elektronická verzia publikácie - FIIT STU - Slovenská technická ...

234 Selected Studies on Software and Information Systems
The approach could also benefit from the “classical” social network analysis. Papers
scheduled by somebody who is considered as an authority in the network could be considered
as more interesting to see (so they could have yet distinct annotation so the user does not
overlook them).
The ISM framework [61] is a mix of several approaches to cold-start problem solving: it
employs explicit user modeling by letting the user to pass an interview as well as a pre-test.
The knowledge acquired from the user is used to assign her a stereotype. However, this
stereotype is not used directly to initialize the student model. The default assumptions of
each stereotype are refined by taking into account the actual behavior of the other students
belonging to the stereotype. Moreover, the contribution of these students to the initialization
of the new student model is weighted based on their similarity with the new student [61].
The information acquired from both the interview and the preliminary test (see Figure
8-6) is represented as a feature vector, which contains student code, name, stereotype and
a set of N characteristics (student’s attributes). The initial information that has been acquired
directly from the student, as well as information from existing students is then used in order to
produce a second vector that represents the system’s estimations of certain domain dependent
attributes of the new student. The second student model vector contains student code and
a set of M domain-related characteristics. While the first vector contains information such
as mother tongue or level of carefulness, the second vector contains information referring to
domain concepts: the degree of knowledge and error proneness for each concept.
Interview
Preliminary
test
Stereotypes
Knowledge
base
Personal
characteristics
Prior
knowledge
First
student model
Generation
of the first
student model
Students of
the same Knowledge
Level Stereotype
Category
Generation
of the second
student model
using clustering
algorithm
Stereotypes
Knowledge
base
Second
student model
Figure 8-6. ISM Architecture, according to [61].
The second vector is computed using the distance weighted k-Nearest Neighbors (k-NN)
algorithm. The actual number of neighbors is the number of students within a stereotype.
The system presumes, that users belonging to different stereotypes do not have similar
knowledge of the domain.