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Competence-based knowledge structures for personalised learning.

Competence-based extensions of Knowledge Space Theory are suggested as a formal framework for implementing key features of personalised learning Personalised Learning is the tailoring of pedagogy, curriculum and learning support to meet the needs and aspirations of individual learners.

Personalised learning is a hot topic within the debate on education taking place in the UK at present (2006).
 in technology-enhanced learning Technology-Enhanced Learning (TEL) is any learning situation involving the use of technology. Technology used need not be computer technology, but this is often the case. Branches of TEL include CALL (Computer-Assisted Language Learning), although the latter term is often used to . The approach links learning objects and assessment problems to the relevant skills that are taught or required. Various ways to derive these skills from domain ontologies are discussed in detail. Moreover, it is shown that the approach induces structures on the assessment problems and learning objects, respectively, that can serve as a basis for an efficient adaptive assessment of the learners' skills, and for selecting personalised learning paths.


Personalised learning aims to tailor teaching to individual needs, interests, and aptitude to ensure that every learner achieves and reaches the highest standards possible. It usually proceeds by assessing the learner's current knowledge state and probably other individual characteristics or preferences, and by using the results of this assessment to inform further teaching. Knowledge Space Theory (Doignon & Falmagne, 1985, 1999; Falmagne, Koppen, Villano, Doignon, & Johannesen, 1990) provides a foundation for personalising the learning experience. The theory, in its original formalisation Noun 1. formalisation - the act of making formal (as by stating formal rules governing classes of expressions)

systematisation, systematization, rationalisation, rationalization - systematic organization; the act of organizing something
, is purely behaviouristic Adj. 1. behaviouristic - of or relating to behaviorism; "behavioristic psychology"
behaviorist, behavioristic, behaviourist
. Various approaches have been devised in order to theoretically explain the observed behaviour by considering underlying cognitive constructs (e.g. Falmagne et al., 1990). These approaches focus on items' difficulty components, their underlying demands, and skills or competencies, and processes for performing them.

The following section will give an introduction to the basic concepts of Knowledge Space Theory. Subsequently, an extension of Knowledge Space Theory is suggested as a formal framework that can serve as a basis for implementing personalised learning into a technology-enhanced learning system. This approach incorporates explicit reference See explicit link.  to underlying skills and competencies and integrates learning objects into an originally behaviouristic formal psychological theory with its focus on knowledge assessment. Its discussion covers the derivation derivation, in grammar: see inflection.  of skills and their structure from ontological on·to·log·i·cal  
1. Of or relating to ontology.

2. Of or relating to essence or the nature of being.

 information, and elaborates on the impact of skill assignments on both the assessment problems and the learning objects. It is shown that these assignments induce structures, which allow for designing efficient procedures for adaptive assessment of the learner's competencies, and for generating personalised learning paths.


Knowledge Space Theory provides a set-theoretic framework for representing the knowledge of a learner in a certain domain, which is characterised by a set of assessment problems (subsequently denoted by Q). In this framework the knowledge state of an individual is identified with the set of problems the person is capable of solving. Due to mutual (psychological) dependencies between the problems not all potential knowledge states (i.e., subsets of problems) will actually be observed. If a correct solution to a certain problem can be inferred given another problem is mastered, then each knowledge state will contain the first problem whenever it contains the second one (i.e. the first problem may be considered a prerequisite pre·req·ui·site  
Required or necessary as a prior condition: Competence is prerequisite to promotion.

 to the second). To capture the relationships between the problems of a domain the notion of a surmise relation was introduced. Two problems a and b are in a surmise relation whenever from a correct solution to problem b the mastery of problem a can be surmised. A surmise relation can be illustrated by a so-called Hasse diagram In the mathematical discipline known as order theory, a Hasse diagram (pronounced HAHS uh, named after Helmut Hasse (1898–1979)) is a simple picture of a finite partially ordered set, forming a drawing of the transitive reduction of the partial order.  (see Figure 1 for an example), where descending descending /des·cend·ing/ (de-send´ing) extending inferiorly.  sequences of line segments indicate a surmise relation. According to according to
1. As stated or indicated by; on the authority of: according to historians.

2. In keeping with: according to instructions.

 the surmise relation shown in Figure 1, from a correct solution to problem b the correct answer to problem a can be surmised, while the mastery of problem e implies correct answers to problems a, b, and c. A surmise relation restricts the number of possible knowledge states and forms a quasi-order on the set of assessment problems.

The collection of possible knowledge states of a given domain Q is called a knowledge structure, whenever it contains the empty set [empty set] and the whole set Q. The knowledge structure K induced by the surmise relation depicted de·pict  
tr.v. de·pict·ed, de·pict·ing, de·picts
1. To represent in a picture or sculpture.

