CN104318253A - MOOC online learning pattern recognition system and method - Google Patents
MOOC online learning pattern recognition system and method Download PDFInfo
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- CN104318253A CN104318253A CN201410634428.1A CN201410634428A CN104318253A CN 104318253 A CN104318253 A CN 104318253A CN 201410634428 A CN201410634428 A CN 201410634428A CN 104318253 A CN104318253 A CN 104318253A
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Abstract
The invention discloses an MOOC online learning pattern recognition system and method. The recognition system comprises a head-wearable EEG collecting instrument, a blink detection and analyzing instrument and a pattern recognizing and classifying device. The head-wearable EEG collecting instrument is used for obtaining EEG signals of a tested object in the online learning process in real time and is provided with a wireless communication module for transmitting the EEG signals. The blink detection and analyzing instrument is used for receiving the EEG signals sent by the head-wearable EEG collecting instrument, processing the EEG signals through a standard time frequency analytical algorithm so as to obtain the blink time information and obtain the blink time sequence, conducting statistic analysis on the blink time sequence and obtaining feature parameters of online learning state patterns. The pattern recognizing and classifying device is used for conducting pattern recognizing and classifying on the online learning state of a user according to the spatial position where the feature parameters are located, and therefore real-time feedback information is provided for the user. According to the MOOC online learning pattern recognition system and method, the structure is simple, the application range is wide, the performance is superior, the pattern recognizing problem of the MOOC online learning state can be solved, and the good market prospect is achieved.
Description
Technical field
The invention belongs to education evaluation and test field, especially a kind of MOOC on-line study pattern recognition system and method.
Background technology
MOOC is also known as " admiring class ", and be that the English opening online course (Massive Open Online Course) is on a large scale write a Chinese character in simplified form, it is a kind of online course towards masses.MOOC breaches the restriction in space-time boundary line, provides a kind of brand-new knowledge dissemination pattern and mode of learning, significant and bright prospects.
But the correlative study of MOOC still not deeply, and have also appeared the solution of some problems demand, such as high " dropping rate ".The raw institute of University of Pennsylvania's educational research is investigated for 1,000,000 MOOC users.Result shows, and only have the learner of 4% to complete curriculum, only about half of learner only listened a class.What the Instructional Design of current MOOC Open Course adopted mostly is traditional teaching mode, do not take into full account the feature mostly just utilizing the fragmentation time to learn in user learning process, the content of courses and educational mode therefore how to formulate applicable user learning will be the gordian techniquies of MOOC.
More it is emphasized that the open not just resource of MOOC, more emphasize the process learnt, and the open class of domestic video in fashion is more static resource, lacks process that is interactive and that evaluate.
Summary of the invention
Goal of the invention: provide a kind of MOOC on-line study pattern recognition system on the one hand, to solve the problems referred to above of prior art, for MOOC on-line study person provides evaluation and test in real time and feedback.On the other hand, a kind of method of estimation of MOOC on-line study pattern recognition system is provided.
Technical scheme: a kind of MOOC on-line study pattern recognition system, comprising:
Wear-type electroencephalogramdata data collector, for the EEG signals in Real-time Obtaining measurand on-line study process, and has the wireless communication module can launching described EEG signals;
Blink detection and analyser, for receiving the EEG signals that wear-type electroencephalogramdata data collector sends, and by standardized time frequency analysis algorithm process, with obtain nictation time information and and then obtain moment sequence of blinking; Statistical study is carried out to wink time sequence, obtains the characteristic parameter of the online learning state pattern of reflection;
Pattern recognition and classification device, realizes the pattern recognition and classification to user's on-line study state for the locus residing for characteristic parameter, and then provides real-time feedback information to user.
In a further embodiment, described wear-type electroencephalogramdata data collector comprises microprocessor, and be connected with microprocessor two lead brain wave acquisition module and radio receiving transmitting module; Lead brain wave acquisition module for gathering original EEG signals for described pair; Described radio receiving transmitting module is used for original EEG signals to send to blink detection and analyser in real time; Above-mentioned each module package of wear-type electroencephalogramdata data collector is in wear-type shell.
