CN109447858B - Ubiquitous learning environment construction method based on block chain - Google Patents
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Abstract
The invention discloses a block chain-based ubiquitous learning environment construction method, which comprises the following steps: the node records the transaction of the learner based on a consensus mechanism; when a preset number of transactions are collected or a certain time interval is reached since the last transaction is collected, the node with the highest credit examines each transaction, and the transactions meeting the requirements are recorded in the block of the block. By the method disclosed by the invention, because the block chain has a distributed storage characteristic, a learning resource producer can conveniently store the learning resources at any node, and a user can acquire a large amount of learning resources through the block chain network. Because a large amount of learning resources are concentrated in the intelligent ubiquitous learning environment based on the block chain, the classification granularity of the resources can be fully refined, and the learning requirement of personalized customization is met. Meanwhile, an intermediate platform does not exist in the intelligent ubiquitous learning environment based on the block chain, and a direct communication channel is provided for users and learning resource producers.
Description
Technical Field
The invention relates to the technical field of block chains, in particular to a block chain-based ubiquitous learning environment construction method.
Background
The ubiquitous learning is the application of ubiquitous computing in the learning field, and the ubiquitous learning aims to construct a seamless learning space with the integration of an information space and a physical space through a pervasive computing technology, so that the learning requirements, resources and processes are naturally integrated into daily learning, work and life.
Learning resources are core elements of a ubiquitous learning ecosystem, the experience and efficiency of a user are directly influenced by the organization mode of the resources, and the ubiquitous learning needs learning resources which are dynamically generated, continuously evolved and developed and have an open structure, and needs an efficient and strong interactive resource organization mode. At present, the ubiquitous learning can sense learning equipment, learning time, learning content and the like of a user through sensing technologies such as two-dimensional codes and postal codes, and is shown in fig. 1 according to the current situation time, location, equipment, content and learning characteristics of the user. And pushing required learning content and services to the user students through the decision of the cloud computing center. Although the perception technology is more advanced and the decision-making capability of the cloud computing center is stronger, the organization mode of the ubiquitous learning resources does not bypass the centralized platform of the ubiquitous learning center. The 'ubiquitous learning center' platform is a centralized learning resource medium platform which is gathered by various intermediary education institutions and is independently established, the learning contents of the platforms are different,
at present, the platform of the "ubiquitous learning center" is shown in fig. 2, and has the following problems:
firstly, the learning resources are dispersed and limited, and the learning requirements are difficult to meet. The ubiquitous learning platforms are respectively camping and concentrating on different learning resources, and the learning resources stored by the platforms are limited, so that the massive learning requirements of users are difficult to meet;
and secondly, the granularity of the learning resources is too large, so that the requirement of personalized learning customization is difficult to meet. The ubiquitous learning platform has a large content classification granularity, cannot provide precise and accurate subject classification, and cannot meet the requirements of user personalized learning customization.
And thirdly, the platform has poor interactivity, and no interaction channel exists between the user and the learning resource producer. The ubiquitous learning platform is used as an intermediate platform, a bridge is built between the user and the learning resource producer, but obstacles are caused for communication between the user and the learning resource producer, and poor interactive experience is brought.
Disclosure of Invention
In view of the above, the present invention provides a block chain-based ubiquitous learning environment construction method, so as to overcome the problem that it is difficult to flexibly construct a strong interactive intelligent ubiquitous learning environment in the prior art because a direct interaction channel between a learner and a learning resource is obstructed by an intermediary education platform.
In order to achieve the purpose, the invention provides the following technical scheme:
a ubiquitous learning environment construction method based on block chains comprises the following steps: the node records the transaction of the learner based on a consensus mechanism;
when a preset number of transactions are collected or a certain time interval has elapsed since the last transaction was collected, the node with the highest credit reviews each transaction and the satisfactory transactions are recorded in the block of the block.
Preferably, the node with the highest credit reviews each transaction, and satisfactory transactions are recorded in the block of blocks, including:
extracting semantic features of intelligent learning element contents and semantic features of updated contents in each transaction, and performing similarity calculation on the semantic features of the updated contents and the semantic features of the intelligent learning element contents, wherein the content transactions exceeding a credibility threshold are accepted;
and scoring the trust level of the learner through the resource trust level and the user trust level as well as time influence, difference influence and most reliable assumptions, wherein the learner with the highest trust level obtains the updating right of the block.
