Learning analytics and machine learning

Dragan Gasevic; Annika Wolff; Carolyn Rose; Zdenek Zdrahal; George Siemens

doi:10.1145/2567574.2567633

Learning analytics and machine learning

Dragan Gasevic
, Annika Wolff
, Carolyn Rose
, Zdenek Zdrahal
, George Siemens

Moray House School of Education and Sport

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Learning analytics (LA) as a field remains in its infancy. Many of the techniques now prominent from practitioners have been drawn from various fields, including HCI, statistics, computer science, and learning sciences. In order for LA to grow and advance as a discipline, two significant challenges must be met: 1) development of analytics methods and techniques that are native to the LA discipline, and 2) practitioners in LA to develop algorithms and models that reflect the social and computational dimensions of analytics. This workshop introduces researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models.

Original language	English
Title of host publication	ACM International Conference Proceeding Series
Publisher	ACM Association for Computing Machinery
Pages	287-288
Number of pages	2
ISBN (Print)	1595930361, 9781595930361
DOIs	https://doi.org/10.1145/2567574.2567633
Publication status	Published - 1 Jan 2014
Event	4th International Conference on Learning Analytics and Knowledge, LAK 2014 - Indianapolis, IN, United Kingdom Duration: 24 Mar 2014 → 28 Mar 2014

Conference

Conference	4th International Conference on Learning Analytics and Knowledge, LAK 2014
Country/Territory	United Kingdom
City	Indianapolis, IN
Period	24/03/14 → 28/03/14

Keywords / Materials (for Non-textual outputs)

Collaboration
Learning analytics
Machine learning
Theory

Access to Document

10.1145/2567574.2567633

Cite this

@inproceedings{c1c81b305f12439991df0e0464c31061,

title = "Learning analytics and machine learning",

abstract = "Learning analytics (LA) as a field remains in its infancy. Many of the techniques now prominent from practitioners have been drawn from various fields, including HCI, statistics, computer science, and learning sciences. In order for LA to grow and advance as a discipline, two significant challenges must be met: 1) development of analytics methods and techniques that are native to the LA discipline, and 2) practitioners in LA to develop algorithms and models that reflect the social and computational dimensions of analytics. This workshop introduces researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models.",

keywords = "Collaboration, Learning analytics, Machine learning, Theory",

author = "Dragan Gasevic and Annika Wolff and Carolyn Rose and Zdenek Zdrahal and George Siemens",

year = "2014",

month = jan,

day = "1",

doi = "10.1145/2567574.2567633",

language = "English",

isbn = "1595930361",

pages = "287--288",

booktitle = "ACM International Conference Proceeding Series",

publisher = "ACM Association for Computing Machinery",

note = "4th International Conference on Learning Analytics and Knowledge, LAK 2014 ; Conference date: 24-03-2014 Through 28-03-2014",

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TY - GEN

T1 - Learning analytics and machine learning

AU - Gasevic, Dragan

AU - Wolff, Annika

AU - Rose, Carolyn

AU - Zdrahal, Zdenek

AU - Siemens, George

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Learning analytics (LA) as a field remains in its infancy. Many of the techniques now prominent from practitioners have been drawn from various fields, including HCI, statistics, computer science, and learning sciences. In order for LA to grow and advance as a discipline, two significant challenges must be met: 1) development of analytics methods and techniques that are native to the LA discipline, and 2) practitioners in LA to develop algorithms and models that reflect the social and computational dimensions of analytics. This workshop introduces researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models.

AB - Learning analytics (LA) as a field remains in its infancy. Many of the techniques now prominent from practitioners have been drawn from various fields, including HCI, statistics, computer science, and learning sciences. In order for LA to grow and advance as a discipline, two significant challenges must be met: 1) development of analytics methods and techniques that are native to the LA discipline, and 2) practitioners in LA to develop algorithms and models that reflect the social and computational dimensions of analytics. This workshop introduces researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models.

KW - Collaboration

KW - Learning analytics

KW - Machine learning

KW - Theory

UR - https://www.scopus.com/pages/publications/84898830612

U2 - 10.1145/2567574.2567633

DO - 10.1145/2567574.2567633

M3 - Conference contribution

AN - SCOPUS:84898830612

SN - 1595930361

SN - 9781595930361

SP - 287

EP - 288

BT - ACM International Conference Proceeding Series

PB - ACM Association for Computing Machinery

T2 - 4th International Conference on Learning Analytics and Knowledge, LAK 2014

Y2 - 24 March 2014 through 28 March 2014

ER -

Learning analytics and machine learning

Abstract

Conference

Keywords / Materials (for Non-textual outputs)

Access to Document

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