2. To represent in words; describe. See Synonyms at represent.
 in Figure 1 is given by

K = {[empty set], {a}, {c}, {a, c}, {a, b}, {a, b, c}, {a, b, d}, {a, b, c, e}, {a, b, c, d}, Q}.


The possible knowledge states are naturally ordered by set-inclusion, which results in the diagram shown in Figure 2.


Figure 2 illustrates that there are various possible learning paths for moving from the naive knowledge state (empty set [empty set]) to the knowledge state of full mastery (set Q). One of the possible learning paths is indicated by arrows describing the possible steps of a learning process. It suggests to initially present material related to problem a (or, equivalently, c), followed by material related to problems b or c (a, respectively), and so on. Notice that the knowledge structure of Figure 2 is somehow special, as it allows for gradual learning. On the one hand, each knowledge state (except state Q) has at least one immediate successor state A successor state is a state that takes over some or all of the territory, assets, treaty obligations and rights from a previously well-established state (the predecessor state).  that comprises all the same problems plus exactly one. On the other hand, each knowledge state (except state [empty set]) has at least one predecessor state A predecessor state is an established state in international law that is succeeded by a new state or states. Example
  • China
 that contains exactly the same problems, except one. A knowledge structure with these properties, in which learning can take place step by step, is called well-graded. According to Figure 2, for instance, the states {a, b, c, d} and {a, b, c, e} are the immediate successor states In the fictional BattleTech universe, the Successor States (named such due to their being the "Successors" of the Star League) are the major military powers of the Inner Sphere, each governed by one of the Great Houses. Each Successor State has its own culture and customs.  to the knowledge state {a, b, c}. The set {d, e} constitutes the so-called outer fringe Fringe (optics)

One of the light or dark bands produced by interference or diffraction of light. Distances between fringes are usually very small, because of the short wavelength of light.
 of the knowledge state {a, b, c}. It consists of exactly those problems that a learner having knowledge state {a, b, c} should tackle next, and can thus form a basis for generating personalised learning paths. The knowledge state {a, b, c} has also two predecessor states, which are {a, b} and {a, c}. The set {b, c} represents the so-called inner fringe of the knowledge state {a, b, c}. Its problems may be seen as corresponding to the most sophisticated content that has been learned recently. This is the content that the learner should revisit re·vis·it  
tr.v. re·vis·it·ed, re·vis·it·ing, re·vis·its
To visit again.

A second or repeated visit.

, when previously learned material is to be reviewed.

Besides providing the information relevant for generating personalised learning paths, a knowledge structure is at the core of an efficient adaptive procedure for knowledge assessment. It allows for uniquely determining the knowledge state by presenting the learner with only a subset A group of commands or functions that do not include all the capabilities of the original specification. Software or hardware components designed for the subset will also work with the original.  of the problems (for more details see "Problem-Based Skill Assessment").


Although there is a commercial learning system that is based on Knowledge Space Theory, which is the ALEKS system (, this approach suffers from its limitation to a purely behaviouristic perspective. In its original formalisation, Knowledge Space Theory focuses completely on the observable ob·serv·a·ble  
1. Possible to observe: observable phenomena; an observable change in demeanor. See Synonyms at noticeable.

 solution behaviour, and does not refer to both learning objects and skills or competencies that are to be taught. To overcome these limitations Knowledge Space Theory may be extended so that it incorporates explicit reference to learning objects and underlying skills and competencies. The subsequent considerations are based on previous work by Falmagne et al. (1990), Doignon (1994), Duntsch and Gediga (1995), Korossy (1997, 1999), Albert and Held (1994, 1999), Hockemeyer (2003), and Hockemeyer, Conlan, Wade, and Albert (2003). It not only integrates these different contributions, but also derives their implications for implementing a personalised learning system, and clarifies the role of domain ontologies.

Extended Knowledge Space Theory is dealing with three different sorts of entities, which are:

1. the set Q of assessment problems,

2. the set L of learning objects (LOs),

3. the set S of skills relevant for solving the problems, and taught by the LOs.

Notice that the skills in the set S are meant to provide a fine-grained, low-level description of the learner's capabilities. Usually, it is a whole bunch of skills that is tested by an assessment problem, or taught by a LO.

Each of these basic sets is assumed to be endowed en·dow  
tr.v. en·dowed, en·dow·ing, en·dows
1. To provide with property, income, or a source of income.

 with a structure, which we conceive conceive /con·ceive/ (kon-sev´)
1. to become pregnant.

2. take in, grasp, or form in the mind.

