METHODS, SYSTEMS, APPARATUSES, AND DEVICES FOR FACILITATING EVALUATION OF USER KNOWLEDGE USING MULTIPLE-CHOICE QUESTIONS

Disclosed herein is a method of evaluating user knowledge using multiple-choice questions. Accordingly, the method may include retrieving, using a storage device, an assessment module and transmitting, using the communication device, the assessment module to a student device, wherein the assessment module comprises a multiple choice question and options. Further, the method may include receiving, using the communication device, bets corresponding to the options from the at least one student device, retrieving, using the storage device, an assessment answer module comprising correct probabilities, analyzing, using the processing device, the bets and the correct probabilities, generating, using the processing device, a knowledge score based on the analyzing of the plurality of bets and the correct probabilities, transmitting, using the communication device, the knowledge score to an examiner device and the student device, and storing, using the storage device, the bets and the knowledge score.

FIELD OF THE INVENTION

Generally, the present disclosure relates to the field of data processing. More specifically, the present disclosure relates to methods, systems, apparatuses, and devices for facilitating evaluation of user knowledge using multiple-choice questions.

BACKGROUND OF THE INVENTION

The field of data processing is technologically important to several industries, business organizations, and/or individuals.

In connection with the verification of training, applications such as online education require users to answer correctly through MCQ (Multiple Choice Questions), picking the right answer from the available choices of a list.

However, using MCQs for evaluating user knowledge is deficient in regard to several aspects. For instance, the MCQs do not adequately evaluate problem-solving skills. Furthermore, the MCQs do not evaluate higher-order reasoning skills. Further, in a tricky MCQ, where the answer to select is not trivial, only “right” or “wrong” is not enough to judge the user's knowledge. Moreover, the MCQs do not determine how close the user is to be right or wrong, i.e., it does not provide information about the degree of a user's knowledge. Users can just guess and a lucky user could be incorrectly evaluated. A technique to discover who is guessing requires a certain amount of answers already given from the user, which means a short verification of knowledge is not applicable. Another technique to mitigate the guessing is to adopt a “slider” of confidence, where the user indicates their certainty about the answer, however, this is typically misused in practice (most users choose always 100% confidence) because unusual and counterintuitive in the case of negative questions (called “odd-one-out” questions, in which the correct answer is the false one).

Therefore, there is a need for improved methods, systems, apparatuses, and devices for facilitating evaluation of user knowledge using multiple-choice questions that may overcome one or more of the above-mentioned problems and/or limitations.

SUMMARY OF THE INVENTION

Disclosed herein is a method of evaluating user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, the method may include retrieving, using a storage device, at least one assessment module. Further, the at least one assessment module may include a multiple-choice question and a plurality of options associated with the multiple-choice question. Further, the method may include transmitting, using a communication device, the at least one assessment module to at least one student device associated with at least one student. Further, the method may include receiving, using the communication device, a plurality of bets corresponding to the plurality of options from the at least one student device. Further, a first bet of the plurality of bets corresponding to a first option of the plurality of options may be a first measure of correctness of the first option in relation to the multiple-choice question. Further, a second bet of the plurality of bets corresponding to a second option of the plurality of options may be a second measure of correctness of the second option in relation to the multiple-choice question. Further, the method may include retrieving, using the storage device, at least one assessment answer module. Further, the method may include analyzing, using a processing device, the plurality of bets and the plurality of correct probabilities. Further, the method may include generating, using the processing device, a knowledge score based on the analyzing of the plurality of bets and the plurality of correct probabilities. Further, the method may include transmitting, using the communication device, the knowledge score to at least one of at least one examiner device and the at least one student device. Further, the method may include storing, using the storage device, at least one of the plurality of bets and the knowledge score.

