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The experiment was conducted for computer graduate learners with a sample size of 40. The learners are subjected to register for the course endorsement session. During the class, the cognitive values are taken from the BCI machine. In the form of digital signals procured for a sign for eveiy second, learning is subjected to answer online questionnaire on the particular subject. Brain signals are accumulated in hertz. The average value of the attention span and the score attained at the end of the session. The brain wave visualizes as shown in Figure 2.4 by the learner, while the cognition action is performed, but for the researcher, data have to be converted into the digital format. The brain wave visualizes are represented in different frequencies as alpha, beta, gamma, and theta waves in e-sense of attention and meditation meters from 0 to 100 Hz.
Attention and meditation are resolved and documented on an interface measuring scale with a relative e-sense scale ranging from 1 to 100. The values between 20 and 40 are diminished levels, and values fr om 1 to 20 are treated as weakly lowered signals. A neutral value is considered in the range of 40-60. Values above 60 are considered to be values higher than the standard. The frequency is to interpret to an algorithm depending upon the alpha, beta, gamma, and theta waves; we can determine the concentration depending upon the spatial memory of the cognitive skills. The data as per Table 2.4 are the attention span for one single learner having a duration of 26 s. Twenty-six frequency signals are recorded in terms of attention and
FIGURE 2.4 Brain wave visualizer.
meditation. As the scale ranges from 0 to 100, each value is multiplied with 100 to get attention span. It is organized to be ahead for operating systems such as Windows and MAC OS and statistical analysis by the WEKA tool.
TABLE 2.4 Attention Span of a Single Learner
2.9.1 RESULTS AND DISCUSSION
The results for the cognizance factors for AI and DM following Likert scale ranging from strong competent to not competent are given in Tables 2.5-2.14. These cognizance factors are performance indicators for self-assessment of the learner to identify the prerequisites owned by a learner before a course endorsement, which is summarized in Table 2.5. The performance is analyzed in the form of a Likert scale ranging from 1 to 5:1 signifies strong competent and 5 signifies not competent. The proprietary algorithm for the BCI is used to get the data saved for analytical purposes.
2.9.2 COGNIZANCE FACTOR FOR AI
The cognizance factors ensuring the willingness of the learner to endorse a new course such as AI are shown in Table 2.5. These cognizance factors are performance indicators for self-assessment of the learner to identify the prerequisites owned by a learner before a course endorsement, which is summarized in Table 2.5. The performance is analyzed in the form of a Likert scale ranging from 1 to 5:1 signifies strong competent and 5 signifies not competent.
The cognizance factor concerning each student is obtained by classifying into the scale of strong competitors to no competency, as shown in Table 2.6. The learners are given a suffix of L1-L40 with a prefix of the course code AI denotes artificial intelligence.
2.9.3 COURSE ENDORSEMENT COMPETENCY FOR AI
The EEG values obtained from the BCI device termed as “Cognitive (COG).’’ The scores are obtained by the evaluation of 50 MCQs; each phase has 10 questions from the AI concept. The first 10 questions assess the learning ability through an audio-video credential, followed by 10 MCQs to answer. The second phases of indicators procure from the existing knowledge domain of the learner under the hardware and software compatibility, as shown in Table 2.7. The performance indicator values are shown in Table 2.8. The last 10 questions on the applicability have to be answered.
TABLE 2.6 Result for the Learner Competency Final Outcome for AI
TABLE 2.7 Result for the Learner Competency Based on Parameters for AI
2.9.4 COGNIZANCE FACTOR FOR DM
The cognizance factors for DM include good communication, reading, and writing skills. These cognizance factors show flexibility for learning. The performance is analyzed in the form of a Likert scale ranging from 1 to 5:1 signifies strong competent and 5 signifies not competent.
The analyzed data shown in Table 2.8 throw light on the competency of each learner while answering the 10 cognizance questions. Table 2.8 describes the learner’s competency level based on performance indicators. The learners are given a suffix of L1-L40 with a prefix of the course code DM denotes digital marketing.
2.9.5 COURSE ENDORSEMENT COMPETENCY FOR DM
The EEG values are obtained from the BCI device. The scores are obtained by the evaluation of 50 MCQs; each phase has 10 questions from the DM concepts, as shown in Table 2.8. The first 10 questions assess the learning ability through an audio-video credential, followed by 10 MCQs. The second phase of indicators procures from the existing knowledge domain of the learner following the hardware and software compatibility. The last 10 questions on the applicability have to be answered.