1. To become pregnant.

 as a collection of subsets of the respective set. In particular, we consider

* a knowledge structure on the set Q of assessment problems,

* a learning structure on the set L of LOs,

* a competence structure on the set of skills S.

As outlined, the knowledge structure constitutes the collection of possible knowledge states and forms the basis of the problem-based assessment of a student's competency COMPETENCY, evidence. The legal fitness or ability of a witness to be heard on the trial of a cause. This term is also applied to written or other evidence which may be legally given on such trial, as, depositions, letters, account-books, and the like.
 (see "Problem-based Skill Assessment"). Usage of the notion "competency" in the present context is in line with the terminology of Doignon and Falmagne (1999), which refers to subsets of skills that are collected in the competence structure, and which may also be called competence states. A competence structure may either be explicitly established by identifying prerequisite relationships between skills (see "Deriving Skills and their Structure from Domain Ontologies") that restrict the set of possible competence states, or it may be indirectly induced by assigning skills to assessment problems (or LOs) (see "Assigning Skills to Assessment Problems" and "Assigning Skills to Learning Objects"). The learning structure together with a student's current competence state is used to generate a personalised learning path. Learning and competence structures are defined in complete analogy analogy, in biology, the similarities in function, but differences in evolutionary origin, of body structures in different organisms. For example, the wing of a bird is analogous to the wing of an insect, since both are used for flight.  to the knowledge structure previously introduced. Now, the main goal is to identify the pieces of information that are needed for establishing those structures.


Deriving Skills and their Structure from Domain Ontologies

This section addresses the question of how to identify skills that are relevant and suitable for modelling the underlying constructs of assessment problems and learning object regarding a certain domain. As an alternative to cognitive task analysis (Korossy, 1999), querying experts (Zaluski, 2001), and systematic problem construction by applying the component-attribute approach (Albert & Held, 1994), we propose to utilise information coming from domain ontologies.

An ontology ontology: see metaphysics.

Theory of being as such. It was originally called “first philosophy” by Aristotle. In the 18th century Christian Wolff contrasted ontology, or general metaphysics, with special metaphysical theories
 allows structuring a domain of knowledge with respect to its conceptual organization. It constitutes a specification of the concepts in a domain and the relations among them and thus, defines a common vocabulary of the knowledge domain. A common and natural way of representing ontologies is by concept maps. The ontological information provided by a concept map can be used for identifying skills and for establishing a competence structure, respectively. In the sequel we outline two approaches, which differ with respect to the level of granularity The degree of modularity of a system. More granularity implies more flexibility in customizing a system, because there are more, smaller increments (granules) from which to choose.  of the underlying concept map.

Identifying skills with substructures of a concept map. Skills in terms of competence-based Knowledge Space Theory may be identified with substructures of a concept map representing the ontological information of the respective domain. This actually assumes a quite fine-grained representation, as it is necessary for a detailed characterisation of learning content, for example. A specific skill that is required for solving problems, or that is taught by learning objects, can be identified with a subset of the propositions represented by the concept map. Consider, for instance, the knowledge domain of right triangles. Figure 3 illustrates a possible assessment problem from this domain.

Solving this geometry geometry [Gr.,=earth measuring], branch of mathematics concerned with the properties of and relationships between points, lines, planes, and figures and with generalizations of these concepts.  problem requires to know the Pythagorean Theorem Pythagorean theorem

Rule relating the lengths of the sides of a right triangle. It says that the sum of the squares of the lengths of the legs is equal to the square of the length of the hypotenuse (the side opposite the right angle).
 and how to apply it. Knowing the Pythagorean Theorem may be assumed to constitute a skill, which corresponds to a substructure substructure /sub·struc·ture/ (-struk-chur) the underlying or supporting portion of an organ or appliance; that portion of an implant denture embedded in the tissues of the jaw.

 of a concept map. Figure 4 provides an exemplary concept map that highlights the substructure representing this skill. Note that not all the relevant skills can be constructed in this way. The ability of applying the Pythagorean Theorem, for example, may be regarded as a related, but separate skill, which has to be added to the set of considered skills.

The representation of skills in the concept map may also be used for deriving dependencies between skills, e.g. by set inclusion. If the representation of a skill x in the concept map is a subset of that of a skill y, then skill x constitutes a prerequisite to skill y.

