AU2020101287A4 - Method and device for constructing an educational cyber-physical space fusion learning environment - Google Patents
Method and device for constructing an educational cyber-physical space fusion learning environment Download PDFInfo
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Abstract
The present disclosure provides a method and a device for constructing an
educational cyber-physical space fusion learning environment, in which the method
includes the following steps. A plurality of spaces in the educational cyber-physical
space fusion learning environment and learning environment construction parameters
of each of the spaces are predefined. A semantic network model of each of the spaces
is constructed. Learning subject-related events and learning environment-related
events are collected, and a data fusion model of a cyber-physical space semantic level
consistency for learning subjects is constructed. An integrated embodied model based
on learning scenarios is constructed. The learning environment construction
parameters for the learning subjects are set according to the integrated embodied
model. Therefore, the disclosure can dynamically customize an educational
cyber-physical space fusion learning environment for the learning subjects.
12553799_1 (GHMatters) P113901.AU
1/4
Predefine educational and learning
spatial scenarios
Construct a semantic network model of
educational learning context space
Perform multi-spatial data fusion
with consistent semantic level
Customize a space integrated embodied
model
Customize a multi-spatial learning
environment
FIG1
- Social space Resource space Network space
Inside school
In class Self-study in library Lecture-basedlearning in classroom Afterclass
Training outside school Asynchronous classroom outside school
Outside school
FIG 2
Description
1/4
Predefine educational and learning spatial scenarios
Construct a semantic network model of educational learning context space
Perform multi-spatial data fusion with consistent semantic level
Customize a space integrated embodied model
Customize a multi-spatial learning environment
FIG1
- Social space Resource space Network space
Inside school
In class Self-study in library Lecture-basedlearning in classroom Afterclass
Training outside school Asynchronous classroom outside school Outside school
FIG 2
The present application relates to information technologies, and more
particularly to a method and a device for constructing an educational cyber-physical
space fusion learning environment.
Information technology develops very fast and has been widely applied in
education, which facilitates the digitalization and intelligence of traditional teaching
environment. Currently, the learning can be conducted offline in physical spaces such
as classrooms, or conducted online using interactive whiteboards, portable tablets and
other smart devices. Therefore, in students' learning environments, offline physical
spaces are fused with online spaces, such as digital classrooms, various intelligent
terminal devices and teaching appliances.
However, in the prior art, the offline physical space and the online space are
independent of each other without seamless interaction. An educational
cyber-physical space fusion learning environment cannot be personalized according to
students' behaviors in any one of the spaces. For example, since offline education
resources and online education resources are independent of each other, it is hard to
adjust the online learning resources to adapt the offline learning as required.
In order to overcome the above-mentioned defects in the prior art, the present
disclosure provides a method and a system for constructing an educational
cyber-physical space fusion learning environment according to students' behaviors.
In a first aspect, the present disclosure provides a method for constructing an
educational cyber-physical space fusion learning environment, comprising:
13201450_1 (GHMatters) P113901.AU
(1) predefining a plurality of spaces in the educational cyber-physical space
fusion learning environment and learning environment construction parameters of
each of the spaces, wherein the spaces comprise at least two of a physical space, a
network space, a resource space, and a social space;
(2) predefining service contents of the spaces, and constructing a semantic
network model of each of the spaces according to the service contents;
(3) collecting learning subject-related events and learning environment-related
events, and constructing a data fusion model of a cyber-physical space semantic level
consistency for learning subjects;
(4) constructing an integrated embodied model based on learning scenarios,
wherein the integrated embodied model is for describing relationships between the
learning subjects and the learning environment construction parameters in the learning
scenarios, and is capable of being dynamically adjusted according to learning
subject-related events or learning environment-related events collected in any one of
the spaces; and
(5) setting the learning environment construction parameters for the learning
subjects according to the integrated embodied model.
In an embodiment, the step (2) comprises:
(2.1) predefining the service contents of the spaces according to the learning
scenarios; performing a semantic calibration on the service contents of each of the
spaces; establishing semantic subject units of the service contents; and determining
semantic units of the service contents;
(2.2) analyzing semantic relationships of the service contents of the spaces;
determining sequence relationships, hierarchical relationships and inclusion
relationships between the semantic subject units; and constructing a semantic
relationship table; and
(2.3) constructing a semantic network organization structure according to the
semantic relationship table for each of the semantic units of the service contents.
In an embodiment, the step (3) comprises:
2
13201450_1 (GHMatters) P113901.AU
(3.1) creating embodied objects of a learning subject in different spaces, and determining a consistent expression of the same learning subject in different spaces; (3.2) collecting the learning subject-related events and the learning environment-related events, and converting the learning subject-related events and learning environment-related events into data according to the semantic network model to form a learning behavior data pool; and (3.3) constructing the data fusion model of the cyber-physical space semantic level consistency based on the learning behavior data pool. In an embodiment, the step (3.3) comprises: extracting and classifying learning behavior data in the learning behavior data pool using a long short-term memory model and a convolutional neural network with three channels comprising a spatial domain, a local time domain and a global time domain, to construct the data fusion model of the cyber-physical space semantic level consistency. In an embodiment, the step (4) comprises: (4.1) converting the learning subject-related events and the learning environment-related events into standard format data according to the semantic network model and the data fusion model; and (4.2) converting relationships between the learning environment-related events and the learning subject-related events into a graph network model; expressing spatial environments using vertexes of the graph network model, and expressing embodied relationships among different spaces using edges of the graph network model; constructing the integrated embodied model based on the learning scenarios using the graph network mode, wherein the integrated embodied model is used for describing the relationships between the learning subjects and the learning environment construction parameters in the learning scenarios, and is capable of being dynamically adjusted according to learning subject-related events or learning environment-related events collected in any one of the spaces. In an embodiment, the learning environment construction parameters are one or
3
13201450_1 (GHMatters) P113901.AU more of drive parameters of hardware or software of the physical space, the network space or the social space, or resource acquisition parameters of the resource space.
