Berri

User Interface

implement core game design principles that primarily optimize for maintaining user flow during a state of solving math problems
optimize for recurrent feedback loops for user's learning and performance metrics to maintain engagement in respect to frictionless app usability
maximize data visibility and user retention to optimize user / student performance metrics, and further optimize internal algorithm assuming productionization and scale, similar to Brainscape's Confidence-Based Repetition Algorithm, but in respect to optimizing for algorithm-style questions that apply to mathematics.
To view a live demonstration of the features, click on the video below.

render tutor profiles in grid, allow students / users to filter by category & availability
allow users to select and view tutor_profiles and join freemium sessions (enable multi-sided platform for educators, students, and tutors)
store profile metadata and allow tutors to rank on the berri platform and gain traffic given their engagement and value creation for students.

store a comprehensive set of subject categories which can handle LaTeX equations for higher-level mathematics courses beyond high school
Store problem sets that were hardcoded with a mixture of problems varied by difficulty
store confidence_scores given by the user for each problem they solve with respect to maintaining their flow without hitting maximum point of interruption that'd break their train of focus (attention span)
store session_time, confidence_score for each problem they solve, track problem_completion, accuracy_instance_count (number of questions they solve correctly in a problem set)
compute metrics that were converted and stored in pandas.dataframe at the end of each user session for the problem_set
display short term performance metrics (specific to problem_set they complete)
user will access math training gateway for problem_sets
users can practice with currently existing problem_sets
users will soon be able to add in their own problem sets from their math classes to offer a more personalized training experience in respect to the problem types they come across in their school, which varies between other districts and states

render graphs, charts (data visualization) of the user's performance metrics / user analytics / for total # of questions solved correctly, est_mastery_time, user_xp (computed from # of correctly solved questions in respect to session_time)
display graphs that track their performance with hardcoded metadata (category, difficulty) and display trends in respect to their accuracy
graph more longer term trends and metrics that allow the user to view their performance in a clear manner with the problem sets they are doing in their mathematics class

real-time competitive gateway for online matches
users will soon access marketplace to allow students / educators to create their own problem sets that can be shared with other students from different schools and grades
users will soon be able to join & create public / private matches either locally within their classrooms, districts, and with other schools

randomization for problems imported from OCR or direct user input to automatically simulate test conditions
public / private matches with keys generated from invite codes
simulating test conditions for users via CRUD setup for problem sets (OCR trained model to identify question to prevent friction, user manually adds in multiple choice answers