Using the component-attribute approach. Concept maps provide a tool for modelling the content of a knowledge domain, which is an essential part of curriculum and content analysis. Within this context the construction of concept maps aims at uncovering the prerequisite relations among the basic concepts within a topic, and between different topics of a subject. Such a concept map most probably will contain concepts on a higher level of abstraction The level of complexity by which a system is viewed. The higher the level, the less detail. The lower the level, the more detail. The highest level of abstraction is the single system itself. , for example, Theorem theorem, in mathematics and logic, statement in words or symbols that can be established by means of deductive logic; it differs from an axiom in that a proof is required for its acceptance.  of Pythagoras. This is in contrast to the more fine-grained concept map presented before, which also captures the definition or content of these general concepts.



Curriculum and content analysis not only reveal the basic concepts of a domain, but also the learning objectives that are related to these concepts. Learning objectives include required activities of the learner and may be captured by so-called action verbs. Action verbs (e.g., state, or apply a theorem) describe the observable student performance or behaviour and may be annotated to the nodes of the concept map representing the concepts that are to be taught. The information provided by the concept map then again can be used for establishing a competence structure in the sense of Knowledge Space Theory.

The concept map provides a hierarchical structure See hierarchical.  on the concepts of a domain. For instance, according to the curriculum the Pythagorean Theorem constitutes a prerequisite to the Altitude altitude, vertical distance of an object above some datum plane, such as mean sea level or a reference point on the earth's surface. It is usually measured by the reduction in atmospheric pressure with height, as shown on a barometer or altimeter.  Theorem. This induces an order on the set of concepts C. The relation between the concepts may be represented graphically as in Figure 5(a). Additionally, a relation may be introduced on the set of action verbs A that induces a structure on it. For instance, to "state" a particular theorem (Math.) a theorem which extends only to a particular quantity.

See also: Theorem
 is most likely a prerequisite to "apply" the respective theorem, and therefore, the action verb verb, part of speech typically used to indicate an action. English verbs are inflected for person, number, tense and partially for mood; compound verbs formed with auxiliaries (e.g., be, can, have, do, will) provide a distinction of voice.  "state" can be considered as a prerequisite to the action verb "apply." The structure defined on the action verbs can also be illustrated by a graph (see Figure 5(b) for an example).

Based on these considerations, a skill in terms of extended Knowledge Space Theory may be identified with a pair consisting of a concept and an action verb (e.g. [c.sub.1][a.sub.2]). As an example for a skill consider "apply the Pythagorean Theorem," which consists of the concept "Pythagorean Theorem" and the action verb "apply." Formally we define the set of skills by S [??] C x A to reflect the fact that not all combinations of concepts and action verbs may be meaningful, or even realisable. A crucial question is how to merge the two kinds of structures, that is, the structure on the set of concepts and the structure on the set of action verbs, to establish a structure on the set of skills.

To resolve this issue we suggest the component-attribute approach (Albert & Held, 1994, 1999). According to this approach components are understood as dimensions, while attributes are the different values these dimensions can take on. In the present context, the set C of concepts and the set A of action verbs are considered as the components, and the attributes are identified with the respective elements (e.g. [c.sub.1], [c.sub.2], [c.sub.3], [c.sub.4] in C and [a.sub.1], [a.sub.2] in A). On each component a relation is defined that orders the attributes (see Figure 5). A structure on the set of skills is then established by forming the direct product of these two components, which results in a prerequisite relation on the Cartesian product (mathematics) Cartesian product - (After Renee Descartes, French philosper and mathematician) The Cartesian product of two sets A and B is the set

A x B = a, b) | a in A, b in .

I.e. the product set contains all possible combinations of one element from each set.
 C x A. The product of the two graphs displayed in Figure 5 is the relation depicted in Figure 6. From this you can see, e.g. that skill [c.sub.2][a.sub.2] is a prerequisite to the skills [c.sub.2][a.sub.1], [c.sub.1][a.sub.1], and [c.sub.1][a.sub.2], but to none of the other skills.

If S is a proper subset of the Cartesian product C x A then we consider the prerequisite relation that the direct product shown in Figure 6 induces on S. In the framework of extended Knowledge Space Theory the prerequisite relation on the skills is interpreted as a surmise relation that gives rise to the competence structure. The competence states contained in it have to respect the ordering illustrated in Figure 6, which means, for example, that with the skill [c.sub.3][a.sub.1] each competence state has to contain the skills [c.sub.3][a.sub.2], [c.sub.4][a.sub.1], and [c.sub.4][a.sub.2], too.



Note, that from a psychological point of view, pairs consisting of a concept and an action verb, like "state Pythagorean Theorem" or "apply Pythagorean Theorem," describe rather global skills. Applying the Pythagorean Theorem might require several more elementary skills, which are in correspondence with the distinct steps in a solution path (e.g., extracting a root, transforming). It may thus be necessary to characterise Verb 1. characterise - be characteristic of; "What characterizes a Venetian painting?"

differentiate, distinguish, mark - be a distinctive feature, attribute, or trait; sometimes in a very positive sense; "His modesty distinguishes him from his
 the skills at a more fine-grained level. Further research is needed to decide upon an optimal level of granularity of the skills.

