Cross-Algorithm Ranking for Automated Multiple-Choice Distractors
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Solution Overview
Problem
Existing online learning systems lack effective methods for automatically generating high-quality distractors for multiple-choice questions, which are crucial for enhancing the learning experience.
Innovation Solution
A computer-implemented method using a distributed system with a within-set machine learning model and a cross-algorithm ranking model to generate and rank distractors, incorporating user-defined, often-confused, and number replacement algorithms to optimize distractor selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional methods are used to create distractors for multiple-choice questions, then the process requires significant manual effort and time, but the quality and relevance of distractors may be insufficient
Solution Approach 1:
The system automatically generates distractors using machine learning models that analyze the correct answer and generate plausible alternative options without human intervention. The within-set model and cross-algorithm ranking model work together to self-generate high-quality distractors that are contextually relevant and challenging.
Solution Approach 2:
The patent replaces manual human effort in distractor creation with automated machine learning systems. The mechanical process of manual distractor writing is substituted with computational algorithms including the within-set model, cross-algorithm ranking model, and multiple distractor generation algorithms that automatically produce quality distractors.
2Device complexity
If simple distractor generation methods are used, then the implementation is straightforward, but the distractors may not be challenging or relevant enough for effective learning
Solution Approach 1:
The distractor generation system is divided into multiple specialized components: a within-set model that generates distractors from the same term set, a cross-algorithm ranking model that evaluates candidates, and multiple specialized algorithms (user-defined, often-confused, number replacement). Each segment handles a specific aspect of distractor generation to ensure overall effectiveness.
Solution Approach 2:
The cross-algorithm ranking model serves as an intermediary that receives candidate distractors from multiple generation algorithms, evaluates them using learned features, and selects the best candidates. This intermediary component coordinates the work of different algorithms and ensures high-quality output by filtering and ranking candidates systematically.
3Manufacturing precision
If multiple algorithms are used to generate distractors, then the quality and variety of distractors improve, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-training the within-set model and cross-algorithm ranking model on large datasets before actual distractor generation. This preliminary training enables the models to quickly evaluate and generate high-quality distractors during inference, improving processing speed without sacrificing quality.
Solution Approach 2:
The system generates multiple candidate distractors using different algorithms and then selectively processes only the most promising candidates through the ranking model. This partial action approach avoids the computational cost of fully processing all possible distractors while still ensuring high quality output by focusing resources on the most relevant candidates.
Data Source
AI summary
In one embodiment, a computer-implemented method comprises, using an application server computer that is communicatively coupled to a database via a telecommunication network, the database comprising a digitally stored relational table schema storing a plurality of term sets, each of the term sets comprising a plurality of terms, the application server computer hosting an application program programmed with a plurality of distractor selection algorithms; executing each of the distractor algorithms to access a particular term from among the plurality of the terms as an input and to generate a plurality of corresponding first interim sets of candidate distractor terms, for the particular term, as an output; executing a first inference stage of a within-set machine learning model over the same particular term as input to generate classification output comprising a second interim set of candidate distractor terms, for the particular term; executing a second inference stage of a cross-algorithm ranking machine learning model over the plurality of corresponding first interim sets and the second interim set to generate a top-N set of best candidate distractors; digitally storing the top-N set of best candidate distractors in a table of the relational table schema in association with the particular term; formatting the top-N set of best candidate distractors with the particular term as a multiple-choice question set and transmitting, to a client computer, presentation instructions which when rendered using the client computer cause presenting a user interface comprising the particular term as a prompt and the top-N set of best candidate distractors as available answers.