TABLE 2.8 Cognizance Factors for DM
TABLE 2.9 Result for the Learner Competency Outcome for DM
TABLE 2.10 Result for the Learner Competency Based on Parameters for DM
2.9.6 RESULT ANALYSIS THROUGH ML TECHNIQUES
The data received from the proposed model are input to the ML algorithm based on supervised learning decision tree algorithms: J48, RF, random tree, NB classifier, and SVM-implemented cognitive EEG data and performance indicators of learners based on four parameters for the course endorsement. Before implementing data to the ML algorithm based on digital data retrieved from the EEG signals, these have to be converted into numeric data based on the numerosity reduction method of data discretization. The converted numeric and nominal data of cognitive EEG data and performance indicators of the learner through WEKA for further processing based on a supervised and unsupervised learning algorithm. Decision tree techniques, J48 and random tree, identified that the cognizance factor variable is the first decision variable to decide about the competency of the learner for the course, as shown in Figure 2.5.
FIGURE 2.5 Decision tree J48.
Correctly classified instances, predicted value, and sensitivity calculation generated from implementing supervised learning techniques are mentioned in Table 2.11 and Figure 2.6. These statistics represent that the 100% recall value is interpreted as the completeness of the result. The consistent appraisals of class precision 92.3% for decision tree techniques are relevant values of prediction, and 77.3% of NB classifier and 75% of SVM class precision values are appropriate values of the forecast for data acquired from the experiment of AI course endorsement.
FIGURE 2.6 Supervised learning algorithm AI.
Unsupervised ML algorithms /с-means and density-based clustering are applied, and results are mentioned in Table 2.12 and Figure 2.7. Two clusters are represented in Table 2.13 showing the probability from the training data sets and the probability of course competency predicted to be 60% and course incompetency predicted to be 40% for data acquired from the experiment of AI course endorsement.
TABLE 2.12 ML Techniques AI for Unsupervised Learning
FIGURE 2.7 Unsupervised learning algorithm AI.
Correctly classified instances predicted the value and sensitivity calculation generated from implementing supervised learning techniques mentioned in Table 2.13 and Figure 2.8. These statistics represent that the 50%-86% of the recall value interprets the completeness of the result. The consistent appraisals of class precision of 65% for decision tree techniques are relevant values of prediction. Therefore, 87% of NB classifier, 53% of SYM, and class precision values are appropriate values of a forecast for data acquired from the experiment of DM course endorsement.
TABLE 2.13 Class Precision and Class Recall for DM
Unsupervised ML algorithms /г-means and density-based clustering are applied, and results are mentioned in Table 2.14 and Figure 2.9. Table 2.14 represents two clusters’ probability from the training data sets and the probability of course competency predicted to be 40% and course incompetency predicted to be 60% for data acquired from the experiment of DM course endorsement.
FIGURE 2.8 Supervised learning algorithm DM.
TABLE 2.14 ML Techniques DM for Unsupervised Learning
FIGURE 2.9 Unsupervised learning algorithm DM.
STRENGTH AND LAPSE OF THE FRAMEWORK
The proposed model in the CoML framework has an advantage over the other models as the cognition values are derived fr om brain, which speaks the tongue of the mind, unlike the traditional way of questionnaire or survey. The cognizance factors are drawn on to self-assess the learners by reading the EEG signals. The validity of the analysis of the CoML model utilizes as pre- and postevaluation of a course fulfillment. ML algorithms are implemented on the model to check the accuracy and correctly classified instances.
The model mainly requires a BCI headset, without which the cognitive signals cannot be procured, and has a batteiy life of 8 h. If the accurate selections of the frequency are not accumulated in a precise maimer, then there is a loss of data. Data are also lost if the headset gets disconnected during the experiment. The model is implemented on bigger sample size and more courses for more clustering and classification. The automated interface is generated, which reduces the time duration of data procurement.
The proposed model has conceded out the experimental procedure appropriated with cognizance factors, BCI, and ML techniques to classify the competency values for course endorsement. The learners are classified into five competency categories. This chapter also focuses on considering the cognitive ability of the learner with the conceptual capacity of understanding the concepts, hardware, and software compatibility putting forth the applicability. The analytical results from WEKA have confirmed the model accuracy of 92.3% under the ML algorithms and class precision of 87% under the NB classifier. The CoML framework based on neuroeducation, which is the science, deals with the learner’s unique cognitive strengths and their appropriate learning style fostering successful intensification of academic skills and confidence to proceed for the selected course.
Future work can be extended by the automation process of data procurement with the help of user-friendly language such as Phyton. At present, the model is implemented for computer science course, which can be applicable for discrete courses such as management, commerce, etc. Uplifting the course content as per the learning ability of the learners can help gain more knowledge. The automated system implemented on static IP can acquiesce the virtual users having the headset to measure their cognitive ability for endorsing a course.