Lead brain wave acquisition module and adopt dry electrode for described pair, the scalp location of eeg signal acquisition is bilateral prefrontal lobe, and reference electrode and ground electrode respectively position are arranged in left and right ear-lobe
Described mode of learning recognition and classification device calculates based on according to blink detection and analyser the characteristic parameter obtained, the learning state pattern classifier with good generalization ability is obtained by choosing typical large sample amount data and suitable machine learning algorithm, then realize identifying user's on-line study state with described learning state pattern classifier, provide real-time feedback information to user on this basis.Described learning state pattern classifier adopts three layer feedforward neural networks, and hidden layer excitation function is Sigmoid function, and output layer activation function is linear function.Described machine learning algorithm adopts feedforward neural network structural design algorithm, determines hidden neuron quantity, input layer weights and threshold values, hidden layer weights and threshold values, and output layer weights and threshold values.
Based on the method for estimation of above-mentioned MOOC on-line study pattern recognition system, comprise the steps:
Obtain learning state perception information: the original EEG signals adopting wear-type electroencephalogramdata data collector Real-time Obtaining reflection MOOC on-line study person state, and send to blink detection and analyser by wireless communication module;
Obtain characteristic parameter: original EEG signals by standardized time frequency analysis algorithm process, obtain nictation time information and and then obtain moment sequence nictation, by obtaining the characteristic parameter reflecting online learning state pattern to the statistical study of wink time sequence;
Build learning state pattern classifier: adopt three layer feedforward neural networks and feedforward neural network structural design algorithm to obtain neural network structure in learning state pattern classifier, comprise and determine hidden neuron quantity, input layer weights and threshold values, hidden layer weights and threshold values, and output layer weights and threshold values
Wherein: hidden layer excitation function is Sigmoid function
x, f (x) represent input and output respectively, output layer activation function is linear function f (x)=x, x, f (x) represent input and output respectively, and the Function Mapping relation of learning state pattern classifier can be expressed as: out=W
2* f (W
1* in+B
1)+B
2,
Wherein out is the output of the Function Mapping relation of learning state pattern classifier, in is input, the W of the Function Mapping relation of learning state pattern classifier
1for hidden layer weights, W
2for output layer weights, B
1for hidden layer threshold values, B
2for output layer threshold values;
By choosing typical sample size training data, obtaining the learning state pattern classifier with good generalization ability, being wherein input as the characteristic parameter reflecting online learning state pattern, exporting as on-line study state;
The identification of on-line study state and feedback: the locus residing for characteristic parameter of the online learning state pattern of reflection obtained according to blink detection and analyser, learning state pattern classifier realizes identifying user's on-line study state, can provide real-time feedback information on this basis to user.
Beneficial effect: first, the present invention introduces formative evaluation and test and feedback mechanism in MOOC online education process, and utilizes wearable technology to solve MOOC education formative evaluation and test problem, for MOOC on-line study person provides evaluation and test in real time and feedback.Secondly, compared with the affection computation method of complexity, result of calculation of the present invention is stable, brief and practical.Again, the present invention adopts wearable blink detection equipment, avoids many deficiencies that the existing technology based on video analysis exists, and is not subject to the restriction of user field of employment and dimensional orientation, detects also more reliable.Finally, the present invention proposes thinking EEG signals being used for blink detection, and to need to remove the target that eye moves, myoelectricity disturbs totally different with traditional EEG Processing, and, owing to adopting wearable technology to lead acquisition method with two, blink detection result does not benefit from the dynamic impact of account.In a word, structure of the present invention is simple, and applicability is extensive, and superior performance, can solve the pattern recognition problem of MOOC on-line study state, has good market outlook.
Accompanying drawing explanation
Fig. 1 is the theory diagram of the embodiment of the present invention.
Embodiment
As shown in Figure 1, MOOC on-line study pattern recognition system of the present invention, comprises wear-type electroencephalogramdata data collector, blink detection and analyser, mode of learning recognition and classification device.
Wherein, wear-type electroencephalogramdata data collector is used for EEG signals in Real-time Obtaining measurand on-line study process, and by wireless communication module, the EEG signals collected is sent to blink detection and analyser;
In the present embodiment, wear-type electroencephalogramdata data collector overall package, in wear-type shell, comprises microprocessor, twoly leads brain wave acquisition module and radio receiving transmitting module.Two brain wave acquisition module of leading adopts dry electrode, and the scalp location of eeg signal acquisition is bilateral prefrontal lobe, and reference electrode and ground electrode respectively position are arranged in left and right ear-lobe, for gathering original EEG signals; Original EEG signals is sent to blink detection and analyser by radio receiving transmitting module in real time.
