Measures for recommendations based on past students' activity

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F17%3A00309662" target="_blank" >RIV/68407700:21730/17:00309662 - isvavai.cz</a>

Result on the web

<a href="http://dl.acm.org/citation.cfm?id=3027426" target="_blank" >http://dl.acm.org/citation.cfm?id=3027426</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3027385.3027426" target="_blank" >10.1145/3027385.3027426</a>

Result language

angličtina

Original language name

Measures for recommendations based on past students' activity

Original language description

This paper introduces two measures for the recommendation of study materials based on students' past study activity. We use records from the Virtual Learning Environment (VLE) and analyse the activity of previous students. We assume that the activity of past students represents patterns, which can be used as a basis for recommendations to current students. The measures we define are Relevance, for description of a supposed VLE activity derived from previous students of the course, and Effort, that represents the actual effort of individual current students. Based on these measures, we propose a composite measure, which we call Importance. We use data from the previous course presentations to evaluate of the consistency of students' behaviour. We use correlation of the defined measures Relevance and Average Effort to evaluate the behaviour of two different student cohorts and the Root Mean Square Error to measure the deviation of Average Effort and individual student Effort.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

50301 - Education, general; including training, pedagogy, didactics [and education systems]

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the Seventh International Learning Analytics & Knowledge Conference

ISBN

978-1-4503-4870-6

ISSN

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e-ISSN

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Number of pages

5

Pages from-to

404-408

Publisher name

ACM

Place of publication

New York

Event location

Vancouver

Event date

Mar 13, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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