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in reality, learners are often diverse which can be ex-
plored with learner clustering.
Intelligent tutoring systems are digital tools that
can be used as a (partial) alternative to one-to-one hu-
man tutoring and can be used for computer science
education. For example, intelligent tutoring systems
give feedback on the syntax or semantics of code writ-
ten in special environments (Crow et al., 2018). In
general, intelligent tutoring systems have two impor-
tant roles for which student modelling is used: a diag-
nostic role and a strategic role (Chrysafiadi and Vir-
vou, 2013). The diagnostic role means that the sys-
tem understands the knowledge of the student. The
strategic role means that the system can plan a re-
sponse to that knowledge state. In their review of
intelligent tutoring systems for programming educa-
tion, Crow et al. (2018) found that such systems often
lack reference materials for the students and mainly
focus on programming tasks and the required instruc-
tions. Reference material is often closely related to
the domain model, which is an important component
of a student model. User interactions with reference
material can normally be used as input for the stu-
dent model. Although intelligent tutoring systems for
programming often lack reference material, it is still
possible to use student modelling, for example for
generating hints and feedback. However, the underly-
ing knowledge domain model might not be visible to
the student because it cannot be linked to explaining
resources and therefore it might be more difficult to
understand the hints and feedback. When reference
material is available, students can seek clarification
based on the feedback of the student model (Crow
et al., 2018).
3 PLATFORM DESIGN
This section describes the design and the rationale
behind the design of the learning platform. Impor-
tant prerequisites for the newly designed platform are
allowing independent learning and tracing the stu-
dents’ progress, while supporting the specific work-
flow of Co-Teach Informatica. The emphasis on
student progress tracing, using a student model ap-
proach, makes this platform unique compared to other
existing platforms. In the next paragraph, the ratio-
nale behind this is explained in more detail. In the fol-
lowing paragraphs, the main features to facilitate in-
dependent learning within the Co-Teach Informatica
program are explained per user type, i.e., the learners,
the support desk, and the teachers.
3.1 Student Progress Tracing Using
Student Modelling
To trace the progress of the student, a student model
approach is used. This student model represents the
knowledge state of the student and is based on a do-
main model consisting of all learning goals of the
learning materials. This domain model is organized
on different levels. Learning goals are connected with
other learning goals to represent prior knowledge. For
each mandatory domain of the curriculum, the learn-
ing goals are organized by chapter. This helps to
reduce the complexity of the graph and allows easy
and comprehensible filtering. Connections with prior
knowledge can go beyond the learning goals of the
chapter or even the domain.
All learning activities in the online learning mate-
rials are linked to learning goals. For each learning
goal, a so-called probability score can be calculated
based on the student’s achievement on activities re-
lated to the given learning goal. The probability score
expresses the probability that a learner will answer
the next question linked to the learning goal correctly.
Every time a student completes an activity, the prob-
ability score for the linked learning goals (and their
prior knowledge) changes according to the evaluation
of the activity. The exact mathematics behind this will
evolve during initial testing. Figure 2 shows an exam-
ple of what a learning goal graph for a programming
chapter could look like.
As mentioned in the background section, student
models have two roles in intelligent tutoring systems:
a diagnostic role and a strategic role. In our platform,
the student model also has these two roles.
When our student model is used as a diagnosti-
cian it is mainly a tool for reflection, both for the
learner and the teacher or the local remote support
desk. Aside from being a tool that can be used to
give them insight into their progress, it can also stim-
ulate self-regulated learning. Within the learning ma-
terial, there are different types of activities: exercises
for practising and so-called milestone assignments.
Exercises for practising are not mandatory and are
often automatically corrected or manually assessed
by the learner. Milestone assignments are obligatory
and larger activities, often corrected by teaching as-
sistants. Learners are free to decide whether they
want to follow the learning material linearly and com-
plete (all) the exercises for practising before starting
with the milestone assignment or cherry-picking the-
ory and exercises to prepare for the milestone assign-
ment. The information on the learning goals can help
them to make informed decisions about this.
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