Adaptive Assessment Based on Decision Trees and Decision Rules

Šerbec, Irena Nančovska; Zerovnik, Alenka; Rugelj, Jože

doi:10.5220/0003521104730479

Proceedings of the 3rd International Conference on Computer Supported Education 2011

DOI: 10.5220/0003521104730479

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Adaptive Assessment Based on Decision Trees and Decision Rules

Irena Nančovska Šerbec

Alenka Zerovnik²,

Jože Rugelj

Abstract: In the e-learning environment we use adaptive assessment based on machine learning models called decision trees and decision rules. Adaptation of testing procedure relies on performance, current knowledge of test participants, on the goals of educators and on the properties of knowledge shown by participants. The paper presents sequential process of adaptive assessment where human educator or intelligent tutoring system uses different adaptive rules, based on machine learning models, to make formative assessme… Show more

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Cited by 2 publications

References 6 publications

(11 reference statements)

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Investigating the Viability of Alternating Model Tree As An Item Selection Algorithm for Constructing Computerized Adaptive Psychological Testing

Youn¹,

Lee²

2021

KCPA

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Computerized adaptive testing (CAT) is a computer-administered test where the next question for estimating the examinee's trait level is selected depending on his or her reponses to the previous items, resulting in tailored testing for each individual examinee. A defining feature of CAT stems from its item selection algorithms, among which both research interest and practical applications of decision-tree based CAT (DT-based CAT) have been rising recently. In the field of machine learning, however, it is well known that decision-trees, as a form of predictive models with simple and interpretable tree structures, can be vulnerable to the problem of overfitting or the problem of creating overly complex trees that do not generalize to newly observed data. Among various ensemble techniques developed to adequately address this problem, we the authors paid attention to the Alternating Model Tree (AMT) due to its interpretable tree-like structure. The purpose of this article is to investigate the viability of the Alternating Model Tree (AMT) as an item selection algorithm for constructing CAT. To this end, we first presented a detailed exposition of how AMT-based CAT can be constructed and then compared its performance with DT-based CAT using two sets of publicly available psychological test scores. The results provided supportive evidence that AMT-based CAT is viable, and that AMT-based CAT can predict test scores at least as accurate as DT-based CAT does. Based on our findings, we discuss implications, limitations, and directions of future studies.

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Investigating the Viability of Alternating Model Tree As An Item Selection Algorithm for Constructing Computerized Adaptive Psychological Testing

Youn¹,

Lee²

2021

KCPA

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Smart E-Testing: Future Trend of E-Learning or Gentle Deviation

Krašna

Bratina

Kaučič

2012

PEC

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A decade of experience with e-learning material production gives significant trends overview. From the perspective of ICT evolution this means very long time. E-learning is always closely coupled to ICT but with the distinction - it always lags behind the advancement in ICT. Among many unknown issues one thing is certain – e-learning permanently changes. From the past experiences prediction of immediate future e-learning is possible but the prediction for longer time is less accurate. In the article chronological e-learning achievements and their connections are presented in order to give an overview of trends in e-learning in Slovenia. But despite of the detailed past knowledge fundamental prediction error of e-learning future developments was made. Today's hot topics are not better e-learning materials, not even broad application of interactive boards but e-testing. Possible immediate future of e-learning is therefore application of intelligent e-testing systems. Intelligent computer assisted testing (CAT) is not the new topic in e-learning. It is object of many researches but it is not suitable for teachers. It is way too complicated for teachers to apply it successfully in their work. E-learning development process for intelligent computer assisted test production that suits teachers' competences is prepared and explained in the article. Key words: distance education, electronic testing, learning management system, knowledge assessment.

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Adaptive Assessment Based on Decision Trees and Decision Rules

Cited by 2 publications

References 6 publications

Investigating the Viability of Alternating Model Tree As An Item Selection Algorithm for Constructing Computerized Adaptive Psychological Testing

Investigating the Viability of Alternating Model Tree As An Item Selection Algorithm for Constructing Computerized Adaptive Psychological Testing

Smart E-Testing: Future Trend of E-Learning or Gentle Deviation

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