Systematic Review of Context-Aware Systems that use Item Response Theory in Learning Environments
Pages 100 - 104
Abstract
Item response theory (I.R.T.) has been widely used in psychometrics, and the design of adaptive tests has recently been modified to be used in recommender systems of educational environments. This work aims to identify research that combines recommendation strategies for educational environments and item response theory; and find possible research spaces for improving recommendation quality and identifying learning trajectories. The study was carried out between 2015 and 2020 and found an increase in this type of research between 2017 and 2019. The resulting works were classified into three main axes, according to the analysis of the approaches used by the authors: Identification of learning trajectories, Prediction of learning levels (Proficiency, ability, others), and Recommendation of educational resources or learning tasks.
References
[1]
Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen, and Alexander Tuzhilin. 2005. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems 23, 1 (1 2005), 103–145. https://doi.org/10.1145/1055709.1055714
[2]
Jorge Cardoso. 2007. The semantic web vision: Where are we?IEEE Intelligent systems 22, 5 (2007), 84–88.
[3]
Chandrapaul, Rounak Soni, Shubham Sharma, Hemraj Fagna, and Sangeeta Mittal. 2019. News Analysis Using Word Cloud. In Advances in Signal Processing and Communication, Banmali S. Rawat, Aditya Trivedi, Sanjeev Manhas, and Vikram Karwal (Eds.). Springer Singapore, Singapore, 55–64.
[4]
Haitao Cheng, Li Yan, Zongmin Ma, and Slobodan Ribarić. 2019. Fuzzy spatio-temporal ontologies and formal construction based on fuzzy Petri nets. Computational Intelligence 35, 1 (2019), 204–239.
[5]
Douglas H Clements and Julie Sarama. 2004. Learning trajectories in mathematics education. Mathematical thinking and learning 6, 2 (2004), 81–89.
[6]
Dalton Francisco de Andrade, Heliton Ribeiro Tavares, and Raquel da Cunha Valle. 2000. Teoria da Resposta ao Item: conceitos e aplicações. ABE, Sao Paulo (2000).
[7]
José Muñiz Fernández and Ronald K Hambleton. 1992. Medio siglo de teoría de respuesta a los ítems. Anuario de psicología/The UB Journal of psychology52 (1992), 41–66.
[8]
David Goldberg, David Nichols, Brian M Oki, and Douglas Terry. 1992. Using collaborative filtering to weave an information tapestry. Commun. ACM 35, 12 (12 1992), 61–70. https://doi.org/10.1145/138859.138867
[9]
Lawrence W Green. 2008. Making research relevant: if it is an evidence-based practice, where’s the practice-based evidence?Family practice 25, suppl_1 (2008), i20–i24.
[10]
Thomas R Gruber. 1993. A translation approach to portable ontology specifications. Knowledge acquisition 5, 2 (1993), 199–220.
[11]
Nicola Guarino and Pierdaniele Giaretta. 1995. Ontologies and knowledge bases. Towards very large knowledge bases(1995), 1–2.
[12]
Florian Heimerl, Steffen Lohmann, Simon Lange, and Thomas Ertl. 2014. Word cloud explorer: Text analytics based on word clouds. In 2014 47th Hawaii International Conference on System Sciences. IEEE, 1833–1842.
[13]
K.L. James, N.P. Randall, and N.R. Haddaway. 2016. A Methodology for Systematic Mapping in Environmental Sciences. Environmental Evidences 5 (2016). Issue 7. https://doi.org/10.1186/s13750-016-0059-6
[14]
Dietmar Jannach, Lukas Lerche, and Michael Jugovac. 2015. Adaptation and evaluation of recommendations for short-term shopping goals. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 211–218.
[15]
Barbara Kitchenham. 2004. Technical Report: Procedures for performing Systematic Reviews. Technical Report TR/SE-0401. Department of Computer Science, Keele University, East Lansing, Michigan. 28 pages.
[16]
Antonio Martire 2013. Espacio físico y espacio virtual en prácticas educativas avanzadas. (2013).
[17]
Patrick E Parrish. 2004. The trouble with learning objects. Educational technology research and development 52, 1(2004), 49–67.
[18]
Kai Petersen, Sairam Vakkalanka, and Ludwik Kuzniarz. 2015. Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology 64 (2015), 1 – 18. https://doi.org/10.1016/j.infsof.2015.03.007
[19]
Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work. ACM, 175–186.
[20]
Sampaio R.F. and Mancini M.C.2007. Estudos de Revisão Sistemática: Um Guia para Síntese Criteriosa da Evidência Científica. Revista Brasileira de Fisioterapia 11 (2007). Issue 1. http://www.scielo.br/pdf/rbfis/v11n1/12.pdf
[21]
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2015. Recommender systems: introduction and challenges. In Recommender systems handbook. Springer, 1–34.
[22]
Graeme Shanks, Elizabeth Tansley, and Ron Weber. 2003. Using ontology to validate conceptual models. Commun. ACM 46, 10 (2003), 85–89.
[23]
Martin A Simon. 1995. Reconstructing mathematics pedagogy from a constructivist perspective. Journal for research in mathematics education 26, 2 (1995), 114–145.
[24]
Don Slater and Fran Tonkiss. 2013. Market society: Markets and modern social theory. John Wiley & Sons.
[25]
John Sweller. 1988. Cognitive load during problem solving: Effects on learning. Cognitive science 12, 2 (1988), 257–285.
[26]
Ana tomé Klock. 2018. Mapeamentos e Revisões Sistemáticos da Literatura: um Guia Teórico e Prático. Cadernos de Informática 10 (2018). Issue 1. https://seer.ufrgs.br/cadernosdeinformatica/article/view/v10n1201801-09
[27]
C. M. M. Vendramini. 2002. Aplicação da Teoria de Resposta ao Item na avaliação educacional. Temas em avaliação psicológica(2002), 116–130.
[28]
Peter Holt II Wilson 2009. Teachers’ uses of a learning trajectory for equipartitioning. (2009).
Index Terms
- Systematic Review of Context-Aware Systems that use Item Response Theory in Learning Environments
Index terms have been assigned to the content through auto-classification.
Recommendations
A Learning Ability Evaluation Method Based On Item Response Theory and Machine Learning Method
ICBDE '20: Proceedings of the 2020 3rd International Conference on Big Data and EducationIn recent years, online education and MOOC (Massive Open Online Courses) education have arisen. This paper proposes a learner ability modeling method based on item response theory and machine learning method. We use item response theory to calculate ...
Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems
AbstractAdaptive learning systems aim to provide learning items tailored to the behavior and needs of individual learners. However, one of the outstanding challenges in adaptive item selection is that often the corresponding systems do not ...
Highlights- A hybrid learning system is built integrating machine learning and IRT.
- Machine ...
A Context-Aware User-Item Representation Learning for Item Recommendation
Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. That ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
June 2021
431 pages
ISBN:9781450383677
DOI:10.1145/3450614
Copyright © 2021 ACM.
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 June 2021
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Funding Sources
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Brasil
Conference
UMAP '21: 29th ACM Conference on User Modeling, Adaptation and Personalization
June 21 - 25, 2021
Utrecht, Netherlands
Acceptance Rates
Overall Acceptance Rate 162 of 633 submissions, 26%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 94Total Downloads
- Downloads (Last 12 months)12
- Downloads (Last 6 weeks)1
Reflects downloads up to 18 Nov 2024
Other Metrics
Citations
Cited By
View allView Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format