In a second aspect, the present disclosure provides a device for constructing an
educational cyber-physical space fusion learning environment, comprising:
a space predefining module for predefining a plurality of spaces in the
educational cyber-physical space fusion learning environment and learning
environment construction parameters of each of the spaces, wherein the spaces
comprise at least two of a physical space, a network space, a resource space, and a
social space;
a semantic network model constructing module for predefining service contents
of the spaces, and constructing a semantic network model of each of the spaces
according to the service contents;
a data fusion model constructing module for collecting learning subject-related
events and learning environment-related events, and constructing a data fusion model
of a cyber-physical space semantic level consistency for learning subjects;
an integrated embodied model constructing module for constructing an integrated
embodied model based on learning scenarios, wherein the integrated embodied model
is for describing relationships between the learning subjects and the learning
environment construction parameters in the learning scenarios, and is capable of being
dynamically adjusted according to learning subject-related events or learning
environment-related events collected in any one of the spaces; and
a learning environment constructing module for setting the learning environment
construction parameters for the learning subjects according to the integrated embodied
model.
Compared to the prior art, the present invention has the following beneficial
effects.
(1) Based on spaces involved in education and teaching, the present invention
analyzes the physical space, network space, resource space, social space, etc. involved
in the educational scenarios, and explores a semantic analysis method based on the
4
13201450_1 (GHMatters) P113901.AU contents of education and teaching services to realize an educational cyber-physical space unified expression and data fusion method with a consistent learning subject, which can construct an educational cyber-physical space fusion learning environment according to students' behaviors in any one of the spaces.
(2) The present invention introduces a multi-level data fusion method into the
application of educational scenarios, making it possible to construct a consistent
cyber-physical space learning environment for learners.
(3) The present invention proposes a learning space fusion method for learners,
and expression methods such as semantic network model and traditional independent
spatial expression methods are fused.
(4) The present disclosure constructs a personalized cyber-physical space
learning environment and designs an educational cyber-physical space fusion system
for learners, which supports a personalized learning environment under different
physical conditions.
FIG. 1 is a flowchart of a method for constructing an educational cyber-physical
space fusion learning environment according to an embodiment of the present
disclosure.
FIG. 2 schematically shows an application of the method for constructing the
educational cyber-physical space fusion learning environment according to the
embodiment of the present disclosure.
FIG. 3 schematically shows a semantic network model according to an
embodiment of the present disclosure.
FIG. 4 schematically shows another semantic network model according to
another embodiment of the present disclosure.
FIG. 5 schematically shows the application of a device for constructing the
educational cyber-physical space fusion learning environment according to an
embodiment of the present disclosure.
5
13201450_1 (GHMatters) P113901.AU
FIG. 6 schematically shows the device for constructing the educational
cyber-physical space fusion learning environment according to the embodiment of the
present disclosure.
In order to make the object, technical solutions and advantages of the disclosure
more clearly, the present disclosure will be further described in detail below with
reference to the accompanying drawings and embodiments. It should be understood
that the embodiments described herein are only intended to explain the present
disclosure, but not to limit the scope of the present disclosure. In addition, the
technical features involved in various embodiments of the present disclosure
described below can be combined with each other as long as there is no conflict with
each other.
The technical solutions provided by the embodiments of the present disclosure
can be applied to education informatization, so that students can not only learn in
offline physical spaces such as classrooms, but also use interactive whiteboards,
portable tablets and other smart devices for online learning. Therefore, in students'
learning environments, offline physical spaces are fused with network spaces,
resource spaces and social spaces, such as digital classrooms, various intelligent
terminal devices and teaching appliances, etc., so that an educational cyber-physical
space learning environment is personalized according to students' behaviors in any
one of the spaces.
As shown in Fig. 1, the embodiment of the disclosure provides a method for
constructing an educational cyber-physical space fusion learning environment,
including the following steps.
(1) A plurality of spaces in the educational cyber-physical space fusion learning
environment and learning environment construction parameters of each of the spaces
are respectively predefined, where the plurality of spaces comprise at least two of a
physical space, a network space, a resource space, and a social space.
6
13201450_1 (GHMatters) P113901.AU
The learning environment construction parameters are at least one of drive
parameters of hardware or software of the physical space, the network space or the
social space, or resource acquisition parameters of the resource space.
Through the analysis of educational and learning situations, different situations
and learning spaces involved in education and learning scenarios are determined and
divided into the physical space, the network space, the resource space, the social
space, etc. The specific medium and presentation form of each of the spaces are
defined, and typical learning scenarios in each of the spaces and the learning
situations under the learning scenarios are determined.
As shown in Fig. 2, the step (1) includes the following steps.
(1.1) A learning environment is defined and spaces in the learning environment
are clarified; where the spaces include: the physical space, the network space, the
resource space and the social space; the physical space is an actual environment; the
network space (or cloud space, cyberspace) is a virtual space formed by Internet; and
the resource space and the social space are virtual spaces formed by knowledge and
social relations.
(1.2) The physical space in the learning situation is defined, and the meaning of
the physical space, contents involved in the physical space and the contents and
conditions of the learning environment in the physical space are clarified. In this
embodiment, it is preferable to define the physical space for the environment of
education and learning, and describe the physical world where students are located.
The physical space is a physical existence of the actual environment. Due to
differences in learning contents, physical locations of the physical space during
learning are different, that is, the physical locations can be a classroom, places for
learning or activities, such as classrooms, libraries, reading rooms, sports fields,
dormitories, lecture halls, etc.
