@inproceedings{d1645ea34b674eb48a3a210972088b1a,
title = "Infret: Preliminary Findings of a Tool for Explorative Learning of Information Retrieval Concepts",
abstract = "To help students better understand concepts in an information search and retrieval (ISR) class using the Motivational Active Learning (MAL) pedagogical approach, a tool called Infret was developed. It serves as the interactive experimentation and visualization component of MAL. The design of Infret is based on feedback collected from a testing session with a first proof of concept tool built in Java a year earlier. Infret was developed using Web technologies in order to have a simpler update process and be accessible from any Internet connected device with reasonable screen sizes (PC or tablet) without the need for manual installation. It was used and evaluated as part of a text statistics exercise in which students explored different properties of a text-based document collection. When students completed the exercise, they filled out a multi-part survey about Infret. Findings revealed that Infret helped participants gain a better understanding of text statistics and that the activity was well received by them. The usability score of Infret was 76.9, which indicates a usability level that is slightly above average and the emotions scale results were mostly positive. Additionally, the most commonly experienced emotion by students was happiness followed by negative emotions such as sadness, anxiety and anger which were experienced almost none of the time. There were multiple improvement suggestions such as improving the responsiveness of the UI on small screens, offering the option to see the content of text collections and the addition of formula explanations. These suggestions will be considered for improving Infret in future versions. Additionally, students expressed they would like to use Infret in other areas of information retrieval and also in other subjects. ",
author = "Aleksandar Bobic and Christian G{\"u}tl and Christoper Cheong",
year = "2021",
month = jan,
day = "1",
doi = "10.1007/978-3-030-52575-0_70",
language = "English",
isbn = "978-3-030-52574-3",
volume = "1231",
series = "Advances in Intelligent Systems and Computing, AISC",
publisher = "Springer",
pages = "849--865",
editor = "Auer, {Michael E} and Dominik May",
booktitle = "Cross Reality and Data Science in Engineering",
note = "17th International Conference on Remote Engineering and Virtual Instrumentation, REV 2020 ; Conference date: 26-02-2020 Through 28-02-2020",
}