Blink detection and analyser obtain by radio receiving transmitting module the original EEG signals that wear-type electroencephalogramdata data collector sends.Original EEG signals first by standardized time frequency analysis algorithm process, obtain nictation time information and and then obtain moment sequence nictation, then the characteristic parameter of the online learning state pattern of reflection is obtained to the statistical study of wink time sequence.
Described mode of learning recognition and classification device locus residing for described characteristic parameter realizes the pattern recognition and classification to user's on-line study state, provides real-time feedback information to improve on-line study efficiency on this basis to user.
Described mode of learning recognition and classification device calculates according to blink detection and analyser the characteristic parameter that obtains first will by choosing typical large sample amount data and suitable machine learning algorithm obtains the learning state pattern classifier with good generalization ability, then realize identifying user's on-line study state with described learning state pattern classifier, real-time feedback information can be provided to improve on-line study efficiency to user on this basis.
Learning state pattern classifier adopts three layer feedforward neural networks, and hidden layer excitation function is Sigmoid function
output layer activation function is linear function f (x)=x, and so the Function Mapping relation of learning state pattern classifier can be expressed as: out=W
2* f (W
1* in+B
1)+B
2,
Wherein, out is the output of the Function Mapping relation of learning state pattern classifier, in is input, the W of the Function Mapping relation of learning state pattern classifier
1for hidden layer weights, W
2for output layer weights, B
1for hidden layer threshold values, B
2for output layer threshold values.
Described machine learning algorithm have employed feedforward neural network structural design algorithm, determines the structural informations such as hidden neuron quantity, input layer weights and threshold values, hidden layer weights and threshold values, output layer weights and threshold values.
The method of estimation of the MOOC on-line study pattern recognition system of the embodiment of the present invention, comprises the following steps:
The acquisition of learning state perception information: the original EEG signals of wear-type electroencephalogramdata data collector Real-time Obtaining reflection MOOC on-line study person state, and send to blink detection and analyser by wireless communication technology;
The acquisition of characteristic parameter: the original EEG signals of MOOC on-line study person by standardized time frequency analysis algorithm process obtain nictation time information and and then obtain moment sequence nictation, by can obtain the characteristic parameter reflecting online learning state pattern to the statistical study of wink time sequence;
The structure of learning state pattern classifier: learning state pattern classifier have employed standardized three layer feedforward neural networks, and adopt standardized feedforward neural network structural design algorithm to solve Neural Network Structure Design problem (that is: determining the structural informations such as hidden neuron quantity, input layer weights and threshold values, hidden layer weights and threshold values, output layer weights and threshold values) in learning state pattern classifier, wherein: hidden layer excitation function is Sigmoid function
output layer activation function is linear function f (x)=x, and the Function Mapping relation of learning state pattern classifier can be expressed as: out=W
2* f (W
1* in+B
1)+B
2, wherein out is the output of the Function Mapping relation of learning state pattern classifier, in is input, the W of the Function Mapping relation of learning state pattern classifier
1for hidden layer weights, W
2for output layer weights, B
1for hidden layer threshold values, B
2for output layer threshold values.By choosing typical large sample amount training data, the learning state pattern classifier with good generalization ability can be obtained, being wherein input as the characteristic parameter reflecting online learning state pattern, export as on-line study state;
The identification of on-line study state and feedback: the locus residing for characteristic parameter of the online learning state pattern of reflection obtained according to blink detection and analyser, realization identifies user's on-line study state by learning state pattern classifier, real-time feedback information can be provided to improve on-line study efficiency to user on this basis.
It is to be noted: with regard to blink detection technology itself, the existing technology be widely adopted is the technology based on video analysis, and the face of this technical limitation user must be in suitable spatial dimension (go beyond the scope and just cannot realize detecting) and video camera is wanted to photograph direct picture (non-frontal facial recognition techniques also exists the insurmountable great drawback of short-term at present).Present invention employs wearable blink detection equipment, be not subject to the restriction of user field of employment and dimensional orientation, detect also more reliable.
More than describe the preferred embodiment of the present invention in detail; but the present invention is not limited to the detail in above-mentioned embodiment, within the scope of technical conceive of the present invention; can carry out multiple equivalents to technical scheme of the present invention, these equivalents all belong to protection scope of the present invention.It should be noted that in addition, each the concrete technical characteristic described in above-mentioned embodiment, in reconcilable situation, can be combined by any suitable mode.In order to avoid unnecessary repetition, the present invention illustrates no longer separately to various possible array mode.In addition, also can carry out combination in any between various different embodiment of the present invention, as long as it is without prejudice to thought of the present invention, it should be considered as content disclosed in this invention equally.