Preferably, the transaction is a transaction performed to fulfill an intelligent contract charged by the learner for a learning service provided by a teaching party.
Preferably, the learning service provided by the teaching party is to generate adaptive learning content for the learner and supplement the adaptive teaching strategy according to the characteristics and the learning track of the learner, and record the result of formative evaluation.
Preferably, the tile comprises a tile head and a tile body;
the block head includes: a version number, a hash pointer pointing to a previous block, a hash value of a current block, a tree root pointer of a Merkel tree, and a timestamp;
the block body includes: performing operation transaction on the intelligent learning element; the operation transaction constitutes a Merkel tree whose root is recorded in a block header.
Preferably, the operation record of the node on the intelligent learning element is broadcast to all nodes as a transaction;
the node collects all received transactions.
Preferably, the operation transaction of the intelligent learning element comprises: the method comprises the steps of intelligent learning element creation, updating of existing intelligent learning element content, exercise processes and results, formative evaluation and teaching strategies.
Preferably, the learner uses any node ID to perform operation transaction on the intelligent learning element;
the learner saves all used node IDs in the node;
and the intelligent learning element recommends learning content, generates exercise questions, selects a teaching strategy and generates an evaluation result for the learner according to the corresponding relation between the node ID and the learner.
Preferably, the node recording the learner's transaction based on a consensus mechanism comprises:
the node records each of the learned transactions once.
According to the ubiquitous learning environment construction method based on the block chain, provided by the invention, the block chain has a distributed storage characteristic, a learning resource producer can conveniently store learning resources at any node, the learning resources are very concentrated, the quantity is not limited, and a user can obtain a large amount of learning resources through the block chain network. Because a large amount of learning resources are concentrated in the intelligent ubiquitous learning environment based on the block chain, the classification granularity of the resources can be fully refined, and the learning requirement of personalized customization is met. Meanwhile, an intermediate platform does not exist in the intelligent ubiquitous learning environment based on the block chain, a direct communication channel is provided for the user and a learning resource producer, and better user experience is achieved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a diagram of a ubiquitous learning platform framework disclosed in the prior art;
FIG. 2 is a schematic diagram of the problem of the ubiquitous learning disclosed in the prior art
Fig. 3 is a block chain-based ubiquitous learning environment construction method according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of a consensus mechanism provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of an intelligent contract provided by an embodiment of the invention;
FIG. 6 is a diagram of a ubiquitous learning environment built based on block chains according to an embodiment of the present invention;
fig. 7 is a schematic diagram of an intelligent learning unit according to an embodiment of the present invention;
fig. 8 is a schematic diagram illustrating an effect of the intelligent learning environment according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a ubiquitous learning environment construction method based on a block chain, and as shown in fig. 3, in step S101, a node records the transaction of a learner based on a consensus mechanism;
step S102, when the preset number of transactions are collected or a certain time interval is reached from the last transaction collection, the node with the highest credit examines each transaction, and the transactions meeting the requirements are recorded in the block body of the block.
Bitcoin blockchain platforms initially employed a Proof of Work (PoW) consensus mechanism that was highly dependent on node computing power to ensure consistency of ledgers in distributed systems. In the workload proving mechanism, individual computer nodes (miners) compete to solve a complex and easily verifiable mathematical problem (mine excavation), the node that solves the mathematical problem the fastest gains the right to write the most recent transaction record into the block and thus gains the reward. The PoW mechanism also ensures the safety of the bitcoin through the examination of the long-term stable operation of the bitcoin. However, the PoW consensus mechanism causes serious power waste, and the problem solving process for as long as 10 minutes reduces the frequency of transaction confirmation. Another Proof of rights (PoS) consensus mechanism employs Proof of rights instead of an algorithm-based consensus mechanism in a workload Proof mechanism, where a node with the highest rights, but not the highest algorithm, in the system obtains block accounting rights. In addition to the above-mentioned workload certification and equity certification mechanisms, a granted Proof of identity (DPoS), a PoW and PoS combined identity mechanism, a Proof of action (PoA) identity mechanism, and the like have appeared. As shown in fig. 4, in the present invention, the node records each of the learned transactions once.