Assigning Skills to Assessment Problems

Let us now consider the assignment of skills to the set of assessment problems. The relationship between assessment problems and skills can be formalised Adj. 1. formalised - concerned with or characterized by rigorous adherence to recognized forms (especially in religion or art); "highly formalized plays like `Waiting for Godot'"
formalistic, formalized
 by two mappings.

* The mapping s (skill function) associates to each problem a collection of subsets of skills. Each of these subsets (i.e., each competency) consists of those skills that are sufficient for solving the problem. Assigning more than one competency to a problem takes care of the fact that there may be more than one way to solve it.

* The mapping p (problem function) associates to each subset of skills the set of problems that can be solved in it. It defines a knowledge structure because the associated subsets actually are nothing else but the possible knowledge states.

It has been shown that both notions are equivalent (Duntsch & Gediga, 1995), which means that, given the skill function, the problem function is uniquely determined, and vice versa VICE VERSA. On the contrary; on opposite sides. . Consequently, only one of the two functions needs to be known to build the respective knowledge structure. Consideration is confined con·fine  
v. con·fined, con·fin·ing, con·fines
1. To keep within bounds; restrict: Please confine your remarks to the issues at hand. See Synonyms at limit.
 to the skill function, because it may be interpreted as representing the assignment of metadata (1) (meta-data) Data that describes other data. The term may refer to detailed compilations such as data dictionaries and repositories that provide a substantial amount of information about each data element.  to the problems. It follows that assigning (semantic See semantics. See also Symantec. ) metadata to assessment problems puts constraints CONSTRAINTS - A language for solving constraints using value inference.

["CONSTRAINTS: A Language for Expressing Almost-Hierarchical Descriptions", G.J. Sussman et al, Artif Intell 14(1):1-39 (Aug 1980)].
 on the possible knowledge states that can occur.

We illustrate the intimate relationship An intimate relationship is a particularly close interpersonal relationship. It is a relationship in which the participants know or trust one another very well or are confidants of one another, or a relationship in which there is physical or emotional intimacy.  between skill function and problem function by a simple example. Consider the knowledge domain Q = {a, b, c, d}, and let the skill function s on the set S = {x, y, z} of skills be given by

s(a) = {{x, y},{x, z}}, s(b) = {{x, z}}, s(c) = {{x},{y}}, s(d) = {{y, z}}.

This means, for example, that each of the skill sets {x, y} and {x, z} is sufficient for solving problem a. From the skill function we can derive the corresponding problem function, which yields

p([empty set]) = [empty set], p({x}) = {c}, p({y}) = {c}, p({z}) = [empty set],

p({x,y}) = {a, c}, p({x, z}) = {a, b, c}, p({y, z}) = {c, d}, p(S) = Q.

The assignment of skills to the assessment problems induces a knowledge structure on the set of problems, which is actually given by the subsets of problems in the range of the problem function. The knowledge structure for these examples is given by {[empty set], {c}, {a, c}, {c, d}, {a, b, c}, Q}. Whenever a competence structure is available, e.g. as a result of exploiting ontological information (see "Deriving Skills and their Structure from Domain Ontologies"), the domain of the problem function is restricted to the actually occurring competence states. This puts additional constraints on the set of possible knowledge states.

In principle, the skill function for a given set Q of assessment problems may introduce dependencies between skills, too. It may be the case that a certain skill is required for solving a problem only in connection with another skill. In the above example the skill z is available only if either x or y is available. These dependencies, however, may only crop up in the given set Q, and it remains unclear whether they are valid in general. If capitalising on incidental Contingent upon or pertaining to something that is more important; that which is necessary, appertaining to, or depending upon another known as the principal.

Under Workers' Compensation statutes, a risk is deemed incidental to employment when it is related to whatever a
 dependencies between problems is to be avoided then the constraints the skill function puts on the possible subsets of skills should be neglected.

Problem-Based Skill Assessment

A knowledge structure can form the basis for devising an efficient adaptive procedure for knowledge assessment (Doignon & Falmagne, 1999; Dowling & Hockemeyer, 2001). Problem-based skill assessment proceeds in two steps. First, the knowledge state of a learner, which refers to the observable behaviour, is adaptively assessed. After identifying a learner's knowledge state, the knowledge state can be mapped to the corresponding competence state in a second step.

