On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees

Crockett, Keeley; Latham, Annabel; Whitton, Nicola

doi:10.1016/j.ijhcs.2016.08.005

Cited by 95 publications

(66 citation statements)

References 51 publications

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“…The appropriate teaching methods of student LS will enable students to accept and understand the concepts taught, and contribute to better achievement indirectly [36], [37], [38]. [26] reported that by identifying learning style, making each of student know of their strengths and weaknesses in learning and they have the option to personalize their learning environment.…”

Section: Learning Style Detectionmentioning

confidence: 99%

An Architecture of Decision Support System for Visual-Auditory-Kinesthetic (VAK) Learning Styles Detection Through Behavioral Modelling

Mohd¹,

Yahya²,

Ismail³

et al. 2019

IJIES

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Learning style (LS) is a description of the attitudes and behaviors which determine an individual’s preferred way of learning. Since each student has different LS, it is important for the teacher to recognize the differences in LS. Thus, an appropriate technique to detect students' LS, improve the motivation and academic achievement are required. The common approach using questionnaires to identify LS is less accurate due to complete the questionnaire is a tedious task for students and tend to choose answers randomly without understanding the questions. Emotions such as anger, sadness, and happiness resulting the different questionnaire answers. Due to the approach constrains, this study has focused on automated approaches that identify student LS from student behavior in the learning process. Implementation of decision support system (DSS) as automated application systems is needed to help teachers make decisions in determining students' LS. Thus, the objective of this study is to propose the architecture of LS detection automatically using decision support system. The development of the architecture is applying the behavioral modelling, that are contained student’s behavior parameters for visual-auditory-kinesthetic (VAK) model. Evaluation of the architecture is tested with the precision DSS engine. The accuracy of the rule technique achieves significant 80% accuracy. This study aims to help teachers to identify the ability of the student through the learning style (LS) in order to create effectiveness of learning and improving student’s achievement indirectly. Keywords— decision support system, reasoning engines, learning style detection, user behavior, visual-auditory-kinesthetic (VAK) model

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Section: Learning Style Detectionmentioning

confidence: 99%

An Architecture of Decision Support System for Visual-Auditory-Kinesthetic (VAK) Learning Styles Detection Through Behavioral Modelling

Mohd¹,

Yahya²,

Ismail³

et al. 2019

IJIES

View full text Add to dashboard Cite

show abstract

“…Fuzzy trees have been induced to predict the learning style of individuals. Outcome classification has been observed due to the automatic behavior of learning from a collection of data [16].…”

Section: Related Workmentioning

confidence: 99%

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Abidi

Hussain

et al. 2018

Sustainability

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Incorporating substantial, sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study was to identify the confused students who had failed to master the skill(s) given by the tutors as homework using the Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study, and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models including: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated, and tested learning algorithms, performed stratified cross-validation, and measured the performance of the models through various performance metrics, i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity, and Specificity. We found RF, GLM, XGBoost, and DL were high accuracy-achieving classifiers. However, other perceptions such as detecting unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students that were confused when attempting the homework exercise, to help foster their knowledge and talent to play a vital role in environmental development.

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“…In other words, decision tree models are so transparent that people can clearly identify how outputs are mapped from inputs (Liu et al 2015(Liu et al , 2016c. In practice, applications of decision tree learning are extensive and varied, such as text classification (Khan et al 2015), biomedicine (Tayefi et al 2017), intelligent tutoring systems (Crockett et al 2017) and transient stability assessment (Rahmatian et al 2017).…”

Section: Related Workmentioning

confidence: 99%

Granular computing-based approach of rule learning for binary classification

Liu

Cocea

2018

Granul. Comput.

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Rule learning is one of the most popular types of machine-learning approaches, which typically follow two main strategies: 'divide and conquer' and 'separate and conquer'. The former strategy is aimed at induction of rules in the form of a decision tree, whereas the latter one is aimed at direct induction of if-then rules. Due to the case that the divide and conquer strategy could result in the replicated sub-tree problem, which not only leads to overfitting but also increases the computational complexity in classifying unseen instances, researchers have thus been motivated to develop rule learning approaches through the separate and conquer strategy. In this paper, we focus on investigation of the Prism algorithm, since it is a representative one that follows the separate and conquer strategy, and is aimed at learning a set of rules for each class in the setting of granular computing, where each class (referred to as target class) is viewed as a granule. The Prism algorithm shows highly comparable performance to the most popular algorithms, such as ID3 and C4.5, which follow the divide and conquer strategy. However, due to the need to learn a rule set for each class, Prism usually produces very complex rule-based classifiers. In real applications, there are many problems that involve one target class only, so it is not necessary to learn a rule set for each class, i.e., only a set of rules for the target class needs to be learned and a default rule is used to indicate the case of non-target classes. To address the above issues of Prism, we propose a new version of the algorithm referred to as PrismSTC, where 'STC' stands for 'single target class'. Our experimental results show that PrismSTC leads to production of simpler rule-based classifiers without loss of accuracy in comparison with Prism. PrismSTC also demonstrates sufficiently good performance comparing with C4.5.

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On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees

Cited by 95 publications

References 51 publications

An Architecture of Decision Support System for Visual-Auditory-Kinesthetic (VAK) Learning Styles Detection Through Behavioral Modelling

An Architecture of Decision Support System for Visual-Auditory-Kinesthetic (VAK) Learning Styles Detection Through Behavioral Modelling

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Granular computing-based approach of rule learning for binary classification

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