Claims (7)
1. a MOOC on-line study pattern recognition system, is characterized in that, comprising:
Wear-type electroencephalogramdata data collector, for the EEG signals in Real-time Obtaining measurand on-line study process, and has the wireless communication module can launching described EEG signals;
Blink detection and analyser, for receiving the EEG signals that wear-type electroencephalogramdata data collector sends, and by standardized time frequency analysis algorithm process, with obtain nictation time information and and then obtain moment sequence of blinking; Statistical study is carried out to wink time sequence, obtains the characteristic parameter of the online learning state pattern of reflection;
Pattern recognition and classification device, realizes the pattern recognition and classification to user's on-line study state for the locus residing for characteristic parameter, and then provides real-time feedback information to user.
2. MOOC on-line study pattern recognition system as claimed in claim 1, it is characterized in that, described wear-type electroencephalogramdata data collector comprises microprocessor, and is connected with microprocessor pair lead brain wave acquisition module and radio receiving transmitting module; Lead brain wave acquisition module for gathering original EEG signals for described pair; Described radio receiving transmitting module is used for original EEG signals to send to blink detection and analyser in real time; Above-mentioned each module package of wear-type electroencephalogramdata data collector is in wear-type shell.
3. MOOC on-line study pattern recognition system as claimed in claim 2, is characterized in that, lead brain wave acquisition module and adopt dry electrode for described pair, the scalp location of eeg signal acquisition is bilateral prefrontal lobe, and reference electrode and ground electrode respectively position are arranged in left and right ear-lobe.
4. MOOC on-line study pattern recognition system as claimed in claim 1, it is characterized in that, described mode of learning recognition and classification device calculates based on according to blink detection and analyser the characteristic parameter obtained, the learning state pattern classifier with good generalization ability is obtained by choosing typical large sample amount data and suitable machine learning algorithm, then realize identifying user's on-line study state with described learning state pattern classifier, provide real-time feedback information to user on this basis.
5. MOOC on-line study pattern recognition system as claimed in claim 4, is characterized in that, described learning state pattern classifier adopts three layer feedforward neural networks, and hidden layer excitation function is Sigmoid function, and output layer activation function is linear function.
6. MOOC on-line study pattern recognition system as claimed in claim 4, it is characterized in that, described machine learning algorithm adopts feedforward neural network structural design algorithm, determines hidden neuron quantity, input layer weights and threshold values, hidden layer weights and threshold values, and output layer weights and threshold values.
7., based on the method for estimation of the MOOC on-line study pattern recognition system of any one of claim 1 to 6, it is characterized in that, comprise the steps:
Obtain learning state perception information: the original EEG signals adopting wear-type electroencephalogramdata data collector Real-time Obtaining reflection MOOC on-line study person state, and send to blink detection and analyser by wireless communication module;
Obtain characteristic parameter: original EEG signals by standardized time frequency analysis algorithm process, obtain nictation time information and and then obtain moment sequence nictation, by obtaining the characteristic parameter reflecting online learning state pattern to the statistical study of wink time sequence;
Build learning state pattern classifier: adopt three layer feedforward neural networks and feedforward neural network structural design algorithm to obtain neural network structure in learning state pattern classifier, comprise and determine hidden neuron quantity, input layer weights and threshold values, hidden layer weights and threshold values, and output layer weights and threshold values
Wherein: hidden layer excitation function is Sigmoid function
output layer activation function is linear function f (x)=x, and the Function Mapping relation of learning state pattern classifier can be expressed as: out=W
2* f (W
1* in+B
1)+B
2,
Wherein out is the output of the Function Mapping relation of learning state pattern classifier, in is input, the W of the Function Mapping relation of learning state pattern classifier
1for hidden layer weights, W
2for output layer weights, B
1for hidden layer threshold values, B
2for output layer threshold values;
By choosing typical sample size training data, obtaining the learning state pattern classifier with good generalization ability, being wherein input as the characteristic parameter reflecting online learning state pattern, exporting as on-line study state;
The identification of on-line study state and feedback: the locus residing for characteristic parameter of the online learning state pattern of reflection obtained according to blink detection and analyser, learning state pattern classifier realizes identifying user's on-line study state, can provide real-time feedback information on this basis to user.
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