to a minimal answer questions which are posed by the Simmons (1) provides a very comprehensive review of extent. In addition, they are, difficult to modify and research in the areas of natural language communica- tend to provide a fairly fixed instructional sequence. tion and question-answering. Recently, generative CAI systems have appeared. These are systems for CAI which are capable of gener- Carbonell (2,3) has designed an extremely SCHOLAR is an ating their own questions or problems and deriving versatile-system called SCHOLAR. the Hence, they can provide un- example of a 'mixed- initiative" system in that their own solutions. generated limited drill_and tutoring with little effort required student can interrupt the flow-of questions from the course-author to define an instructional by SCHOLAR at any time and interrogate SCHOLAR. - SCHOLAR relies heavily on prior research byQuillian sequence. (4,5) in the area of semantic memory and utilizes a of facts These systems :re supplied with knowledge of semantic network for storage of a data base their-subject matter. Therefore, they can often concerning South America. interpret and answer questions or problems posed by They are also capable of diagnosing A semantic network is an organization of units -the student. mutual the degree of inaccuracy in a student response-and of information in terms of their meaning and providing remedial feedback on an individual basis. interrelationships. Each unit or node in the memory Starting Most of these systems incorporate techniques and contains pointers to other related units. concepts which are outgrowths of research in Artifi- from a given unit, one can trace out a set of rela- tionships by following the diverging trailsof cial Intelligence. pointers. See Figure 1. This paper will describe some of the generative systems which have recently been designed. A gener- For example, the unit Argentina, denoted as an ative tutor under development.by the author, which object, might have attributes such as superconcept, The Values has been used to teach digital computer concepts, superpart, location, capital city, etc. of these attributes would be other units in thenet- will also be examined in detail. work-and, hence, there would be-pointers to thenodes lati- These systems can be divided into two classes. representing the units-country, South America, Also, Those which are oriented towards the humanities and tude and longitude, Buenos Aires, respectively. textual material and those which are more concerned associated with each attribute is a tag indicating unit. with numerical manipulations and quantitativematerial. its irrelevancy with respect to the parent The former have been termed information-Structure both for Oriented systems (ISO) and will be discussed next. The Semantic network is used by SCHOLAR generating and answering questions. The initial con- text of the questions is determined by theteacher. II. ISO GZNERATIVE SYSTEMS The teacher's input "South America"specifies that node Research in the area of Artificial Intelligence the initial unit to be investigated is the has provided most of the background for the ISO Gen- South America. An attribuce is selected and a ,ques- These systems are intelligent in tion is generated concerning the value;ofthat erative Systems. is 4) the sense that they have knowledge of thesubject- Jtcribute. At the same time, the correct answer matter they are designed to teach. This data base retrieved for comparison with the student response. of facts is stored in memory in an informationnetwork. For example, given the initial context"South This research -is sponsored by the U.S.Office of America" and the attribute "Countries" SCHOLARmight Education under Grant 0EG-0-72-0895. Computer services ask one of the following: were supported by NSF Grant GS-9. What are the countries in South America? 0 Session 2.3.1 FILMED FROMrBEST AVAILABLE COPY . r- Is it true that Argentina is a country in South this can be cut by a factor of 15 if compiled code America (T/F) is used. A Country in South America is--- (fill in). There is a certain probability that Another example of an ISO Teacher is the" system he cdntext designed by Wexler (7,8). will change from the initial unit to Wexlei uses an information a related unit. structure that consists of classes, objects within Hence, a subsequent question might be: those classes, and links between objects. Sample Select an Alternative from the list: classes from the-geography of Canada might be Paysardu "Provinces" and "Cities" with "Newfoundland" and Rio De Janerio "Quebec" examples of objects in theformer class Buenos Aires and "St. John's" and "Quebec City" examples of Uruguay Riirer objects in the latter class.There could be a link In the question: between the objects St. John's and Newfoundland and between the objects Quebec City and Quebec. The What is the capital of Argentina? course-author constructs a chain of links in the net- work by specifying an object together with its class The actual generation of questions is handled name and the next object (with associated claSs name) by a set of procedures which are available for forming in the chain. each of the above question types. Figure2 gives an example of an The question type information structure from this system. to be asked is determined probabilistically.As has been mentioned, the correct answer to a question is derived when the question is generated. The information structure utilized might be Since all though' of as a skeletal representation of relation- expected student responses are numbers or object ships. It is considerably more streamlined than the names, the problem of recognizing a correct student response isminimized. semantic network and, consequently,_ additional data The teacher can specify the must be supplied in order to interpret it. acceptable degree of inaccuracy in numericalanswers and the degree of misspelling allowed for object This data consists of a set of skeleton segments names. and their interpretation strings. A sample segment might be the following: Since SCHOLAR is a mixed-initiative system, the student can interrupt with questions of hisown such STATE *V1 LINK TO CITY *V3 as "Tell me-about Argentina" or "What is the capital city in Argentina". Associated with this segment might be the interpreta- SCHOLAR analyzes the student's tion string question and determines which of the elements in the set (attribute, object, value) are missing.SCHOLAR *V3 is a city in *VI then generates an appropriate reply. This statement pair means that if there isa link in the information network,between VI, a member In the case of "Tell me, about Argentina," of the class "state", and V3,a member of the class SCHOLAR would trace through the portion of the net- "city", then the assertion_V3 isa city in V1 is work originating,at the node for the unit Argentina valid. Skeleton-segments an be combined into skele- and generate a set of statements. SCHOLAR keeps ton patterns of 011 form-"If A then B" where A isa track of the irrelevancy tags to makesure that all statements generated-are pertinent. boolean expression formed from a set of skeleton seg- If the student ments and B is the explanation string for the complete cosies back with "Tell me more about-Argentina," .then skeleton pattern.
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