short-paper

Implementing predictive learning analytics on a large scale: the teacher's perspective

Authors:

Christothea Herodotou,

Avinash Boroowa,

Zdenek Zdrahal,

Martin Hlosta, and

Galina NaydenovaAuthors Info & Claims

LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference

March 2017

Pages 267 - 271

https://doi.org/10.1145/3027385.3027397

Published: 13 March 2017 Publication History

Abstract

In this paper, we describe a large-scale study about the use of predictive learning analytics data with 240 teachers in 10 modules at a distance learning higher education institution. The aim of the study was to illuminate teachers' uses and practices of predictive data, in particular identify how predictive data was used to support students at risk of not completing or failing a module. Data were collected from statistical analysis of 17,033 students' performance by the end of the intervention, teacher usage statistics, and five individual semi-structured interviews with teachers. Findings revealed that teachers endorse the use of predictive data to support their practice yet in diverse ways and raised the need for devising appropriate intervention strategies to support students at risk.

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Queiroga ESantana Dda Silva Mde Aguiar Mdos Santos VMello RBittencourt ICechinel C(2024)Anticipating Student Abandonment and Failure: Predictive Models in High School SettingsArtificial Intelligence in Education10.1007/978-3-031-64302-6_25(351-364)Online publication date: 2-Jul-2024
https://doi.org/10.1007/978-3-031-64302-6_25
Knobbout Jvan Teylingen T(2023)What Challenges Are Holding Us Back From Adopting Learning Analytics?Perspectives on Learning Analytics for Maximizing Student Outcomes10.4018/978-1-6684-9527-8.ch003(44-63)Online publication date: 24-Oct-2023
https://doi.org/10.4018/978-1-6684-9527-8.ch003
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Index Terms

Implementing predictive learning analytics on a large scale: the teacher's perspective
1. Applied computing
  1. Education
    1. Distance learning

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cover image ACM Other conferences

LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference

March 2017

631 pages

ISBN:9781450348706

DOI:10.1145/3027385

General Chairs:
Alyssa Wise
New York University, USA
,
Phillip H. Winne
Simon Fraser University, Canada
,
Grace Lynch
Society for Learning Analytics Research
,
Program Chairs:
Xavier Ochoa
Escuela Superior Politecnica del Litoral, Ecuador
,
Inge Molenaar
Radboud University, The Netherlands
,
Shane Dawson
University of South Australia, Australia
,
Publications Chair:
Marek Hatala
Simon Fraser University, Canada

Copyright © 2017 ACM.

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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Association for Computing Machinery

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Published: 13 March 2017

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LAK '17

LAK '17: 7th International Learning Analytics and Knowledge Conference

March 13 - 17, 2017

British Columbia, Vancouver, Canada

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LAK '17 Paper Acceptance Rate 36 of 114 submissions, 32%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Queiroga ESantana Dda Silva Mde Aguiar Mdos Santos VMello RBittencourt ICechinel C(2024)Anticipating Student Abandonment and Failure: Predictive Models in High School SettingsArtificial Intelligence in Education10.1007/978-3-031-64302-6_25(351-364)Online publication date: 2-Jul-2024
https://doi.org/10.1007/978-3-031-64302-6_25
Knobbout Jvan Teylingen T(2023)What Challenges Are Holding Us Back From Adopting Learning Analytics?Perspectives on Learning Analytics for Maximizing Student Outcomes10.4018/978-1-6684-9527-8.ch003(44-63)Online publication date: 24-Oct-2023
https://doi.org/10.4018/978-1-6684-9527-8.ch003
Ngoon TKovalev DPatidar PHarrison CAgarwal YZimmerman JOgan A(2023)"An Instructor is [already] able to keep track of 30 students": Students’ Perceptions of Smart Classrooms for Improving Teaching & Their Emergent Understandings of Teaching and LearningProceedings of the 2023 ACM Designing Interactive Systems Conference10.1145/3563657.3596079(1277-1292)Online publication date: 10-Jul-2023
https://dl.acm.org/doi/10.1145/3563657.3596079
Gelorme Silveira MDe Souza LDe Oliveira Brandão LFranco Brandão A(2023)Learning Analytics to Support Education for All: Learning from the Past2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343491(1-8)Online publication date: 18-Oct-2023
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Biancato GMarques Queiroga EMuñoz RRamos VThompsen Primo TRodés VCechinel C(2023)Expectations of High School Teachers Regarding the Use of Learning AnalyticsProceedings of the 18th Latin American Conference on Learning Technologies (LACLO 2023)10.1007/978-981-99-7353-8_34(459-471)Online publication date: 17-Oct-2023
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Nurmalitasari Awang Long ZMohd Noor M(2023)The Predictive Learning Analytics for Student Dropout Using Data Mining Technique: A Systematic Literature ReviewAdvances in Technology Transfer Through IoT and IT Solutions10.1007/978-3-031-25178-8_2(9-17)Online publication date: 1-Apr-2023
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Li QJung Yd'Anjou BWise AWise AMartinez-Maldonado RHilliger I(2022)Unpacking Instructors’ Analytics Use: Two Distinct Profiles for Informing TeachingLAK22: 12th International Learning Analytics and Knowledge Conference10.1145/3506860.3506905(528-534)Online publication date: 21-Mar-2022
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Li XRosson MRobert JKizilcec RDavis KOchoa X(2022)A Scenario-based Exploration of Expected Usefulness, Privacy Concerns, and Adoption Likelihood of Learning AnalyticsProceedings of the Ninth ACM Conference on Learning @ Scale10.1145/3491140.3528271(48-59)Online publication date: 1-Jun-2022
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