Further disclosed herein is a system of evaluating user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, the system may include a communication device configured for transmitting at least one assessment module to at least one student device associated with at least one student. Further, the communication device may be configured for receiving a plurality of bets corresponding to a plurality of options from the at least one student device. Further, a first bet of the plurality of bets corresponding to a first option of the plurality of options may be a first measure of correctness of the first option in relation to a multiple-choice question. Further, a second bet of the plurality of bets corresponding to a second option of the plurality of options may be a second measure of correctness of the second option in relation to the multiple-choice question. Further, the communication device may be configured for transmitting a knowledge score to at least one of at least one examiner device and the at least one student device. Further, the system may include a processing device communicatively coupled with the communication device. Further, the processing device may be configured for analyzing the plurality of bets and a plurality of correct probabilities. Further, the processing device may be configured for generating the knowledge score based on the analyzing of the plurality of bets and the plurality of correct probabilities. Further, the system may include a storage device communicatively coupled with the processing device. Further, the storage device may be configured for retrieving the at least one assessment module. Further, the at least one assessment module may include the multiple-choice question and the plurality of options associated with the multiple-choice question. Further, the storage device may be configured for retrieving at least one assessment answer module. Further, the at least one assessment answer module may include the plurality of correct probabilities corresponding to the plurality of options of the multiple-choice question. Further, the storage device may be configured for storing at least one of the plurality of bets and the knowledge score.

DETAIL DESCRIPTIONS OF THE INVENTION

The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of methods, systems, apparatuses, and devices of facilitating evaluation of user knowledge using multiple-choice questions, embodiments of the present disclosure are not limited to use only in this context.

The present disclosure describes methods, systems, apparatuses, and devices for facilitating evaluation of user knowledge using multiple-choice questions.

In this context, the disclosed system may be associated with following user types:an evaluated user (generally a student or a learner)an evaluator user (generally a teacher or examiner or the author or creator of the MCQ)

In this context, the disclosed methods may include following processes:bet on the right answercontinue to submit the answertop-up credit when the budget is running outskip to the next question when the topic of the assessment is unknownask for a hint when the topic of the assessment is not fully unknownMCQ selector (machine learning algorithm selecting MCQ based on difficulty)answer generator (machine learning algorithm generating MCQ answers)

In this context coins may include but are not limited to:fiat and digital currencies, real or virtual tokens, cryptocurrencies, digital reward tokens, physical tokens, etc.

Further, the disclosed system may be configured to automatically generate multiple answers to MCQ that exhibit the desired variability in correctness using a machine learning algorithm. Further, the disclosed system may be configured to automatically generate wrong answers via a second machine learning algorithm based on a corpus of learning material and the correct answer provided by the user evaluator. Further, the disclosed system may be configured to analyze the betting behavior of the evaluated user and automatically adapt the variability in the correctness of subsequent question-answers using a machine-learning algorithm. Further, the machine learning algorithm is trained to provide difficult MCQ only at the end of the learning journey to stimulate evaluated users to continue the learning process (avoiding making them frustrated quickly) but also to motivate on top-up when the evaluated user is close to the end.

In order to have honest, unbiased, cheat-free, non-random answers, the disclosed system asks the evaluated user to bet a certain amount of coins from a budget received at the start of the assessment. Further, the budget may not be paid by the evaluated user, it is the promised win for a perfect knowledge verification during the assessment phase (or learning journey). Further, the disclosed method may include an “earn to learn” process, that promotes learning without any payment or even receiving payment that may give resources to deserving students.

However, if the evaluated user ends up consuming all the available budget, the evaluated user may top up to continue with the examination. In case the evaluated user does not know the answer, the evaluated user may skip the question and go to the next MCQ, paying a micro fee as a penalty. Further, the disclosed system may provide an opportunity to receive hints for a correct answer in exchange for payment of extra coins, coming from the budget (or available budget) or a top-up. This is the way the disclosed system may become lucrative and sustainable.

Further, the disclosed system may allow the student (or examined student) to save the budget from wrong bets and cashout at the end of the evaluation. Further, the coins lost from wrong answers go to an evaluator that created the MCQ. Further, the scope of the evaluator is to provide as much as possible difficult/non-trivial MCQ, in order to cash out at the end of every examination process.

