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Algorithms
Special Issues
Algorithms in Educational Data Mining
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Algorithms in Educational Data Mining

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 2514

Special Issue Editors

Dr. Jakub Kuzilek

E-Mail Website
Guest Editor
Didaktik der Informatik/Informatik und Gesellschaft, Humbold-University of Berlin, 10099 Berlin, Germany
Interests: learning analytics; machine learning; signal processing; educational data mining
Dr. Niels Seidel

E-Mail Website
Co-Guest Editor
Department of Cooperative Systems,FernUniversität in Hagen, 58097 Hagen, Germany
Interests: adaptive learning environments; learning analytics; video-based learning environments; computer-aided collaborative learning

Special Issue Information

Dear Colleagues,

The past decade has brought the extensive use of information and communication technologies into education. With that, vast amounts of student- and study-related data have been collected. This data provides institutions with an opportunity to gain new perspectives on learning and provide students with extended learning experience and gains. With the rapid advancement in machine learning and deep learning, the opportunities to use information technologies in education are expanding. Therefore, we would like to invite you to contribute to this Special Issue on “Algorithms in Educational Data Mining”. Potential topics include, but are not limited to, data-informed learning, learning and teaching modelling, assessing student learning, personalized and adaptive learning, insights to learning processes, learning analytics, recommender systems, and student support systems.

Dr. Jakub Kuzilek
Dr. Niels Seidel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data-informed learning
  • learning and teaching modeling
  • assessing student learning
  • personalized and adaptive learning
  • insights into learning processes
  • learning analytics
  • recommender systems
  • student support systems

Published Papers (1 paper)

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Research

20 pages, 625 KiB  
Article
Rapid Guessing in Low-Stakes Assessments: Finding the Optimal Response Time Threshold with Random Search and Genetic Algorithm
by Okan Bulut, Guher Gorgun, Tarid Wongvorachan and Bin Tan
Algorithms 2023, 16(2), 89; https://doi.org/10.3390/a16020089 - 7 Feb 2023
Cited by 3 | Viewed by 1776
Abstract
Rapid guessing is an aberrant response behavior that commonly occurs in low-stakes assessments with little to no formal consequences for students. Recently, the availability of response time (RT) information in computer-based assessments has motivated researchers to develop various methods to detect rapidly guessed [...] Read more.
Rapid guessing is an aberrant response behavior that commonly occurs in low-stakes assessments with little to no formal consequences for students. Recently, the availability of response time (RT) information in computer-based assessments has motivated researchers to develop various methods to detect rapidly guessed responses systematically. These methods often require researchers to identify an RT threshold subjectively for each item that could distinguish rapid guessing behavior from solution behavior. In this study, we propose a data-driven approach based on random search and genetic algorithm to search for the optimal RT threshold within a predefined search space. We used response data from a low-stakes math assessment administered to over 5000 students in 658 schools across the United States. As we demonstrated how to use our data-driven approach, we also compared its performance with those of the existing threshold-setting methods. The results show that the proposed method could produce viable RT thresholds for detecting rapid guessing in low-stakes assessments. Moreover, compared with the other threshold-setting methods, the proposed method yielded more liberal RT thresholds, flagging a larger number of responses. Implications for practice and directions for future research were discussed. Full article
(This article belongs to the Special Issue Algorithms in Educational Data Mining)
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