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We observe that nowadays many students cannot apply what they have learned, or are learning to existing problems
or contexts, and many students cannot appreciate the applicability of their reading materials. That is to say that the
transfer of learning seems not to be effectively present among students. Through the multi-subject learning
sequencing model, learners can clearly recognize the relationships between several courses and see how to apply
what they are learning to other fields or courses. In addition, by seeing the connections between the subject being
studied and the subjects in which they experienced the most success, they are better informed in choosing other
related subjects to study in the future.
Definition
The application of the Markov chain model in a learning system has the advantage that the likelihood of the grouplearning
patterns is simply a product derived from the collective likelihoods of individual instances. It can provide
learners in need of assistance with several learning sequences that are based on actual learners’ experiences, and in
accordance with group-learning patterns. In a Web-Based learning environment, an LO is first used by individual
learners as a learning course related to a topic or issue. LOs are placed in a learning repository and the learners’
profiles are recorded in a transaction database. Thus, we can retrieve the learning data of each learner, which can be
viewed as a learning sequence. Moreover, we give a definition that an LO, denoted as o i , belongs to the record of a
learner, and each learner produces their own learning sequence 1 ,..., n
< o o > during the learning process for
learning a specific concept. Furthermore, the dynamic learning sequence can be formally defined as:
Definition 1: Dynamic Learning Sequence (DLS)
Let O be a set of LOs from a learning repository. Given a set of LOs 1 2
the learning sequence at time t is denoted as LS t = x1 x2 x3 ... xi
time t + 1,
where 1 ≤i ≤ l .
X = { x, x ,..., x }, x ∈O, 1 ≤i≤ m,
then
m i
→ → → → , and the end is x x i l
Figure 2. The methodology of the proposed learning mechanism
→ at the
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