Non-binary information increases deeply the understanding of the evaluated user's knowledge and it can be automatically managed using the disclosed system, preserving fast evaluation and fast administration. Further, the disclosed system may be configured for the evaluation of problem-solving and higher-order reasoning skills.

Further, the disclosed system may improve the quality of knowledge verification.

Referring now to figures,FIG.1is a flowchart of a method100of evaluating user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, at102, the method100may include retrieving, using a storage device (such as a storage device806), at least one assessment module. Further, the at least one assessment module may include a multiple-choice question and a plurality of options associated with the multiple-choice question.

Further, at104, the method100may include transmitting, using a communication device (such as a communication device802), the at least one assessment module to at least one student device (such as at least one student device902) associated with at least one student.

Further, at106, the method100may include receiving, using the communication device, a plurality of bets corresponding to the plurality of options from the at least one student device. Further, a first bet of the plurality of bets corresponding to a first option of the plurality of options may be a first measure of correctness of the first option in relation to the multiple-choice question. Further, a second bet of the plurality of bets corresponding to a second option of the plurality of options may be a second measure of correctness of the second option in relation to the multiple-choice question.

Further, at108, the method100may include retrieving, using the storage device, at least one assessment answer module. Further, the at least one assessment answer module may include a plurality of correct probabilities corresponding to the plurality of options of the multiple-choice question.

Further, at110, the method100may include analyzing, using a processing device (such as a processing device804), the plurality of bets and the plurality of correct probabilities.

Further, at112, the method100may include generating, using the processing device, a knowledge score based on the analyzing of the plurality of bets and the plurality of correct probabilities.

Further, at114, the method100may include transmitting, using the communication device, the knowledge score to at least one of at least one examiner device (such as at least one examiner device904) and the at least one student device.

Further, at116, the method100may include storing, using the storage device, at least one of the plurality of bets and the knowledge score.

Further, in some embodiments, the multiple-choice question may include a first multiple-choice question and a second multiple-choice question. Further, the first multiple-choice question may be associated with a first plurality of options. Further, the second multiple-choice question may be associated with a second plurality of options. Further, the first multiple-choice question may be presented on the at least one student device at a first time instant. Further, the second multiple-choice question may be presented on the at least one student device at a second time instant later than the first time instant. Further, the knowledge score may include a first knowledge score corresponding to the first multiple-choice question. Further, the method100may include generating, using the processing device, the second plurality of options based on the first knowledge score.

Further, in some embodiments, the method100may include receiving, using the communication device, a specified variability of correctness corresponding to the second plurality of options from the at least one examiner device. Further, the generating of the second plurality of options may be based on the specified variability of correctness.

FIG.2is a flowchart of a method200of evaluating user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, at202, the method200may include performing, using processing the device, a first machine learning based on a domain specific corpus comprising a plurality of questions and a plurality of answers corresponding to each of the plurality of questions.

Further, at204, the method200may include generating, using the processing device, the multiple-choice question and the plurality of options associated with the multiple-choice question based on the first machine learning.

Further, in some embodiments, the method200may include receiving, using the communication device, a specified variability of correctness corresponding to the plurality of options from the at least one examiner device. Further, the generating of the multiple-choice question and the plurality of options associated with the multiple-choice question may be based on the specified variability of correctness.

FIG.3is a flowchart of a method300of evaluating user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, at302, the method300may include retrieving, using the storage device, a budget data associated with a budget available for the at least one student. Further, the at least one student places the plurality of bets using the budget. Further, the at least one student places the plurality of bets using the budget.

Further, at304, the method300may include updating, using the processing device, the budget data based on the first knowledge score.

Further, at306, the method300may include generating, using the processing device, a revised budget data based on the updating of the budget data. Further, the revised budget data corresponds to a revised budget. Further, the transmitting of the second multiple-choice question and the second plurality of options may be based on the revised budget data.