The transaction may be a transaction conducted to fulfill an intelligent contract charged by the learner for a learning service provided by a teaching party. An intelligent contract is a set of commitments (promises) defined in digital form, including agreements on which contract participants can enforce the commitments. A set of commitments refers to the rights and obligations agreed upon (often mutually) by contract participants, which commitments define the nature and purpose of the contract. Taking a teaching contract as a typical example, a teaching party promises to provide a learning service, and a learner promises to pay a reasonable amount of money. Digital means that contracts must be written into computer readable code, and as long as the participants agree, the rights and obligations for intelligent contract establishment are performed by a computer or computer network and recorded into the blockchain platform, which is not tamper-able, and has trust and security. An agreement is a technical implementation on the basis of which a contract commitment is implemented or on which a contract commitment implementation is recorded. Intelligent contracts are constrained by markets, participants, blockchain platforms, and legal parties, as shown in fig. 5.
The learning service provided by the teaching party generates adaptive learning content for the learner according to the characteristics and the learning track of the learner, is supplemented with an adaptive teaching strategy, and records the formative evaluation result. Based on the ether house, an intelligent ubiquitous learning environment based on the block chain is realized, as shown in fig. 6.
The learning elements are oriented to specific learning targets and can be communicated with each other to construct a personalized knowledge network taking a learner as a center. The content of the learning element comprises metadata, an aggregation model, a domain ontology, content, exercises, evaluations, activities, generative information, a multi-element format, a service interface and the like. The intelligent learning element is added with two elements of teaching strategy and intelligent teaching on the basis of the learning element. The teaching strategy has a plurality of teaching strategies suitable for learning contents, and the intelligent teaching matches the learner with the suitable teaching strategies, exercises and contents of the next step of learning according to the characteristics and the learning track of the learner, as shown in fig. 7.
The operation transaction of the intelligent learning element comprises the following steps: the method comprises the steps of intelligent learning element creation, updating of existing intelligent learning element content, exercise processes and results, formative evaluation and teaching strategies.
The intelligent learning elements take the learner as the center, the cognitive load of each learning element is small enough, and a certain logical relationship is formed between the learning elements, so that the learning gradient of the learner is small enough. The learning unit provides each learner with proper learning content, the number of exercises and the learning unit needed to learn next step. The intelligent learning unit also provides proper teaching strategies such as sample learning, self explanation, instant feedback and the like according to the mastery degree of the learner. The intelligent learning unit enables the ubiquitous learning environment to have certain teaching capacity, performs adaptive learning and adaptive teaching, further reduces the learning difficulty of learners, and truly achieves personalized learning.
In a ubiquitous learning environment based on blockchains, each tile includes two parts, a tile header and a tile body.
The contents of the block header include: a version number, a hash pointer to the previous block, a hash value of the current block, a root pointer of the mekerr tree, and a timestamp.
The block body part comprises: and performing operation transaction aiming at the intelligent learning element. These transactions constitute the meikel tree. The root of the mekerr tree is recorded in the block header.
In a ubiquitous learning environment based on blockchains, a newly generated block is linked to a previous block, thus forming a unidirectional blockchain. Each node maintains a complete blockchain. The nodes communicate in a point-to-point mode, and the operation record of any node on the intelligent learning element can be broadcast to all nodes as a transaction. The node collects all received transactions, and when a set number of transactions are collected or a certain time interval is reached, the node with the highest credit will review each transaction record, and the transactions that meet the requirements are recorded in the block of the block.
The operation on the intelligent learning element comprises the following steps: the method comprises the steps of intelligent learning element creation, updating of the content of the existing intelligent learning element, exercise process and result, formative evaluation, teaching strategy and the like. Each operation on the intelligent learning element is associated with a specific learner through the ID of the node.
For the purpose of privacy protection, a learner can use any node ID to operate the intelligent learning element, the learner stores all used IDs in the node, and the intelligent learning element corresponds all the IDs related to the learner according to the corresponding relation between the node ID and the learner, so that learning content is recommended, exercise questions are generated, teaching strategies are selected, evaluation results are generated and the like.