(1.3) The network space in the learning situation is defined, the meaning of the
network space including contents involved in the network space and the contents of
the learning environment supported in the network space are clarified; the network
7 13201450_1 (GHMatters) P113901.AU space is the Internet connected by wired or wireless means, and connected to Internet services through corresponding devices; essentially, the network space is a network place for studying through service partitions and organization of different levels of the Internet. The network space is generally used to express the situation of learning with network, processing capabilities, such as network computing capabilities, network storage capabilities, and network service capabilities. Besides, the network space shows a situation of the network for the learning, such as bandwidths and speeds of the network, computing and processing capabilities of the network, storage capacities and read and write speeds of the network, service contents and service qualities of the network, etc. (1.4) The resource space in the learning situation is defined; the meaning of the resource space is clarified; and the characteristics of the resource space and its supporting elements for the construction of the learning environment are determined. The resource space, which is defined as the space of the contents that students learn and calibrated according to different learning contents (i.e., the disciplines or knowledge meanings), is reflected in a unique resource organization structure space formed by contents of each discipline, such as the knowledge map space of the discipline; and the resource space is generally embodied as the knowledge space formed by the semantics of the resource. Essentially, the resource space is to show semantics spaces of different knowledge contents, and its expression form is a knowledge graph. At present, since there is no unified knowledge graph that can express everything, it is necessary to construct the knowledge graph according to different disciplines and research contents. In the embodiment, in an application scenario, the resource space is expressed as a knowledge graph of specific contents. In particular, this embodiment does not carry out any innovation and research on the generation of the knowledge graph, and thus this embodiment only uses the knowledge graph of well-known contents. (1.5) The social space in the learning situation is defined; the meaning of the social space is clarified, and the characteristics of the social space and its construction
8
13201450_1(GHMtters) P113901.AU elements for the learning environment are determined. The social space is a virtual space in which the students communicate with people when learning, where the communication includes social activities among students, social activities among students and teachers, and other related activities. It may be peer-to-peer communication and group communication formed by online learning forums and social networking tools, etc. The social space is a space for socializing through tools such as forums and instant messaging, in which the space records social contents, such as post text, chat text, voice, or pictures and videos shared in moments of Wechat, and the social contents are mainly the personalized expression of the learners to the learning contents or other learners.
(1.6) Other dimensional spaces in the learning situation are defined, and the
meaning of the space, the basis for setting up the space, and its elements for
constructing the learning environment are clarified. In a preferred embodiment, the
learning space includes at least the physical space, the network space, the resource
space and the social space described as above, but is not limited to the
above-mentioned spaces, which may also include other spaces which are not defined
to support the learning process.
(1.7) The presentation form and presentation medium of different spaces in a
specific learning scenario are defined. In particular, in the embodiment, the physical
space is a place where specific information equipment and conditions are equipped,
such as smart classrooms which are equipped with traditional blackboards or
whiteboards and various output devices such as acoustic sensors, video sensors,
biosensors, lights, projections, etc. The network space expresses cloud storage
scenarios and cloud service scenarios, where the cloud storage scenarios include
learning contents storage space, learning process storage space, etc.; and the cloud
service scenarios include online course autonomous learning scenarios, online
classroom learning scenarios, online tutoring teaching scenarios. The learning
scenarios of the resource space include online learning scenarios, learning contents
construction scenarios, and learning contents management scenarios, where the online
9 13201450_1 (GHMatters) P113901.AU learning scenarios are expressed as a learner's online learning process for a certain subject and a certain course; the learning construction scenarios are expressed as a construction scenario of knowledge of specific subjects; and the learning contents management scenarios are configured to manage the user's operation and processing of resources during the learning process. The social space can be divided into different learning scenarios, mainly including: course forum discussion scenarios, chatting scenarios for individual learners, teachers or classes on social networking tools, and in the physical space.
(2) The service contents of the spaces are predefined, and a semantic network
model of each of the spaces is constructed according to the service contents.
As shown in Fig. 3, the step (2) includes the following steps.
(2.1) The service contents of the spaces are predefined according to the learning
scenarios; a semantic calibration is performed on the service contents of each of the
spaces; semantic subject units of the service contents are established; and semantic
units of the service contents are determined.
(2.2) Semantic relationships of the service contents of the spaces are analyzed;
sequence relationships, hierarchical relationships and inclusion relationships between
the semantic subject units are determined, and a semantic relationship table is
constructed.
(2.3) A semantic network organization structure is constructed according to the
semantic relationship table for each of the semantic units of the service contents, so as
to express and store semantics.
The method for constructing the semantic network model of the physical space is
as follows.
The physical space in the learning situation is defined, the service contents
provided by the physical space when providing education and learning services are
clarified; the equipment and hardware elements involved in providing the service are
clarified, and the content semantics of the physical space is analyzed, thereby
constructing the semantic network model. Through the technologies such as
10 13201450_1 (GHMatters) P113901.AU intelligent sensing, electronic tags, image/voice collection, video monitoring, etc., classroom teaching scenarios perception achieve intelligent sensing of classroom teaching environment parameters and automatic identification of collected target information to provide classroom recording video and teaching courseware video of different perspectives, and customized synthetic video, and record contextual information such as teaching contents, learning styles, teaching methods, etc. in real time. The multi-scenario online learning process perception will adopt the "activity stream" to track the learner's effective behavior information collection in all activities in real time, to achieve the unified processing, quantification and recording of structured, semi-structured and unstructured behavior data, forming a panoramic learning behavior data pool which is reusable and computable.
Classroom teaching behavior perception can perceive typical classroom teaching
behavior information such as classroom interaction, question answering, discussion,
etc., between teachers and students and automatically extracts interactive behavior
data such as interactive frequencies, interactive subjects and interactive contents in
different stages. The generation format of "activity stream", such as [subject, action,
object, result, scene, timestamp, authority], is transformed into well-structured data
that are applicable for modeling through semantics.
The method for constructing the semantic network model of the network space is
as follows.
The network space in the learning situation is defined; the services contents
provided by the network space when providing education and learning services is
clarified; the hardware elements of the network space services and the service
elements of the network space are clarified; the content semantics based on services is
analyzed to construct a semantic network model of the network space. At present,
personalized services in the network space mostly involve personalized learning
diagnosis, personalized learning path planning, personalized resource
recommendation, and visualization of learning status, but rarely involve personalized
learning intervention. Personalized learning in the network space focuses on
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13201450_1(GHMtters) P113901.AU improving the online experience of netizen groups, mostly based on social computing, complex networks and other technologies to construct netizen group models; and provide customized learning path planning and other personalized service contents for different groups as needed by analyzing group needs and interaction between groups, and classifying groups according to different needs.
The method for constructing the semantic network model of the resource space is
as follows.