FIG.4is a flowchart of a method400of evaluating user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, at402, the method400may include comparing, using the processing device, the revised budget data and a threshold budget. Further, the threshold budget may include a minimum budget required for betting to answer the at least one question.

Further, at404, the method400may include generating, using the processing device, a budget alert based on the comparing of the revised budget data and the threshold budget. Further, the budget alert may inform the at least one student that the revised budget is low than the minimum budget for answering the at least one question. Further, the generating of the knowledge score may be based on the revised budget.

Further, at406, the method400may include transmitting, using the communication device, the budget alert to the at least one student device. Further, in some embodiments, the method400may include transmitting, using the communication device, the revised budget data to the at least one student device.

FIG.5is a flowchart of a method500of evaluating user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, at502, the method500may include generating, using the processing device, a top-up request based on the budget alert.

Further, at504, the method500may include transmitting, using the communication device, the top-up request to the at least one student device.

Further, at506, the method500may include receiving, using the communication device, a payment information from the at least one student device.

Further, at508, the method500may include processing, using the processing device, a transaction based on the payment information and the top-up request. Further, the generating of the revised budget data may be based on the processing of the transaction.

FIG.6is a flowchart of a method600of evaluating user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, at602, the method600may include determining, using the processing device, at least one examiner share based on the analyzing of the plurality of bets and the plurality of correct probabilities. Further, the at least one examiner share reflects money distributed among at least one examiner. Further, the money corresponds to at least one bet of the plurality of bets placed on at least one wrong option of the plurality of options.

Further, at604, the method600may include transmitting, using the communication device, the at least one examiner share to the at least one examiner device.

FIG.7is a flowchart of a method700of evaluating user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, at702, the method700may include receiving, using the communication device, a tip request associated with the multiple-choice question from the at least one student device. Further, the tip request may indicate that the at least one student may be facing difficulty in the at least one question and want to receive a hint or tip to the correct answer for the at least one question.

Further, at704, the method700may include analyzing, using the processing device, the tip request and the multiple-choice question.

Further, at706, the method700may include retrieving, using the storage device, a tip data corresponding to the multiple-choice question based on the analyzing of the tip request and the multiple-choice question.

Further, at708, the method700may include transmitting, using the communication device, the tip data to the at least one student device.

FIG.8is a block diagram of a system800of evaluating user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, the system800may include a communication device802configured for transmitting at least one assessment module to at least one student device902(as shown inFIG.9) associated with at least one student. Further, the communication device802may be configured for receiving a plurality of bets corresponding to a plurality of options from the at least one student device902. Further, a first bet of the plurality of bets corresponding to a first option of the plurality of options may be a first measure of correctness of the first option in relation to a multiple-choice question. Further, a second bet of the plurality of bets corresponding to a second option of the plurality of options may be a second measure of correctness of the second option in relation to the multiple-choice question. Further, the communication device802may be configured for transmitting a knowledge score to at least one of at least one examiner device904(as shown inFIG.9) and the at least one student device902.

Further, the system800may include a processing device804communicatively coupled with the communication device802. Further, the processing device804may be configured for analyzing the plurality of bets and a plurality of correct probabilities. Further, the processing device804may be configured for generating the knowledge score based on the analyzing of the plurality of bets and the plurality of correct probabilities.

Further, the system800may include a storage device806communicatively coupled with the processing device804. Further, the storage device806may be configured for retrieving the at least one assessment module. Further, the at least one assessment module may include the multiple-choice question and the plurality of options associated with the multiple-choice question. Further, the storage device806may be configured for retrieving at least one assessment answer module. Further, the at least one assessment answer module may include the plurality of correct probabilities corresponding to the plurality of options of the multiple-choice question. Further, the storage device806may be configured for storing at least one of the plurality of bets and the knowledge score.

Further, in some embodiments, the processing device804may be configured for performing a first machine learning based on a domain specific corpus comprising a plurality of questions and a plurality of answers corresponding to each of the plurality of questions. Further, the processing device804may be configured for generating the multiple-choice question and the plurality of options associated with the multiple-choice question based on the first machine learning.