In the embodiment of the invention, the semantic features of the intelligent learning element content and the semantic features of the updated content in each transaction can be specifically extracted, the similarity calculation is carried out on the semantic features of the updated content and the semantic features of the intelligent learning element content, and the content transaction exceeding the credibility threshold is accepted;
and scoring the trust level of the learner through the resource trust level and the user trust level as well as time influence, difference influence and most reliable assumptions, wherein the learner with the highest trust level obtains the updating right of the block.
Factors that affect confidence include: accuracy of content, objectivity of content, completeness of content, frequency of use, results of formative evaluations, period of learning, number of interactions with other learners, and the like.
The embodiment of the invention provides a ubiquitous learning environment construction method based on a block chain. Because a large amount of learning resources are concentrated in the intelligent ubiquitous learning environment based on the block chain, the classification granularity of the resources can be fully refined, and the learning requirement of personalized customization is met. Meanwhile, an intermediate platform does not exist in the intelligent ubiquitous learning environment based on the block chain, a direct communication channel is provided for the user and a learning resource producer, and better user experience is achieved.
The concept of the intelligent learning element is provided, and self-adaptive learning and self-adaptive teaching are realized through the intelligent contract of the block chain. Transaction legality is checked through a semantic gene similarity model, and generation of a new block is achieved through a consensus mechanism based on the trust degree. The system realizes a decentralized open learning environment, and can realize personalized learning at any place and any time. The system is realized based on an EtherFang blockchain system. The reason for using etherhouses is its well-defined intelligent contract mechanism. The intelligent teaching and personalized learning mentioned herein can be realized by using an intelligent contract, and fig. 8 is an effect schematic diagram of an intelligent learning environment provided by an embodiment of the present invention.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A ubiquitous learning environment construction method based on block chains is characterized by comprising the following steps: the node records the transaction of the learner based on a consensus mechanism;
when the transaction reaching the preset number is collected or a certain time interval is reached from the last transaction collection, the node with the highest credit examines each transaction, and the transaction meeting the requirement is recorded in the block body of the block;
the highest credit node reviews each transaction, and satisfactory transactions are recorded in a block of blocks, including:
extracting semantic features of intelligent learning element contents and semantic features of updated contents in each transaction, and performing similarity calculation on the semantic features of the updated contents and the semantic features of the intelligent learning element contents, wherein the content transactions exceeding a credibility threshold are accepted;
scoring the learner for trust level through resource trust level and user trust level as well as time influence, difference influence and most reliable assumptions, and the learner with the highest trust level obtains the update right of the block;
the transaction is a transaction performed to fulfill an intelligent contract for the learner to pay for learning services provided by the learner for the teaching party;
the learning service provided by the teaching party generates adaptive learning content for the learner according to the characteristics and the learning track of the learner, is supplemented with an adaptive teaching strategy, and records the formative evaluation result.
2. The method of claim 1, wherein the tile comprises a tile header and a tile body;
the block head includes: a version number, a hash pointer pointing to a previous block, a hash value of a current block, a tree root pointer of a Merkel tree, and a timestamp;
the block body includes: performing operation transaction on the intelligent learning element; the operation transaction constitutes a Merkel tree whose root is recorded in a block header.
3. The method of claim 2, wherein the operation transaction of the node on the smart learning element is broadcast as one transaction to all nodes;
the node collects all received transactions.
4. The method of claim 3, wherein the operational transaction for the smart learning element comprises: the method comprises the steps of intelligent learning element creation, updating of existing intelligent learning element content, exercise processes and results, formative evaluation and teaching strategies.
5. The method of claim 4, wherein the learner conducts an operation transaction on the smart learning element using an arbitrary node ID;
the learner saves all used node IDs in the node;
and the intelligent learning element recommends learning content, generates exercise questions, selects a teaching strategy and generates an evaluation result for the learner according to the corresponding relation between the node ID and the learner.
6. The method of claim 1, wherein the node recording a learner's transaction based on a consensus mechanism comprises:
the node records each learner's transaction once.
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| CN107864198A (en) * | 2017-11-07 | 2018-03-30 | 济南浪潮高新科技投资发展有限公司 | A kind of block chain common recognition method based on deep learning training mission |
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| CN107864198A (en) * | 2017-11-07 | 2018-03-30 | 济南浪潮高新科技投资发展有限公司 | A kind of block chain common recognition method based on deep learning training mission |
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