A Learning S hell must provide an adaptable environ ment which the student can ind ividualise to suit his or her own learning preferences and needs. Murray et a l . (2000)
Chapter 5: Towards a specification 1 04
h i g h l ig ht advantages of the adaptable approach to individualising a learning system s uch as retaining the locus of control with the user and being simpler to i mplement.
This section specifies the key elements for achieving this goal with in the I M M E D IATE p rototype .
5.6 . 1 Student Model
Ind ividualisation of the Lea rn ing Shell is made possible th rough reference to a locally stored Student Mode l .
Adaptive teach ing systems model the student's knowledge s o that the system can dynamically adapt its teach ing strateg ies to suit the individual student (AIIessi et a l . ,
1 99 1 , p. 463). I M M E D IATE models the student's knowledge s o that the system can help the student adjust their learn ing strateg ies and priorities.
The Student Model records the student's current state vis-a-vis the course, wh ich incl udes :
• their name and password;
• their current position i n the cou rse (section and topic);
• their cu rrent study mode;
• the sections and topics they have attempted o r completed ;
• their progress in meeting key learning goals and concepts; and
• any other information necessary for configu ring the user's environment.
The information in the Model is saved to permanent storage whenever the student's state is changed.
The Student Model enables the system to track the student through the course in o rder to p reserve their position when changing study mode or quitting the system . On re entering the Learning Shell, the u se r can be g iven the options of returning to whe re they left off previously, o r of viewi ng how far they have progressed th rough the course a nd selecting a new topic or mode of study.
5 . 6 . 2 I nteg rated Learn i n g S u pport
Providing learning support to individual students is faci litated by the integ ration of this support with the commu n ication and collaboration facilities.
Chapter 5 : Towards a specification 1 05
a tutor across a public commun ications network are well-established a nd are now a basic component of courseware packages. The simplest method is to provide email based correspondence and discussion g roups . Cou rseware systems may utilise thei r database capabilities t o organise these discussions i n specific th reads or l i n k them with specific parts of a cou rse (Goldberg , 1 997).
Extramural Su pport should enable direct student queries on a pa rticular subject . And it must work when the student is off-line and therefore cannot be connected to a central database.
To meet these requirements the integrated system should be built upon a re lational database, which stores all correspondence, discussion and learning support i nformation. SOL queries offer a straig htforward mechanism for provid ing ind ividual student and tutor views of this database , and for search ing for answers to student q ueries.
This database should be organised a long the lines of IMME D IATE's distributed logic architecture. The best fit would be along the lines of models that have been developed to support mobile computing such as the "Briefcase model" ( l n prise, 1 999a, pp. 1 3. 1 7-
1 3. 1 8) o r "asynchronous replication" (Con nolly et a l . , 1 999 , p. 7 1 2) . These represent a more peer-to-peer style of distributed database organisation in which each user has a local copy of the data base and some mechanism is provided for caching of updates to allow asynchronous, off-line remote functioning.
Separate email-based commun ications are not necessary in I M M E D IATE beca use all students can be li nked to a central copy of the database over the I nternet. Loca l copies of the database on student machines ca n then be synchronised and updated a utomatically whenever the student logs onto the network.
5.6.3 I ndividualised S u p port
As well as integrating learning support with the system's communications and collaboration, IMMEDIATE must individualise it so that a student can receive mean ingful, relevant help from the system itself, without having to tu rn to the cou rse tutor or fellow students with every query .
I M M E D IATE should provide an adaptable mechanism through which t h e user in itiates a dialogue with the system by defining a query on a particular su bject a nd then receives a specific, context-sensitive response. Laddering, i . e . the repeated p robing of a n issue via Why-type q uestions (Reynolds et a l . , 1 988) should be supported. I n other words, if the student is not satisfied with the system respon se then it should be
Chapter 5 : Towards a specification 1 06
possible for the student to repeat o r re-define the q uery to mu ltiple depths, in add ition to being a ble to pass the q uery on to the tutor.
Extramural S u pport should be able to learn from its experience and improve its responses to the same or similar queries over time.
The starting point for meeting these req uirements is recognising that because the lea rning computer tracks the student as they work, all communications -- including q ueries and thei r responses -- are linked to a particu lar position in the course . This provides the framework for organising these com munications into a coa rse-grained knowledge base .