Further, in some embodiments, the communication device802may be configured for receiving a specified variability of correctness corresponding to the plurality of options from the at least one examiner device904. Further, the generating of the multiple-choice question and the plurality of options associated with the multiple-choice question may be based on the specified variability of correctness.

Further, in some embodiments, the multiple-choice question may include a first multiple-choice question and a second multiple-choice question. Further, the first multiple-choice question may be associated with a first plurality of options. Further, the second multiple-choice question may be associated with a second plurality of options. Further, the first multiple-choice question may be presented on the at least one student device902at a first time instant. Further, the second multiple-choice question may be presented on the at least one student device902at a second time instant later than the first time instant. Further, the knowledge score may include a first knowledge score corresponding to the first multiple-choice question. Further, the processing device804may be configured for generating the second plurality of options based on the first knowledge score.

Further, in some embodiments, the communication device802may be configured for receiving a specified variability of correctness corresponding to the second plurality of options from the at least one examiner device904. Further, the generating of the second plurality of options may be based on the specified variability of correctness.

Further, in some embodiments, the storage device806may be configured for retrieving a budget data associated with a budget available for the at least one student. Further, the processing device804may be configured for updating the budget data based on the first knowledge score. Further, the processing device804may be configured for generating a revised budget data based on the updating of the budget data. Further, the transmitting of the second multiple-choice question and the second plurality of options may be based on the revised budget data.

Further, in some embodiments, the processing device804may be configured for comparing the revised budget data and a threshold budget. Further, the processing device804may be configured for generating a budget alert based on the comparing of the revised budget data and the threshold budget. Further, the communication device802may be configured for transmitting the budget alert to the at least one student device902. Further, in some embodiments, the communication device802may be configured for transmitting the revised budget data to the at least one student device902.

Further, in some embodiments, the processing device804may be configured for generating a top-up request based on the budget alert. Further, the processing device804may be configured for processing a transaction based on a payment information and the top-up request. Further, the generating of the revised budget data may be based on the processing of the transaction. Further, the communication device802may be configured for transmitting the top-up request to the at least one student device902. Further, the communication device802may be configured for receiving the payment information from the at least one student device902.

Further, in some embodiments, the processing device804may be configured for determining at least one examiner share based on the analyzing of the plurality of bets and the plurality of correct probabilities. Further, the at least one examiner share reflects money distributed among at least one examiner. Further, the money corresponds to at least one bet of the plurality of bets placed on at least one wrong option of the plurality of options. Further, the communication device802may be configured for transmitting the at least one examiner share to the at least one examiner device904.

Further, in some embodiments, the communication device802may be configured for receiving a tip request associated with the multiple-choice question from the at least one student device902. Further, the tip request may indicate that the at least one student may be facing difficulty in the at least one question and want to receive a hint or tip to the correct answer for the at least one question. Further, the communication device802may be configured for transmitting a tip data to the at least one student device902. Further, the processing device804may be configured for analyzing the tip request and the multiple-choice question. Further, the storage device806may be configured for retrieving the tip data corresponding to the multiple-choice question based on the analyzing of the tip request and the multiple-choice question. Further, the tip data may include a textual content, a graphical content, etc. associated with the correct answer. Further, the graphical content may include an image, a video, an audio, an audio-video, etc.

Further, in some embodiments, the generating of the revised budget data may be based on the transmitting of the tip data. Further, an amount equivalent to a bet placed for the tip may be deducted from a revised budget corresponding to the revised budget data.

FIG.9is a block diagram of the system800of evaluating user knowledge using multiple-choice questions, in accordance with some embodiments.

