Smart learning analytics

David Boulanger; Jeremie Seanosky; Vive Kumar; Kinshuk; Karthikeyan Panneerselvam; Selvi Somasundaram Thamarai Selvi Somasundaram

doi:10.1007/978-3-662-44188-6_39

Smart learning analytics

David Boulanger
, Jeremie Seanosky
, Vive Kumar
, Kinshuk
, Karthikeyan Panneerselvam
, Selvi Somasundaram Thamarai Selvi Somasundaram

School of Computing and Information systems

Athabasca University
Anna University

Research output: Contribution to journal › Journal Article › peer-review

22 Citations (Scopus)

Abstract

A smart learning environment (SLE) is characterized by the key provision of personalized learning experiences. To approach different degrees of personalization in online learning, this paper introduces a framework called SCALE that tracks finer level learning experiences and translates them into opportunities for custom feedback. A prototype version of the SCALE system has been used in a study to track the habits of novice programmers. Growth of coding competencies of first year engineering students has been captured in a continuous manner. Students have been provided with customized feedback to optimize their learning path in programming. This paper describes key aspects of our research with the SCALE system and highlights results of the study.

Original language	English
Pages (from-to)	289-296
Number of pages	8
Journal	Lecture Notes in Educational Technology
Issue number	9783662441879
DOIs	https://doi.org/10.1007/978-3-662-44188-6_39
Publication status	Published - 2015

Keywords

Big data learning analytics
E-Learning technologies
Novice programming
Programming
SCALE framework
Smart learning environment

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10.1007/978-3-662-44188-6_39

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title = "Smart learning analytics",

abstract = "A smart learning environment (SLE) is characterized by the key provision of personalized learning experiences. To approach different degrees of personalization in online learning, this paper introduces a framework called SCALE that tracks finer level learning experiences and translates them into opportunities for custom feedback. A prototype version of the SCALE system has been used in a study to track the habits of novice programmers. Growth of coding competencies of first year engineering students has been captured in a continuous manner. Students have been provided with customized feedback to optimize their learning path in programming. This paper describes key aspects of our research with the SCALE system and highlights results of the study.",

keywords = "Big data learning analytics, E-Learning technologies, Novice programming, Programming, SCALE framework, Smart learning environment",

author = "David Boulanger and Jeremie Seanosky and Vive Kumar and Kinshuk and Karthikeyan Panneerselvam and \{Thamarai Selvi Somasundaram\}, \{Selvi Somasundaram\}",

note = "Publisher Copyright: {\textcopyright} 2015, Springer-Verlag Berlin Heidelberg.",

year = "2015",

doi = "10.1007/978-3-662-44188-6\_39",

language = "English",

pages = "289--296",

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T1 - Smart learning analytics

AU - Boulanger, David

AU - Seanosky, Jeremie

AU - Kumar, Vive

AU - Kinshuk,

AU - Panneerselvam, Karthikeyan

AU - Thamarai Selvi Somasundaram, Selvi Somasundaram

PY - 2015

Y1 - 2015

N2 - A smart learning environment (SLE) is characterized by the key provision of personalized learning experiences. To approach different degrees of personalization in online learning, this paper introduces a framework called SCALE that tracks finer level learning experiences and translates them into opportunities for custom feedback. A prototype version of the SCALE system has been used in a study to track the habits of novice programmers. Growth of coding competencies of first year engineering students has been captured in a continuous manner. Students have been provided with customized feedback to optimize their learning path in programming. This paper describes key aspects of our research with the SCALE system and highlights results of the study.

AB - A smart learning environment (SLE) is characterized by the key provision of personalized learning experiences. To approach different degrees of personalization in online learning, this paper introduces a framework called SCALE that tracks finer level learning experiences and translates them into opportunities for custom feedback. A prototype version of the SCALE system has been used in a study to track the habits of novice programmers. Growth of coding competencies of first year engineering students has been captured in a continuous manner. Students have been provided with customized feedback to optimize their learning path in programming. This paper describes key aspects of our research with the SCALE system and highlights results of the study.

KW - Big data learning analytics

KW - E-Learning technologies

KW - Novice programming

KW - Programming

KW - SCALE framework

KW - Smart learning environment

UR - https://www.scopus.com/pages/publications/84966488552

U2 - 10.1007/978-3-662-44188-6_39

DO - 10.1007/978-3-662-44188-6_39

M3 - Journal Article

AN - SCOPUS:84966488552

SN - 2196-4963

SP - 289

EP - 296

JO - Lecture Notes in Educational Technology

JF - Lecture Notes in Educational Technology

IS - 9783662441879

ER -

Smart learning analytics

Abstract

Keywords

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