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The Prediction of Student First Response Using Prerequisite Skills

Published: 14 March 2015 Publication History

Abstract

A large amount of research in the field of educational data analytics has focused primarily on student next problem correctness. Although the prediction of such information is useful in assessing current student performance, it is better for teachers and instructors to place attention on student knowledge over a longer period of time. Several researchers have articulated that it is important to predict aspects that are more meaningful, inspiring our work here to utilize the large amounts of student data available to derive more substantial predictions over student knowledge. Our goal in this paper is to utilize prerequisite information to better predict student knowledge quantitatively as a subsequent skill is begun. Learning systems like ASSISTments and Khan Academy already record such prerequisite information, and can therefore be used to construct a method of prediction as described in this paper. Using these inter-skill relationships, our method estimates students' initial knowledge based on performance on each prerequisite skill. We compare our method with the standard Knowledge Tracing (KT) model and majority class in terms of the predictive accuracy of students' first responses on subsequent skills. Our results support our method as a viable means of representing student prerequisite knowledge in a subsequent skill, leading to results that outperform the majority class and that are comparably superior to KT by providing more definitive student knowledge estimates without sacrificing predictive accuracy.

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  • (2023)The effects of operator position and superfluous brackets on student performance in simple arithmeticJournal of Numerical Cognition10.5964/jnc.95359:1(107-128)Online publication date: 31-Mar-2023
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  • (2022)Detecting threshold concepts through Bayesian knowledge tracing: examining research skill development in biological sciences at the doctoral levelInstructional Science10.1007/s11251-022-09578-550:3(475-497)Online publication date: 14-Mar-2022
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cover image ACM Conferences
L@S '15: Proceedings of the Second (2015) ACM Conference on Learning @ Scale
March 2015
438 pages
ISBN:9781450334112
DOI:10.1145/2724660
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 14 March 2015

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Author Tags

  1. first response prediction
  2. initial knowledge
  3. knowledge tracing
  4. mastery speed
  5. predicting student knowledge
  6. prerequisite
  7. subsequent skills

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L@S 2015
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L@S 2015: Second (2015) ACM Conference on Learning @ Scale
March 14 - 18, 2015
BC, Vancouver, Canada

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L@S '15 Paper Acceptance Rate 23 of 90 submissions, 26%;
Overall Acceptance Rate 117 of 440 submissions, 27%

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Cited By

View all
  • (2023)The effects of operator position and superfluous brackets on student performance in simple arithmeticJournal of Numerical Cognition10.5964/jnc.95359:1(107-128)Online publication date: 31-Mar-2023
  • (2023)Evaluation and modeling of students’ persistence and wheel-spinning propensities in formative assessmentsSmart Learning Environments10.1186/s40561-023-00283-510:1Online publication date: 4-Dec-2023
  • (2022)Detecting threshold concepts through Bayesian knowledge tracing: examining research skill development in biological sciences at the doctoral levelInstructional Science10.1007/s11251-022-09578-550:3(475-497)Online publication date: 14-Mar-2022
  • (2020)Spacing out! Manipulating spatial features in mathematical expressions affects performanceJournal of Numerical Cognition10.5964/jnc.v6i2.2436:2(186-203)Online publication date: 9-Sep-2020
  • (2019)Start of a ScienceProceedings of the Sixth (2019) ACM Conference on Learning @ Scale10.1145/3330430.3333631(1-10)Online publication date: 24-Jun-2019
  • (2019)Developing Early Detectors of Student Attrition and Wheel Spinning Using Deep LearningIEEE Transactions on Learning Technologies10.1109/TLT.2019.291216212:2(158-170)Online publication date: 1-Apr-2019
  • (2018)Students, systems, and interactionsProceedings of the Fifth Annual ACM Conference on Learning at Scale10.1145/3231644.3231662(1-10)Online publication date: 26-Jun-2018
  • (2017)One Decision Tree is Enough to Make CustomizationProceedings of the Fourth (2017) ACM Conference on Learning @ Scale10.1145/3051457.3053983(193-196)Online publication date: 12-Apr-2017
  • (2015)Big Data Analytics in MOOCs2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing10.1109/CIT/IUCC/DASC/PICOM.2015.98(681-686)Online publication date: Oct-2015

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