FIG.10is a flow chart of a method1000for economic incentivisation for evaluation of user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, at1002, the method1000may include receiving, using a communication device, a request from at least one student device associated with at least one student. Further, the at least one student device may include a smartphone, a tablet, a mobile, a computer, a laptop, and so on. Further, the at least one student may include an individual that may want to undergo at least one assessment. Further, the request may indicate that the at least one student may want to initiate the at least one assessment. Further, the request may include a student data comprising student name, a student learning standard, an institute name, an email address, an address, a contact number, etc. Further, the request may include a budget data comprising a budget equivalent to a certain amount of coins (or digital coins).

Further, at1004, the method1000may include analyzing, using a processing device, the request using a machine learning algorithm.

Further, at1006, the method1000may include retrieving, using a storage device, at least one assessment data associated with the at least one assessment based on the analyzing of the request. Further, the at least one assessment data may include at least one question such as multiple-choice questions (MCQs).

Further, at1008, the method1000may include transmitting, using the communication device, the at least one assessment data to the at least one student device.

Further, at1010, the method1000may include receiving, using the communication device, at least one response corresponding to the at least one assessment data from the at least one student device. Further, the at least one response may include at least one option chosen by the at least one student for the at least one question. Further, the at least one student may bet a certain amount of the coins for the at least answer. Further, the at least one answer may include a probability corresponding to each option of at least one option being correct. Further, the at least one question may include the at least one option.

Further, at1012, the method1000may include analyzing, using the processing device, the response and the at least one assessment data using at least one second machine learning algorithm.

Further, at1014, the method1000may include generating, using the processing device, a knowledge result based on the analyzing of the response and the at least one assessment data. Further, the knowledge result may evaluate user knowledge for the at least one assessment. Further, the knowledge result may indicate a level of problem-solving skills and higher-order reasoning skills of the at least one student.

Further, at1016, the method1000may include transmitting, using the communication device, the knowledge result to at least one of at least one examiner device and the at least one student device. Further, the at least one examiner device may be associated with at least one examiner comprising an individual, an institution, and an organization that may want to conduct the at least one assessment. Further, the at least one examiner device may include a smartphone, a mobile, a tablet, a computer, and so on.

Further, at1018, the method1000may include storing, using the storage device, the request and the knowledge result.

FIG.11is a flow chart of a method1100for economic incentivisation for evaluation of user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, at1102, the method1100may include analyzing, using the processing device, the response and the at least one assessment data to generate a revised budget. Further, the revised budget may indicate the budget left after answering a first question of the at least one question. Further, at1104, the method1100may include comparing, using the processing device, the revised budget and a threshold budget. Further, the threshold budget may include a minimum budget required for betting to answer the at least one question. Further, at1106, the method1100include generating, using the processing device, a budget alert based on the comparing of the revised budget and the threshold budget. Further, the budget alert may inform the at least one student that the revised budget is low than the minimum budget for answering the at least one question. Further, the generating of the knowledge result may be based on the revised budget. Further, at1108, the method1100may include transmitting, using the communication device, the budget alert to the at least one student device. Further, in some embodiments, the method1100may include transmitting, using the communication device, the revised budget to the at least one student device.

Further, in some embodiments, the method1100may include generating, using the processing device, a top-up request based on the budget alert. Further, the top-up request may inform the at least one student to top-up the revised budget to continue the at least one assessment. Further, the method1100may include transmitting, using the communication device, the top-up request to the at least one student device. Further, the method1100may include receiving, using the communication device, a payment information from the at least one student device. Further, the method1100may include processing, using the processing device, a transaction based on the payment information and the top-up request. Further, the generating of the revised budget may be based on the processing of the transaction.

Further, in some embodiments, the method1100may include receiving, using the communication device, the at least one question and a correct answer from the at least one examiner device. Further, the method1100may include analyzing, using the processing device, the at least one question and the correct answer based on at least one third machine learning algorithm. Further, the method1100may include generating, using the processing device, the least one option based on the analyzing of the at least one question and the correct answer. Further, the at least one option may exhibit a desired variability of correctness with respect to the correct answer. Further, the at least one question and the at least one option may be comprised in the at least one assessment data. Further, the at least one option may include the correct answer along with wrong answers. Further, the method1100may include storing, using the storage device, the at least one assessment data.

