Adaptive presentation

Following the definition provided in (Brusilovsky & Peylo, 2003), the aim of the adaptive presentation is to adapt the content that is presented by system to the student characteristics stored in the SM. While this definition refers in many systems to the selection and formatting of multimedia content, in this thesis it is focused exclusively in the selection of the objects that are used at last instance for the instruction process, i.e. the LOs.

4.2.1 Incorporation of Learning Objects

As mentioned in a very rough way on section 1.2.11, LOs may be defined as digital resources that are used to support teaching/learning process. To extent this definition, and with the aim of differentiate them from the notion of “simple” resources that were also

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mentioned on last section, it may be said that LOs have a particular instructional purpose (or they should) and are described in great detail using metadata.

This proposal does not specify which standard or specification must be used for LO metadata because that is a designer decision which mainly depends on the detail level and specificity he wants. What this proposal does specify is the minimum information that such metadata must contain and how such information is used in the adaptation process. As mentioned on last chapter, there are two sources from the SM that are considered for adaptation: 1) some of the personal information measured with scalar values from different formats and 2) psycho-cognitive information measured with array values. In both cases there must be at least one attribute in the LO metadata for each characteristic, not necessarily with the same name, but with the same meaning.

In order to clarify this idea, consider an example where age and language data from SMPI are considered for adaptation (Q = 2), then the LO metadata should contain at least two attributes that refers to those two characteristics. Apart of that, in the psycho-cognitive information, it is used a particular learning style model with five dichotomies along with some particular psycho-technical model with three profiles (S = 1, D(1) = 5, D(2) = 3), then the LO metadata should contain also at least two attributes with scaled array values that refers to those two characteristics. For this example, a match between a hypothetic student and a hypothetic LO is illustrated on figure 4.3; the detailed adaptation technique that is used to perform such matching is presented in the next section.

Figure 4.2: Student – LO matching example 1

A very important consideration in this point is that attributes values in the LO metadata that are used for adaptation must be determined carefully whether by the LO author, system administrators or teacher users, hopefully with some pedagogical guidance.

Personal information … Age = 14 Language = English … Psycho-cognitive information Learning style = {0.8, 1.0, 0.3, 0.1, 0.5} Psycho-technical profile = {0.2, 0.7, 0.4} Knowledge information … Metadata …

Age range = teenager Language = en LS = {0.7, 0.9, 0.2, 0.1, 0.4} PT = {0.2, 0.8, 0.3} … Student LO

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4.2.2 Selection of Learning Objects

Once the knowledge and task sequencing process have been performed according to the procedure described in section 4.1, it is necessary to determine which LO (one or and ordered subset) would be presented to student when a specific Activity has been selected and it contains more than one LO. As mentioned on chapter 3, the two sources from the SM that are considered for adaptation are the personal and the psycho-cognitive information that in both cases has a one-to-one relationship between student’s characteristic and LOs attributes as it was stated in previous section. With this in mind, a sequential process is proposed where, for each specific activity, the list of available LOs are filtered according to student personal information and later, from the LOs that pass such filter, the more appropriates are presented to student according to his/her psycho-cognitive information.

In the first part, once such relationships are defined according to designer decisions, simple IF <antecedent> THEN <consequent> adaptation rules may be defined. The antecedent determines a specific category or range for the student characteristic and the consequent the corresponding desired attribute of the LO. Going back to the example presented on section 4.2.1, imagine the next rules for the age characteristic:

IF student age ≤ 12 THEN LO should be for “children”

ELSE IF 12 < student age ≤ 17 THEN LO should be for “teenager” ELSE IF 17 < student age ≤ 22 THEN LO should be for “young” ELSE THEN LO should be for “adult”

Those rules implies that at least one of these four values must be present in the corresponding LO attributes for that example: “children”, “teenager”, “young” and “adult”. Similar rules could be defined for the language characteristic:

IF student language = “spanish” THEN LO language should be “es” ELSE THEN LO language should be “en”

In this simple example it is clear that in this case for each activity at least eight LOs would be necessary to cover all possible student alternatives with regard to the corresponding characteristics. In general, if there are Q characteristics and each one has C(1), C(2), … , C(Q) categories or ranges, the minimum number of LOs that would be required to cover all possible student’s alternatives is:

C(1)×C(2)×…×C(Q) (Equation 4.1)

For example, if S = 3 characteristics are considered for adaptation from personal information of SM and each one has four categories, such number would ascend to 4×4×4 = 64. This is an issue that designer must consider carefully because it affects directly the difficulty of the courses construction. In Duque’s words:

“This is one of the problems that construction of adaptive systems exhibits: the exponential growth of resources when adaptation components increase” (2009, p. 38).

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Once this first filter has been done and a subset of all available LOs for a specific activity has been selected, the second part starts where the psycho-cognitive information criterion is used. In this case, the IF THEN rules approach is not used. A mathematical approach is proposed here instead, taking advantage of the array representation of these student characteristics.

