US 2025/0148929 A1 · SYSTEM OF GENERATING MULTI-STYLE LEARNING TUTORIALS BASED ON LEARNING PREFERENCE EVALUATION

Abstract

Provided is a system of generating multi-style learning tutorials based on learning preference evaluation. The system dynamically explores preferences of learners in a learning process from a time dimension through a proposed deep reinforcement learning model, so that a deep reinforcement learning agent can accurately express learning behavior features of learners and realize the personalized and accurate construction of course tutorials. At the same time, using an internal knowledge base and an external knowledge base as resource support, a multi-modal knowledge map with a plurality of definition relationships is designed to overcome the limitations of construction of a course tutorial method.

A system of generating multi-style learning tutorials based on learning preference evaluation, wherein the system comprises a learning preference evaluation module, a tutorial matching module, a match revision module and a teaching evaluation module;

wherein
the learning preference evaluation module is internally provided with a learning style evaluation scale for evaluating learning preferences of students;
the tutorial matching module is internally provided with a matching rule of a learning preference type and a content style for generating learning style tutorial contents corresponding to learning preferences of users;
wherein the matching rule of the learning preference type and the content style is designed as follows:
the learning preference type comprises A <active type A1, reflective type A2>, B <feeling type B1, intuitive type B2>, C <visual type C1, literal type C2>, D <sequential type D1, overall type D2>, the eight types are mutually exclusive, and the final evaluation results comprise only four types out of the eight types;
the content style comprises four styles:
a text style, a picture style, a video style and an audio style;
the weights of four content styles corresponding to the active type A1 are [50%, 50%, 50%, 50%, 50%], respectively;
the weights of four content styles corresponding to the reflective type A2 are [60%, 50%, 50%, 50%], respectively;
the weights of four content styles corresponding to the feeling type B1 are [40%, 60%, 60%, 50%], respectively;
the weights of four content styles corresponding to the intuitive type B2 are [40%, 80%, 80%, 60%], respectively;
the weights of four content styles corresponding to the visual type C1 are [30%, 90%, 90%, 50%], respectively;
the weights of four content styles corresponding to the literal type C2 are [90%, 50%, 30%, 40%], respectively;
the weights of four content styles corresponding to the sequential type D1 are [50%, 50%, 30%, 30%], respectively;
the weights of four content styles corresponding to the overall type D2 are [50%, 50%, 50%, 50%, 50%], respectively;
after obtaining the combination of four learning preference types, the text t_p, the picture p_p, the video v_p and the audio a_p are obtained by adding the weights of four content styles and dividing the total weights by 4, that is:
t_p
=
A
i
(
1
)
+
B
i
(
1
)
+
C
i
(
1
)
+
D
i
(
1
)
4
;
p_p
=
A
i
(
2
)
+
B
i
(
2
)
+
C
i
(
2
)
+
D
i
(
2
)
4
;
v_p
=
A
i
(
3
)
+
B
i
(
3
)
+
C
i
(
3
)
+
D
i
(
3
)
4
;
a_p
=
A
i
(
4
)
+
B
i
(
4
)
+
C
i
(
4
)
+
D
i
(
4
)
4
;
where i=1, 2;
the match revision module is internally provided with a style adaptability star-rating and revision rule for revising the learning style tutorial contents and generating the learning style tutorial contents in the next section;
wherein the revision rule comprises the following steps:
S11, giving a critical definition of the revision rule:
definition of a state:
S=(U, V, T) is used to represent the current dynamic state based on a learning path, where U denotes advice to learners, V denotes the entity that a deep reinforcement learning agent arrives at, and T denotes the entity accessed by the agent in which an access path is recorded;
definition of an action space:
after the reinforcement learning agent is in a specific state, it is necessary to obtain the action space in this state, select an action from this space, start the action, and then transmit the action to the next state;
wherein the action space is defined as follows:
A
s
⁢
t
=
{
(
r
,
V
t
+
1
)
,
(
V
t
,
r
,
V
t
+
1
)
∈
R
}
;
where r denotes the relationship between entities, r∈R, R denotes the number of relationship types, t denotes the current training of t-step reinforcement learning, and Vt+1 denotes the entity that will arrive next time after selecting a certain state;
definition of a reward and penalty model:
a reward mechanism based on user historical interactive data and random reasoning search is designed, a personalized tutorial is constructed based on learning preferences of learners, and the next state is inferred in conjunction with objective learning results;
therefore, for the state St of a subject, a reward function is defined:
F
⁡
(
U
,
T
)
=
s
⁢
〈
U
,
V
〉
+
∑
i
=
1
t
-
1
⁢
s
⁢
〈
V
i
,
V
t
〉
the reward and penalty model is expressed as:
R
⁡
(
s
t
)
=
{
max
⁡
(
0
,
F
⁡
(
U
,
T
)
)
,
if
⁢
β
>
θ
-
γ
,
otherwise
;
where γ is an arbitrary penalty constant, which is usually used to modify a random selection path of the model, and is set to 1 here;
β indicates the satisfaction with learning from learners;
θ is an ideal satisfaction constant with a value of 70;
a cumulative reward is expressed as:
∑
n
=
1
t
⁢
R
=
1
n
⁢
∑
a
,
t
=
0
⁢
(
a
|
s
t
,
A
⁡
(
s
t
)
)
⁢
σ
⁢
R
⁡
(
s
t
+
1
)
;
definition of a transition strategy network:
a transition strategy network based on the current state S is constructed based on the reward result;
the strategy network takes the current state and the complete path space as inputs, outputs the probability of each action, and then selects the following transition paths:
L
[
s
t
+
1
|
s
t
,
s
t
-
1
,
…
,
s
0
,
a
t
,
a
t
-
1
,
…
,
a
0
]
=
L
[
s
t
,
a
t
]
;
hierarchical definition of a deep reinforcement learning model:
a prediction layer is added, and the ReLU function is used as an activation function;
and the final prediction result is output as follows:
yui=α(PiTCu);
where yui∈(0, 1) denotes a learning style tutorial recommended for the next section, a denotes an activation function of converting the input into different contents to form probability, PiT denotes a vector feature obtained from the learning style tutorial during T training, and Cu denotes the learning tutorial of the previous section;
S12, learning initial learning preferences of users according to evaluation results of learning preferences of users, forming an initial personalized tutorial C0, and performing style adaptability star-rating after the course learning is completed;
S13, combining a multi-modal knowledge map and a reinforcement learning model, starting personalized tutorial revision, inputting two matrices by the model:
“learning preferences of learners” and “a course content label”, calculating a weight of a learning style of learning preferences according to the input feature matrix, selecting actions, and constructing course contents;
S14, simulating the interactive data between users and course contents, and generating rewards and states;
S15, iteratively optimizing S12-S15 until the model is capable of automatically constructing a tutorial satisfying the satisfaction of users;
the teaching evaluation module is internally provided with a summary test and a learning effect evaluation method of the learning style tutorial for comparing a teaching effect of a learning style tutorial mode and a teaching effect of a traditional classroom teaching mode.

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System of generating multi-style learning tutorials based on learning preference evaluation

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System of generating multi-style learning tutorials based on learning preference evaluation

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Abstract

Claims6