Considering the knowledge structure given in Figure 2 for the knowledge domain Q = {a, b, c, d, e}, in the beginning of an assessment phase all states of the structure may correspond to the knowledge state of an individual learner. According to a deterministic 1. (probability) deterministic - Describes a system whose time evolution can be predicted exactly.

Contrast probabilistic.
2. (algorithm) deterministic - Describes an algorithm in which the correct next step depends only on the current state.
 procedure, the assessment starts by selecting a problem that is contained approximately in half of the states of this structure and by posing this problem to the learner. Dependent on the learner's answer, the next problem will be selected. If the learner is capable of solving problem b, for example, then only the knowledge states containing problem b are still feasible. If subsequently problem e is solved, states {a, b, c, e} and {a, b, c, d, e} remain. The learner's knowledge state is uniquely identified after presenting problem d. For instance, state {a, b, c, e} results if problem d cannot be solved by this learner. Thus, for a set of five assessment problems, the presentation of only three problems allows for identifying the knowledge state of a learner. Formally, the number of questions for determining the knowledge state of a learner is approximately the dual logarithm logarithm (lŏg`ərĭthəm) [Gr.,=relation number], number associated with a positive number, being the power to which a third number, called the base, must be raised in order to obtain the given positive number.  of the total number of knowledge states.

Aside from the outlined deterministic assessment procedure, assessment may also be embedded Inserted into. See embedded system.  into a probabilistic (probability) probabilistic - Relating to, or governed by, probability. The behaviour of a probabilistic system cannot be predicted exactly but the probability of certain behaviours is known. Such systems may be simulated using pseudorandom numbers.  framework. A probabilistic assessment method allows for considering that the knowledge states may occur with different frequencies within a population as well as that a subject sometimes may be careless careless adj., adv. 1) negligent. 2) the opposite of careful. A careless act can result in liability for damages to others. (See: negligent, negligence, care)  in answering a problem or may guess the correct answer. Such an assessment method assumes an a priori a priori

In epistemology, knowledge that is independent of all particular experiences, as opposed to a posteriori (or empirical) knowledge, which derives from experience.
 likelihood function (e.g. probability distribution Probability distribution

A function that describes all the values a random variable can take and the probability associated with each. Also called a probability function.

probability distribution 
) on the knowledge states. Initially, this likelihood may depend on the learner's profile, for example, the age, or grade of this learner. Later, this probability distribution is updated consistent with the learner's answers to the posed problems. The questioning continues until there is a pronounced peak in the likelihood function that suggests a unique knowledge state for an individual learner.

The knowledge state identified for a learner then can be mapped to his/her competence state by using the skill function. This means that, given a knowledge state, we are looking for Looking for

In the context of general equities, this describing a buy interest in which a dealer is asked to offer stock, often involving a capital commitment. Antithesis of in touch with.
 the subset of skills that are sufficient for solving the problems contained in the knowledge state. However, there may be more than one such subset. In this case the skills cannot be recovered uniquely given the assessed knowledge state. To provide an example, consider the skill function defined in "Assigning Skills to Assessment Problems." If we assume that the assessment converged to the knowledge state {c} then it is unclear, which skills the learner is endowed with. According to the skill function either skill x or skill y may be responsible for solving problem c. This nonuniqueness occurs whenever a problem function is not one-to-one. Using additional information may lead to a unique identification of the available skills (e.g. looking up the learning history, checking for the skills actually taught). The best strategy, however, would be to select a proper set of assessment problems that avoids the nonuniqueness. Once the competence state of a learner has been determined it may serve as a basis for selecting a personalised learning path.

Assigning Skills to Learning Objects

The relationship between learning objects and skills is different from that between assessment problems and skills. The relationship between the set L of LOs and the skills in S is mediated me·di·ate  
v. me·di·at·ed, me·di·at·ing, me·di·ates
1. To resolve or settle (differences) by working with all the conflicting parties:
 by two mappings (Hockemeyer, 2003; Hockemeyer et al. 2003). The mapping r associates to each LO a subset of skills (required skills), which characterise the prerequisites for dealing with it, or understanding it. The mapping t associates to each LO a subset of skills (taught skills), which refer to the content actually taught by the LO. In a similar way as previously outlined, the mappings r and t induce a learning structure on the set of LOs, which plays a central role for generating personalised learning paths. The pair of mappings r and t also imposes constraints on the competence states that can occur. Again, these constraints are tied to the given set L of LOs. The imposed competence structure characterises the learning progress that may be achieved by studying the learning objects in L.

