When mock-set-plus Insists For A TrustSpot Review?

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Questionbang mock-set-plus is a premier assessment platform for various competitive exams including NEET, JEE and Bank exams. The application offers a feature to provide feedback once a user attempts a free mock exam. These reviews are captured and recorded through TrustSpot.

As a part of internship, I wanted to use machine learning (ML) to decide whether a user is a potential reviewer and be insisted for a feedback.


Importance of user reviews

The success of any application depends on how it meets user expectations. It is vital for any application to constantly evolve to address changing user requirements and expectations. This can be possible only by gathering regular reviews from the user community.

Every topic in mock-set-plus has a free mock exam. These are of short duration. Most users who visit mock-set-plus attempt free papers to have a quick overview of online mock exams. They are asked to give a feedback and a star rating immediately after completing the exam session.

Figure 1. mock-set-plus asking for TrustSpot reviews.

Not many users give feedback or rating, they simply skip over to the next stage for reviewing results. Now, whether a user be reminded to give feedback? It is not going to be a good user experience, if an application regularly asks users for reviews or other feedback. So, we need to be careful while seeking second time for the review. To make this process restrained, we wanted to classify users based on whether they are potential reviewers or not.


Scenario 1

A user who hardly attempted questions, ends a session without giving any feedback.


Scenario 2

A user who does extremely well with an exam, but skips over without giving feedback.


In scenario (1), let us assume, the user did not like any of the questions or the user felt the mock exam is not worthy of attempting. But, a feedback on why the application has been disliked, can be a valuable input. We should insist such a user for a feedback.

In scenario (2), the user might have found the questions to be obvious or ordinary. The application would really benefit by having this feedback.

In short, every user can be a source of valuable feedback. The only concern now is whether a user be insisted to give a feedback?

Let us use historical data from TrustSpot reviews to decide whether a user is a potential reviewer.


User Q attempted
Time spent
(T) in minutes
Reviewed or not
 Sonal S  \frac{5}{5}  \frac{3}{5}  \frac{1}{5}   Yes
krishna  \frac{0}{5}  \frac{1}{5}  \frac{0}{5}   No
Meghana R  \frac{5}{5}  \frac{3}{5}  \frac{1}{5}   Yes
Pooja Thathera  \frac{5}{5}  \frac{2}{5}  \frac{3}{5}   No
Akanksha Bangera  \frac{5}{5}  \frac{0}{5}  \frac{2}{5}   Yes
Rajesh Gaddanaker  \frac{5}{5}  \frac{3}{5}  \frac{1}{5}   Yes
guruprasad  \frac{5}{5}  \frac{2}{5}  \frac{3}{5}   No
Illa Durgesh  \frac{5}{5}  \frac{2}{5}  \frac{3}{5}   Yes
Murali Manohar  \frac{12}{20}  \frac{14}{20}  \frac{-2}{5}   Yes
Sandip Ingle Thakur  \frac{6}{20}  \frac{1}{20}  \frac{4}{80}   No
Niranjan Kannanugo  \frac{5}{5}  \frac{2}{5}  \frac{2}{5}   Yes
koushik  \frac{15}{15}  \frac{5}{15}  \frac{6}{15}   Yes
Isabella Dawn  \frac{5}{5}  \frac{5}{5}  \frac{2}{5}   No
Sachith Jaugar  \frac{5}{5}  \frac{2}{5}  \frac{1}{5}   Yes
Asad Inamdar  \frac{10}{10}  \frac{7}{9}  \frac{4}{10}   Yes
Sweety S  \frac{15}{15}  \frac{15}{15}  \frac{3}{15}   No
ramdas gavit  \frac{15}{15}  \frac{6}{10}  \frac{4}{5}   Yes

Table 1: A table showing snapshots of free mock exam sessions and TrustSpot reviews.


Choosing a ML technique – whether a user is a potential reviewer?


Our requirement is to assess whether a user will rate mock-set-plus on TrustSpot. What we have is historical ratings from TrustSpot. As we can see above, there are multiple factors (probably dependent) that contribute in a user’s decision to rate or not.

– How many questions user attempted?

– How much time a user has spent?

– What is the score?

The requirement for now is to connect historical trend to compute a probability of an outcome. We will use Naive-Bayes technique.




Let us categorize above data (table 1) into the following groups:

  • Total number of questions attempted –

Low:   if it is less than or equal to 50%,

High: if it is more than 50%.