The resource space in the learning situation is defined, the service contents
provided by the resource space when providing education and learning services is
clarified; the hardware elements and service elements of the resource space service
are clarified; a graph relationship of the resource semantic in the resource space is
analyzed to construct a semantic network model. Based on the contents of course
knowledge, including nodes (domain concepts, knowledge fragments) and inter-node
relationships (relationships among domain concepts, relationships between courses,
relationships between knowledge fragments and domain concepts, etc.), a semantic
network of knowledge is constructed; the text contents are judged sentence by
sentence using the existing single-sentence semantic type classification method to
discover the indicator sentences of various types of domain concepts; secondly, a
lexical chain method is adopted to analyze the lexical chain of the adjacent sentences
of the indicator to find a lexical dependency relationship between sentences; finally,
based on the analyzed results of the lexical chain, a starting point and an ending point
of domain concept text are labeled using sequence labeling models such as
Conditional Random Fields (CRF), Hidden Model HMM (HMM), Maximum Entropy
Markov Model (MEMM), and a domain concept boundary division method is
proposed by comparatively analyzing the labeled results and the analyzed results of
lexical chain. On this basis, according to the text characteristics such as word
frequency, word order, context, etc., the term competitive learning method is used to
identify the core terms, and the multi-class classifier is used to identify the semantics
type of the domain concept. In the tracking of each learner's online learning process,
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13201450_1 (GHMatters) P113901.AU according to the characteristics of the user's click behavior, click resource type (text, image, exercise/test question of domain concept), forum discussion knowledge sharing method, etc., combining with the learning resource metadata (including keywords), the degree of mastery of knowledge concepts learned by users, etc., and using conditional constrained sequential pattern mining methods, learners' click behavior patterns are discovered, learner's cognitive strategies are analyzed, and the typical mass learners model is discovered; and by borrowing Felder-Silverman's learning style classification method, different learner styles are summarized. The method of constructing the semantic network model of the social space is as follows: the social space in the learning situation is defined, and the contents of the education and learning services provided by the social space are clarified; the hardware elements and service elements of the resource space service are clarified, and the individuals of the social space and their relationship semantics are analyzed to construct the semantic network model. (3) Learning subject-related events and learning environment-related events are collected, and a data fusion model of a cyber-physical space semantic level consistency for learning subjects is constructed. As shown in Fig. 4, the step (3) includes the following steps. (3.1) Embodied objects of a learning subject in different spaces are created, and a consistent expression of the same learning subject in different spaces is determined. The embodied objects of unified learning subjects in different spaces are constructed to form learner subjects; individual static characteristics (background information, early knowledge ability, learning style, etc.) and dynamic features (current knowledge ability, learning motivation, cognitive level, emotional attitude, interest preference, etc.) in multiple learning scenarios (online resource browsing, collaborative mutual evaluation, question and answer, offline classroom interaction, outdoor learning, etc.) are analyzed; individual key learning features are extracted. Based on theoretical methods such as contents analysis, cognitive classification theory, sentiment analysis, deep learning, etc., key elements representing individual
13
13201450_1 (GHMatters) P113901.AU characteristics are extracted from the multi-source learning activity data, and key factors affecting individual learning process are analyzed, thereby exploring the characteristics and personalities of learners in different scenarios in terms of knowledge ability, cognitive level, emotional attitude, etc. Taking the timing, scene and deep semantic characteristics of the learning state features into account, the learning state of individuals under a specific time and space is identified. Finally, a dynamic unified model of learners based on specific instructional designs and ability evaluation is constructed.
(3.2) The learning subject-related events and the learning environment-related
events are collected, and the learning subject-related events and learning
environment-related events are converted into data according to the semantic network
model to form a learning behavior data pool.
The learning subject-related events and the learning environment-related events
are learner behaviors/actions collected by various sensors and network monitoring
devices in the environment or monitoring parameters related to the learning
environment. For example, through the technologies such as intelligent sensing,
electronic tags, image/voice collection, video monitoring, etc., the classroom teaching
scenarios perception achieve intelligent sensing of classroom teaching environment
parameters and automatic identification of collected target information to provide
classroom recording video and teaching courseware video of different perspectives,
and customized synthetic video, and records contextual information such as teaching
contents, learning methods, teaching methods, etc. in real time. The multi-scenario
online learning process perception will adopt the "activity stream" to track the
learner's effective behavior information collection in all activities in real time, to
achieve the unified processing, quantification and recording of structured, semi-structured and unstructured behavior data, forming a panoramic learning
behavior data pool which is reusable and computable. The classroom teaching
behavior perception can perceive typical classroom teaching behavior information
such as classroom interaction, question and answer, discussion, etc. between students
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13201450_1 (GHMatters) P113901.AU and teachers, and automatically extracts interactive behavior data such as interactive frequencies, interactive subjects and interactive contents in different stages. The generation formats "activity stream", such as [subject, action, object, result, scene, timestamp, authority], is transformed into well-structured data that are applicable for modeling through semantics. (3.3) The data fusion model of the cyber-physical space semantic level consistency is constructed based on the learning behavior data pool. In the embodiment, the semantics of objects and learning behaviors of the same learning subject in different spaces is clarified; the semantics of different spaces describing the same event, the same behavior and activities is analyzed; the data entities of each semantic object are clarified, and cyber-physical space data fusion standards are constructed. Different classroom teaching scenarios, different network platforms and social organization spaces carry out the automatic extraction, intelligent identification and automatic record for classroom teaching situations, teaching subjects, teaching status and learning behavior, so that structured, semi-structured and unstructured behavior data are uniformly processed, quantified and recorded, thereby forming a panoramic learning behavior data pool which is reusable and computable. A long short-term memory model and three-stream convolutional neural networks (three-stream CNNs) with three channels of space, local time domain and global time domain are used to extract and classify the learning behavior data, thereby constructing a data fusion model of the cyber-physical space semantic level consistency. The three-stream CNNs framework including three channels of space, local time domain and global time domain is used to extract the spatiotemporal features of learners' behaviors. The three-stream CNNs framework includes 4 convolutional layers (Conv 1-4), where 2 convolutional layers (Norm 1 and Norm 2) are normalized, and connected to 2 pooling layers (Pooling 1 and Pooling 2); after the three channels (the spatial channel, the local time domain channel and the global time domain channel) undergo convolution and pooling operations, deep features are obtained;
15
13201450_1 (GHMatters) P113901.AU where the spatial channel CNNs perform deep learning for learner behavior images; the local time domain channels CNNs perform deep learning for optical flow features; and the global time domain channel CNNs perform deep learning for learner's behavior and differential image products.
