LADA

A learning analytics dashboard for academic advising

Francisco Gutiérrez, Karsten Seipp, Xavier Ochoa, Katherine Chiluiza, Tinne De Laet, Katrien Verbert

Research output: Contribution to journalArticle

Abstract

From the perspective of Learning and Educational Technologies, academic advising has been one of the most overlooked aspects of academic support systems, despite being critical for the learning process and final success of students. The majority of higher education institutions provides simple technical support to academic advisers with basic descriptive statistics. This article presents the general design and implementation of a Learning Analytics Dashboard for Advisers (LADA), to support the decision-making process of academic advisers through comparative and predictive analysis. Moreover, this work evaluates the use of this tool to support decision-making of actual advisers in two different higher education institutions (University A, University B), compared with more traditional procedures and tools. Results indicate that LADA enables expert advisers to evaluate significantly more scenarios (Median = 2), especially for high advising difficulty cases with students that failed many courses (MedianA=3,MedianB=2.5), in a not-significantly different amount of time. For inexperienced advisers, LADA is perceived as a valuable tool for more accurate and efficient decision-making, as they were able to make informed decisions in a similar amount of time compared to the experts. These results are encouraging for further developments in the field.

Original languageEnglish (US)
JournalComputers in Human Behavior
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Decision making
Learning
Education
Decision Making
Students
Educational technology
Educational Technology
Statistics
Advisers
Predictive analytics

Keywords

  • Academic adviser
  • Academic advising
  • Data-driven decision-making
  • Learning analytics
  • Visualization

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • Psychology(all)

Cite this

Gutiérrez, F., Seipp, K., Ochoa, X., Chiluiza, K., De Laet, T., & Verbert, K. (Accepted/In press). LADA: A learning analytics dashboard for academic advising. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2018.12.004

LADA : A learning analytics dashboard for academic advising. / Gutiérrez, Francisco; Seipp, Karsten; Ochoa, Xavier; Chiluiza, Katherine; De Laet, Tinne; Verbert, Katrien.

In: Computers in Human Behavior, 01.01.2018.

Research output: Contribution to journalArticle

Gutiérrez, Francisco ; Seipp, Karsten ; Ochoa, Xavier ; Chiluiza, Katherine ; De Laet, Tinne ; Verbert, Katrien. / LADA : A learning analytics dashboard for academic advising. In: Computers in Human Behavior. 2018.
@article{03addb34b7434f55a0c20dd3bd252978,
title = "LADA: A learning analytics dashboard for academic advising",
abstract = "From the perspective of Learning and Educational Technologies, academic advising has been one of the most overlooked aspects of academic support systems, despite being critical for the learning process and final success of students. The majority of higher education institutions provides simple technical support to academic advisers with basic descriptive statistics. This article presents the general design and implementation of a Learning Analytics Dashboard for Advisers (LADA), to support the decision-making process of academic advisers through comparative and predictive analysis. Moreover, this work evaluates the use of this tool to support decision-making of actual advisers in two different higher education institutions (University A, University B), compared with more traditional procedures and tools. Results indicate that LADA enables expert advisers to evaluate significantly more scenarios (Median = 2), especially for high advising difficulty cases with students that failed many courses (MedianA=3,MedianB=2.5), in a not-significantly different amount of time. For inexperienced advisers, LADA is perceived as a valuable tool for more accurate and efficient decision-making, as they were able to make informed decisions in a similar amount of time compared to the experts. These results are encouraging for further developments in the field.",
keywords = "Academic adviser, Academic advising, Data-driven decision-making, Learning analytics, Visualization",
author = "Francisco Guti{\'e}rrez and Karsten Seipp and Xavier Ochoa and Katherine Chiluiza and {De Laet}, Tinne and Katrien Verbert",
year = "2018",
month = "1",
day = "1",
doi = "10.1016/j.chb.2018.12.004",
language = "English (US)",
journal = "Computers in Human Behavior",
issn = "0747-5632",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - LADA

T2 - A learning analytics dashboard for academic advising

AU - Gutiérrez, Francisco

AU - Seipp, Karsten

AU - Ochoa, Xavier

AU - Chiluiza, Katherine

AU - De Laet, Tinne

AU - Verbert, Katrien

PY - 2018/1/1

Y1 - 2018/1/1

N2 - From the perspective of Learning and Educational Technologies, academic advising has been one of the most overlooked aspects of academic support systems, despite being critical for the learning process and final success of students. The majority of higher education institutions provides simple technical support to academic advisers with basic descriptive statistics. This article presents the general design and implementation of a Learning Analytics Dashboard for Advisers (LADA), to support the decision-making process of academic advisers through comparative and predictive analysis. Moreover, this work evaluates the use of this tool to support decision-making of actual advisers in two different higher education institutions (University A, University B), compared with more traditional procedures and tools. Results indicate that LADA enables expert advisers to evaluate significantly more scenarios (Median = 2), especially for high advising difficulty cases with students that failed many courses (MedianA=3,MedianB=2.5), in a not-significantly different amount of time. For inexperienced advisers, LADA is perceived as a valuable tool for more accurate and efficient decision-making, as they were able to make informed decisions in a similar amount of time compared to the experts. These results are encouraging for further developments in the field.

AB - From the perspective of Learning and Educational Technologies, academic advising has been one of the most overlooked aspects of academic support systems, despite being critical for the learning process and final success of students. The majority of higher education institutions provides simple technical support to academic advisers with basic descriptive statistics. This article presents the general design and implementation of a Learning Analytics Dashboard for Advisers (LADA), to support the decision-making process of academic advisers through comparative and predictive analysis. Moreover, this work evaluates the use of this tool to support decision-making of actual advisers in two different higher education institutions (University A, University B), compared with more traditional procedures and tools. Results indicate that LADA enables expert advisers to evaluate significantly more scenarios (Median = 2), especially for high advising difficulty cases with students that failed many courses (MedianA=3,MedianB=2.5), in a not-significantly different amount of time. For inexperienced advisers, LADA is perceived as a valuable tool for more accurate and efficient decision-making, as they were able to make informed decisions in a similar amount of time compared to the experts. These results are encouraging for further developments in the field.

KW - Academic adviser

KW - Academic advising

KW - Data-driven decision-making

KW - Learning analytics

KW - Visualization

UR - http://www.scopus.com/inward/record.url?scp=85058469687&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058469687&partnerID=8YFLogxK

U2 - 10.1016/j.chb.2018.12.004

DO - 10.1016/j.chb.2018.12.004

M3 - Article

JO - Computers in Human Behavior

JF - Computers in Human Behavior

SN - 0747-5632

ER -