Generally, the assignment of skills to learning objects allows for deciding upon which learning objects are to be presented next, given a certain competence state. The concepts of inner and outer fringes (see "Basic Notions of Knowledge Space Theory") of a competence state may provide the basis for implementing personalised learning. The inner fringe of a competence state may be interpreted as "what a learner can do," while the outer fringe represents "what this learner is ready to learn." Therefore, proceeding in the learning process the next skills to be learned should be chosen from the outer fringe of the current competence state. Thus, a suitable learning object has to be selected that is characterized char·ac·ter·ize  
tr.v. character·ized, character·iz·ing, character·iz·es
1. To describe the qualities or peculiarities of: characterized the warden as ruthless.

 by required skills that the learner has already available and by taught skills that correspond to the outer fringe of the current competence state. If previously learned material has to be reviewed, then the content corresponding to the inner fringe of a learner's actual competence state seems to be a natural choice, because it contains the most sophisticated skills acquired by the learner.


The present article proposes a competence-based extension of Knowledge Space Theory that provides a formal framework for explicitly linking assessment problems and learning objects to the relevant skills and competencies. It is demonstrated that the assignment of skills to assessment problems (which are sufficient for their solution) induces a knowledge structure characterising the possible answer patterns of the learners. Moreover, it is shown that assigning required and taught skills to learning objects allows for generating personalised learning paths. Introducing skills provides a general framework for relating models of the domain, the learners, and the learning objects, as described by Bouzeghoub, Defude, Duitama, and Lecocq (2006, this issue). These authors also refer to information about what is required and what is provided by a LO, which is perfectly in line with the assignment of required and taught skills to LOs as discussed in "Assigning Skills to Learning Objects." The proposed skill assignments also contribute to the reusability The ability to use all or the greater part of the same programming code or system design in another application.

reusability - reuse
 of LOs (see Strijker & Collis, 2006, this issue).

The article provides a detailed discussion of how to derive relevant skills and their structure from domain ontologies. Two possible approaches are outlined. On the one hand, skills are identified with substructures of a concept map. On the other hand, skills are identified with pairs of concepts and action verbs, and a skill structure is established by merging the structures given on both sets. Assigning these skills to assessment problems and LOs, as suggested by the competence-based extension of Knowledge Space Theory, yields a framework for an efficient adaptive assessment of the skills and competencies of a learner, and for selecting personalised learning paths. This framework constitutes a valuable model for implementing personalised learning within an open technology-enhanced learning system. The implementation of the outlined theoretical framework within the iClass project is discussed by Turker, Gorgun, and Conlan (2006, this issue), while Brady, O'Keeffe, Conlan, and Wade (2006, this issue) focus on the personalisation of the presented learning material via skill- or concept-based services offered by the Selector (programming) selector - 1. In Smalltalk or Objective C, the syntax of a message which selects a particular method in the target object.

2. An operation that returns the state of an object but does not alter that state.
 and the LO Generator module of the iClass system. A discussion of how to handle and integrate multiple skill assignments that characterize (partially overlapping) learning material coming from distributed resources is contained in Heller, Mayer, Hockemeyer, and Albert (2005).


Albert, D., & Held, T. (1994). Establishing knowledge spaces by systematical problem construction. In D. Albert (Ed.), Knowledge structures (pp. 78-112). New York New York, state, United States
New York, Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of
: Springer springer

a North American term commonly used to describe heifers close to term with their first calf.

Albert, D., & Held, T. (1999). Component based knowledge spaces in problem solving problem solving

Process involved in finding a solution to a problem. Many animals routinely solve problems of locomotion, food finding, and shelter through trial and error.
 and inductive reasoning Inductive reasoning

The attempt to use information about a specific situation to draw a conclusion.
. In D. Albert & J. Lukas (Eds.), Knowledge spaces: Theories, empirical research Noun 1. empirical research - an empirical search for knowledge
inquiry, research, enquiry - a search for knowledge; "their pottery deserves more research than it has received"
 applications (pp. 15-40). Mahwah, NJ: Lawrence Erlbaum.

Bouzeghoub, A., Defude, B., Duitama, J.F., & Lecocq, C. (2006). A knowledge-based approach to describe and adapt learning objects. International Journal on E-Learning, 5(1).

Doignon, J. (1994). Knowledge spaces and skill assignments. In G. H. Fischer & D. Laming (Eds.), Contributions to mathematical psychology Mathematical Psychology is an approach to psychological research that is based on mathematical modeling of perceptual, cognitive and motor processes, and on the establishment of law-like rules that relate quantifiable stimulus characteristics with quantifiable behavior. , psychometrics psychometrics

Science of psychological measurement. Psychometricians design and administer psychological tests (see psychological testing), both to generate empirical data on mental processes and to refine their understanding of measurement techniques and the
 and methodology (pp. 111-121). New York: Springer Verlag.