With the advantages in dealing with time series problems, the long short-term
memory model (LSTM) is introduced into the training of the classification module to
recognize the learner's behavior. The features extracted by 3DCNN are input into the
LSTM model for learning. Time-domain information is introduced into sequence
learning, thereby bringing more accurate results to the classification. Then, a spatial
pyramid pooling layer (SPP) is added between the fully connected layer (FC) and the
LSTM model. After the SPP layer calculates the feature maps of different sizes, a
fixed-length feature vector can be obtained. Then, through the fully connected layer,
3DCNN+LSTM is learned to classify a single type of features. Finally, the
classification results of the independent features of the three channels are voted to
obtain behavior action categories, so as to summarize and store the corresponding
behavior data in the form of tags. Through the two-way long short-term memory
neural network to learn the data of learners in different learning spaces, the single
form features of the learning process data and the shared features that show the
correlation between different data forms can be effectively learned, so that relevance
between relevant learning contents and learning behavior data at different times and in
different spaces can be captured, thereby aggregating the data with the corresponding
"activity stream" specifications. The relationship between different data is
automatically sorted out using the learned multi-level features. On this basis, Deep
Boltzmann Machine (DBM) is used to fuse feature-level data from different learning
spaces.
(4) An integrated embodied model is constructed based on learning scenarios,
where the integrated embodied model is used for describing relationships between the
learning subjects and the learning environment construction parameters in the learning
scenarios, and is capable of being dynamically adjusted according to learning
16
13201450_1 (GHMatters) P113901.AU subject-related events or learning environment-related events collected in any one of the spaces. Specifically, the step (4) includes the following steps. (4.1) The learning subject-related events and the learning environment-related events are converted into standard format data according to the semantic network model and the data fusion model. (4.2) The relationships between the learning environment-related events and the learning subject-related events are converted into a graph network model; the spatial environments are expressed using vertexes of the graph network model, and embodied relationships among different spaces are expressed using edges of the graph network model; the integrated embodied model is constructed based on the learning scenarios using the graph network model, where the integrated embodied model is used for describing relationships between the learning subjects and the learning environment construction parameters in the learning scenarios, and is capable of being dynamically adjusted according to the learning subject-related events or the learning environment-related events collected in any one of the spaces. Specifically, an integrated embodied model is constructed based on the integration of scene-based space and environment structure, resource and content semantics, learning activities and behaviors; data of the embodied learning environment is fused using the graph network model, that is, a physical embodied model of the intelligent partner system. Using the graph neural network, the embodied learning environment and embodied relationship are expressed as follows:
G= (N, E) ; where G (term: graph) is a set of embodied environments and embodied relationship of the cyber-physical space fusion; N (term: vertex) is expressed as a spatial set of embodied learning environments; and E (term: edge) is a set of embodied relationships. ne[n] represents an adjacent space of a space n (represented by vertices), and co[n] relates the relationship of the space n (represented by edges).
17 13201450_1 (GHMatters) P113901.AU
The space n and the corresponding attribute of the relationship (nI,n_2) are
respectively expressed as _nE RA(lN) and (n_1, n_2)E R(lE), 1 represents the
tensor formed by stacking all the attributes in the graph.
The embodied learning environments of multiple spaces and the relationships
therebetween are generalized into a graph neural network model, where the vertex is
used to express the spatial environment, and the edges are used to express the
embodied relations of different spaces. Based on the computational model of the
graph neural network, the calculation rule for perception and fusion of embodied data
of multiple spaces is constructed to obtain a solution of personalized personal
environments.
Based on technologies such as voice interaction, action interaction, holographic
visual interaction, wearable sensor interaction, etc., data perception of all elements of
the environment and the entire process of behavior is constructed to realize
cyber-physical space embodied interaction of learners through the multi-model
interaction technology. The learner's cognitive results are perceived through the
embodied learning environment, and a feedback mechanism of the cognitive results to
the embodied learning environment and intelligent services is constructed. Cognitive
practice results are fed back to the learning environment to realize customization of
the learning environment; and cognitive results are fed back to the intelligent service
to realize the resource customization of the intelligent service and achieve customized
learning.
The cyber-physical space embodied model further includes data standard
specification of fusion of spatial hierarchy, resource graph and learning interaction
behavior.
Aiming at unstructured, distributed, heterogeneous, and source-scattered features
of data, under the guidance of learning science and education and teaching related
theories, an educational cyber-physical space data fusion standard system is formed,
where the system includes education-related standards, such as subject standards,
resource standards, and evaluation standards, management standards, teaching process
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13201450_1 (GHMatters) P113901.AU standards, data processing and data quality standards, and data interoperability standards. Through the unified conversion gateway for education big data standardization, the standardized processing of heterogeneous education data is realized, including the information extraction, data storage and retrieval of structured, unstructured and semi-structured data, as well as the steps of data cleaning and data verification, so that usable and trusted data sources for data modeling, analysis and application are provided. An information association model of cross-scenario and cross-temporal education big data is established by acquiring the multi-source data entities and their multi-level association relationships, so as to realize the information extraction and aggregation of multi-source heterogeneous educational data, so that data after processed can meet the needs of applications such as data analysis and modeling. Data exchange and sharing services based on application requirements are realized, and an on-demand data exchange for multiple data sources is supported through unified standard data interfaces and standard data formats, including data aggregation, data distribution, data update, data conversion, etc., thereby supporting identity authentication, user authorization, transmission encryption, data integrity, data credibility, and data validity. As for teaching subject standards, the basic information of the teaching subject is described, and the continuous recording and data association of the active subject across platforms and systems are realized. The teaching subject is the subject of teaching activities, including students, parents, teachers, teaching researchers and teaching managers. Teaching resource standards include a unified description, encapsulation and reorganization of teaching resources of different forms, different granularities, and different formats, such as courses, videos, and exercises. The teaching resource standard not only includes the metadata description of the resource attributes, but also includes the semantics attributes of the resources to support automatic recognition and processing of machines, so that the personalized intelligent push of resources is realized. The teaching process standard is to describe any interaction or related experience between the teaching subject and teaching contents
19 13201450_1 (GHMatters) P113901.AU
(such as courses, resources, etc.), teaching environment (such as traditional classrooms, outdoor learning environment), and other participants in the teaching
activities during the teaching process. semantic The teaching process standards are
oriented to traditional teaching environments (such as schools, classrooms) and
non-traditional teaching environments (such as online learning environments, outdoor
learning environments). It mainly includes how and when teaching activities occur,
contextual information, and the results of the teaching process. Data processing
standards mainly regulate data collection, preprocessing, analysis, visualization, and
access. Data quality standards mainly propose specific management requirements and
corresponding index requirements for data quality to ensure the quality of data in
various links such as generation, storage, exchange and use, and lay a good
foundation for the application of educational big data. Education data interoperability
standards are mainly aimed at the heterogeneity of education data to achieve the
connection between massive data sets and the interoperability requirements of
coupling, fusion, migration and information extraction of education data.
