A Novel Hybrid Student Model for Personalized Education - Advances in Personalized Web-Based Education - page 77

Table 3.2 Values of the KL stereotype category
KL stereotype Knowledge type

McGill and Volet (1997)

Knowledge level
de Raadt (2007)

Stereotype 1 : novice No knowledge Level 1

Declarative—syntactic Pre-structural

Stereotype 2 : the learner knows the basics
of the programming language C and the
sequence structure of programming

Declarative—conceptual One-structural

Stereotype 3 : the learner knows basics of
the programming language C, the sequence
structure and the structures of choice

Stereotype 4 : the learner knows basics of
the programming language C, the sequence
structure, the structures of choice and the
iteration structure with concrete number
of loops

Procedural—syntactic One-structural

Procedural—syntactic Multi-structural

Stereotype 5 : the learner knows basics of
the programming language C, the sequence
structure, the structures of choice and the
iteration structures with concrete or unknown
number of loops

Procedural—conceptual Multi-structural

Stereotype 6 : the learner knows basics of
the programming language C, the sequence
structure, the structures of choice, the iteration
structures and one-dimensional arrays

Stereotype 7 : the learner/he knows basics of
the programming language C, the sequence
structure, the structures of choice, the itera-
tion structures and all the type of arrays

Procedural—conceptual Relational

Strategic Relational

Stereotype 8 : experts

in case of misconceptions of the program and of the semantics and operation of
the commands. They, usually, indicate that the learner has a difficulty in under-
standing the instructions and their logic.

3.5.3.3 PrK Stereotype Category

Furthermore, the system should known if a learner has prior knowledge of another
programming language in order to be able to distinguish if an error occurs due to
non-learning or due to affecting by another language. This kind of information is
derived by the 3rd-dimension of the stereotype model (PrK). Therefore, the PrK
stereotype category is associated with other programming languages that the
learner may already know. In particular, PrK takes the following values: 'none',
'Basic', 'Pascal', 'Java'. If a learner does not know a programming language, PrK
takes the value “none”. If a learner knows more than one programming language of
the above, PrK takes two or all these programming languages. So, it takes either the
value 'none', or one or more values of the set {Basic, Pascal, Java}.