Organising Learning Support as Knowledge System
There a re th ree elements to be considered in util ising the system database for a knowledge system :
• Provid ing a more u ser-friendly interface query interface than standard SQL or Query By Example can provide;
• Deve loping a d i rected sea rch strategy which a ims to return a more mea n ingful result set than is possible th rough a blind or brute force search of the entire database.
• Modifying the structure of the database (e. g . by adding fields) to facilitate implementing either of the above strategies.
User-Friendly Query Interfaces
H a rmon et al. ( 1 985, p. 262) note that: " I n many cases, small knowledge systems derive thei r util ity from their user-friendly nature rather than from their ability to capture knowledge that wou ld be d ifficult to rep resent in a conventional program."
Querying d atabases is considered a promising arena for applying Artificial I ntelligence techniques for natural lang uage parsing because databases usually represent a coherent a nd restricted domain (Luger et a l . , 1 998 , C h . 8). Another simpler approach allows a subset of English to be used in natural-language-like q ueries which may be parsed by their syntax or their semantics (Heinrich et a l . , 2 000; Heinrich et a l . , 200 1 ) . For t h e purposes o f th is i n itial prototype a highly-simplified modification o f the semantic q uerying approach in Heinrich et al. (2001 ) will be used . Queries a re limited to fou r pre defined types "What?", "Why?", "Where?", "Who?" . The semantics of these queries a re summarised in Table 5 . 3 . The user selects one of these query-types and then
Chapter 5: Towards a specification 1 07
associates it with a key word or phrase in the cou rse material . The efficacy of this approach may then be eva luated , a nd the need for a more sophisticated approach assessed.
Dimension Mean ing Comment
What? What is the meaning of this I nvolves finding the best match for the phrase (or its
word or phrase? synonym) in a course glossary.
Why? Explain this concept I nvolves sea rching a database for the m ost further. appropriate explanation or com bination of
explanations of a particular concept (phrase).
Where? Where can I find further I nvolves searching a database for further references
reading on this concept? (li nks) to the current phrase, subtopic or topic.
Who? Who has written further on I nvolves searching a database (or i nternetwork of
this concept? databases) for further references (links) to authors on
the current phrase, subtopic or topic.
Table 5.3: The four d imensions of the I ntegrated Help syste m.
Directed Search
A perusal of Table 5 . 3 shows that the concept of searching a database is at the centre of Extramural Support. The simplest method is a text search of the entire database looking for key word s , and retu rn ing every match ing record . This blind or brute force sea rch strategy will find any and all references to the key words in the database but will have no way of selecting and ranking those resu lts likely to be most helpfu l to the user. A more directed sea rch strategy is req uired .
Gonzalez et a l . ( 1 993, Ch. 1 ) describe search strategies as the "foundation of a rtificial intelligence" (p. 3). The principal components of a Knowledge-Based System a re a database, a user i nterface, and an intelligent program, or i nferencing mecha nism, mediating between the user and the database (Figure 5 . 9) .
Within the Extra mural Support system , the inferencing mechan ism consists of the ru les for searching the database to provide the most mean ingfu l possible result. A directed search strategy includes what to look for, where to look for it, and how to rank/present resu lts? Extram u ral Support implements these in the following manner:
Chapter 5 : Towards a specification 1 08 Intelligent program User User I nterface Database
F i g u re 5.9: Knowledge-based system components (Gonza lez et al., 1 993).
• What to look for. The user selects what type of query to make and on what subject
(i.e. what key word or phrase). The system maps the key word or ph rase to related terms.
• Where to look for. The system uses the system tree to search the database,
beg inning with the cu rrent topic, then the closest adjacent topics (i.e. leaf nodes in the System Tree), u ntil sufficient results h ave been found.
• How to present/rank results. The q uery results a re ranked so that the most relevant
res ult is pre se nted first a nd the least relevant, last. At its simplest level, the results are automatica lly ran ked by their proxim ity in the System Tree to the cu rrent node. The i mplementation should allow for more sophisticated ranking algorithms to be added d u ring the prototyping process.
Modifications to the database
Becau se the System Tree is the meta-description of the learning computer - used by the student model , extram u ral support and the directory system - text and keyword searches of such a structured database are d i rected in a way not otherwise possible. The teacher edits his o r her responses to student q ueries and other usefu l discussion items, including by adding keyword and other links between entries to facilitate various q uery types and deeper search ing. In this way the integ rated help system is built dynamica lly and collabo ratively by the tutor and students.
Chapter 5 : Towards a specification 1 09