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Generating actionable predictive models of academic performance

Published: 25 April 2016 Publication History

Abstract

The pervasive collection of data has opened the possibility for educational institutions to use analytics methods to improve the quality of the student experience. However, the adoption of these methods faces multiple challenges particularly at the course level where instructors and students would derive the most benefit from the use of analytics and predictive models. The challenge lies in the knowledge gap between how the data is captured, processed and used to derive models of student behavior, and the subsequent interpretation and the decision to deploy pedagogical actions and interventions by instructors. Simply put, the provision of learning analytics alone has not necessarily led to changing teaching practices. In order to support pedagogical change and aid interpretation, this paper proposes a model that can enable instructors to readily identify subpopulations of students to provide specific support actions. The approach was applied to a first year course with a large number of students. The resulting model classifies students according to their predicted exam scores, based on indicators directly derived from the learning design.

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    cover image ACM Other conferences
    LAK '16: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge
    April 2016
    567 pages
    ISBN:9781450341905
    DOI:10.1145/2883851
    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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    Publication History

    Published: 25 April 2016

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    Author Tags

    1. feedback
    2. learning analytics
    3. personalization
    4. recursive partitioning

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    LAK '16 Paper Acceptance Rate 36 of 116 submissions, 31%;
    Overall Acceptance Rate 236 of 782 submissions, 30%

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    • (2023)Explainable AI In Education : Current Trends, Challenges, And OpportunitiesSoutheastCon 202310.1109/SoutheastCon51012.2023.10115140(232-239)Online publication date: 1-Apr-2023
    • (2023)Genetic Algorithm-Based Approach for Predicting Student Academic Success2023 24th International Arab Conference on Information Technology (ACIT)10.1109/ACIT58888.2023.10453789(1-5)Online publication date: 6-Dec-2023
    • (2023)Analysing student performance for online education using the computational modelsUniversal Access in the Information Society10.1007/s10209-023-01033-7Online publication date: 19-Aug-2023
    • (2022)E-Learning Performance Prediction: Mining the Feature Space of Effective Learning BehaviorEntropy10.3390/e2405072224:5(722)Online publication date: 19-May-2022
    • (2022)Thinking with causal models: A visual formalism for collaboratively crafting assumptionsLAK22: 12th International Learning Analytics and Knowledge Conference10.1145/3506860.3506899(250-259)Online publication date: 21-Mar-2022
    • (2022)Connecting the dots – A literature review on learning analytics indicators from a learning design perspectiveJournal of Computer Assisted Learning10.1111/jcal.1271640:6(2432-2470)Online publication date: 26-Jul-2022
    • (2022)A neuro-fuzzy model for predicting and analyzing student graduation performance in computing programsEducation and Information Technologies10.1007/s10639-022-11205-228:3(2455-2484)Online publication date: 18-Aug-2022
    • (2022)Predicting Student Rankings Based on the Dual-Student Performance Comparison ModelData Science10.1007/978-981-19-5209-8_21(309-322)Online publication date: 10-Aug-2022
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