(5) The learning environment construction parameters for the learning subjects
are set according to the integrated embodied model.
(5.1) An automatic construction technology of personalized cyber-physical space
learning environment for learners is established; based on technologies such as voice
interaction, action interaction, holographic visual interaction, wearable sensor
interaction, etc., data perception of all elements of the environment and the entire
process of behavior is constructed to realize cyber-physical space embodied
interaction of learners through the multi-model interaction technology. The learner's
cognitive results are perceived through the embodied learning environment, and a
feedback mechanism of the cognitive results to the embodied learning environment
and intelligent services is constructed. Cognitive results are fed back to the learning
environment to realize customization of the learning environment.
The situation of the learning space and the behavior of the learning subject are
perceived; data such as environmental data, learning process data, learning behavior
20
13201450_1 (GHMatters) P113901.AU data is obtained; cyber-physical space embodied entities are constructed and are expressed as vertexes of the graph network; the edge structures of the graph network are formed by the relationship between the spatial embodied entities to describe the relationship between the vertexes, and finally an educational cyber-physical space learning environment graph network structure is formed. Using the topology calculation method of the graph, an educational cyber-physical space data fusion algorithm is developed to achieve subject-based data fusion. Learner feature extraction algorithm, learner state recognition algorithm, learner portrait technology and learning analysis technology are applied to analyze the state of the learning process of learning subjects in multiple spaces and realize the visualization of the learning process of learning subjects in multiple spaces. In the learner's learning process, data mining and in-depth analysis are performed to draw the learner's learning curve, so as to diagnose the students' knowledge structure in detail, find blind spots in learning, and design personalized learning plan that is more targeted at students' weak knowledge to provide learners with personalized learning diagnosis. (5.2) An educational cyber-physical space fusion learning environment creation device that meets the learner's embodied requirements is developed and integrated; an intelligent learning partner system for embodied recognition is perceived and fused through the cyber-physical space personal cognitive data perception and fusion module to realize perception and fusion for the learning environment and learning process data; the learner learning analysis module based on multi-source data is adopted to realize the recognition of the characteristics and status of the learning subject, so as to accurately characterize the learner and its learning behavior feedback, the intelligent tutoring module is applied to realize autonomous learning. According to the individual needs of learners, data mining and semantic search are used to continuously improve the course knowledge map. Combining the learning process, forgetting rules and success rate, the node relationships in the course knowledge map are dynamically updated, and genetic algorithms (GA) are used to
21
13201450_1 (GHMatters) P113901.AU optimize the path, so that a learning path adapted to the learner's personality is outputted. Through the optimized learning path and high-quality adaptive learning resources, the generalized resource recommendation method is applied to achieve personalized intelligent tutoring for learners.
FIG. 5 schematically shows the application of a device for constructing the
educational cyber-physical space fusion learning environment according to an
embodiment of the present disclosure, where the device is connected to a video
module, a network module and an Internet of things (IoT) module, etc. in the learning
environment through interfaces. The video module, the network module and the IoT
module, etc. are configured to collect learning subject-related events and learning
environment-related events in different spaces, and are controlled to construct
parameters to configure the learning environment according to a learning environment
of the device.
As shown in Fig. 6, the device includes the following modules.
(1) A space predefining module for predefining a plurality of spaces in the
educational cyber-physical space fusion learning environment and learning
environment construction parameters of each of the spaces, wherein the spaces
include at least two of a physical space, a network space, a resource space, and a
social space.
(2) A semantic network model constructing module for predefining service
contents of the spaces according to a learning scenario, and constructing a semantic
network model of each of the spaces according to the service contents.
Specifically, the semantic network model constructing module includes the
following modules.
(2.1) A spatial content semantic calibrating module for predefining the service
contents of the spaces according to the learning scenario, performing a semantic
calibration on the service contents of each of the spaces, establishing semantic subject
units of the service contents, and determining semantic units of the service contents.
(2.2) A spatial content semantic analyzing module for analyzing semantic
22
13201450_1 (GHMatters) P113901.AU relationships of the service contents of the spaces; clarifying sequence relationships, hierarchical relationships and inclusion relationships between the semantic subject units to construct a semantic relationship table.
(2.3) A spatial content semantic network module for constructing a semantic
network organization structure according to the semantic relationship table for each of
the semantic subject units of the service contents.
The semantic network constructing module performs a semantic calibration and
hierarchical organization of contents of different spaces, constructs a semantic
network model based on the semantic relationship between spatial contents,
establishes a content-based organizational structure, and establishes a semantically
consistent expression of cyber-physical space services and contents; and converts the
connection between cyber-physical space learning environment, learning behavior and
cognitive results (learning results) into a graph network model; where a vertex of the
graph network model expresses the cognitive result (knowledge) of a space and
expresses learning behavior; a certain subnet in the graph network expresses a certain
learning space (that is, an embodied learning environment), and the aggregation
operation of the nodes between the subnets is the personalized solution of the learning
space. A spanning tree after the multi-subnet personalized solution is expressed as a
personalized cyber-physical space model. The frame and feedback mechanism of the
embodied interaction are the constraints of aggregate computing.