Further, in some embodiments, the at least one assessment data may include a sequence of the at least one question that may be presented to the at least one student (such as the evaluated user). Further, in an instance, the sequence may include presenting a difficult question of the least one question only at the end of the assessment to stimulate the at least one evaluated user to continue the assessment (avoiding making him/her frustrated quickly) but also to motivate on top-up when the at least one evaluated user is close to the end. Further, the each of the at least one question may be characterized by a difficulty level. Further, the method1100may include determining, using the processing device, a betting behavior of the at least one student based on the analyzing of the at least one response and the at least one assessment data. Further, the method1100may include updating, using the processing device, the sequence based on the betting behavior. Further, the method1100may include generating, using the processing device, an updated sequence based on the updating. Further, the transmitting of the at least one assessment data may be based on the updated sequence.

Further, in some embodiments, the generating of the at least one option may be based on the betting behavior.

Further, in some embodiments, the method1100may include receiving, using the communication device, at least one sensor data from at least one sensor disposed in a surrounding of the at least one student. Further, the at least one sensor may include an audio sensor, an image sensor, a motion sensor, etc. Further, the at least one sensor may be configured for generating at least one sensor data based on determining a behavior of the at least one student. Further, the method1100may include analyzing, using the processing device, the at least one sensor data. Further, the method1100may include determining, using the processing device, a physical behavior characteristic of the at least one student based on the analyzing of the at least one sensor data. Further, the physical behavior characteristic may include the movements of the at least one student during the assessment. Further, the movements may include movement of the head, hands, etc. Further, the method1100may include generating, using the processing device, an unfair practice alert based on the determining of the physical behavior characteristic. Further, the generating of the knowledge result may be based on the unfair practice alert.

Further, in some embodiments, the method1100may include receiving, using the communication device, a tip request from the at least one student device. Further, the tip request may indicate that the at least one student may be facing difficulty in the at least one question and want to receive a hint or tip to the correct answer for the at least one question. Further, the method1100may include analyzing, using the processing device, the tip request, the at least one question, and the revised budget. Further, the method1100may include determining, using the processing device, a tip approval based on the analyzing of the tip request, the at least one question, and the revised budget. Further, the method1100may include retrieving, using the storage device, the tip data corresponding to the at least one question. Further, the tip data may include a textual content, a graphical content, etc. associated with the correct answer. Further, the graphical content may include an image, a video, an audio, an audio-video, etc. Further, the method1100may include transmitting, using the communication device, the tip data to the at least one student device. Further, the generating of the revised budget may be based on the transmitting of the tip data. Further, an amount equivalent to a bet placed for the tip may be deducted from the revised budget.

Further, in some embodiments, the at least one response may include a skipping response. Further, the skipping response may indicate that the at least one student didn't give an answer to the at least one question and proceeded to a next question of the at least one question. Further, the method1100may include deducting, using the processing device, a penalty amount from the revised budget based on the skipping response. Further, the penalty may be distributed across the at least one examiner (or evaluator) or a creator of the exam. Further, the method1100may include transmitting, using the processing device, the penalty amount to the at least one examiner device.