Doignon, J.P., & Falmagne, J.C. (1985). Spaces for the assessment of knowledge. International Journal of Man-Machine Studies, 23, 175-196.

Doignon, J.P. & Falmagne, J.C. (1999). Knowledge spaces. Berlin: Springer.

Dowling, C. E., & Hockemeyer, C. (2001). Automata automata - automaton  for the assessment of knowledge. IEEE (Institute of Electrical and Electronics Engineers, New York, A membership organization that includes engineers, scientists and students in electronics and allied fields.  Transactions on Knowledge and Data Engineering, 13, 451-461.

Duntsch, I., & Gediga, G. (1995). Skills and knowledge structures. British Journal of Mathematical and Statistical Psychology, 48, 9-27.

Falmange, J.C., Koppen, M., Villano, M., Doignon, J.P. & Johannesen, L. (1990). Introduction to knowledge spaces: How to build, test, and search them. Psychological Review, 97, 201-224.

Heller, J., Mayer, B., Hockemeyer, C., & Albert, D. (2005). Competence-based knowledge structures for personalised learning: Distributed resources and virtual experiments. In G. Albano, P. Ritrovato, & S. Salerno (Eds.), Proceedings of the 1st International EleGl Conference on Advanced Technology for Enhanced Learning. Swindon Wiltshire, UK: The British Computer Society Established in 1957, The British Computer Society (BCS) is a body that represents those working in Information and Communications Technology ICT. It is the largest United Kingdom-based professional body for computing.  (BCS (1) (The British Computer Society, Swindon, Wiltshire, England, The chartered body for information technology professionals in the U.K., founded in 1957. ).

Hockemeyer, C. (2003). Competence based adaptive e-learning in dynamic domains. In F. W. Hesse & Y. Tamura (Ed.), The Joint Workshop of Cognition cognition

Act or process of knowing. Cognition includes every mental process that may be described as an experience of knowing (including perceiving, recognizing, conceiving, and reasoning), as distinguished from an experience of feeling or of willing.
 and Learning through Media-Communication for Advanced E-Learning (JWCL), 2003, 79-82, Berlin.

Hockemeyer, C., Conlan, O., Wade, V., & Albert, D. (2003). Applying competence prerequisite structures for eLearning and skill management. Journal of Universal Computer Science, 9, 1428-1436.

Korossy, K. (1997). Extending the theory of knowledge spaces: A competence-performance approach. Zeitschrift fur Psychologie, 205, 53-82.

Korossy, K. (1999). Modeling knowledge as competence and performance. In D. Albert & J. Lukas (Eds.), Knowledge spaces: Theories, empirical research, applications (pp. 103-132). Mahwah, NJ: Lawrence Erlbaum.

O'Keeffe, I., Brady, A., Conlan, O., & Wade, V. (2006). Just-in-time generation of pedagogically ped·a·gog·ic   also ped·a·gog·i·cal
1. Of, relating to, or characteristic of pedagogy.

2. Characterized by pedantic formality: a haughty, pedagogic manner.
 sound, context sensitive personalized per·son·al·ize  
tr.v. per·son·al·ized, per·son·al·iz·ing, per·son·al·iz·es
1. To take (a general remark or characterization) in a personal manner.

2. To attribute human or personal qualities to; personify.
 learning experiences. International Journal on E-Learning, 5(1).

Strijker, A., & Collis, B. (2006). Strategies for reuse reuse - Using code developed for one application program in another application. Traditionally achieved using program libraries. Object-oriented programming offers reusability of code via its techniques of inheritance and genericity.  of learning objects: Context dimensions. International Journal on E-Learning, 5(1).

Turker, A., Gorgun, I., & Conlan, O. (2006). The challenge of content creation to facilitate personalized eLearning experiences. International Journal on E-Learning, 5(1).

Zaluski, A. (2001). Knowledge spaces mathematica package. In Proceedings of PrimMath 2001--Mathematica in Science, Technology and Education. Zagreb, Croatia, University of Zagreb.


The work presented in this paper is partially supported by the European Community European Community: see European Union.
European Community (EC)

Organization formed in 1967 with the merger of the European Economic Community, European Coal and Steel Community, and European Atomic Energy Community.
 under the Information Society Technologies (IST) program of the 6th FP for RTD--project iClass contract IST-507922. The authors are solely responsible for the content of this paper. It does not represent the opinion of the European Community, and the European Community is not responsible for any use that might be made of data appearing therein.


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Author:Albert, Dietrich
Publication:International Journal on E-Learning
Geographic Code:1USA
Date:Jan 1, 2006
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