In this paper, we describe a scalable learning analytics platform which runs generalized analytics models on educational data in parallel. As a proof of concept, we use this platform as a base for an end-to-end automated writing feedback system. The system allows students to view feedback on their writing in near real-time, edit their writing based on the feedback provided, and observe the progression of their performance over time. Providing students with detailed feedback is an important part of improving writing skills and an essential component towards solving Bloom's "two sigma" problem in education. We evaluate the effectiveness of the feedback for students with an ongoing pilot study with 800 students who are using the learning analytics platform in a college English course.

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Cited By

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Duncan ATang THuang YLuu JThakkar NJoyner DJoyner DKim MWang XXia M(2024)Forums, Feedback, and Two Kinds of AI: A Selective History of Learning @ ScaleProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3664667(376-382)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3657604.3664667
Raković MWinne PMarzouk ZChang D(2021)Automatic identification of knowledge‐transforming content in argument essays developed from multiple sourcesJournal of Computer Assisted Learning10.1111/jcal.1253137:4(903-924)Online publication date: 15-Feb-2021
https://doi.org/10.1111/jcal.12531
Cavalcanti ADiego AMello RMangaroska KNascimento AFreitas FGašević DRensing CDrachsler HKovanović VPinkwart NScheffel MVerbert K(2020)How good is my feedback?Proceedings of the Tenth International Conference on Learning Analytics & Knowledge10.1145/3375462.3375477(428-437)Online publication date: 23-Mar-2020
https://dl.acm.org/doi/10.1145/3375462.3375477
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Index Terms

A Scalable Learning Analytics Platform for Automated Writing Feedback
1. Applied computing
  1. Education
    1. Computer-assisted instruction
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Parallel computing methodologies
    1. Parallel programming languages

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Published In

L@S '16: Proceedings of the Third (2016) ACM Conference on Learning @ Scale

April 2016

446 pages

ISBN:9781450337267

DOI:10.1145/2876034

General Chair:
Jeff Haywood
The University of Edinburgh, UK
,
Program Chairs:
Vincent Aleven
Carnegie Mellon University, USA
,
Judy Kay
University of Sydney, Australia
,
Ido Roll
University of British Columbia, Canada

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 25 April 2016

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NSERC/CNRL/Xerox/McGraw Hill Research Chair for Adaptivity and Personalization in Informatics

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L@S 2016

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L@S 2016: Third (2016) ACM Conference on Learning @ Scale

April 25 - 26, 2016

Scotland, Edinburgh, UK

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L@S '16 Paper Acceptance Rate 18 of 79 submissions, 23%;

Overall Acceptance Rate 117 of 440 submissions, 27%

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Duncan ATang THuang YLuu JThakkar NJoyner DJoyner DKim MWang XXia M(2024)Forums, Feedback, and Two Kinds of AI: A Selective History of Learning @ ScaleProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3664667(376-382)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3657604.3664667
Raković MWinne PMarzouk ZChang D(2021)Automatic identification of knowledge‐transforming content in argument essays developed from multiple sourcesJournal of Computer Assisted Learning10.1111/jcal.1253137:4(903-924)Online publication date: 15-Feb-2021
https://doi.org/10.1111/jcal.12531
Cavalcanti ADiego AMello RMangaroska KNascimento AFreitas FGašević DRensing CDrachsler HKovanović VPinkwart NScheffel MVerbert K(2020)How good is my feedback?Proceedings of the Tenth International Conference on Learning Analytics & Knowledge10.1145/3375462.3375477(428-437)Online publication date: 23-Mar-2020
https://dl.acm.org/doi/10.1145/3375462.3375477
Muslim AChatti MGuesmi M(2020)Open Learning Analytics: A Systematic Literature Review and Future PerspectivesArtificial Intelligence Supported Educational Technologies10.1007/978-3-030-41099-5_1(3-29)Online publication date: 30-Apr-2020
https://doi.org/10.1007/978-3-030-41099-5_1
Boulanger DClemens CSeanosky JFraser SKumar V(2019)Performance Analysis of a Serial Natural Language Processing Pipeline for Scaling Analytics of Academic Writing ProcessLearning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment10.1007/978-3-030-15130-0_8(123-151)Online publication date: 25-May-2019
https://doi.org/10.1007/978-3-030-15130-0_8
Ferreira‐Mello RAndré MPinheiro ACosta ERomero C(2019)Text mining in educationWIREs Data Mining and Knowledge Discovery10.1002/widm.13329:6Online publication date: 4-Aug-2019
https://doi.org/10.1002/widm.1332
Estvez-Ayres IArias Fisteus JDelgado-Kloos C(2018)LostregoJournal of Network and Computer Applications10.1016/j.jnca.2017.10.014100:C(56-68)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1016/j.jnca.2017.10.014
Clemens CKumar VBoulanger DSeanosky JKinshuk (2018)Learning Traces, Competence Assessment, and Causal Inference for English CompositionFrontiers of Cyberlearning10.1007/978-981-13-0650-1_3(49-67)Online publication date: 4-Nov-2018
https://doi.org/10.1007/978-981-13-0650-1_3
Jeske DBagher MPantidi NHall LTurner P(2017)HCI expertise needed!Proceedings of the 31st British Computer Society Human Computer Interaction Conference10.14236/ewic/HCI2017.15(1-4)Online publication date: 3-Jul-2017
https://dl.acm.org/doi/10.14236/ewic/HCI2017.15
Essa A(2016)A possible future for next generation adaptive learning systemsSmart Learning Environments10.1186/s40561-016-0038-y3:1Online publication date: 14-Nov-2016
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