For doing so, it is necessary to remember that there are R psycho-cognitive characteristics represented trough arrays SMCIr for each student. A subset of S1 (S1 ≤ R)

characteristics may be used in this point, each one having D(s) dimensions with scaled values in the range 0 – 1. Figure 4.2 shows examples of the graphical representation of such arrays for D(s) = 1, 2 and 3.

Figure 4.3: Graphical representation of the psycho-cognitive characteristics

For example, if in a particular system the Gardner’ Multiples Intelligences Theory (Gardner, 1999) was considered within student’s psycho-cognitive information, each one of the considered particular LOs in such a system should have an array representation for the corresponding dimensions: Verbal – Linguistic, Logical – Mathematical, Visual – Spatial, Bodily – Kinesthetic, Musical, Interpersonal, Intrapersonal, Naturalistic and Existential. In this hypothetical case, an activity like “Storytelling” could have a general array like {1.0, 0.1, 0.7, 0.1, 0.0, 0.4, 0.4, 0.1, 0.1}, whereas another like “Physics experiment” could have one like {0.3, 1.0, 0.3, 0.7, 0.0, 0.0, 0.0, 0.5, 0.1}. Although this example was presented only for explanatory purposes, readers interested on this particular theory and its applications on content design could consult (Kelly & Tangney, 2006; Visser et al., 2006).

Once the psycho-cognitive characteristics have been defined for the LOs, the comparison with the student may be performed using the typical Euclidian distance. However, due to arrays may have different dimensions numbers, it is necessary to unify the distance measure dividing for the corresponding D(s) square root (maximum distance between two vectors in D(s)-dimensional space inside range 0-1). In more detail, if for a specific activity there are H LOs, the next distance formula for each LO must be calculated with regard to the student’s characteristic s:

√∑ √ (Equation 4.2) 0 1 0 1 0 1 1 1 1

D(s) = 1 dimension D(s) = 2 dimensions D(s) = 3 dimensions

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In the case of S1 > 1, all s distances could be summed for each LO in which case the value of the sum would be inside the range 0 – S1. With the aim of incorporating designer considerations about relative importance of each characteristic, a pondered sum is proposed instead to calculate the total distance:

∑

∑

(Equation 4.3)

This way such final value would be inside range 0 – 1, where in the extreme cases a value of 0 would mean a total compatibility between student characteristics and the corresponding LO, whereas a value of 1 would mean a total dissonance. Once this value has been calculated, the sorting technique is as simple as presenting all m activities in ascending order with regard to that value.

An important issue about this procedure is that, differently to the IF THEN rules for the personal information criteria, it does not exclude any LO; instead it allows giving them a relative importance order. This is very important because it means such procedure does not have the “dimensionality curse” explained with equation 4.1 and then, it may be used considering any number of characteristics whether there are just one, two or thousands of available LOs.

In order to clarify more the whole LO adaptation process presented on this section, consider the hypothetical example presented on figure 4.4 where just one personal information data (SMPI1) and two psycho-cognitive characteristics (SMCI1 and SMCI2) are

used from SM as the adaptation criteria. Consider also that for a specific activity there are three available LOs with the corresponding attributes (assume that order is the same than in the student’s characteristics).

Figure 4.4: Student – LO matching example 2

To select which of the LOs is more appropriate in that moment for that student, the personal information filter must be accomplished first. In this case, assuming that a simple categories rule exists, LO2 would be discarded. From the remaining two LOs the distance

Student SMPI1 = yes SMCI1 = {0.4, 0.7} SMCI2 = {1, 0.3} LO1 Atr1 = “yes” Atr2 = {0, 0.9} Atr3 = {0.6, 0.6} LO2 Atr1 = “no” Atr2 = {0.1, 0.1} Atr3 = {0.6, 0.2} LO3 Atr1 = “yes” Atr2 = {0.5, 0.3} Atr3 = {0.1, 0.4} ? ? ?

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measure described in equation 4.2 must be calculated. Such values along with the vectors that represent student and LOs are presented on figure 4.5.

Figure 4.5: Graphical representation of student and LOs from example

In this case DLO11 ≈ 0.32, DLO12 ≈ 0.35 for LO1; and DLO31 ≈ 0.29, DLO32 ≈ 0.64 for

LO3. Using these values on equation 4.3 and giving the same importance to both characteristics (α1 = α3) the total distance for the two LOs are TDLO1 ≈ 0.33 and TDLO3 ≈

0.47, with which it may be concluded that LO1 would be more appropriate for that student considering the three criteria of this example. This does not mean that finally only LO1 would be presented to the student in this example (the same apply for a general case), because it is an implementation decision whether select only the more appropriate one, or the two more appropriate, or the three, etc., having the opportunity this way of using a particular technique from the Brusilovsky’s taxonomy: sorting.

Once a subset of the available LOs has been selected, it is also an implementation decision defining how to present them in terms of links and, for doing so, a formatting policy should be considered in a similar way that for curriculum links described in section 4.1.2.