(3) A data fusion model constructing module for collecting learning
subject-related events and learning environment-related events of different spaces,
and constructing a data fusion model of a cyber-physical space semantic level
consistency for learning subjects.
Specifically, the data fusion model constructing module includes the following
modules.
(3.1) A learning subject consistency verification module for creating embodied
objects of a learning subject in different spaces, and determining a consistent
expression of the same learning subject in different spaces.
23
13201450_1 (GHMatters) P113901.AU
(3.2) A learning subject cyber-physical space data collection module for
collecting the learning subject-related events and the learning environment-related
events, and converting the learning subject-related events and learning
environment-related events into data according to the semantic network model to form
a learning behavior data pool.
(3.3) A data fusion module for constructing the data fusion model of the
cyber-physical space semantic level consistency based on the learning behavior data
pool.
The data fusion module of the learner subject constructs an educational
cyber-physical space data fusion method based on the learner subject and the semantic
contents of the same level of learning, learning services and personality characteristics,
to realize the cyber-physical space instance data representation of the learner subject
based on the same semantics; standardized and unified conversion gateway is
constructed to realize the standardized processing of heterogeneous educational data,
including information extraction, data storage and retrieval of structured, unstructured
and semi-structured data, as well as steps of data cleaning and data verification, etc.,
thereby providing usable and trusted data sources for modeling, analysis and
applications of data. The pre-processing process includes steps such as data cleaning,
data verification, and normalization processing to provide usable and trusted data
sources for data modeling, analysis, and applications of data. Erroneous data and
redundant data in the data is cleared; the consistency and completeness of the original
data are verified; and the data are processed using unified standards, such as text
information format conversion, consistency of unit of measurement, etc., thereby
making the data set to be processed more complete, and transforming heterogeneous
and complex data into analyzable and applicable information.
(4) An integrated embodied model constructing module for constructing an
integrated embodied model based on learning scenarios, where the integrated
embodied model is used for describing relationships between the learning subjects and
the learning environment construction parameters in the learning scenarios, and is
24
13201450_1 (GHMatters) P113901.AU capable of dynamically adjusting according to learning subject-related events or learning environment-related events newly collected in any one of the spaces.
(5) A learning environment constructing module for setting the learning
environment construction parameters for the learning subjects according to the
integrated embodied model.
The learning environment construction module constructs an educational
cyber-physical space fusion learning environment with learners as the main body,
realizes the fusion expression, display and data services of different spaces, and
proposes a personalized learning environment.
Cyber-physical space personalized environment construction: the construction
parameters of different learning spaces are calculated based on the learner's subject,
and the parameter-driven environment construction technology is applied to realize
the environment setting and customization of different learning spaces. Learners can
learn in different spaces, where their learning experience should be expressed as a
unified whole, and its overall effect needs to be embodied in each space through the
embodied model. In a certain space, the resources or abilities of other spaces can be
obtained through the embodied object, and activities in a space can be fed back to
different spaces through the embodied object. For example, the learning behavior of
the cloud space can be fed back to the learning activities of the physical space, so that
the learning is carried out through embodied objects of multiple spaces and is no
longer the learning behavior or process of a single space.
Cyber-physical space learning environment fusion: a learning environment
fusion device is developed, in which a non-physical space is fused in a certain
physical space, and holographic technology and multi-modal interaction technology
are applied to realize the integrated presentation of multiple spaces to provide a
personalized intelligent learning environment. According to the learner's personalized
characteristics, the cyber-physical space learning environment that matches the
personality is customized, such as the learning environment that conforms to the
learner's personal interaction mode (such as voice interaction, somatosensory
25
13201450_1 (GHMatters) P113901.AU interaction, etc.), which emphasizes the impact of feedback on the learning process on the user's sense, touch, vision, etc.
The device for constructing the educational cyber-physical space fusion learning
environment of the present disclosure has the following beneficial effects.
(1) Based on spaces involved in education and teaching, the present disclosure
analyzes the physical space, network space, resource space, social space, etc. involved
in the educational scenarios, and explores a semantic analysis method based on the
contents of education and teaching services to realize an educational cyber-physical
space unified expression and data fusion method with a consistent learning subject,
which can construct an educational cyber-physical space fusion learning environment
according to students' behaviors in any one of the spaces.
(2) The present disclosure introduces a method of multi-level data fusion into the
application of educational scenarios, making it possible to construct a consistent
cyber-physical space learning environment with learners as the learning subject.
(3) The present disclosure proposes a learning space fusion method for learners,
and expression methods such as semantic network model and traditional independent
spatial expression methods are fused.
(4) The present disclosure constructs a personalized cyber-physical space
learning environment and designs an educational cyber-physical space fusion system
for learner subjects, which supports a personalized learning environment under
different physical conditions.
It should be understood that the steps are not necessarily executed in the order
indicated by the arrows. Unless specified, the execution order of these steps is not
limited herein.
It should be understood that described above is only preferred embodiments of
the present disclosure, but not intended to limit the scope of the present disclosure.
Any modification, equivalent replacement and improvement made within the spirit
and principle of the present disclosure shall fall within the scope of present disclosure
defined by the appended claims.
26
13201450_1 (GHMatters) P113901.AU
Claims (10)
1. A method for constructing an educational cyber-physical space fusion learning
environment, comprising:
(1) predefining a plurality of spaces in the educational cyber-physical space
fusion learning environment and learning environment construction parameters of
each of the spaces; wherein the spaces comprise at least two of a physical space, a
network space, a resource space, and a social space;
(2) predefining service contents of the spaces, and constructing a semantic
network model of each of the spaces according to the service contents;
(3) collecting learning subject-related events and learning environment-related
events, and constructing a data fusion model of a cyber-physical space semantic level
consistency for learning subjects;
(4) constructing an integrated embodied model based on learning scenarios,
wherein the integrated embodied model is for describing relationships between the
learning subjects and the learning environment construction parameters in the learning
scenarios, and is capable of being dynamically adjusted according to learning
subject-related events or learning environment-related events collected in any one of
the spaces; and
(5) setting the learning environment construction parameters for the learning
subjects according to the integrated embodied model.