  • Total time spent –

Low:   if it is less than or equal to 50%,

High: if it is more than 50%.


  • Score –

Low :   if it is less than or equal to 50%,

High:  if it is more than 50%.


Table of preprocessed data with low and high values:

User Q attempted
Time spent
Reviewed or not
Sonal S High High Low Yes
krishna Low Low Low No
Meghana R High High Low Yes
Pooja Thathera High Low High No
Akanksha Bangera High Low Low Yes
Rajesh Gaddanaker High High Low Yes
guruprasad High Low High No
Illa Durgesh High Low High Yes
Murali Manohar High High Low Yes
Sandip Ingle Thakur Low Low Low No
Niranjan Kannanugo High Low Low Yes
koushik High Low High Yes
Isabella Dawn High High Low No
Sachith Jaugar High Low Low Yes
Asad Inamdar High High Low Yes
Sweety S High High Low No
ramdas gavit High High High Yes

Table 2: Table showing classification of features.


Using Naive Bayes classifier to identify potential reviewers

Naive Bayes is a statistical classification technique based on Bayes Theorem. The classifier assumes that the effect of a particular feature in a class is independent of other features. In our case, the features Q, S and T can have some role in a rating event. As per Naive-Bayes classifier,  these features are assumed to be independent while computing probability. This assumption simplifies the computation, and that’s why it is considered as naive.

    \begin{equation*} P( h\mid D) = \frac{P( D \h)P(h)}{P(D)}   \end{equation}


  • P(h): the probability of hypothesis h being true (regardless of the data). This is known as the prior probability of h.
  • P(D): the probability of the data (regardless of the hypothesis). This is known as the prior probability.
  • P(h|D): the probability of hypothesis h given the data D. This is known as posterior probability.
  • P(D|h): the probability of data d given that the hypothesis h was true. This is known as posterior probability.


In our case,

likelihood of giving a review

    \begin{equation*} P( Yes\mid Q, S, T) = \frac{P( Q, S, T \mid Yes)P( Yes)}{P(Q, S, T)}  , \end{equation}


likelihood of not giving a review

    \begin{equation*} P( No\mid Q, S, T) = \frac{P( Q, S, T \mid No)P( No)}{P(Q, S, T)}  .         \end{equation}



Next, let us check whether a user will rate based on following inputs:


Total questions attempted (Q) = High  (Q_{H}),

Total marks scored (S) = Low  (S_{L}),

Total time taken (T) = High  (T_{H}).


For above condition,


likelihood of giving a review


P( Yes\mid Q_{H}, S_{L},T_{H}) = \frac{P( Q_{H}, S_{L},T_{H}\mid Yes)P( Yes)}{P(Q_{H}, S_{L},T_{H})}

                                             = \frac{P( Q_{H}\mid Yes) \,P( S_{L}\mid Yes) \, P( T_{H}\mid Yes)\,  P( Yes)}{P(Q_{H},\, S_{L},\,T_{H})}

                                             = \frac{ 1\ast\,0.7272\ast 0.5454 \ast 0.6470}{ 0.8823\ast00.7058 \ast0.4705     }

                                             = 0.8760,


likelihood of not giving a review


P( No\mid Q_{H}, S_{L},T_{H}) = \frac{P( Q_{H}, S_{L},T_{H}\mid Yes)P( No)}{P(Q_{H}, S_{L},T_{H})}

                                              = \frac{P( Q_{H}\mid No) P( S_{L}\mid Yes)  P( T_{H}\mid No)  P( No)}{P(Q_{H}, S_{L},T_{H})}

                                             = \frac{ 0.66\ast\,0.66\ast 0.33 \ast0.3529}{0.8823\ast0.7058 \ast0.4705   }

                                             = 0.1730


For above condition there is 87% chance that the user is going to rate mock-set-plus.


Final thoughts

As the application is going to constantly accumulate rating data, the prediction accuracy may improve over a period of time. More importantly, how accurate, these predictions in a production setup are something to watch for.

The classification of features (Q, T, S) for now is very broad (low/high) based. Ideally, this should be on a wider scale, e.g., poor, low, average, good, excellent,  or something similar. This is something,  we hope can achieve better accuracy in  predictions.

Lastly, I would like to thank Questionbang team, esp.,   Raksha Raikar for all the guidance and help throughout the internship.