FIG.12is a flow diagram of a process1200for economic incentivisation for evaluation of user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, at1202, the process1200may include presenting an assessment title of an assessment journey to an evaluated user using a user interface1204. Further, at1206, the process1200may include asking an evaluated user to bet a certain amount of coins from a budget received at the start of the assessment journey. Further, in an instance, the bet value may be $100 (as an example), but this value changes based on the MCQ difficulty). Further, the budget may not be paid by the evaluated user. Further, it is the promised win for perfect knowledge verification during a learning journey or assessment. Further, at1210, the user interface1204may show the available budget. However, if the evaluated user ends up consuming all the available budget, at1214, the process1200may allow the evaluated user to top up to continue with the assessments or examination. Further, at1208, upon clicking on Accept button, the evaluated user may agree to access the question corresponding to the assessment title. Further, at1222, the process1200may include presenting a question to the evaluated user. Further, the process1200may include receiving bets for at least one of multiple choices available for the question (or MCQ) using a user interface1216. Further, in an instance, at1218and1220, the process1200may allow the evaluated user to place $70 (as an example) on a first answer of the multiple choices (or answers) and $30 (as an example) on a second answer of the multiple choices. In case the evaluated user doesn't know the topic in relation to the title, at1202, of the question, at1224, the process1200may allow the evaluated user to always skip it and go to a next question (MCQ) or next learning material by paying a micro fee as a penalty. Further, at1212, the process1200may include providing a hint or tip to the evaluated user for a correct answer in exchange for payment of extra coins, coming from the available budget or a top-up. Further, in an instance, the tip may cost 10% of the bet for the question. Further, at1226, the process1200may allow the evaluated user to continue to view the correct answer to the question by clicking on a continue button.

FIG.13is a continuous flow diagram of the process1200for economic incentivisation for evaluation of user knowledge using multiple-choice questions, in accordance with some embodiments. Accordingly, the process1200may include presenting the correct answer to the evaluated user using a user interface1228. Further, the amount has to be spread only across the correct answers. Further, at1230, the process1200may include distributing coins, lost by giving a wrong answer, to all evaluator users of the assessment. Further, at1232, the process1200may include the evaluated user saving the budget by betting on the correct answer. Further, at1234, the process1200may include presenting an explanation of the right answer corresponding to the question. Further, at1236, the user interface1228may include a continue button to allow the evaluated user to continue to the next question. Further, the scope of the evaluator user is to provide as much as possible difficult/not-trivial MCQ in order to cash out at the end of every assessment.

FIG.14is an illustration of an online platform1400consistent with various embodiments of the present disclosure. By way of non-limiting example, the online platform1400for facilitating evaluation of user knowledge using multiple-choice questions may be hosted on a centralized server1402, such as, for example, a cloud computing service. The centralized server1402may communicate with other network entities, such as, for example, a mobile device1406(such as a smartphone, a laptop, a tablet computer, etc.), other electronic devices1410(such as desktop computers, server computers, etc.), databases1414, and sensors1416over a communication network1404, such as, but not limited to, the Internet. Further, users of the online platform1400may include relevant parties such as, but not limited to, end-users, administrators, service providers, service consumers, and so on. Accordingly, in some instances, electronic devices operated by the one or more relevant parties may be in communication with the platform.

A user1412, such as the one or more relevant parties, may access online platform1400through a web-based software application or browser. The web-based software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device1500.

With reference toFIG.15, a system consistent with an embodiment of the disclosure may include a computing device or cloud service, such as computing device1500. In a basic configuration, computing device1500may include at least one processing unit1502and a system memory1504. Depending on the configuration and type of computing device, system memory1504may comprise, but is not limited to, volatile (e.g. random-access memory (RAM)), non-volatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory1504may include operating system1505, one or more programming modules1506, and may include a program data1507. Operating system1505, for example, may be suitable for controlling computing device1500's operation. In one embodiment, programming modules1506may include image-processing module, machine learning module. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated inFIG.15by those components within a dashed line1508.

Computing device1500may have additional features or functionality. For example, computing device1500may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated inFIG.15by a removable storage1509and a non-removable storage1510. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. System memory1504, removable storage1509, and non-removable storage1510are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device1500. Any such computer storage media may be part of device1500. Computing device1500may also have input device(s)1512such as a keyboard, a mouse, a pen, a sound input device, a touch input device, a location sensor, a camera, a biometric sensor, etc. Output device(s)1514such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.

As stated above, a number of program modules and data files may be stored in system memory1504, including operating system1505. While executing on processing unit1502, programming modules1506(e.g., application1520such as a media player) may perform processes including, for example, one or more stages of methods, algorithms, systems, applications, servers, databases as described above. The aforementioned process is an example, and processing unit1502may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include machine learning applications.