2. The method according to claim 1, characterized in that the step (2) comprises:
(2.1) predefining the service contents of the spaces according to the learning
scenarios; performing a semantic calibration on the service contents of each of the
spaces; establishing semantic subject units of the service contents; and determining semantic units of the service contents; (2.2) analyzing semantic relationships of the service contents of the spaces; determining sequence relationships, hierarchical relationships and inclusion
27
12553799_1 (GHMatters) P113901.AU relationships between the semantic subject units; and constructing a semantic relationship table; and (2.3) constructing a semantic network organization structure according to the semantic relationship table for each of the semantic units of the service contents.
3. The method according to any one of claims 1-2, characterized in that the step (3) comprises: (3.1) creating embodied objects of a learning subject in different spaces, and determining a consistent expression of the same learning subject in different spaces; (3.2) collecting the learning subject-related events and the learning environment-related events, and converting the learning subject-related events and learning environment-related events into data according to the semantic network model to form a learning behavior data pool; and (3.3) constructing the data fusion model of the cyber-physical space semantic level consistency based on the learning behavior data pool.
4. The method according to claim 3, characterized in that the step (3.3) comprises: extracting and classifying learning behavior data in the learning behavior data pool using a long short-term memory model and a convolutional neural network with three channels comprising a spatial domain, a local time domain and a global time domain, to construct the data fusion model of the cyber-physical space semantic level consistency.
5. The method according to any one of claims 1-2, characterized in that the step (4) comprises: (4.1) converting the learning subject-related events and the learning environment-related events into standard format data according to the semantic network model and the data fusion model; and
28
12553799_1 (GHMatters) P113901.AU
(4.2) converting relationships between the learning environment-related events
and the learning subject-related events into a graph network model; expressing spatial
environments using vertexes of the graph network model, and expressing embodied
relationships among different spaces using edges of the graph network model;
constructing the integrated embodied model based on the learning scenarios using the
graph network mode, wherein the integrated embodied model is for describing the
relationships between the learning subjects and the learning environment construction
parameters in the learning scenarios, and is capable of being dynamically adjusted
according to learning subject-related events or learning environment-related events
collected in any one of the spaces.
6. The method according to any one of claims 1-2, characterized in that the
learning environment construction parameters are one or more of drive parameters of
hardware or software of the physical space, the network space or the social space, or
resource acquisition parameters of the resource space.
7. A device for constructing an educational cyber-physical space fusion learning
environment, comprising:
a space predefining module for predefining a plurality of spaces in the
educational cyber-physical space fusion learning environment and learning
environment construction parameters of each of the spaces; wherein the spaces
comprise at least two of a physical space, a network space, a resource space, and a
social space;
a semantic network model constructing module for predefining service contents
of the spaces, and constructing a semantic network model of each of the spaces
according to the service contents;
a data fusion model constructing module for collecting learning subject-related
events and learning environment-related events, and constructing a data fusion model
of a cyber-physical space semantic level consistency for learning subjects;
29
12553799_1 (GHMatters) P113901.AU an integrated embodied model constructing module for constructing an integrated embodied model based on learning scenarios, wherein the integrated embodied model is for describing relationships between the learning subjects and the learning environment construction parameters in the learning scenarios, and is capable of being dynamically adjusted according to learning subject-related events or learning environment-related events collected in any one of the spaces; and a learning environment constructing module for setting the learning environment construction parameters for the learning subjects according to the integrated embodied model.
8. The device according to claim 7, characterized in that the semantic network model constructing module comprises: a spatial content semantics calibrating module for predefining the service contents of the spaces according to the learning scenarios; performing a semantic calibration on the service contents of each of the spaces; establishing semantic subject units of the service contents; and determining semantic units of the service contents; a spatial content semantics analyzing module for analyzing semantic relationships of the service contents of the spaces; clarifying sequence relationships, hierarchical relationships and inclusion relationships between the semantic subject units, and constructing a semantic relationship table; and a spatial content semantics network constructing module for constructing a semantic network organization structure according to the semantic relationship table for the semantic units of the service contents of the spaces.
9. The device according to claim 7, characterized in that the data fusion model constructing module comprises: a learning subject consistency verification module for creating embodied objects of a learning subject in different spaces, and determining a consistent expression of the same learning subject in different spaces;
30
12553799_1 (GHMatters) P113901.AU a learning subject cyber-physical space data collection module for collecting the learning subject-related events and the learning environment-related events, and converting the learning subject-related events and learning environment-related events into data according to the semantic network model to form a learning behavior data pool; and a data fusion module for constructing the data fusion model of the cyber-physical space semantic level consistency based on the learning behavior data pool.
10. The device according to claim 7, characterized in that the learning
environment construction parameters are one or more of drive parameters of hardware
or software of the physical space, the network space or the social space, or resource
acquisition parameters of the resource space.
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|---|---|---|---|---|
| CN113065986A (en) * | 2021-03-22 | 2021-07-02 | 深圳童年科技有限公司 | Educational resource generation method based on intelligent interaction |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113065986A (en) * | 2021-03-22 | 2021-07-02 | 深圳童年科技有限公司 | Educational resource generation method based on intelligent interaction |
| CN114898156A (en) * | 2022-05-21 | 2022-08-12 | 山东大学 | Image classification method and system based on cross-modal semantic representation learning and fusion |
| CN114898156B (en) * | 2022-05-21 | 2024-06-04 | 山东大学 | Cross-modal semantic representation learning and fusion-based image classification method and system |
| CN116719957A (en) * | 2023-08-09 | 2023-09-08 | 广东信聚丰科技股份有限公司 | Learning content distribution method and system based on portrait mining |
| CN116719957B (en) * | 2023-08-09 | 2023-11-10 | 广东信聚丰科技股份有限公司 | Learning content distribution method and system based on portrait mining |
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